diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..38754209ebee5d64f860b0c14a611507fb26626d 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.o filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..c18dd8d83ceed1806b50b0aaa46beb7e335fff13 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +__pycache__/ diff --git a/README.md b/README.md index ff3aa3abd4fc51a96b6832f11c9b50d403dad391..71837cdc9f5fe007f213086dc81655f407d759e9 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ --- title: LucidDreamer -emoji: ๐Ÿ‘ +emoji: ๐Ÿ˜ด colorFrom: purple colorTo: pink sdk: gradio @@ -11,3 +11,5 @@ license: cc-by-nc-sa-4.0 --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference + +More information in https://luciddreamer-cvlab.github.io diff --git a/ZoeDepth/.gitignore b/ZoeDepth/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..832b9d621852cb1036394519f5fc9c43744516fd --- /dev/null +++ b/ZoeDepth/.gitignore @@ -0,0 +1,148 @@ +*.png +**.gif +.vscode/ +*.rdb +**.xml +wandb/ +slurm/ +tmp/ +.logs/ +checkpoints/ +external_jobs/ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class +ptlflow_logs/ +output/ +log/ +.idea/ +# C extensions +*.so +results/ +**.DS_Store +**.pt +demo/ +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST +~shortcuts/ +**/wandb_logs/ +**.db +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/ZoeDepth/LICENSE b/ZoeDepth/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..70a2068262a774e0e179b9c898ff38a665f59884 --- /dev/null +++ b/ZoeDepth/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Intelligent Systems Lab Org + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/ZoeDepth/README.md b/ZoeDepth/README.md new file mode 100644 index 0000000000000000000000000000000000000000..446dae32b63740cc42fd0874848e1f56488d5665 --- /dev/null +++ b/ZoeDepth/README.md @@ -0,0 +1,248 @@ +# **ZoeDepth: Combining relative and metric depth** (Official implementation) +[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/isl-org/ZoeDepth) +[![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/shariqfarooq/ZoeDepth) + +[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT) ![PyTorch](https://img.shields.io/badge/PyTorch_v1.10.1-EE4C2C?&logo=pytorch&logoColor=white) +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/zoedepth-zero-shot-transfer-by-combining/monocular-depth-estimation-on-nyu-depth-v2)](https://paperswithcode.com/sota/monocular-depth-estimation-on-nyu-depth-v2?p=zoedepth-zero-shot-transfer-by-combining) + +>#### [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://arxiv.org/abs/2302.12288) +> ##### [Shariq Farooq Bhat](https://shariqfarooq123.github.io), [Reiner Birkl](https://www.researchgate.net/profile/Reiner-Birkl), [Diana Wofk](https://dwofk.github.io/), [Peter Wonka](http://peterwonka.net/), [Matthias Mรผller](https://matthias.pw/) + +[[Paper]](https://arxiv.org/abs/2302.12288) + +![teaser](assets/zoedepth-teaser.png) + +## **Table of Contents** +- [**Usage**](#usage) + - [Using torch hub](#using-torch-hub) + - [Using local copy](#using-local-copy) + - [Using local torch hub](#using-local-torch-hub) + - [or load the models manually](#or-load-the-models-manually) + - [Using ZoeD models to predict depth](#using-zoed-models-to-predict-depth) +- [**Environment setup**](#environment-setup) +- [**Sanity checks** (Recommended)](#sanity-checks-recommended) +- [Model files](#model-files) +- [**Evaluation**](#evaluation) + - [Evaluating offical models](#evaluating-offical-models) + - [Evaluating local checkpoint](#evaluating-local-checkpoint) +- [**Training**](#training) +- [**Gradio demo**](#gradio-demo) +- [**Citation**](#citation) + + +## **Usage** +It is recommended to fetch the latest [MiDaS repo](https://github.com/isl-org/MiDaS) via torch hub before proceeding: +```python +import torch + +torch.hub.help("intel-isl/MiDaS", "DPT_BEiT_L_384", force_reload=True) # Triggers fresh download of MiDaS repo +``` +### **ZoeDepth models** +### Using torch hub +```python +import torch + +repo = "isl-org/ZoeDepth" +# Zoe_N +model_zoe_n = torch.hub.load(repo, "ZoeD_N", pretrained=True) + +# Zoe_K +model_zoe_k = torch.hub.load(repo, "ZoeD_K", pretrained=True) + +# Zoe_NK +model_zoe_nk = torch.hub.load(repo, "ZoeD_NK", pretrained=True) +``` +### Using local copy +Clone this repo: +```bash +git clone https://github.com/isl-org/ZoeDepth.git && cd ZoeDepth +``` +#### Using local torch hub +You can use local source for torch hub to load the ZoeDepth models, for example: +```python +import torch + +# Zoe_N +model_zoe_n = torch.hub.load(".", "ZoeD_N", source="local", pretrained=True) +``` + +#### or load the models manually +```python +from zoedepth.models.builder import build_model +from zoedepth.utils.config import get_config + +# ZoeD_N +conf = get_config("zoedepth", "infer") +model_zoe_n = build_model(conf) + +# ZoeD_K +conf = get_config("zoedepth", "infer", config_version="kitti") +model_zoe_k = build_model(conf) + +# ZoeD_NK +conf = get_config("zoedepth_nk", "infer") +model_zoe_nk = build_model(conf) +``` + +### Using ZoeD models to predict depth +```python +##### sample prediction +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +zoe = model_zoe_n.to(DEVICE) + + +# Local file +from PIL import Image +image = Image.open("/path/to/image.jpg").convert("RGB") # load +depth_numpy = zoe.infer_pil(image) # as numpy + +depth_pil = zoe.infer_pil(image, output_type="pil") # as 16-bit PIL Image + +depth_tensor = zoe.infer_pil(image, output_type="tensor") # as torch tensor + + + +# Tensor +from zoedepth.utils.misc import pil_to_batched_tensor +X = pil_to_batched_tensor(image).to(DEVICE) +depth_tensor = zoe.infer(X) + + + +# From URL +from zoedepth.utils.misc import get_image_from_url + +# Example URL +URL = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS4W8H_Nxk_rs3Vje_zj6mglPOH7bnPhQitBH8WkqjlqQVotdtDEG37BsnGofME3_u6lDk&usqp=CAU" + + +image = get_image_from_url(URL) # fetch +depth = zoe.infer_pil(image) + +# Save raw +from zoedepth.utils.misc import save_raw_16bit +fpath = "/path/to/output.png" +save_raw_16bit(depth, fpath) + +# Colorize output +from zoedepth.utils.misc import colorize + +colored = colorize(depth) + +# save colored output +fpath_colored = "/path/to/output_colored.png" +Image.fromarray(colored).save(fpath_colored) +``` + +## **Environment setup** +The project depends on : +- [pytorch](https://pytorch.org/) (Main framework) +- [timm](https://timm.fast.ai/) (Backbone helper for MiDaS) +- pillow, matplotlib, scipy, h5py, opencv (utilities) + +Install environment using `environment.yml` : + +Using [mamba](https://github.com/mamba-org/mamba) (fastest): +```bash +mamba env create -n zoe --file environment.yml +mamba activate zoe +``` +Using conda : + +```bash +conda env create -n zoe --file environment.yml +conda activate zoe +``` + +## **Sanity checks** (Recommended) +Check if models can be loaded: +```bash +python sanity_hub.py +``` +Try a demo prediction pipeline: +```bash +python sanity.py +``` +This will save a file `pred.png` in the root folder, showing RGB and corresponding predicted depth side-by-side. +## Model files +Models are defined under `models/` folder, with `models/_.py` containing model definitions and `models/config_.json` containing configuration. + +Single metric head models (Zoe_N and Zoe_K from the paper) have the common definition and are defined under `models/zoedepth` while as the multi-headed model (Zoe_NK) is defined under `models/zoedepth_nk`. +## **Evaluation** +Download the required dataset and change the `DATASETS_CONFIG` dictionary in `utils/config.py` accordingly. +### Evaluating offical models +On NYU-Depth-v2 for example: + +For ZoeD_N: +```bash +python evaluate.py -m zoedepth -d nyu +``` + +For ZoeD_NK: +```bash +python evaluate.py -m zoedepth_nk -d nyu +``` + +### Evaluating local checkpoint +```bash +python evaluate.py -m zoedepth --pretrained_resource="local::/path/to/local/ckpt.pt" -d nyu +``` +Pretrained resources are prefixed with `url::` to indicate weights should be fetched from a url, or `local::` to indicate path is a local file. Refer to `models/model_io.py` for details. + +The dataset name should match the corresponding key in `utils.config.DATASETS_CONFIG` . + +## **Training** +Download training datasets as per instructions given [here](https://github.com/cleinc/bts/tree/master/pytorch#nyu-depvh-v2). Then for training a single head model on NYU-Depth-v2 : +```bash +python train_mono.py -m zoedepth --pretrained_resource="" +``` + +For training the Zoe-NK model: +```bash +python train_mix.py -m zoedepth_nk --pretrained_resource="" +``` +## **Gradio demo** +We provide a UI demo built using [gradio](https://gradio.app/). To get started, install UI requirements: +```bash +pip install -r ui/ui_requirements.txt +``` +Then launch the gradio UI: +```bash +python -m ui.app +``` + +The UI is also hosted on HuggingFace๐Ÿค— [here](https://huggingface.co/spaces/shariqfarooq/ZoeDepth) +## **Citation** +``` +@misc{https://doi.org/10.48550/arxiv.2302.12288, + doi = {10.48550/ARXIV.2302.12288}, + + url = {https://arxiv.org/abs/2302.12288}, + + author = {Bhat, Shariq Farooq and Birkl, Reiner and Wofk, Diana and Wonka, Peter and Mรผller, Matthias}, + + keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, + + title = {ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth}, + + publisher = {arXiv}, + + year = {2023}, + + copyright = {arXiv.org perpetual, non-exclusive license} +} + +``` + + + + + + + + + + + + + diff --git a/ZoeDepth/environment.yml b/ZoeDepth/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..60d27771ee57eb447fc568af916b67d5583e4577 --- /dev/null +++ b/ZoeDepth/environment.yml @@ -0,0 +1,26 @@ +name: zoe +channels: + - pytorch + - nvidia + - conda-forge +dependencies: + - cuda=11.7.1 + - h5py=3.7.0 + - hdf5=1.12.2 + - matplotlib=3.6.2 + - matplotlib-base=3.6.2 + - numpy=1.24.1 + - opencv=4.6.0 + - pip=22.3.1 + - python=3.9.7 + - pytorch=1.13.1 + - pytorch-cuda=11.7 + - pytorch-mutex=1.0 + - scipy=1.10.0 + - torchaudio=0.13.1 + - torchvision=0.14.1 + - pip: + - huggingface-hub==0.11.1 + - timm==0.6.12 + - tqdm==4.64.1 + - wandb==0.13.9 diff --git a/ZoeDepth/evaluate.py b/ZoeDepth/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..902b6aedf8f709449d90d5e2009efc781557b7c6 --- /dev/null +++ b/ZoeDepth/evaluate.py @@ -0,0 +1,160 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import argparse +from pprint import pprint + +import torch +from zoedepth.utils.easydict import EasyDict as edict +from tqdm import tqdm + +from zoedepth.data.data_mono import DepthDataLoader +from zoedepth.models.builder import build_model +from zoedepth.utils.arg_utils import parse_unknown +from zoedepth.utils.config import change_dataset, get_config, ALL_EVAL_DATASETS, ALL_INDOOR, ALL_OUTDOOR +from zoedepth.utils.misc import (RunningAverageDict, colors, compute_metrics, + count_parameters) + + +@torch.no_grad() +def infer(model, images, **kwargs): + """Inference with flip augmentation""" + # images.shape = N, C, H, W + def get_depth_from_prediction(pred): + if isinstance(pred, torch.Tensor): + pred = pred # pass + elif isinstance(pred, (list, tuple)): + pred = pred[-1] + elif isinstance(pred, dict): + pred = pred['metric_depth'] if 'metric_depth' in pred else pred['out'] + else: + raise NotImplementedError(f"Unknown output type {type(pred)}") + return pred + + pred1 = model(images, **kwargs) + pred1 = get_depth_from_prediction(pred1) + + pred2 = model(torch.flip(images, [3]), **kwargs) + pred2 = get_depth_from_prediction(pred2) + pred2 = torch.flip(pred2, [3]) + + mean_pred = 0.5 * (pred1 + pred2) + + return mean_pred + + +@torch.no_grad() +def evaluate(model, test_loader, config, round_vals=True, round_precision=3): + model.eval() + metrics = RunningAverageDict() + for i, sample in tqdm(enumerate(test_loader), total=len(test_loader)): + if 'has_valid_depth' in sample: + if not sample['has_valid_depth']: + continue + image, depth = sample['image'], sample['depth'] + image, depth = image.cuda(), depth.cuda() + depth = depth.squeeze().unsqueeze(0).unsqueeze(0) + focal = sample.get('focal', torch.Tensor( + [715.0873]).cuda()) # This magic number (focal) is only used for evaluating BTS model + pred = infer(model, image, dataset=sample['dataset'][0], focal=focal) + + # Save image, depth, pred for visualization + if "save_images" in config and config.save_images: + import os + # print("Saving images ...") + from PIL import Image + import torchvision.transforms as transforms + from zoedepth.utils.misc import colorize + + os.makedirs(config.save_images, exist_ok=True) + # def save_image(img, path): + d = colorize(depth.squeeze().cpu().numpy(), 0, 10) + p = colorize(pred.squeeze().cpu().numpy(), 0, 10) + im = transforms.ToPILImage()(image.squeeze().cpu()) + im.save(os.path.join(config.save_images, f"{i}_img.png")) + Image.fromarray(d).save(os.path.join(config.save_images, f"{i}_depth.png")) + Image.fromarray(p).save(os.path.join(config.save_images, f"{i}_pred.png")) + + + + # print(depth.shape, pred.shape) + metrics.update(compute_metrics(depth, pred, config=config)) + + if round_vals: + def r(m): return round(m, round_precision) + else: + def r(m): return m + metrics = {k: r(v) for k, v in metrics.get_value().items()} + return metrics + +def main(config): + model = build_model(config) + test_loader = DepthDataLoader(config, 'online_eval').data + model = model.cuda() + metrics = evaluate(model, test_loader, config) + print(f"{colors.fg.green}") + print(metrics) + print(f"{colors.reset}") + metrics['#params'] = f"{round(count_parameters(model, include_all=True)/1e6, 2)}M" + return metrics + + +def eval_model(model_name, pretrained_resource, dataset='nyu', **kwargs): + + # Load default pretrained resource defined in config if not set + overwrite = {**kwargs, "pretrained_resource": pretrained_resource} if pretrained_resource else kwargs + config = get_config(model_name, "eval", dataset, **overwrite) + # config = change_dataset(config, dataset) # change the dataset + pprint(config) + print(f"Evaluating {model_name} on {dataset}...") + metrics = main(config) + return metrics + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("-m", "--model", type=str, + required=True, help="Name of the model to evaluate") + parser.add_argument("-p", "--pretrained_resource", type=str, + required=False, default=None, help="Pretrained resource to use for fetching weights. If not set, default resource from model config is used, Refer models.model_io.load_state_from_resource for more details.") + parser.add_argument("-d", "--dataset", type=str, required=False, + default='nyu', help="Dataset to evaluate on") + + args, unknown_args = parser.parse_known_args() + overwrite_kwargs = parse_unknown(unknown_args) + + if "ALL_INDOOR" in args.dataset: + datasets = ALL_INDOOR + elif "ALL_OUTDOOR" in args.dataset: + datasets = ALL_OUTDOOR + elif "ALL" in args.dataset: + datasets = ALL_EVAL_DATASETS + elif "," in args.dataset: + datasets = args.dataset.split(",") + else: + datasets = [args.dataset] + + for dataset in datasets: + eval_model(args.model, pretrained_resource=args.pretrained_resource, + dataset=dataset, **overwrite_kwargs) diff --git a/ZoeDepth/hubconf.py b/ZoeDepth/hubconf.py new file mode 100644 index 0000000000000000000000000000000000000000..6067c8fa96577893379e01bdcd3744682d7cda27 --- /dev/null +++ b/ZoeDepth/hubconf.py @@ -0,0 +1,154 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +dependencies=['torch'] +from zoedepth.utils.config import get_config +from zoedepth.models.builder import build_model +import numpy as np +import torch + + +# ZoeD_N +def ZoeD_N(pretrained=False, midas_model_type="DPT_BEiT_L_384", config_mode="infer", **kwargs): + """Zoe_M12_N model. This is the version of ZoeDepth that has a single metric head + Args: + pretrained (bool): If True, returns a model pre-trained on NYU-Depth-V2 + midas_model_type (str): Midas model type. Should be one of the models as listed in torch.hub.list("intel-isl/MiDaS"). Default: DPT_BEiT_L_384 + config_mode (str): Config mode. Should be one of "infer", "train" or "eval". Default: "infer" + + Keyword Args: + **kwargs: Additional arguments to pass to the model + The following arguments are supported: + train_midas (bool): If True, returns a model that with trainable midas base. Default: False + use_pretrained_midas (bool): If True, returns a model that uses pretrained midas base. Default: False + n_bins (int): Number of bin centers. Defaults to 64. + bin_centers_type (str): "normed" or "softplus". Activation type used for bin centers. For "normed" bin centers, linear normalization trick is applied. This results in bounded bin centers. + For "softplus", softplus activation is used and thus are unbounded. Defaults to "softplus". + bin_embedding_dim (int): bin embedding dimension. Defaults to 128. + min_depth (float): Lower bound for normed bin centers. Defaults to 1e-3. + max_depth (float): Upper bound for normed bin centers. Defaults to 10. + n_attractors (List[int]): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1]. + attractor_alpha (int): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 1000. + attractor_gamma (int): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2. + attractor_kind (str): Attraction aggregation "sum" or "mean". Defaults to 'mean'. + attractor_type (str): Type of attractor to use; "inv" (Inverse attractor) or "exp" (Exponential attractor). Defaults to 'inv'. + min_temp (int): Lower bound for temperature of output probability distribution. Defaults to 0.0212. + max_temp (int): Upper bound for temperature of output probability distribution. Defaults to 50. + force_keep_ar (bool): If True, the model will keep the aspect ratio of the input image. Defaults to True. + """ + if pretrained and midas_model_type != "DPT_BEiT_L_384": + raise ValueError(f"Only DPT_BEiT_L_384 MiDaS model is supported for pretrained Zoe_N model, got: {midas_model_type}") + + if not pretrained: + pretrained_resource = None + else: + pretrained_resource = "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_N.pt" + + config = get_config("zoedepth", config_mode, pretrained_resource=pretrained_resource, **kwargs) + model = build_model(config) + return model + +# ZoeD_K +def ZoeD_K(pretrained=False, midas_model_type="DPT_BEiT_L_384", config_mode="infer", **kwargs): + """Zoe_M12_K model. This is the version of ZoeDepth that has a single metric head + Args: + pretrained (bool): If True, returns a model pre-trained on NYU-Depth-V2 + midas_model_type (str): Midas model type. Should be one of the models as listed in torch.hub.list("intel-isl/MiDaS"). Default: DPT_BEiT_L_384 + config_mode (str): Config mode. Should be one of "infer", "train" or "eval". Default: "infer" + + Keyword Args: + **kwargs: Additional arguments to pass to the model + The following arguments are supported: + train_midas (bool): If True, returns a model that with trainable midas base. Default: False + use_pretrained_midas (bool): If True, returns a model that uses pretrained midas base. Default: False + n_bins (int): Number of bin centers. Defaults to 64. + bin_centers_type (str): "normed" or "softplus". Activation type used for bin centers. For "normed" bin centers, linear normalization trick is applied. This results in bounded bin centers. + For "softplus", softplus activation is used and thus are unbounded. Defaults to "softplus". + bin_embedding_dim (int): bin embedding dimension. Defaults to 128. + min_depth (float): Lower bound for normed bin centers. Defaults to 1e-3. + max_depth (float): Upper bound for normed bin centers. Defaults to 10. + n_attractors (List[int]): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1]. + attractor_alpha (int): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 1000. + attractor_gamma (int): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2. + attractor_kind (str): Attraction aggregation "sum" or "mean". Defaults to 'mean'. + attractor_type (str): Type of attractor to use; "inv" (Inverse attractor) or "exp" (Exponential attractor). Defaults to 'inv'. + min_temp (int): Lower bound for temperature of output probability distribution. Defaults to 0.0212. + max_temp (int): Upper bound for temperature of output probability distribution. Defaults to 50. + force_keep_ar (bool): If True, the model will keep the aspect ratio of the input image. Defaults to True. + + """ + if pretrained and midas_model_type != "DPT_BEiT_L_384": + raise ValueError(f"Only DPT_BEiT_L_384 MiDaS model is supported for pretrained Zoe_K model, got: {midas_model_type}") + + if not pretrained: + pretrained_resource = None + else: + pretrained_resource = "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_K.pt" + + config = get_config("zoedepth", config_mode, pretrained_resource=pretrained_resource, config_version="kitti", **kwargs) + model = build_model(config) + return model + +# Zoe_NK +def ZoeD_NK(pretrained=False, midas_model_type="DPT_BEiT_L_384", config_mode="infer", **kwargs): + """ZoeDepthNK model. This is the version of ZoeDepth that has two metric heads and uses a learned router to route to experts. + Args: + pretrained (bool): If True, returns a model pre-trained on NYU-Depth-V2 + midas_model_type (str): Midas model type. Should be one of the models as listed in torch.hub.list("intel-isl/MiDaS"). Default: DPT_BEiT_L_384 + + Keyword Args: + **kwargs: Additional arguments to pass to the model + The following arguments are supported: + train_midas (bool): If True, returns a model that with trainable midas base. Defaults to True + use_pretrained_midas (bool): If True, returns a model that uses pretrained midas base. Defaults to True + bin_conf (List[dict]): A list of dictionaries that contain the bin configuration for each metric head. Each dictionary should contain the following keys: + "name" (str, typically same as the dataset name), "n_bins" (int), "min_depth" (float), "max_depth" (float) + The length of this list determines the number of metric heads. + bin_centers_type (str): "normed" or "softplus". Activation type used for bin centers. For "normed" bin centers, linear normalization trick is applied. This results in bounded bin centers. + For "softplus", softplus activation is used and thus are unbounded. Defaults to "softplus". + bin_embedding_dim (int): bin embedding dimension. Defaults to 128. + + n_attractors (List[int]): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1]. + attractor_alpha (int): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 1000. + attractor_gamma (int): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2. + attractor_kind (str): Attraction aggregation "sum" or "mean". Defaults to 'mean'. + attractor_type (str): Type of attractor to use; "inv" (Inverse attractor) or "exp" (Exponential attractor). Defaults to 'inv'. + + min_temp (int): Lower bound for temperature of output probability distribution. Defaults to 0.0212. + max_temp (int): Upper bound for temperature of output probability distribution. Defaults to 50. + + memory_efficient (bool): Whether to use memory efficient version of attractor layers. Memory efficient version is slower but is recommended incase of multiple metric heads in order save GPU memory. Defaults to True. + + """ + if pretrained and midas_model_type != "DPT_BEiT_L_384": + raise ValueError(f"Only DPT_BEiT_L_384 MiDaS model is supported for pretrained Zoe_NK model, got: {midas_model_type}") + + if not pretrained: + pretrained_resource = None + else: + pretrained_resource = "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_NK.pt" + + config = get_config("zoedepth_nk", config_mode, pretrained_resource=pretrained_resource, **kwargs) + model = build_model(config) + return model \ No newline at end of file diff --git a/ZoeDepth/notebooks/ZoeDepth_quickstart.ipynb b/ZoeDepth/notebooks/ZoeDepth_quickstart.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9b7bf7ac060affc940abec7f2f780e7f0dbd128d --- /dev/null +++ b/ZoeDepth/notebooks/ZoeDepth_quickstart.ipynb @@ -0,0 +1,455 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "collapsed_sections": [ + "OF0_pagYc1NR", + "wqWnTZHqcaRR" + ] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU", + "gpuClass": "standard" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Welcome to ZoeDepth Colab notebook" + ], + "metadata": { + "id": "eHpqqdvza03n" + } + }, + { + "cell_type": "markdown", + "source": [ + "This notebook provides an easy to use interface for the depth estimation model \"ZoeDepth\".\n", + "\n", + "It accompanies our paper : [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://arxiv.org/abs/2302.12288v1)" + ], + "metadata": { + "id": "qnfC4dBNbTMh" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Setup" + ], + "metadata": { + "id": "OF0_pagYc1NR" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KX21oVM6SvSq", + "outputId": "580cb3bb-e1c5-4fa5-f3cd-587d3b2c4566" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: timm in /usr/local/lib/python3.8/dist-packages (0.6.12)\n", + "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.8/dist-packages (from timm) (0.12.1)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.8/dist-packages (from timm) (6.0)\n", + "Requirement already satisfied: torch>=1.7 in /usr/local/lib/python3.8/dist-packages (from timm) (1.13.1+cu116)\n", + "Requirement already satisfied: torchvision in /usr/local/lib/python3.8/dist-packages (from timm) (0.14.1+cu116)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch>=1.7->timm) (4.5.0)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface-hub->timm) (2.25.1)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface-hub->timm) (3.9.0)\n", + "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub->timm) (23.0)\n", + "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub->timm) (4.64.1)\n", + "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.8/dist-packages (from torchvision->timm) (7.1.2)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from torchvision->timm) (1.22.4)\n", + "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub->timm) (4.0.0)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub->timm) (1.24.3)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub->timm) (2.10)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub->timm) (2022.12.7)\n" + ] + } + ], + "source": [ + "!pip install --upgrade timm" + ] + }, + { + "cell_type": "code", + "source": [ + "!git clone https://github.com/isl-org/ZoeDepth.git" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2Mwb90-BTC9w", + "outputId": "030b9b43-3c04-42d2-ad65-485a717e8a6b" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "fatal: destination path 'ZoeDepth' already exists and is not an empty directory.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "cd ZoeDepth" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZFLgRBC3TVL2", + "outputId": "ad805cce-67e9-4d7e-d5ca-0a3742bc41f5" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/ZoeDepth\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Sanity checks, to make sure everything is working" + ], + "metadata": { + "id": "9MEhLSXKcAzE" + } + }, + { + "cell_type": "code", + "source": [ + "!python sanity.py" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-c7C22G1Tncc", + "outputId": "4ad4775f-b784-4d7e-acdb-bfeaeebdc235" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading: \"https://github.com/intel-isl/MiDaS/zipball/master\" to /root/.cache/torch/hub/master.zip\n", + "******************** Testing zoedepth ********************\n", + "Config:\n", + "{'attractor_alpha': 1000,\n", + " 'attractor_gamma': 2,\n", + " 'attractor_kind': 'mean',\n", + " 'attractor_type': 'inv',\n", + " 'aug': True,\n", + " 'bin_centers_type': 'softplus',\n", + " 'bin_embedding_dim': 128,\n", + " 'clip_grad': 0.1,\n", + " 'dataset': 'nyu',\n", + " 'distributed': True,\n", + " 'force_keep_ar': True,\n", + " 'gpu': None,\n", + " 'img_size': [384, 512],\n", + " 'inverse_midas': False,\n", + " 'log_images_every': 0.1,\n", + " 'max_temp': 50.0,\n", + " 'max_translation': 100,\n", + " 'memory_efficient': True,\n", + " 'midas_model_type': 'DPT_BEiT_L_384',\n", + " 'min_temp': 0.0212,\n", + " 'model': 'zoedepth',\n", + " 'n_attractors': [16, 8, 4, 1],\n", + " 'n_bins': 64,\n", + " 'name': 'ZoeDepth',\n", + " 'notes': '',\n", + " 'output_distribution': 'logbinomial',\n", + " 'prefetch': False,\n", + " 'pretrained_resource': 'url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_N.pt',\n", + " 'print_losses': False,\n", + " 'project': 'ZoeDepth',\n", + " 'random_crop': False,\n", + " 'random_translate': False,\n", + " 'root': '.',\n", + " 'save_dir': '/root/shortcuts/monodepth3_checkpoints',\n", + " 'shared_dict': None,\n", + " 'tags': '',\n", + " 'train_midas': False,\n", + " 'translate_prob': 0.2,\n", + " 'uid': None,\n", + " 'use_amp': False,\n", + " 'use_pretrained_midas': False,\n", + " 'use_shared_dict': False,\n", + " 'validate_every': 0.25,\n", + " 'version_name': 'v1',\n", + " 'workers': 16}\n", + "img_size [384, 512]\n", + "Using cache found in /root/.cache/torch/hub/intel-isl_MiDaS_master\n", + "/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)\n", + " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n", + "Params passed to Resize transform:\n", + "\twidth: 512\n", + "\theight: 384\n", + "\tresize_target: True\n", + "\tkeep_aspect_ratio: True\n", + "\tensure_multiple_of: 32\n", + "\tresize_method: minimal\n", + "Using pretrained resource url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_N.pt\n", + "Loaded successfully\n", + "--------------------Testing on a random input--------------------\n", + "metric_depth torch.Size([1, 1, 384, 512])\n", + "\n", + "\n", + "\n", + "-------------------- Testing on an indoor scene from url --------------------\n", + "X.shape torch.Size([1, 3, 193, 260])\n", + "predicting\n", + "output.shape torch.Size([1, 1, 193, 260])\n", + "saved pred.png\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Download file, only works on Chrome\n", + "from google.colab import files\n", + "files.download(\"pred.png\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "JVv53wUATtdi", + "outputId": "1542ef79-5f46-4f17-c973-0e56194c53b8" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_7bc911fa-a244-4e92-ab42-87f4c43ce7ac\", \"pred.png\", 80289)" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Load the model" + ], + "metadata": { + "id": "wqWnTZHqcaRR" + } + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from zoedepth.utils.misc import get_image_from_url, colorize\n", + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "zoe = torch.hub.load(\".\", \"ZoeD_N\", source=\"local\", pretrained=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FHxbIe1-VXVp", + "outputId": "ee333166-5718-4572-e30c-99ee9897f148" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "img_size [384, 512]\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Using cache found in /root/.cache/torch/hub/intel-isl_MiDaS_master\n", + "/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)\n", + " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Params passed to Resize transform:\n", + "\twidth: 512\n", + "\theight: 384\n", + "\tresize_target: True\n", + "\tkeep_aspect_ratio: True\n", + "\tensure_multiple_of: 32\n", + "\tresize_method: minimal\n", + "Using pretrained resource url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_N.pt\n", + "Loaded successfully\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "zoe = zoe.to('cuda')" + ], + "metadata": { + "id": "4roIdsrSW6wK" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Prediction" + ], + "metadata": { + "id": "BsJ1dyJtcpDM" + } + }, + { + "cell_type": "code", + "source": [ + "#@title Predicting depth from a url image\n", + "img_url = \"http://static1.squarespace.com/static/6213c340453c3f502425776e/62f2452bc121595f4d87c713/62f3c63c5eec2b12a333f851/1661442296756/Screenshot+2022-08-10+at+15.55.27.png?format=1500w\" #@param {type:\"string\"}\n", + "img = get_image_from_url(img_url)\n", + "depth = zoe.infer_pil(img)\n", + "\n", + "\n", + "colored_depth = colorize(depth)\n", + "fig, axs = plt.subplots(1,2, figsize=(15,7))\n", + "for ax, im, title in zip(axs, [img, colored_depth], ['Input', 'Predicted Depth']):\n", + " ax.imshow(im)\n", + " ax.axis('off')\n", + " ax.set_title(title)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 427 + }, + "cellView": "form", + "id": "OJ9bY7rrVuAq", + "outputId": "b33a6b54-fdbb-4515-b0fd-ff3ede3ad9b9" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "yGmrrFowXYNM" + }, + "execution_count": 8, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/ZoeDepth/sanity.py b/ZoeDepth/sanity.py new file mode 100644 index 0000000000000000000000000000000000000000..a4915dfb23048ada91f28af83de858185f77d4cf --- /dev/null +++ b/ZoeDepth/sanity.py @@ -0,0 +1,98 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import numpy as np +from torchvision.transforms import ToTensor +from PIL import Image +from zoedepth.utils.misc import get_image_from_url, colorize +import torch + +from zoedepth.models.builder import build_model +from zoedepth.utils.config import get_config +from pprint import pprint + + +torch.hub.help("intel-isl/MiDaS", "DPT_BEiT_L_384", force_reload=True) + +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +if DEVICE == "cpu": + print("WARNING: Running on CPU. This will be slow. Check your CUDA installation.") + +print("*" * 20 + " Testing zoedepth " + "*" * 20) +conf = get_config("zoedepth", "infer") + + +print("Config:") +pprint(conf) + +model = build_model(conf).to(DEVICE) +model.eval() +x = torch.rand(1, 3, 384, 512).to(DEVICE) + +print("-"*20 + "Testing on a random input" + "-"*20) + +with torch.no_grad(): + out = model(x) + +if isinstance(out, dict): + # print shapes of all outputs + for k, v in out.items(): + if v is not None: + print(k, v.shape) +else: + print([o.shape for o in out if o is not None]) + +print("\n\n") +print("-"*20 + " Testing on an indoor scene from url " + "-"*20) + +# Test img +url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS4W8H_Nxk_rs3Vje_zj6mglPOH7bnPhQitBH8WkqjlqQVotdtDEG37BsnGofME3_u6lDk&usqp=CAU" +img = get_image_from_url(url) +orig_size = img.size +X = ToTensor()(img) +X = X.unsqueeze(0).to(DEVICE) + +print("X.shape", X.shape) +print("predicting") + +with torch.no_grad(): + out = model.infer(X).cpu() + +# or just, +# out = model.infer_pil(img) + + +print("output.shape", out.shape) +pred = Image.fromarray(colorize(out)) +# Stack img and pred side by side for comparison and save +pred = pred.resize(orig_size, Image.ANTIALIAS) +stacked = Image.new("RGB", (orig_size[0]*2, orig_size[1])) +stacked.paste(img, (0, 0)) +stacked.paste(pred, (orig_size[0], 0)) + +stacked.save("pred.png") +print("saved pred.png") + + +model.infer_pil(img, output_type="pil").save("pred_raw.png") \ No newline at end of file diff --git a/ZoeDepth/sanity_hub.py b/ZoeDepth/sanity_hub.py new file mode 100644 index 0000000000000000000000000000000000000000..c6d41be85dbbd8d1ed294761a4e6f172a9bdfbb4 --- /dev/null +++ b/ZoeDepth/sanity_hub.py @@ -0,0 +1,43 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import numpy as np +from torchvision.transforms import ToTensor +from PIL import Image +from zoedepth.utils.misc import get_image_from_url, colorize + +from zoedepth.models.builder import build_model +from zoedepth.utils.config import get_config +from pprint import pprint + + + +# Trigger reload of MiDaS +torch.hub.help("intel-isl/MiDaS", "DPT_BEiT_L_384", force_reload=True) + + +model = torch.hub.load(".", "ZoeD_K", source="local", pretrained=True) +model = torch.hub.load(".", "ZoeD_NK", source="local", pretrained=True) +model = torch.hub.load(".", "ZoeD_N", source="local", pretrained=True) diff --git a/ZoeDepth/train_mix.py b/ZoeDepth/train_mix.py new file mode 100644 index 0000000000000000000000000000000000000000..a6496e267d75e01e737edf6a6462fcbe0dbc21c4 --- /dev/null +++ b/ZoeDepth/train_mix.py @@ -0,0 +1,179 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +from zoedepth.utils.misc import count_parameters, parallelize +from zoedepth.utils.config import get_config +from zoedepth.utils.arg_utils import parse_unknown +from zoedepth.trainers.builder import get_trainer +from zoedepth.models.builder import build_model +from zoedepth.data.data_mono import MixedNYUKITTI +import torch.utils.data.distributed +import torch.multiprocessing as mp +import torch +import numpy as np +from pprint import pprint +import argparse +import os + +os.environ["PYOPENGL_PLATFORM"] = "egl" +os.environ["WANDB_START_METHOD"] = "thread" + + +def fix_random_seed(seed: int): + """ + Fix random seed for reproducibility + + Args: + seed (int): random seed + """ + import random + + import numpy + import torch + + random.seed(seed) + numpy.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def load_ckpt(config, model, checkpoint_dir="./checkpoints", ckpt_type="best"): + import glob + import os + + from zoedepth.models.model_io import load_wts + + if hasattr(config, "checkpoint"): + checkpoint = config.checkpoint + elif hasattr(config, "ckpt_pattern"): + pattern = config.ckpt_pattern + matches = glob.glob(os.path.join( + checkpoint_dir, f"*{pattern}*{ckpt_type}*")) + if not (len(matches) > 0): + raise ValueError(f"No matches found for the pattern {pattern}") + + checkpoint = matches[0] + + else: + return model + model = load_wts(model, checkpoint) + print("Loaded weights from {0}".format(checkpoint)) + return model + + +def main_worker(gpu, ngpus_per_node, config): + try: + fix_random_seed(43) + + config.gpu = gpu + + model = build_model(config) + model = load_ckpt(config, model) + model = parallelize(config, model) + + total_params = f"{round(count_parameters(model)/1e6,2)}M" + config.total_params = total_params + print(f"Total parameters : {total_params}") + + train_loader = MixedNYUKITTI(config, "train").data + test_loader = MixedNYUKITTI(config, "online_eval").data + + trainer = get_trainer(config)( + config, model, train_loader, test_loader, device=config.gpu) + + trainer.train() + finally: + import wandb + wandb.finish() + + +if __name__ == '__main__': + mp.set_start_method('forkserver') + + parser = argparse.ArgumentParser() + parser.add_argument("-m", "--model", type=str, default="synunet") + parser.add_argument("-d", "--dataset", type=str, default='mix') + parser.add_argument("--trainer", type=str, default=None) + + args, unknown_args = parser.parse_known_args() + overwrite_kwargs = parse_unknown(unknown_args) + + overwrite_kwargs["model"] = args.model + if args.trainer is not None: + overwrite_kwargs["trainer"] = args.trainer + + config = get_config(args.model, "train", args.dataset, **overwrite_kwargs) + # git_commit() + if config.use_shared_dict: + shared_dict = mp.Manager().dict() + else: + shared_dict = None + config.shared_dict = shared_dict + + config.batch_size = config.bs + config.mode = 'train' + if config.root != "." and not os.path.isdir(config.root): + os.makedirs(config.root) + + try: + node_str = os.environ['SLURM_JOB_NODELIST'].replace( + '[', '').replace(']', '') + nodes = node_str.split(',') + + config.world_size = len(nodes) + config.rank = int(os.environ['SLURM_PROCID']) + # config.save_dir = "/ibex/scratch/bhatsf/videodepth/checkpoints" + + except KeyError as e: + # We are NOT using SLURM + config.world_size = 1 + config.rank = 0 + nodes = ["127.0.0.1"] + + if config.distributed: + + print(config.rank) + port = np.random.randint(15000, 15025) + config.dist_url = 'tcp://{}:{}'.format(nodes[0], port) + print(config.dist_url) + config.dist_backend = 'nccl' + config.gpu = None + + ngpus_per_node = torch.cuda.device_count() + config.num_workers = config.workers + config.ngpus_per_node = ngpus_per_node + print("Config:") + pprint(config) + if config.distributed: + config.world_size = ngpus_per_node * config.world_size + mp.spawn(main_worker, nprocs=ngpus_per_node, + args=(ngpus_per_node, config)) + else: + if ngpus_per_node == 1: + config.gpu = 0 + main_worker(config.gpu, ngpus_per_node, config) diff --git a/ZoeDepth/train_mono.py b/ZoeDepth/train_mono.py new file mode 100644 index 0000000000000000000000000000000000000000..eb21dc8d3b7f369827288130454f07692b24b30d --- /dev/null +++ b/ZoeDepth/train_mono.py @@ -0,0 +1,174 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +from zoedepth.utils.misc import count_parameters, parallelize +from zoedepth.utils.config import get_config +from zoedepth.utils.arg_utils import parse_unknown +from zoedepth.trainers.builder import get_trainer +from zoedepth.models.builder import build_model +from zoedepth.data.data_mono import DepthDataLoader +import torch.utils.data.distributed +import torch.multiprocessing as mp +import torch +import numpy as np +from pprint import pprint +import argparse +import os + +os.environ["PYOPENGL_PLATFORM"] = "egl" +os.environ["WANDB_START_METHOD"] = "thread" + + +def fix_random_seed(seed: int): + import random + + import numpy + import torch + + random.seed(seed) + numpy.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = True + + +def load_ckpt(config, model, checkpoint_dir="./checkpoints", ckpt_type="best"): + import glob + import os + + from zoedepth.models.model_io import load_wts + + if hasattr(config, "checkpoint"): + checkpoint = config.checkpoint + elif hasattr(config, "ckpt_pattern"): + pattern = config.ckpt_pattern + matches = glob.glob(os.path.join( + checkpoint_dir, f"*{pattern}*{ckpt_type}*")) + if not (len(matches) > 0): + raise ValueError(f"No matches found for the pattern {pattern}") + + checkpoint = matches[0] + + else: + return model + model = load_wts(model, checkpoint) + print("Loaded weights from {0}".format(checkpoint)) + return model + + +def main_worker(gpu, ngpus_per_node, config): + try: + seed = config.seed if 'seed' in config and config.seed else 43 + fix_random_seed(seed) + + config.gpu = gpu + + model = build_model(config) + model = load_ckpt(config, model) + model = parallelize(config, model) + + total_params = f"{round(count_parameters(model)/1e6,2)}M" + config.total_params = total_params + print(f"Total parameters : {total_params}") + + train_loader = DepthDataLoader(config, "train").data + test_loader = DepthDataLoader(config, "online_eval").data + + trainer = get_trainer(config)( + config, model, train_loader, test_loader, device=config.gpu) + + trainer.train() + finally: + import wandb + wandb.finish() + + +if __name__ == '__main__': + mp.set_start_method('forkserver') + + parser = argparse.ArgumentParser() + parser.add_argument("-m", "--model", type=str, default="synunet") + parser.add_argument("-d", "--dataset", type=str, default='nyu') + parser.add_argument("--trainer", type=str, default=None) + + args, unknown_args = parser.parse_known_args() + overwrite_kwargs = parse_unknown(unknown_args) + + overwrite_kwargs["model"] = args.model + if args.trainer is not None: + overwrite_kwargs["trainer"] = args.trainer + + config = get_config(args.model, "train", args.dataset, **overwrite_kwargs) + # git_commit() + if config.use_shared_dict: + shared_dict = mp.Manager().dict() + else: + shared_dict = None + config.shared_dict = shared_dict + + config.batch_size = config.bs + config.mode = 'train' + if config.root != "." and not os.path.isdir(config.root): + os.makedirs(config.root) + + try: + node_str = os.environ['SLURM_JOB_NODELIST'].replace( + '[', '').replace(']', '') + nodes = node_str.split(',') + + config.world_size = len(nodes) + config.rank = int(os.environ['SLURM_PROCID']) + # config.save_dir = "/ibex/scratch/bhatsf/videodepth/checkpoints" + + except KeyError as e: + # We are NOT using SLURM + config.world_size = 1 + config.rank = 0 + nodes = ["127.0.0.1"] + + if config.distributed: + + print(config.rank) + port = np.random.randint(15000, 15025) + config.dist_url = 'tcp://{}:{}'.format(nodes[0], port) + print(config.dist_url) + config.dist_backend = 'nccl' + config.gpu = None + + ngpus_per_node = torch.cuda.device_count() + config.num_workers = config.workers + config.ngpus_per_node = ngpus_per_node + print("Config:") + pprint(config) + if config.distributed: + config.world_size = ngpus_per_node * config.world_size + mp.spawn(main_worker, nprocs=ngpus_per_node, + args=(ngpus_per_node, config)) + else: + if ngpus_per_node == 1: + config.gpu = 0 + main_worker(config.gpu, ngpus_per_node, config) diff --git a/ZoeDepth/train_test_inputs/kitti_eigen_test_files_with_gt.txt b/ZoeDepth/train_test_inputs/kitti_eigen_test_files_with_gt.txt new file mode 100644 index 0000000000000000000000000000000000000000..575b66115c160f6c9df37a206ac8ba17e6771ccc --- /dev/null +++ b/ZoeDepth/train_test_inputs/kitti_eigen_test_files_with_gt.txt @@ -0,0 +1,697 @@ +2011_09_26/2011_09_26_drive_0002_sync/image_02/data/0000000069.png 2011_09_26_drive_0002_sync/proj_depth/groundtruth/image_02/0000000069.png 721.5377 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/dev/null +++ b/ZoeDepth/ui/app.py @@ -0,0 +1,66 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import gradio as gr +import torch + +from .gradio_depth_pred import create_demo as create_depth_pred_demo +from .gradio_im_to_3d import create_demo as create_im_to_3d_demo +from .gradio_pano_to_3d import create_demo as create_pano_to_3d_demo + + +css = """ +#img-display-container { + max-height: 50vh; + } +#img-display-input { + max-height: 40vh; + } +#img-display-output { + max-height: 40vh; + } + +""" +DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' +model = torch.hub.load('isl-org/ZoeDepth', "ZoeD_N", pretrained=True).to(DEVICE).eval() + +title = "# ZoeDepth" +description = """Official demo for **ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth**. + +ZoeDepth is a deep learning model for metric depth estimation from a single image. + +Please refer to our [paper](https://arxiv.org/abs/2302.12288) or [github](https://github.com/isl-org/ZoeDepth) for more details.""" + +with gr.Blocks(css=css) as demo: + gr.Markdown(title) + gr.Markdown(description) + with gr.Tab("Depth Prediction"): + create_depth_pred_demo(model) + with gr.Tab("Image to 3D"): + create_im_to_3d_demo(model) + with gr.Tab("360 Panorama to 3D"): + create_pano_to_3d_demo(model) + +if __name__ == '__main__': + demo.queue().launch() \ No newline at end of file diff --git a/ZoeDepth/ui/gradio_depth_pred.py b/ZoeDepth/ui/gradio_depth_pred.py new file mode 100644 index 0000000000000000000000000000000000000000..fb875451effe01cc336f140a5bb6a139b9e18004 --- /dev/null +++ b/ZoeDepth/ui/gradio_depth_pred.py @@ -0,0 +1,52 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import gradio as gr +from zoedepth.utils.misc import colorize +from PIL import Image +import tempfile + +def predict_depth(model, image): + depth = model.infer_pil(image) + return depth + +def create_demo(model): + gr.Markdown("### Depth Prediction demo") + with gr.Row(): + input_image = gr.Image(label="Input Image", type='pil', elem_id='img-display-input').style(height="auto") + depth_image = gr.Image(label="Depth Map", elem_id='img-display-output') + raw_file = gr.File(label="16-bit raw depth, multiplier:256") + submit = gr.Button("Submit") + + def on_submit(image): + depth = predict_depth(model, image) + colored_depth = colorize(depth, cmap='gray_r') + tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False) + raw_depth = Image.fromarray((depth*256).astype('uint16')) + raw_depth.save(tmp.name) + return [colored_depth, tmp.name] + + submit.click(on_submit, inputs=[input_image], outputs=[depth_image, raw_file]) + # examples = gr.Examples(examples=["examples/person_1.jpeg", "examples/person_2.jpeg", "examples/person-leaves.png", "examples/living-room.jpeg"], + # inputs=[input_image]) \ No newline at end of file diff --git a/ZoeDepth/ui/gradio_im_to_3d.py b/ZoeDepth/ui/gradio_im_to_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..242948e9049cd2db046fdfdd8b009d7cc55a4da6 --- /dev/null +++ b/ZoeDepth/ui/gradio_im_to_3d.py @@ -0,0 +1,93 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import gradio as gr +import numpy as np +import trimesh +from zoedepth.utils.geometry import depth_to_points, create_triangles +from functools import partial +import tempfile + + +def depth_edges_mask(depth): + """Returns a mask of edges in the depth map. + Args: + depth: 2D numpy array of shape (H, W) with dtype float32. + Returns: + mask: 2D numpy array of shape (H, W) with dtype bool. + """ + # Compute the x and y gradients of the depth map. + depth_dx, depth_dy = np.gradient(depth) + # Compute the gradient magnitude. + depth_grad = np.sqrt(depth_dx ** 2 + depth_dy ** 2) + # Compute the edge mask. + mask = depth_grad > 0.05 + return mask + + +def predict_depth(model, image): + depth = model.infer_pil(image) + return depth + +def get_mesh(model, image, keep_edges=False): + image.thumbnail((1024,1024)) # limit the size of the input image + depth = predict_depth(model, image) + pts3d = depth_to_points(depth[None]) + pts3d = pts3d.reshape(-1, 3) + + # Create a trimesh mesh from the points + # Each pixel is connected to its 4 neighbors + # colors are the RGB values of the image + + verts = pts3d.reshape(-1, 3) + image = np.array(image) + if keep_edges: + triangles = create_triangles(image.shape[0], image.shape[1]) + else: + triangles = create_triangles(image.shape[0], image.shape[1], mask=~depth_edges_mask(depth)) + colors = image.reshape(-1, 3) + mesh = trimesh.Trimesh(vertices=verts, faces=triangles, vertex_colors=colors) + + # Save as glb + glb_file = tempfile.NamedTemporaryFile(suffix='.glb', delete=False) + glb_path = glb_file.name + mesh.export(glb_path) + return glb_path + +def create_demo(model): + + gr.Markdown("### Image to 3D mesh") + gr.Markdown("Convert a single 2D image to a 3D mesh") + + with gr.Row(): + image = gr.Image(label="Input Image", type='pil') + result = gr.Model3D(label="3d mesh reconstruction", clear_color=[ + 1.0, 1.0, 1.0, 1.0]) + + checkbox = gr.Checkbox(label="Keep occlusion edges", value=False) + submit = gr.Button("Submit") + submit.click(partial(get_mesh, model), inputs=[image, checkbox], outputs=[result]) + # examples = gr.Examples(examples=["examples/aerial_beach.jpeg", "examples/mountains.jpeg", "examples/person_1.jpeg", "examples/ancient-carved.jpeg"], + # inputs=[image]) + diff --git a/ZoeDepth/ui/gradio_pano_to_3d.py b/ZoeDepth/ui/gradio_pano_to_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..2f8fb7ef1c64fdf0a5a2090610e3af433486f213 --- /dev/null +++ b/ZoeDepth/ui/gradio_pano_to_3d.py @@ -0,0 +1,120 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import gradio as gr +import numpy as np +import trimesh +from zoedepth.utils.geometry import create_triangles +from functools import partial +import tempfile + +def depth_edges_mask(depth): + """Returns a mask of edges in the depth map. + Args: + depth: 2D numpy array of shape (H, W) with dtype float32. + Returns: + mask: 2D numpy array of shape (H, W) with dtype bool. + """ + # Compute the x and y gradients of the depth map. + depth_dx, depth_dy = np.gradient(depth) + # Compute the gradient magnitude. + depth_grad = np.sqrt(depth_dx ** 2 + depth_dy ** 2) + # Compute the edge mask. + mask = depth_grad > 0.05 + return mask + + +def pano_depth_to_world_points(depth): + """ + 360 depth to world points + given 2D depth is an equirectangular projection of a spherical image + Treat depth as radius + + longitude : -pi to pi + latitude : -pi/2 to pi/2 + """ + + # Convert depth to radius + radius = depth.flatten() + + lon = np.linspace(-np.pi, np.pi, depth.shape[1]) + lat = np.linspace(-np.pi/2, np.pi/2, depth.shape[0]) + + lon, lat = np.meshgrid(lon, lat) + lon = lon.flatten() + lat = lat.flatten() + + # Convert to cartesian coordinates + x = radius * np.cos(lat) * np.cos(lon) + y = radius * np.cos(lat) * np.sin(lon) + z = radius * np.sin(lat) + + pts3d = np.stack([x, y, z], axis=1) + + return pts3d + + +def predict_depth(model, image): + depth = model.infer_pil(image) + return depth + +def get_mesh(model, image, keep_edges=False): + image.thumbnail((1024,1024)) # limit the size of the image + depth = predict_depth(model, image) + pts3d = pano_depth_to_world_points(depth) + + # Create a trimesh mesh from the points + # Each pixel is connected to its 4 neighbors + # colors are the RGB values of the image + + verts = pts3d.reshape(-1, 3) + image = np.array(image) + if keep_edges: + triangles = create_triangles(image.shape[0], image.shape[1]) + else: + triangles = create_triangles(image.shape[0], image.shape[1], mask=~depth_edges_mask(depth)) + colors = image.reshape(-1, 3) + mesh = trimesh.Trimesh(vertices=verts, faces=triangles, vertex_colors=colors) + + # Save as glb + glb_file = tempfile.NamedTemporaryFile(suffix='.glb', delete=False) + glb_path = glb_file.name + mesh.export(glb_path) + return glb_path + +def create_demo(model): + gr.Markdown("### Panorama to 3D mesh") + gr.Markdown("Convert a 360 spherical panorama to a 3D mesh") + gr.Markdown("ZoeDepth was not trained on panoramic images. It doesn't know anything about panoramas or spherical projection. Here, we just treat the estimated depth as radius and some projection errors are expected. Nonetheless, ZoeDepth still works surprisingly well on 360 reconstruction.") + + with gr.Row(): + input_image = gr.Image(label="Input Image", type='pil') + result = gr.Model3D(label="3d mesh reconstruction", clear_color=[ + 1.0, 1.0, 1.0, 1.0]) + + checkbox = gr.Checkbox(label="Keep occlusion edges", value=True) + submit = gr.Button("Submit") + submit.click(partial(get_mesh, model), inputs=[input_image, checkbox], outputs=[result]) + # examples = gr.Examples(examples=["examples/pano_1.jpeg", "examples/pano_2.jpeg", "examples/pano_3.jpeg"], + # inputs=[input_image]) \ No newline at end of file diff --git a/ZoeDepth/ui/ui_requirements.txt b/ZoeDepth/ui/ui_requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5dc0938dba3efa37e027636be8a293a6dad14d8b --- /dev/null +++ b/ZoeDepth/ui/ui_requirements.txt @@ -0,0 +1,2 @@ +gradio +trimesh==3.9.42 \ No newline at end of file diff --git a/ZoeDepth/zoedepth/data/__init__.py b/ZoeDepth/zoedepth/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5f2668792389157609abb2a0846fb620e7d67eb9 --- /dev/null +++ b/ZoeDepth/zoedepth/data/__init__.py @@ -0,0 +1,24 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + diff --git a/ZoeDepth/zoedepth/data/data_mono.py b/ZoeDepth/zoedepth/data/data_mono.py new file mode 100644 index 0000000000000000000000000000000000000000..80a8486f239a35331df553f490e213f9bf71e735 --- /dev/null +++ b/ZoeDepth/zoedepth/data/data_mono.py @@ -0,0 +1,573 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +# This file is partly inspired from BTS (https://github.com/cleinc/bts/blob/master/pytorch/bts_dataloader.py); author: Jin Han Lee + +import itertools +import os +import random + +import numpy as np +import cv2 +import torch +import torch.nn as nn +import torch.utils.data.distributed +from zoedepth.utils.easydict import EasyDict as edict +from PIL import Image, ImageOps +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + +from zoedepth.utils.config import change_dataset + +from .ddad import get_ddad_loader +from .diml_indoor_test import get_diml_indoor_loader +from .diml_outdoor_test import get_diml_outdoor_loader +from .diode import get_diode_loader +from .hypersim import get_hypersim_loader +from .ibims import get_ibims_loader +from .sun_rgbd_loader import get_sunrgbd_loader +from .vkitti import get_vkitti_loader +from .vkitti2 import get_vkitti2_loader + +from .preprocess import CropParams, get_white_border, get_black_border + + +def _is_pil_image(img): + return isinstance(img, Image.Image) + + +def _is_numpy_image(img): + return isinstance(img, np.ndarray) and (img.ndim in {2, 3}) + + +def preprocessing_transforms(mode, **kwargs): + return transforms.Compose([ + ToTensor(mode=mode, **kwargs) + ]) + + +class DepthDataLoader(object): + def __init__(self, config, mode, device='cpu', transform=None, **kwargs): + """ + Data loader for depth datasets + + Args: + config (dict): Config dictionary. Refer to utils/config.py + mode (str): "train" or "online_eval" + device (str, optional): Device to load the data on. Defaults to 'cpu'. + transform (torchvision.transforms, optional): Transform to apply to the data. Defaults to None. + """ + + self.config = config + + if config.dataset == 'ibims': + self.data = get_ibims_loader(config, batch_size=1, num_workers=1) + return + + if config.dataset == 'sunrgbd': + self.data = get_sunrgbd_loader( + data_dir_root=config.sunrgbd_root, batch_size=1, num_workers=1) + return + + if config.dataset == 'diml_indoor': + self.data = get_diml_indoor_loader( + data_dir_root=config.diml_indoor_root, batch_size=1, num_workers=1) + return + + if config.dataset == 'diml_outdoor': + self.data = get_diml_outdoor_loader( + data_dir_root=config.diml_outdoor_root, batch_size=1, num_workers=1) + return + + if "diode" in config.dataset: + self.data = get_diode_loader( + config[config.dataset+"_root"], batch_size=1, num_workers=1) + return + + if config.dataset == 'hypersim_test': + self.data = get_hypersim_loader( + config.hypersim_test_root, batch_size=1, num_workers=1) + return + + if config.dataset == 'vkitti': + self.data = get_vkitti_loader( + config.vkitti_root, batch_size=1, num_workers=1) + return + + if config.dataset == 'vkitti2': + self.data = get_vkitti2_loader( + config.vkitti2_root, batch_size=1, num_workers=1) + return + + if config.dataset == 'ddad': + self.data = get_ddad_loader(config.ddad_root, resize_shape=( + 352, 1216), batch_size=1, num_workers=1) + return + + img_size = self.config.get("img_size", None) + img_size = img_size if self.config.get( + "do_input_resize", False) else None + + if transform is None: + transform = preprocessing_transforms(mode, size=img_size) + + if mode == 'train': + + Dataset = DataLoadPreprocess + self.training_samples = Dataset( + config, mode, transform=transform, device=device) + + if config.distributed: + self.train_sampler = torch.utils.data.distributed.DistributedSampler( + self.training_samples) + else: + self.train_sampler = None + + self.data = DataLoader(self.training_samples, + batch_size=config.batch_size, + shuffle=(self.train_sampler is None), + num_workers=config.workers, + pin_memory=True, + persistent_workers=True, + # prefetch_factor=2, + sampler=self.train_sampler) + + elif mode == 'online_eval': + self.testing_samples = DataLoadPreprocess( + config, mode, transform=transform) + if config.distributed: # redundant. here only for readability and to be more explicit + # Give whole test set to all processes (and report evaluation only on one) regardless + self.eval_sampler = None + else: + self.eval_sampler = None + self.data = DataLoader(self.testing_samples, 1, + shuffle=kwargs.get("shuffle_test", False), + num_workers=1, + pin_memory=False, + sampler=self.eval_sampler) + + elif mode == 'test': + self.testing_samples = DataLoadPreprocess( + config, mode, transform=transform) + self.data = DataLoader(self.testing_samples, + 1, shuffle=False, num_workers=1) + + else: + print( + 'mode should be one of \'train, test, online_eval\'. Got {}'.format(mode)) + + +def repetitive_roundrobin(*iterables): + """ + cycles through iterables but sample wise + first yield first sample from first iterable then first sample from second iterable and so on + then second sample from first iterable then second sample from second iterable and so on + + If one iterable is shorter than the others, it is repeated until all iterables are exhausted + repetitive_roundrobin('ABC', 'D', 'EF') --> A D E B D F C D E + """ + # Repetitive roundrobin + iterables_ = [iter(it) for it in iterables] + exhausted = [False] * len(iterables) + while not all(exhausted): + for i, it in enumerate(iterables_): + try: + yield next(it) + except StopIteration: + exhausted[i] = True + iterables_[i] = itertools.cycle(iterables[i]) + # First elements may get repeated if one iterable is shorter than the others + yield next(iterables_[i]) + + +class RepetitiveRoundRobinDataLoader(object): + def __init__(self, *dataloaders): + self.dataloaders = dataloaders + + def __iter__(self): + return repetitive_roundrobin(*self.dataloaders) + + def __len__(self): + # First samples get repeated, thats why the plus one + return len(self.dataloaders) * (max(len(dl) for dl in self.dataloaders) + 1) + + +class MixedNYUKITTI(object): + def __init__(self, config, mode, device='cpu', **kwargs): + config = edict(config) + config.workers = config.workers // 2 + self.config = config + nyu_conf = change_dataset(edict(config), 'nyu') + kitti_conf = change_dataset(edict(config), 'kitti') + + # make nyu default for testing + self.config = config = nyu_conf + img_size = self.config.get("img_size", None) + img_size = img_size if self.config.get( + "do_input_resize", False) else None + if mode == 'train': + nyu_loader = DepthDataLoader( + nyu_conf, mode, device=device, transform=preprocessing_transforms(mode, size=img_size)).data + kitti_loader = DepthDataLoader( + kitti_conf, mode, device=device, transform=preprocessing_transforms(mode, size=img_size)).data + # It has been changed to repetitive roundrobin + self.data = RepetitiveRoundRobinDataLoader( + nyu_loader, kitti_loader) + else: + self.data = DepthDataLoader(nyu_conf, mode, device=device).data + + +def remove_leading_slash(s): + if s[0] == '/' or s[0] == '\\': + return s[1:] + return s + + +class CachedReader: + def __init__(self, shared_dict=None): + if shared_dict: + self._cache = shared_dict + else: + self._cache = {} + + def open(self, fpath): + im = self._cache.get(fpath, None) + if im is None: + im = self._cache[fpath] = Image.open(fpath) + return im + + +class ImReader: + def __init__(self): + pass + + # @cache + def open(self, fpath): + return Image.open(fpath) + + +class DataLoadPreprocess(Dataset): + def __init__(self, config, mode, transform=None, is_for_online_eval=False, **kwargs): + self.config = config + if mode == 'online_eval': + with open(config.filenames_file_eval, 'r') as f: + self.filenames = f.readlines() + else: + with open(config.filenames_file, 'r') as f: + self.filenames = f.readlines() + + self.mode = mode + self.transform = transform + self.to_tensor = ToTensor(mode) + self.is_for_online_eval = is_for_online_eval + if config.use_shared_dict: + self.reader = CachedReader(config.shared_dict) + else: + self.reader = ImReader() + + def postprocess(self, sample): + return sample + + def __getitem__(self, idx): + sample_path = self.filenames[idx] + focal = float(sample_path.split()[2]) + sample = {} + + if self.mode == 'train': + if self.config.dataset == 'kitti' and self.config.use_right and random.random() > 0.5: + image_path = os.path.join( + self.config.data_path, remove_leading_slash(sample_path.split()[3])) + depth_path = os.path.join( + self.config.gt_path, remove_leading_slash(sample_path.split()[4])) + else: + image_path = os.path.join( + self.config.data_path, remove_leading_slash(sample_path.split()[0])) + depth_path = os.path.join( + self.config.gt_path, remove_leading_slash(sample_path.split()[1])) + + image = self.reader.open(image_path) + depth_gt = self.reader.open(depth_path) + w, h = image.size + + if self.config.do_kb_crop: + height = image.height + width = image.width + top_margin = int(height - 352) + left_margin = int((width - 1216) / 2) + depth_gt = depth_gt.crop( + (left_margin, top_margin, left_margin + 1216, top_margin + 352)) + image = image.crop( + (left_margin, top_margin, left_margin + 1216, top_margin + 352)) + + # Avoid blank boundaries due to pixel registration? + # Train images have white border. Test images have black border. + if self.config.dataset == 'nyu' and self.config.avoid_boundary: + # print("Avoiding Blank Boundaries!") + # We just crop and pad again with reflect padding to original size + # original_size = image.size + crop_params = get_white_border(np.array(image, dtype=np.uint8)) + image = image.crop((crop_params.left, crop_params.top, crop_params.right, crop_params.bottom)) + depth_gt = depth_gt.crop((crop_params.left, crop_params.top, crop_params.right, crop_params.bottom)) + + # Use reflect padding to fill the blank + image = np.array(image) + image = np.pad(image, ((crop_params.top, h - crop_params.bottom), (crop_params.left, w - crop_params.right), (0, 0)), mode='reflect') + image = Image.fromarray(image) + + depth_gt = np.array(depth_gt) + depth_gt = np.pad(depth_gt, ((crop_params.top, h - crop_params.bottom), (crop_params.left, w - crop_params.right)), 'constant', constant_values=0) + depth_gt = Image.fromarray(depth_gt) + + + if self.config.do_random_rotate and (self.config.aug): + random_angle = (random.random() - 0.5) * 2 * self.config.degree + image = self.rotate_image(image, random_angle) + depth_gt = self.rotate_image( + depth_gt, random_angle, flag=Image.NEAREST) + + image = np.asarray(image, dtype=np.float32) / 255.0 + depth_gt = np.asarray(depth_gt, dtype=np.float32) + depth_gt = np.expand_dims(depth_gt, axis=2) + + if self.config.dataset == 'nyu': + depth_gt = depth_gt / 1000.0 + else: + depth_gt = depth_gt / 256.0 + + if self.config.aug and (self.config.random_crop): + image, depth_gt = self.random_crop( + image, depth_gt, self.config.input_height, self.config.input_width) + + if self.config.aug and self.config.random_translate: + # print("Random Translation!") + image, depth_gt = self.random_translate(image, depth_gt, self.config.max_translation) + + image, depth_gt = self.train_preprocess(image, depth_gt) + mask = np.logical_and(depth_gt > self.config.min_depth, + depth_gt < self.config.max_depth).squeeze()[None, ...] + sample = {'image': image, 'depth': depth_gt, 'focal': focal, + 'mask': mask, **sample} + + else: + if self.mode == 'online_eval': + data_path = self.config.data_path_eval + else: + data_path = self.config.data_path + + image_path = os.path.join( + data_path, remove_leading_slash(sample_path.split()[0])) + image = np.asarray(self.reader.open(image_path), + dtype=np.float32) / 255.0 + + if self.mode == 'online_eval': + gt_path = self.config.gt_path_eval + depth_path = os.path.join( + gt_path, remove_leading_slash(sample_path.split()[1])) + has_valid_depth = False + try: + depth_gt = self.reader.open(depth_path) + has_valid_depth = True + except IOError: + depth_gt = False + # print('Missing gt for {}'.format(image_path)) + + if has_valid_depth: + depth_gt = np.asarray(depth_gt, dtype=np.float32) + depth_gt = np.expand_dims(depth_gt, axis=2) + if self.config.dataset == 'nyu': + depth_gt = depth_gt / 1000.0 + else: + depth_gt = depth_gt / 256.0 + + mask = np.logical_and( + depth_gt >= self.config.min_depth, depth_gt <= self.config.max_depth).squeeze()[None, ...] + else: + mask = False + + if self.config.do_kb_crop: + height = image.shape[0] + width = image.shape[1] + top_margin = int(height - 352) + left_margin = int((width - 1216) / 2) + image = image[top_margin:top_margin + 352, + left_margin:left_margin + 1216, :] + if self.mode == 'online_eval' and has_valid_depth: + depth_gt = depth_gt[top_margin:top_margin + + 352, left_margin:left_margin + 1216, :] + + if self.mode == 'online_eval': + sample = {'image': image, 'depth': depth_gt, 'focal': focal, 'has_valid_depth': has_valid_depth, + 'image_path': sample_path.split()[0], 'depth_path': sample_path.split()[1], + 'mask': mask} + else: + sample = {'image': image, 'focal': focal} + + if (self.mode == 'train') or ('has_valid_depth' in sample and sample['has_valid_depth']): + mask = np.logical_and(depth_gt > self.config.min_depth, + depth_gt < self.config.max_depth).squeeze()[None, ...] + sample['mask'] = mask + + if self.transform: + sample = self.transform(sample) + + sample = self.postprocess(sample) + sample['dataset'] = self.config.dataset + sample = {**sample, 'image_path': sample_path.split()[0], 'depth_path': sample_path.split()[1]} + + return sample + + def rotate_image(self, image, angle, flag=Image.BILINEAR): + result = image.rotate(angle, resample=flag) + return result + + def random_crop(self, img, depth, height, width): + assert img.shape[0] >= height + assert img.shape[1] >= width + assert img.shape[0] == depth.shape[0] + assert img.shape[1] == depth.shape[1] + x = random.randint(0, img.shape[1] - width) + y = random.randint(0, img.shape[0] - height) + img = img[y:y + height, x:x + width, :] + depth = depth[y:y + height, x:x + width, :] + + return img, depth + + def random_translate(self, img, depth, max_t=20): + assert img.shape[0] == depth.shape[0] + assert img.shape[1] == depth.shape[1] + p = self.config.translate_prob + do_translate = random.random() + if do_translate > p: + return img, depth + x = random.randint(-max_t, max_t) + y = random.randint(-max_t, max_t) + M = np.float32([[1, 0, x], [0, 1, y]]) + # print(img.shape, depth.shape) + img = cv2.warpAffine(img, M, (img.shape[1], img.shape[0])) + depth = cv2.warpAffine(depth, M, (depth.shape[1], depth.shape[0])) + depth = depth.squeeze()[..., None] # add channel dim back. Affine warp removes it + # print("after", img.shape, depth.shape) + return img, depth + + def train_preprocess(self, image, depth_gt): + if self.config.aug: + # Random flipping + do_flip = random.random() + if do_flip > 0.5: + image = (image[:, ::-1, :]).copy() + depth_gt = (depth_gt[:, ::-1, :]).copy() + + # Random gamma, brightness, color augmentation + do_augment = random.random() + if do_augment > 0.5: + image = self.augment_image(image) + + return image, depth_gt + + def augment_image(self, image): + # gamma augmentation + gamma = random.uniform(0.9, 1.1) + image_aug = image ** gamma + + # brightness augmentation + if self.config.dataset == 'nyu': + brightness = random.uniform(0.75, 1.25) + else: + brightness = random.uniform(0.9, 1.1) + image_aug = image_aug * brightness + + # color augmentation + colors = np.random.uniform(0.9, 1.1, size=3) + white = np.ones((image.shape[0], image.shape[1])) + color_image = np.stack([white * colors[i] for i in range(3)], axis=2) + image_aug *= color_image + image_aug = np.clip(image_aug, 0, 1) + + return image_aug + + def __len__(self): + return len(self.filenames) + + +class ToTensor(object): + def __init__(self, mode, do_normalize=False, size=None): + self.mode = mode + self.normalize = transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) if do_normalize else nn.Identity() + self.size = size + if size is not None: + self.resize = transforms.Resize(size=size) + else: + self.resize = nn.Identity() + + def __call__(self, sample): + image, focal = sample['image'], sample['focal'] + image = self.to_tensor(image) + image = self.normalize(image) + image = self.resize(image) + + if self.mode == 'test': + return {'image': image, 'focal': focal} + + depth = sample['depth'] + if self.mode == 'train': + depth = self.to_tensor(depth) + return {**sample, 'image': image, 'depth': depth, 'focal': focal} + else: + has_valid_depth = sample['has_valid_depth'] + image = self.resize(image) + return {**sample, 'image': image, 'depth': depth, 'focal': focal, 'has_valid_depth': has_valid_depth, + 'image_path': sample['image_path'], 'depth_path': sample['depth_path']} + + def to_tensor(self, pic): + if not (_is_pil_image(pic) or _is_numpy_image(pic)): + raise TypeError( + 'pic should be PIL Image or ndarray. Got {}'.format(type(pic))) + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img diff --git a/ZoeDepth/zoedepth/data/ddad.py b/ZoeDepth/zoedepth/data/ddad.py new file mode 100644 index 0000000000000000000000000000000000000000..4bd0492bdec767685d3a21992b4a26e62d002d97 --- /dev/null +++ b/ZoeDepth/zoedepth/data/ddad.py @@ -0,0 +1,117 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self, resize_shape): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + self.resize = transforms.Resize(resize_shape) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "ddad"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class DDAD(Dataset): + def __init__(self, data_dir_root, resize_shape): + import glob + + # image paths are of the form /{outleft, depthmap}/*.png + self.image_files = glob.glob(os.path.join(data_dir_root, '*.png')) + self.depth_files = [r.replace("_rgb.png", "_depth.npy") + for r in self.image_files] + self.transform = ToTensor(resize_shape) + + def __getitem__(self, idx): + + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + depth = np.load(depth_path) # meters + + # depth[depth > 8] = -1 + depth = depth[..., None] + + sample = dict(image=image, depth=depth) + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_ddad_loader(data_dir_root, resize_shape, batch_size=1, **kwargs): + dataset = DDAD(data_dir_root, resize_shape) + return DataLoader(dataset, batch_size, **kwargs) diff --git a/ZoeDepth/zoedepth/data/diml_indoor_test.py b/ZoeDepth/zoedepth/data/diml_indoor_test.py new file mode 100644 index 0000000000000000000000000000000000000000..f720ad9aefaee78ef4ec363dfef0f82ace850a6d --- /dev/null +++ b/ZoeDepth/zoedepth/data/diml_indoor_test.py @@ -0,0 +1,125 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + self.resize = transforms.Resize((480, 640)) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "diml_indoor"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class DIML_Indoor(Dataset): + def __init__(self, data_dir_root): + import glob + + # image paths are of the form /{HR, LR}//{color, depth_filled}/*.png + self.image_files = glob.glob(os.path.join( + data_dir_root, "LR", '*', 'color', '*.png')) + self.depth_files = [r.replace("color", "depth_filled").replace( + "_c.png", "_depth_filled.png") for r in self.image_files] + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + depth = np.asarray(Image.open(depth_path), + dtype='uint16') / 1000.0 # mm to meters + + # print(np.shape(image)) + # print(np.shape(depth)) + + # depth[depth > 8] = -1 + depth = depth[..., None] + + sample = dict(image=image, depth=depth) + + # return sample + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_diml_indoor_loader(data_dir_root, batch_size=1, **kwargs): + dataset = DIML_Indoor(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) + +# get_diml_indoor_loader(data_dir_root="datasets/diml/indoor/test/HR") +# get_diml_indoor_loader(data_dir_root="datasets/diml/indoor/test/LR") diff --git a/ZoeDepth/zoedepth/data/diml_outdoor_test.py b/ZoeDepth/zoedepth/data/diml_outdoor_test.py new file mode 100644 index 0000000000000000000000000000000000000000..8670b48f5febafb819dac22848ad79ccb5dd5ae4 --- /dev/null +++ b/ZoeDepth/zoedepth/data/diml_outdoor_test.py @@ -0,0 +1,114 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + return {'image': image, 'depth': depth, 'dataset': "diml_outdoor"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class DIML_Outdoor(Dataset): + def __init__(self, data_dir_root): + import glob + + # image paths are of the form /{outleft, depthmap}/*.png + self.image_files = glob.glob(os.path.join( + data_dir_root, "*", 'outleft', '*.png')) + self.depth_files = [r.replace("outleft", "depthmap") + for r in self.image_files] + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + depth = np.asarray(Image.open(depth_path), + dtype='uint16') / 1000.0 # mm to meters + + # depth[depth > 8] = -1 + depth = depth[..., None] + + sample = dict(image=image, depth=depth, dataset="diml_outdoor") + + # return sample + return self.transform(sample) + + def __len__(self): + return len(self.image_files) + + +def get_diml_outdoor_loader(data_dir_root, batch_size=1, **kwargs): + dataset = DIML_Outdoor(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) + +# get_diml_outdoor_loader(data_dir_root="datasets/diml/outdoor/test/HR") +# get_diml_outdoor_loader(data_dir_root="datasets/diml/outdoor/test/LR") diff --git a/ZoeDepth/zoedepth/data/diode.py b/ZoeDepth/zoedepth/data/diode.py new file mode 100644 index 0000000000000000000000000000000000000000..1510c87116b8f70ce2e1428873a8e4da042bee23 --- /dev/null +++ b/ZoeDepth/zoedepth/data/diode.py @@ -0,0 +1,125 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + self.resize = transforms.Resize(480) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "diode"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class DIODE(Dataset): + def __init__(self, data_dir_root): + import glob + + # image paths are of the form /scene_#/scan_#/*.png + self.image_files = glob.glob( + os.path.join(data_dir_root, '*', '*', '*.png')) + self.depth_files = [r.replace(".png", "_depth.npy") + for r in self.image_files] + self.depth_mask_files = [ + r.replace(".png", "_depth_mask.npy") for r in self.image_files] + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + depth_mask_path = self.depth_mask_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + depth = np.load(depth_path) # in meters + valid = np.load(depth_mask_path) # binary + + # depth[depth > 8] = -1 + # depth = depth[..., None] + + sample = dict(image=image, depth=depth, valid=valid) + + # return sample + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_diode_loader(data_dir_root, batch_size=1, **kwargs): + dataset = DIODE(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) + +# get_diode_loader(data_dir_root="datasets/diode/val/outdoor") diff --git a/ZoeDepth/zoedepth/data/hypersim.py b/ZoeDepth/zoedepth/data/hypersim.py new file mode 100644 index 0000000000000000000000000000000000000000..4334198971830200f72ea2910d03f4c7d6a43334 --- /dev/null +++ b/ZoeDepth/zoedepth/data/hypersim.py @@ -0,0 +1,138 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import glob +import os + +import h5py +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +def hypersim_distance_to_depth(npyDistance): + intWidth, intHeight, fltFocal = 1024, 768, 886.81 + + npyImageplaneX = np.linspace((-0.5 * intWidth) + 0.5, (0.5 * intWidth) - 0.5, intWidth).reshape( + 1, intWidth).repeat(intHeight, 0).astype(np.float32)[:, :, None] + npyImageplaneY = np.linspace((-0.5 * intHeight) + 0.5, (0.5 * intHeight) - 0.5, + intHeight).reshape(intHeight, 1).repeat(intWidth, 1).astype(np.float32)[:, :, None] + npyImageplaneZ = np.full([intHeight, intWidth, 1], fltFocal, np.float32) + npyImageplane = np.concatenate( + [npyImageplaneX, npyImageplaneY, npyImageplaneZ], 2) + + npyDepth = npyDistance / np.linalg.norm(npyImageplane, 2, 2) * fltFocal + return npyDepth + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x: x + self.resize = transforms.Resize((480, 640)) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "hypersim"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class HyperSim(Dataset): + def __init__(self, data_dir_root): + # image paths are of the form //images/scene_cam_#_final_preview/*.tonemap.jpg + # depth paths are of the form //images/scene_cam_#_final_preview/*.depth_meters.hdf5 + self.image_files = glob.glob(os.path.join( + data_dir_root, '*', 'images', 'scene_cam_*_final_preview', '*.tonemap.jpg')) + self.depth_files = [r.replace("_final_preview", "_geometry_hdf5").replace( + ".tonemap.jpg", ".depth_meters.hdf5") for r in self.image_files] + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + + # depth from hdf5 + depth_fd = h5py.File(depth_path, "r") + # in meters (Euclidean distance) + distance_meters = np.array(depth_fd['dataset']) + depth = hypersim_distance_to_depth( + distance_meters) # in meters (planar depth) + + # depth[depth > 8] = -1 + depth = depth[..., None] + + sample = dict(image=image, depth=depth) + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_hypersim_loader(data_dir_root, batch_size=1, **kwargs): + dataset = HyperSim(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) diff --git a/ZoeDepth/zoedepth/data/ibims.py b/ZoeDepth/zoedepth/data/ibims.py new file mode 100644 index 0000000000000000000000000000000000000000..b66abfabcf4cfc617d4a60ec818780c3548d9920 --- /dev/null +++ b/ZoeDepth/zoedepth/data/ibims.py @@ -0,0 +1,81 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms as T + + +class iBims(Dataset): + def __init__(self, config): + root_folder = config.ibims_root + with open(os.path.join(root_folder, "imagelist.txt"), 'r') as f: + imglist = f.read().split() + + samples = [] + for basename in imglist: + img_path = os.path.join(root_folder, 'rgb', basename + ".png") + depth_path = os.path.join(root_folder, 'depth', basename + ".png") + valid_mask_path = os.path.join( + root_folder, 'mask_invalid', basename+".png") + transp_mask_path = os.path.join( + root_folder, 'mask_transp', basename+".png") + + samples.append( + (img_path, depth_path, valid_mask_path, transp_mask_path)) + + self.samples = samples + # self.normalize = T.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + + def __getitem__(self, idx): + img_path, depth_path, valid_mask_path, transp_mask_path = self.samples[idx] + + img = np.asarray(Image.open(img_path), dtype=np.float32) / 255.0 + depth = np.asarray(Image.open(depth_path), + dtype=np.uint16).astype('float')*50.0/65535 + + mask_valid = np.asarray(Image.open(valid_mask_path)) + mask_transp = np.asarray(Image.open(transp_mask_path)) + + # depth = depth * mask_valid * mask_transp + depth = np.where(mask_valid * mask_transp, depth, -1) + + img = torch.from_numpy(img).permute(2, 0, 1) + img = self.normalize(img) + depth = torch.from_numpy(depth).unsqueeze(0) + return dict(image=img, depth=depth, image_path=img_path, depth_path=depth_path, dataset='ibims') + + def __len__(self): + return len(self.samples) + + +def get_ibims_loader(config, batch_size=1, **kwargs): + dataloader = DataLoader(iBims(config), batch_size=batch_size, **kwargs) + return dataloader diff --git a/ZoeDepth/zoedepth/data/preprocess.py b/ZoeDepth/zoedepth/data/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..e08cc309dc823ae6efd7cda8db9eb37130dc5499 --- /dev/null +++ b/ZoeDepth/zoedepth/data/preprocess.py @@ -0,0 +1,154 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import numpy as np +from dataclasses import dataclass +from typing import Tuple, List + +# dataclass to store the crop parameters +@dataclass +class CropParams: + top: int + bottom: int + left: int + right: int + + + +def get_border_params(rgb_image, tolerance=0.1, cut_off=20, value=0, level_diff_threshold=5, channel_axis=-1, min_border=5) -> CropParams: + gray_image = np.mean(rgb_image, axis=channel_axis) + h, w = gray_image.shape + + + def num_value_pixels(arr): + return np.sum(np.abs(arr - value) < level_diff_threshold) + + def is_above_tolerance(arr, total_pixels): + return (num_value_pixels(arr) / total_pixels) > tolerance + + # Crop top border until number of value pixels become below tolerance + top = min_border + while is_above_tolerance(gray_image[top, :], w) and top < h-1: + top += 1 + if top > cut_off: + break + + # Crop bottom border until number of value pixels become below tolerance + bottom = h - min_border + while is_above_tolerance(gray_image[bottom, :], w) and bottom > 0: + bottom -= 1 + if h - bottom > cut_off: + break + + # Crop left border until number of value pixels become below tolerance + left = min_border + while is_above_tolerance(gray_image[:, left], h) and left < w-1: + left += 1 + if left > cut_off: + break + + # Crop right border until number of value pixels become below tolerance + right = w - min_border + while is_above_tolerance(gray_image[:, right], h) and right > 0: + right -= 1 + if w - right > cut_off: + break + + + return CropParams(top, bottom, left, right) + + +def get_white_border(rgb_image, value=255, **kwargs) -> CropParams: + """Crops the white border of the RGB. + + Args: + rgb: RGB image, shape (H, W, 3). + Returns: + Crop parameters. + """ + if value == 255: + # assert range of values in rgb image is [0, 255] + assert np.max(rgb_image) <= 255 and np.min(rgb_image) >= 0, "RGB image values are not in range [0, 255]." + assert rgb_image.max() > 1, "RGB image values are not in range [0, 255]." + elif value == 1: + # assert range of values in rgb image is [0, 1] + assert np.max(rgb_image) <= 1 and np.min(rgb_image) >= 0, "RGB image values are not in range [0, 1]." + + return get_border_params(rgb_image, value=value, **kwargs) + +def get_black_border(rgb_image, **kwargs) -> CropParams: + """Crops the black border of the RGB. + + Args: + rgb: RGB image, shape (H, W, 3). + + Returns: + Crop parameters. + """ + + return get_border_params(rgb_image, value=0, **kwargs) + +def crop_image(image: np.ndarray, crop_params: CropParams) -> np.ndarray: + """Crops the image according to the crop parameters. + + Args: + image: RGB or depth image, shape (H, W, 3) or (H, W). + crop_params: Crop parameters. + + Returns: + Cropped image. + """ + return image[crop_params.top:crop_params.bottom, crop_params.left:crop_params.right] + +def crop_images(*images: np.ndarray, crop_params: CropParams) -> Tuple[np.ndarray]: + """Crops the images according to the crop parameters. + + Args: + images: RGB or depth images, shape (H, W, 3) or (H, W). + crop_params: Crop parameters. + + Returns: + Cropped images. + """ + return tuple(crop_image(image, crop_params) for image in images) + +def crop_black_or_white_border(rgb_image, *other_images: np.ndarray, tolerance=0.1, cut_off=20, level_diff_threshold=5) -> Tuple[np.ndarray]: + """Crops the white and black border of the RGB and depth images. + + Args: + rgb: RGB image, shape (H, W, 3). This image is used to determine the border. + other_images: The other images to crop according to the border of the RGB image. + Returns: + Cropped RGB and other images. + """ + # crop black border + crop_params = get_black_border(rgb_image, tolerance=tolerance, cut_off=cut_off, level_diff_threshold=level_diff_threshold) + cropped_images = crop_images(rgb_image, *other_images, crop_params=crop_params) + + # crop white border + crop_params = get_white_border(cropped_images[0], tolerance=tolerance, cut_off=cut_off, level_diff_threshold=level_diff_threshold) + cropped_images = crop_images(*cropped_images, crop_params=crop_params) + + return cropped_images + \ No newline at end of file diff --git a/ZoeDepth/zoedepth/data/sun_rgbd_loader.py b/ZoeDepth/zoedepth/data/sun_rgbd_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..9e2bdb9aefe68ca4439f41eff3bba722c49fb976 --- /dev/null +++ b/ZoeDepth/zoedepth/data/sun_rgbd_loader.py @@ -0,0 +1,106 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + return {'image': image, 'depth': depth, 'dataset': "sunrgbd"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class SunRGBD(Dataset): + def __init__(self, data_dir_root): + # test_file_dirs = loadmat(train_test_file)['alltest'].squeeze() + # all_test = [t[0].replace("/n/fs/sun3d/data/", "") for t in test_file_dirs] + # self.all_test = [os.path.join(data_dir_root, t) for t in all_test] + import glob + self.image_files = glob.glob( + os.path.join(data_dir_root, 'rgb', 'rgb', '*')) + self.depth_files = [ + r.replace("rgb/rgb", "gt/gt").replace("jpg", "png") for r in self.image_files] + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + depth = np.asarray(Image.open(depth_path), dtype='uint16') / 1000.0 + depth[depth > 8] = -1 + depth = depth[..., None] + return self.transform(dict(image=image, depth=depth)) + + def __len__(self): + return len(self.image_files) + + +def get_sunrgbd_loader(data_dir_root, batch_size=1, **kwargs): + dataset = SunRGBD(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) diff --git a/ZoeDepth/zoedepth/data/transforms.py b/ZoeDepth/zoedepth/data/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..374416dff24fb4fd55598f3946d6d6b091ddefc9 --- /dev/null +++ b/ZoeDepth/zoedepth/data/transforms.py @@ -0,0 +1,481 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import math +import random + +import cv2 +import numpy as np + + +class RandomFliplr(object): + """Horizontal flip of the sample with given probability. + """ + + def __init__(self, probability=0.5): + """Init. + + Args: + probability (float, optional): Flip probability. Defaults to 0.5. + """ + self.__probability = probability + + def __call__(self, sample): + prob = random.random() + + if prob < self.__probability: + for k, v in sample.items(): + if len(v.shape) >= 2: + sample[k] = np.fliplr(v).copy() + + return sample + + +def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA): + """Rezise the sample to ensure the given size. Keeps aspect ratio. + + Args: + sample (dict): sample + size (tuple): image size + + Returns: + tuple: new size + """ + shape = list(sample["disparity"].shape) + + if shape[0] >= size[0] and shape[1] >= size[1]: + return sample + + scale = [0, 0] + scale[0] = size[0] / shape[0] + scale[1] = size[1] / shape[1] + + scale = max(scale) + + shape[0] = math.ceil(scale * shape[0]) + shape[1] = math.ceil(scale * shape[1]) + + # resize + sample["image"] = cv2.resize( + sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method + ) + + sample["disparity"] = cv2.resize( + sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST + ) + sample["mask"] = cv2.resize( + sample["mask"].astype(np.float32), + tuple(shape[::-1]), + interpolation=cv2.INTER_NEAREST, + ) + sample["mask"] = sample["mask"].astype(bool) + + return tuple(shape) + + +class RandomCrop(object): + """Get a random crop of the sample with the given size (width, height). + """ + + def __init__( + self, + width, + height, + resize_if_needed=False, + image_interpolation_method=cv2.INTER_AREA, + ): + """Init. + + Args: + width (int): output width + height (int): output height + resize_if_needed (bool, optional): If True, sample might be upsampled to ensure + that a crop of size (width, height) is possbile. Defaults to False. + """ + self.__size = (height, width) + self.__resize_if_needed = resize_if_needed + self.__image_interpolation_method = image_interpolation_method + + def __call__(self, sample): + + shape = sample["disparity"].shape + + if self.__size[0] > shape[0] or self.__size[1] > shape[1]: + if self.__resize_if_needed: + shape = apply_min_size( + sample, self.__size, self.__image_interpolation_method + ) + else: + raise Exception( + "Output size {} bigger than input size {}.".format( + self.__size, shape + ) + ) + + offset = ( + np.random.randint(shape[0] - self.__size[0] + 1), + np.random.randint(shape[1] - self.__size[1] + 1), + ) + + for k, v in sample.items(): + if k == "code" or k == "basis": + continue + + if len(sample[k].shape) >= 2: + sample[k] = v[ + offset[0]: offset[0] + self.__size[0], + offset[1]: offset[1] + self.__size[1], + ] + + return sample + + +class Resize(object): + """Resize sample to given size (width, height). + """ + + def __init__( + self, + width, + height, + resize_target=True, + keep_aspect_ratio=False, + ensure_multiple_of=1, + resize_method="lower_bound", + image_interpolation_method=cv2.INTER_AREA, + letter_box=False, + ): + """Init. + + Args: + width (int): desired output width + height (int): desired output height + resize_target (bool, optional): + True: Resize the full sample (image, mask, target). + False: Resize image only. + Defaults to True. + keep_aspect_ratio (bool, optional): + True: Keep the aspect ratio of the input sample. + Output sample might not have the given width and height, and + resize behaviour depends on the parameter 'resize_method'. + Defaults to False. + ensure_multiple_of (int, optional): + Output width and height is constrained to be multiple of this parameter. + Defaults to 1. + resize_method (str, optional): + "lower_bound": Output will be at least as large as the given size. + "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) + "minimal": Scale as least as possible. (Output size might be smaller than given size.) + Defaults to "lower_bound". + """ + self.__width = width + self.__height = height + + self.__resize_target = resize_target + self.__keep_aspect_ratio = keep_aspect_ratio + self.__multiple_of = ensure_multiple_of + self.__resize_method = resize_method + self.__image_interpolation_method = image_interpolation_method + self.__letter_box = letter_box + + def constrain_to_multiple_of(self, x, min_val=0, max_val=None): + y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) + + if max_val is not None and y > max_val: + y = (np.floor(x / self.__multiple_of) + * self.__multiple_of).astype(int) + + if y < min_val: + y = (np.ceil(x / self.__multiple_of) + * self.__multiple_of).astype(int) + + return y + + def get_size(self, width, height): + # determine new height and width + scale_height = self.__height / height + scale_width = self.__width / width + + if self.__keep_aspect_ratio: + if self.__resize_method == "lower_bound": + # scale such that output size is lower bound + if scale_width > scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "upper_bound": + # scale such that output size is upper bound + if scale_width < scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "minimal": + # scale as least as possbile + if abs(1 - scale_width) < abs(1 - scale_height): + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + else: + raise ValueError( + f"resize_method {self.__resize_method} not implemented" + ) + + if self.__resize_method == "lower_bound": + new_height = self.constrain_to_multiple_of( + scale_height * height, min_val=self.__height + ) + new_width = self.constrain_to_multiple_of( + scale_width * width, min_val=self.__width + ) + elif self.__resize_method == "upper_bound": + new_height = self.constrain_to_multiple_of( + scale_height * height, max_val=self.__height + ) + new_width = self.constrain_to_multiple_of( + scale_width * width, max_val=self.__width + ) + elif self.__resize_method == "minimal": + new_height = self.constrain_to_multiple_of(scale_height * height) + new_width = self.constrain_to_multiple_of(scale_width * width) + else: + raise ValueError( + f"resize_method {self.__resize_method} not implemented") + + return (new_width, new_height) + + def make_letter_box(self, sample): + top = bottom = (self.__height - sample.shape[0]) // 2 + left = right = (self.__width - sample.shape[1]) // 2 + sample = cv2.copyMakeBorder( + sample, top, bottom, left, right, cv2.BORDER_CONSTANT, None, 0) + return sample + + def __call__(self, sample): + width, height = self.get_size( + sample["image"].shape[1], sample["image"].shape[0] + ) + + # resize sample + sample["image"] = cv2.resize( + sample["image"], + (width, height), + interpolation=self.__image_interpolation_method, + ) + + if self.__letter_box: + sample["image"] = self.make_letter_box(sample["image"]) + + if self.__resize_target: + if "disparity" in sample: + sample["disparity"] = cv2.resize( + sample["disparity"], + (width, height), + interpolation=cv2.INTER_NEAREST, + ) + + if self.__letter_box: + sample["disparity"] = self.make_letter_box( + sample["disparity"]) + + if "depth" in sample: + sample["depth"] = cv2.resize( + sample["depth"], (width, + height), interpolation=cv2.INTER_NEAREST + ) + + if self.__letter_box: + sample["depth"] = self.make_letter_box(sample["depth"]) + + sample["mask"] = cv2.resize( + sample["mask"].astype(np.float32), + (width, height), + interpolation=cv2.INTER_NEAREST, + ) + + if self.__letter_box: + sample["mask"] = self.make_letter_box(sample["mask"]) + + sample["mask"] = sample["mask"].astype(bool) + + return sample + + +class ResizeFixed(object): + def __init__(self, size): + self.__size = size + + def __call__(self, sample): + sample["image"] = cv2.resize( + sample["image"], self.__size[::-1], interpolation=cv2.INTER_LINEAR + ) + + sample["disparity"] = cv2.resize( + sample["disparity"], self.__size[::- + 1], interpolation=cv2.INTER_NEAREST + ) + + sample["mask"] = cv2.resize( + sample["mask"].astype(np.float32), + self.__size[::-1], + interpolation=cv2.INTER_NEAREST, + ) + sample["mask"] = sample["mask"].astype(bool) + + return sample + + +class Rescale(object): + """Rescale target values to the interval [0, max_val]. + If input is constant, values are set to max_val / 2. + """ + + def __init__(self, max_val=1.0, use_mask=True): + """Init. + + Args: + max_val (float, optional): Max output value. Defaults to 1.0. + use_mask (bool, optional): Only operate on valid pixels (mask == True). Defaults to True. + """ + self.__max_val = max_val + self.__use_mask = use_mask + + def __call__(self, sample): + disp = sample["disparity"] + + if self.__use_mask: + mask = sample["mask"] + else: + mask = np.ones_like(disp, dtype=np.bool) + + if np.sum(mask) == 0: + return sample + + min_val = np.min(disp[mask]) + max_val = np.max(disp[mask]) + + if max_val > min_val: + sample["disparity"][mask] = ( + (disp[mask] - min_val) / (max_val - min_val) * self.__max_val + ) + else: + sample["disparity"][mask] = np.ones_like( + disp[mask]) * self.__max_val / 2.0 + + return sample + + +# mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] +class NormalizeImage(object): + """Normlize image by given mean and std. + """ + + def __init__(self, mean, std): + self.__mean = mean + self.__std = std + + def __call__(self, sample): + sample["image"] = (sample["image"] - self.__mean) / self.__std + + return sample + + +class DepthToDisparity(object): + """Convert depth to disparity. Removes depth from sample. + """ + + def __init__(self, eps=1e-4): + self.__eps = eps + + def __call__(self, sample): + assert "depth" in sample + + sample["mask"][sample["depth"] < self.__eps] = False + + sample["disparity"] = np.zeros_like(sample["depth"]) + sample["disparity"][sample["depth"] >= self.__eps] = ( + 1.0 / sample["depth"][sample["depth"] >= self.__eps] + ) + + del sample["depth"] + + return sample + + +class DisparityToDepth(object): + """Convert disparity to depth. Removes disparity from sample. + """ + + def __init__(self, eps=1e-4): + self.__eps = eps + + def __call__(self, sample): + assert "disparity" in sample + + disp = np.abs(sample["disparity"]) + sample["mask"][disp < self.__eps] = False + + # print(sample["disparity"]) + # print(sample["mask"].sum()) + # exit() + + sample["depth"] = np.zeros_like(disp) + sample["depth"][disp >= self.__eps] = ( + 1.0 / disp[disp >= self.__eps] + ) + + del sample["disparity"] + + return sample + + +class PrepareForNet(object): + """Prepare sample for usage as network input. + """ + + def __init__(self): + pass + + def __call__(self, sample): + image = np.transpose(sample["image"], (2, 0, 1)) + sample["image"] = np.ascontiguousarray(image).astype(np.float32) + + if "mask" in sample: + sample["mask"] = sample["mask"].astype(np.float32) + sample["mask"] = np.ascontiguousarray(sample["mask"]) + + if "disparity" in sample: + disparity = sample["disparity"].astype(np.float32) + sample["disparity"] = np.ascontiguousarray(disparity) + + if "depth" in sample: + depth = sample["depth"].astype(np.float32) + sample["depth"] = np.ascontiguousarray(depth) + + return sample diff --git a/ZoeDepth/zoedepth/data/vkitti.py b/ZoeDepth/zoedepth/data/vkitti.py new file mode 100644 index 0000000000000000000000000000000000000000..72a2e5a8346f6e630ede0e28d6959725af8d7e72 --- /dev/null +++ b/ZoeDepth/zoedepth/data/vkitti.py @@ -0,0 +1,151 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +from torch.utils.data import Dataset, DataLoader +from torchvision import transforms +import os + +from PIL import Image +import numpy as np +import cv2 + + +class ToTensor(object): + def __init__(self): + self.normalize = transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + # self.resize = transforms.Resize((375, 1242)) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + # image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "vkitti"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class VKITTI(Dataset): + def __init__(self, data_dir_root, do_kb_crop=True): + import glob + # image paths are of the form /{HR, LR}//{color, depth_filled}/*.png + self.image_files = glob.glob(os.path.join( + data_dir_root, "test_color", '*.png')) + self.depth_files = [r.replace("test_color", "test_depth") + for r in self.image_files] + self.do_kb_crop = True + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = Image.open(image_path) + depth = Image.open(depth_path) + depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR | + cv2.IMREAD_ANYDEPTH) + print("dpeth min max", depth.min(), depth.max()) + + # print(np.shape(image)) + # print(np.shape(depth)) + + # depth[depth > 8] = -1 + + if self.do_kb_crop and False: + height = image.height + width = image.width + top_margin = int(height - 352) + left_margin = int((width - 1216) / 2) + depth = depth.crop( + (left_margin, top_margin, left_margin + 1216, top_margin + 352)) + image = image.crop( + (left_margin, top_margin, left_margin + 1216, top_margin + 352)) + # uv = uv[:, top_margin:top_margin + 352, left_margin:left_margin + 1216] + + image = np.asarray(image, dtype=np.float32) / 255.0 + # depth = np.asarray(depth, dtype=np.uint16) /1. + depth = depth[..., None] + sample = dict(image=image, depth=depth) + + # return sample + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_vkitti_loader(data_dir_root, batch_size=1, **kwargs): + dataset = VKITTI(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) + + +if __name__ == "__main__": + loader = get_vkitti_loader( + data_dir_root="/home/bhatsf/shortcuts/datasets/vkitti_test") + print("Total files", len(loader.dataset)) + for i, sample in enumerate(loader): + print(sample["image"].shape) + print(sample["depth"].shape) + print(sample["dataset"]) + print(sample['depth'].min(), sample['depth'].max()) + if i > 5: + break diff --git a/ZoeDepth/zoedepth/data/vkitti2.py b/ZoeDepth/zoedepth/data/vkitti2.py new file mode 100644 index 0000000000000000000000000000000000000000..9bcfb0414b7f3f21859f30ae34bd71689516a3e7 --- /dev/null +++ b/ZoeDepth/zoedepth/data/vkitti2.py @@ -0,0 +1,187 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import cv2 +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x: x + # self.resize = transforms.Resize((375, 1242)) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + # image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "vkitti"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class VKITTI2(Dataset): + def __init__(self, data_dir_root, do_kb_crop=True, split="test"): + import glob + + # image paths are of the form /rgb///frames//Camera<0,1>/rgb_{}.jpg + self.image_files = glob.glob(os.path.join( + data_dir_root, "rgb", "**", "frames", "rgb", "Camera_0", '*.jpg'), recursive=True) + self.depth_files = [r.replace("/rgb/", "/depth/").replace( + "rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files] + self.do_kb_crop = True + self.transform = ToTensor() + + # If train test split is not created, then create one. + # Split is such that 8% of the frames from each scene are used for testing. + if not os.path.exists(os.path.join(data_dir_root, "train.txt")): + import random + scenes = set([os.path.basename(os.path.dirname( + os.path.dirname(os.path.dirname(f)))) for f in self.image_files]) + train_files = [] + test_files = [] + for scene in scenes: + scene_files = [f for f in self.image_files if os.path.basename( + os.path.dirname(os.path.dirname(os.path.dirname(f)))) == scene] + random.shuffle(scene_files) + train_files.extend(scene_files[:int(len(scene_files) * 0.92)]) + test_files.extend(scene_files[int(len(scene_files) * 0.92):]) + with open(os.path.join(data_dir_root, "train.txt"), "w") as f: + f.write("\n".join(train_files)) + with open(os.path.join(data_dir_root, "test.txt"), "w") as f: + f.write("\n".join(test_files)) + + if split == "train": + with open(os.path.join(data_dir_root, "train.txt"), "r") as f: + self.image_files = f.read().splitlines() + self.depth_files = [r.replace("/rgb/", "/depth/").replace( + "rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files] + elif split == "test": + with open(os.path.join(data_dir_root, "test.txt"), "r") as f: + self.image_files = f.read().splitlines() + self.depth_files = [r.replace("/rgb/", "/depth/").replace( + "rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files] + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = Image.open(image_path) + # depth = Image.open(depth_path) + depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR | + cv2.IMREAD_ANYDEPTH) / 100.0 # cm to m + depth = Image.fromarray(depth) + # print("dpeth min max", depth.min(), depth.max()) + + # print(np.shape(image)) + # print(np.shape(depth)) + + if self.do_kb_crop: + if idx == 0: + print("Using KB input crop") + height = image.height + width = image.width + top_margin = int(height - 352) + left_margin = int((width - 1216) / 2) + depth = depth.crop( + (left_margin, top_margin, left_margin + 1216, top_margin + 352)) + image = image.crop( + (left_margin, top_margin, left_margin + 1216, top_margin + 352)) + # uv = uv[:, top_margin:top_margin + 352, left_margin:left_margin + 1216] + + image = np.asarray(image, dtype=np.float32) / 255.0 + # depth = np.asarray(depth, dtype=np.uint16) /1. + depth = np.asarray(depth, dtype=np.float32) / 1. + depth[depth > 80] = -1 + + depth = depth[..., None] + sample = dict(image=image, depth=depth) + + # return sample + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_vkitti2_loader(data_dir_root, batch_size=1, **kwargs): + dataset = VKITTI2(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) + + +if __name__ == "__main__": + loader = get_vkitti2_loader( + data_dir_root="/home/bhatsf/shortcuts/datasets/vkitti2") + print("Total files", len(loader.dataset)) + for i, sample in enumerate(loader): + print(sample["image"].shape) + print(sample["depth"].shape) + print(sample["dataset"]) + print(sample['depth'].min(), sample['depth'].max()) + if i > 5: + break diff --git a/ZoeDepth/zoedepth/models/__init__.py b/ZoeDepth/zoedepth/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5f2668792389157609abb2a0846fb620e7d67eb9 --- /dev/null +++ b/ZoeDepth/zoedepth/models/__init__.py @@ -0,0 +1,24 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + diff --git a/ZoeDepth/zoedepth/models/base_models/__init__.py b/ZoeDepth/zoedepth/models/base_models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5f2668792389157609abb2a0846fb620e7d67eb9 --- /dev/null +++ b/ZoeDepth/zoedepth/models/base_models/__init__.py @@ -0,0 +1,24 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + diff --git a/ZoeDepth/zoedepth/models/base_models/midas.py b/ZoeDepth/zoedepth/models/base_models/midas.py new file mode 100644 index 0000000000000000000000000000000000000000..e26f8589502f8298bde8820262083f54b254f70e --- /dev/null +++ b/ZoeDepth/zoedepth/models/base_models/midas.py @@ -0,0 +1,377 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn +import numpy as np +from torchvision.transforms import Normalize + + +def denormalize(x): + """Reverses the imagenet normalization applied to the input. + + Args: + x (torch.Tensor - shape(N,3,H,W)): input tensor + + Returns: + torch.Tensor - shape(N,3,H,W): Denormalized input + """ + mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(x.device) + std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(x.device) + return x * std + mean + +def get_activation(name, bank): + def hook(model, input, output): + bank[name] = output + return hook + + +class Resize(object): + """Resize sample to given size (width, height). + """ + + def __init__( + self, + width, + height, + resize_target=True, + keep_aspect_ratio=False, + ensure_multiple_of=1, + resize_method="lower_bound", + ): + """Init. + Args: + width (int): desired output width + height (int): desired output height + resize_target (bool, optional): + True: Resize the full sample (image, mask, target). + False: Resize image only. + Defaults to True. + keep_aspect_ratio (bool, optional): + True: Keep the aspect ratio of the input sample. + Output sample might not have the given width and height, and + resize behaviour depends on the parameter 'resize_method'. + Defaults to False. + ensure_multiple_of (int, optional): + Output width and height is constrained to be multiple of this parameter. + Defaults to 1. + resize_method (str, optional): + "lower_bound": Output will be at least as large as the given size. + "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) + "minimal": Scale as least as possible. (Output size might be smaller than given size.) + Defaults to "lower_bound". + """ + print("Params passed to Resize transform:") + print("\twidth: ", width) + print("\theight: ", height) + print("\tresize_target: ", resize_target) + print("\tkeep_aspect_ratio: ", keep_aspect_ratio) + print("\tensure_multiple_of: ", ensure_multiple_of) + print("\tresize_method: ", resize_method) + + self.__width = width + self.__height = height + + self.__keep_aspect_ratio = keep_aspect_ratio + self.__multiple_of = ensure_multiple_of + self.__resize_method = resize_method + + def constrain_to_multiple_of(self, x, min_val=0, max_val=None): + y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) + + if max_val is not None and y > max_val: + y = (np.floor(x / self.__multiple_of) + * self.__multiple_of).astype(int) + + if y < min_val: + y = (np.ceil(x / self.__multiple_of) + * self.__multiple_of).astype(int) + + return y + + def get_size(self, width, height): + # determine new height and width + scale_height = self.__height / height + scale_width = self.__width / width + + if self.__keep_aspect_ratio: + if self.__resize_method == "lower_bound": + # scale such that output size is lower bound + if scale_width > scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "upper_bound": + # scale such that output size is upper bound + if scale_width < scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "minimal": + # scale as least as possbile + if abs(1 - scale_width) < abs(1 - scale_height): + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + else: + raise ValueError( + f"resize_method {self.__resize_method} not implemented" + ) + + if self.__resize_method == "lower_bound": + new_height = self.constrain_to_multiple_of( + scale_height * height, min_val=self.__height + ) + new_width = self.constrain_to_multiple_of( + scale_width * width, min_val=self.__width + ) + elif self.__resize_method == "upper_bound": + new_height = self.constrain_to_multiple_of( + scale_height * height, max_val=self.__height + ) + new_width = self.constrain_to_multiple_of( + scale_width * width, max_val=self.__width + ) + elif self.__resize_method == "minimal": + new_height = self.constrain_to_multiple_of(scale_height * height) + new_width = self.constrain_to_multiple_of(scale_width * width) + else: + raise ValueError( + f"resize_method {self.__resize_method} not implemented") + + return (new_width, new_height) + + def __call__(self, x): + width, height = self.get_size(*x.shape[-2:][::-1]) + return nn.functional.interpolate(x, (height, width), mode='bilinear', align_corners=True) + +class PrepForMidas(object): + def __init__(self, resize_mode="minimal", keep_aspect_ratio=True, img_size=384, do_resize=True): + if isinstance(img_size, int): + img_size = (img_size, img_size) + net_h, net_w = img_size + self.normalization = Normalize( + mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + self.resizer = Resize(net_w, net_h, keep_aspect_ratio=keep_aspect_ratio, ensure_multiple_of=32, resize_method=resize_mode) \ + if do_resize else nn.Identity() + + def __call__(self, x): + return self.normalization(self.resizer(x)) + + +class MidasCore(nn.Module): + def __init__(self, midas, trainable=False, fetch_features=True, layer_names=('out_conv', 'l4_rn', 'r4', 'r3', 'r2', 'r1'), freeze_bn=False, keep_aspect_ratio=True, + img_size=384, **kwargs): + """Midas Base model used for multi-scale feature extraction. + + Args: + midas (torch.nn.Module): Midas model. + trainable (bool, optional): Train midas model. Defaults to False. + fetch_features (bool, optional): Extract multi-scale features. Defaults to True. + layer_names (tuple, optional): Layers used for feature extraction. Order = (head output features, last layer features, ...decoder features). Defaults to ('out_conv', 'l4_rn', 'r4', 'r3', 'r2', 'r1'). + freeze_bn (bool, optional): Freeze BatchNorm. Generally results in better finetuning performance. Defaults to False. + keep_aspect_ratio (bool, optional): Keep the aspect ratio of input images while resizing. Defaults to True. + img_size (int, tuple, optional): Input resolution. Defaults to 384. + """ + super().__init__() + self.core = midas + self.output_channels = None + self.core_out = {} + self.trainable = trainable + self.fetch_features = fetch_features + # midas.scratch.output_conv = nn.Identity() + self.handles = [] + # self.layer_names = ['out_conv','l4_rn', 'r4', 'r3', 'r2', 'r1'] + self.layer_names = layer_names + + self.set_trainable(trainable) + self.set_fetch_features(fetch_features) + + self.prep = PrepForMidas(keep_aspect_ratio=keep_aspect_ratio, + img_size=img_size, do_resize=kwargs.get('do_resize', True)) + + if freeze_bn: + self.freeze_bn() + + def set_trainable(self, trainable): + self.trainable = trainable + if trainable: + self.unfreeze() + else: + self.freeze() + return self + + def set_fetch_features(self, fetch_features): + self.fetch_features = fetch_features + if fetch_features: + if len(self.handles) == 0: + self.attach_hooks(self.core) + else: + self.remove_hooks() + return self + + def freeze(self): + for p in self.parameters(): + p.requires_grad = False + self.trainable = False + return self + + def unfreeze(self): + for p in self.parameters(): + p.requires_grad = True + self.trainable = True + return self + + def freeze_bn(self): + for m in self.modules(): + if isinstance(m, nn.BatchNorm2d): + m.eval() + return self + + def forward(self, x, denorm=False, return_rel_depth=False): + with torch.no_grad(): + if denorm: + x = denormalize(x) + x = self.prep(x) + # print("Shape after prep: ", x.shape) + + with torch.set_grad_enabled(self.trainable): + + # print("Input size to Midascore", x.shape) + rel_depth = self.core(x) + # print("Output from midas shape", rel_depth.shape) + if not self.fetch_features: + return rel_depth + out = [self.core_out[k] for k in self.layer_names] + + if return_rel_depth: + return rel_depth, out + return out + + def get_rel_pos_params(self): + for name, p in self.core.pretrained.named_parameters(): + if "relative_position" in name: + yield p + + def get_enc_params_except_rel_pos(self): + for name, p in self.core.pretrained.named_parameters(): + if "relative_position" not in name: + yield p + + def freeze_encoder(self, freeze_rel_pos=False): + if freeze_rel_pos: + for p in self.core.pretrained.parameters(): + p.requires_grad = False + else: + for p in self.get_enc_params_except_rel_pos(): + p.requires_grad = False + return self + + def attach_hooks(self, midas): + if len(self.handles) > 0: + self.remove_hooks() + if "out_conv" in self.layer_names: + self.handles.append(list(midas.scratch.output_conv.children())[ + 3].register_forward_hook(get_activation("out_conv", self.core_out))) + if "r4" in self.layer_names: + self.handles.append(midas.scratch.refinenet4.register_forward_hook( + get_activation("r4", self.core_out))) + if "r3" in self.layer_names: + self.handles.append(midas.scratch.refinenet3.register_forward_hook( + get_activation("r3", self.core_out))) + if "r2" in self.layer_names: + self.handles.append(midas.scratch.refinenet2.register_forward_hook( + get_activation("r2", self.core_out))) + if "r1" in self.layer_names: + self.handles.append(midas.scratch.refinenet1.register_forward_hook( + get_activation("r1", self.core_out))) + if "l4_rn" in self.layer_names: + self.handles.append(midas.scratch.layer4_rn.register_forward_hook( + get_activation("l4_rn", self.core_out))) + + return self + + def remove_hooks(self): + for h in self.handles: + h.remove() + return self + + def __del__(self): + self.remove_hooks() + + def set_output_channels(self, model_type): + self.output_channels = MIDAS_SETTINGS[model_type] + + @staticmethod + def build(midas_model_type="DPT_BEiT_L_384", train_midas=False, use_pretrained_midas=True, fetch_features=False, freeze_bn=True, force_keep_ar=False, force_reload=False, **kwargs): + if midas_model_type not in MIDAS_SETTINGS: + raise ValueError( + f"Invalid model type: {midas_model_type}. Must be one of {list(MIDAS_SETTINGS.keys())}") + if "img_size" in kwargs: + kwargs = MidasCore.parse_img_size(kwargs) + img_size = kwargs.pop("img_size", [384, 384]) + print("img_size", img_size) + midas = torch.hub.load("intel-isl/MiDaS", midas_model_type, + pretrained=use_pretrained_midas, force_reload=force_reload) + kwargs.update({'keep_aspect_ratio': force_keep_ar}) + midas_core = MidasCore(midas, trainable=train_midas, fetch_features=fetch_features, + freeze_bn=freeze_bn, img_size=img_size, **kwargs) + midas_core.set_output_channels(midas_model_type) + return midas_core + + @staticmethod + def build_from_config(config): + return MidasCore.build(**config) + + @staticmethod + def parse_img_size(config): + assert 'img_size' in config + if isinstance(config['img_size'], str): + assert "," in config['img_size'], "img_size should be a string with comma separated img_size=H,W" + config['img_size'] = list(map(int, config['img_size'].split(","))) + assert len( + config['img_size']) == 2, "img_size should be a string with comma separated img_size=H,W" + elif isinstance(config['img_size'], int): + config['img_size'] = [config['img_size'], config['img_size']] + else: + assert isinstance(config['img_size'], list) and len( + config['img_size']) == 2, "img_size should be a list of H,W" + return config + + +nchannels2models = { + tuple([256]*5): ["DPT_BEiT_L_384", "DPT_BEiT_L_512", "DPT_BEiT_B_384", "DPT_SwinV2_L_384", "DPT_SwinV2_B_384", "DPT_SwinV2_T_256", "DPT_Large", "DPT_Hybrid"], + (512, 256, 128, 64, 64): ["MiDaS_small"] +} + +# Model name to number of output channels +MIDAS_SETTINGS = {m: k for k, v in nchannels2models.items() + for m in v + } diff --git a/ZoeDepth/zoedepth/models/builder.py b/ZoeDepth/zoedepth/models/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..4363d59689158912a412feb5c296b4a72bc2c608 --- /dev/null +++ b/ZoeDepth/zoedepth/models/builder.py @@ -0,0 +1,51 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +from importlib import import_module +from zoedepth.models.depth_model import DepthModel + +def build_model(config) -> DepthModel: + """Builds a model from a config. The model is specified by the model name and version in the config. The model is then constructed using the build_from_config function of the model interface. + This function should be used to construct models for training and evaluation. + + Args: + config (dict): Config dict. Config is constructed in utils/config.py. Each model has its own config file(s) saved in its root model folder. + + Returns: + torch.nn.Module: Model corresponding to name and version as specified in config + """ + module_name = f"zoedepth.models.{config.model}" + try: + module = import_module(module_name) + except ModuleNotFoundError as e: + # print the original error message + print(e) + raise ValueError( + f"Model {config.model} not found. Refer above error for details.") from e + try: + get_version = getattr(module, "get_version") + except AttributeError as e: + raise ValueError( + f"Model {config.model} has no get_version function.") from e + return get_version(config.version_name).build_from_config(config) diff --git a/ZoeDepth/zoedepth/models/depth_model.py b/ZoeDepth/zoedepth/models/depth_model.py new file mode 100644 index 0000000000000000000000000000000000000000..fc421c108ea3928c9add62b4c190500d9bd4eda1 --- /dev/null +++ b/ZoeDepth/zoedepth/models/depth_model.py @@ -0,0 +1,152 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision import transforms +import PIL.Image +from PIL import Image +from typing import Union + + +class DepthModel(nn.Module): + def __init__(self): + super().__init__() + self.device = 'cpu' + + def to(self, device) -> nn.Module: + self.device = device + return super().to(device) + + def forward(self, x, *args, **kwargs): + raise NotImplementedError + + def _infer(self, x: torch.Tensor): + """ + Inference interface for the model + Args: + x (torch.Tensor): input tensor of shape (b, c, h, w) + Returns: + torch.Tensor: output tensor of shape (b, 1, h, w) + """ + return self(x)['metric_depth'] + + def _infer_with_pad_aug(self, x: torch.Tensor, pad_input: bool=True, fh: float=3, fw: float=3, upsampling_mode: str='bicubic', padding_mode="reflect", **kwargs) -> torch.Tensor: + """ + Inference interface for the model with padding augmentation + Padding augmentation fixes the boundary artifacts in the output depth map. + Boundary artifacts are sometimes caused by the fact that the model is trained on NYU raw dataset which has a black or white border around the image. + This augmentation pads the input image and crops the prediction back to the original size / view. + + Note: This augmentation is not required for the models trained with 'avoid_boundary'=True. + Args: + x (torch.Tensor): input tensor of shape (b, c, h, w) + pad_input (bool, optional): whether to pad the input or not. Defaults to True. + fh (float, optional): height padding factor. The padding is calculated as sqrt(h/2) * fh. Defaults to 3. + fw (float, optional): width padding factor. The padding is calculated as sqrt(w/2) * fw. Defaults to 3. + upsampling_mode (str, optional): upsampling mode. Defaults to 'bicubic'. + padding_mode (str, optional): padding mode. Defaults to "reflect". + Returns: + torch.Tensor: output tensor of shape (b, 1, h, w) + """ + # assert x is nchw and c = 3 + assert x.dim() == 4, "x must be 4 dimensional, got {}".format(x.dim()) + assert x.shape[1] == 3, "x must have 3 channels, got {}".format(x.shape[1]) + + if pad_input: + assert fh > 0 or fw > 0, "atlease one of fh and fw must be greater than 0" + pad_h = int(np.sqrt(x.shape[2]/2) * fh) + pad_w = int(np.sqrt(x.shape[3]/2) * fw) + padding = [pad_w, pad_w] + if pad_h > 0: + padding += [pad_h, pad_h] + + x = F.pad(x, padding, mode=padding_mode, **kwargs) + out = self._infer(x) + if out.shape[-2:] != x.shape[-2:]: + out = F.interpolate(out, size=(x.shape[2], x.shape[3]), mode=upsampling_mode, align_corners=False) + if pad_input: + # crop to the original size, handling the case where pad_h and pad_w is 0 + if pad_h > 0: + out = out[:, :, pad_h:-pad_h,:] + if pad_w > 0: + out = out[:, :, :, pad_w:-pad_w] + return out + + def infer_with_flip_aug(self, x, pad_input: bool=True, **kwargs) -> torch.Tensor: + """ + Inference interface for the model with horizontal flip augmentation + Horizontal flip augmentation improves the accuracy of the model by averaging the output of the model with and without horizontal flip. + Args: + x (torch.Tensor): input tensor of shape (b, c, h, w) + pad_input (bool, optional): whether to use padding augmentation. Defaults to True. + Returns: + torch.Tensor: output tensor of shape (b, 1, h, w) + """ + # infer with horizontal flip and average + out = self._infer_with_pad_aug(x, pad_input=pad_input, **kwargs) + out_flip = self._infer_with_pad_aug(torch.flip(x, dims=[3]), pad_input=pad_input, **kwargs) + out = (out + torch.flip(out_flip, dims=[3])) / 2 + return out + + def infer(self, x, pad_input: bool=True, with_flip_aug: bool=True, **kwargs) -> torch.Tensor: + """ + Inference interface for the model + Args: + x (torch.Tensor): input tensor of shape (b, c, h, w) + pad_input (bool, optional): whether to use padding augmentation. Defaults to True. + with_flip_aug (bool, optional): whether to use horizontal flip augmentation. Defaults to True. + Returns: + torch.Tensor: output tensor of shape (b, 1, h, w) + """ + if with_flip_aug: + return self.infer_with_flip_aug(x, pad_input=pad_input, **kwargs) + else: + return self._infer_with_pad_aug(x, pad_input=pad_input, **kwargs) + + @torch.no_grad() + def infer_pil(self, pil_img, pad_input: bool=True, with_flip_aug: bool=True, output_type: str="numpy", **kwargs) -> Union[np.ndarray, PIL.Image.Image, torch.Tensor]: + """ + Inference interface for the model for PIL image + Args: + pil_img (PIL.Image.Image): input PIL image + pad_input (bool, optional): whether to use padding augmentation. Defaults to True. + with_flip_aug (bool, optional): whether to use horizontal flip augmentation. Defaults to True. + output_type (str, optional): output type. Supported values are 'numpy', 'pil' and 'tensor'. Defaults to "numpy". + """ + x = transforms.ToTensor()(pil_img).unsqueeze(0).to(self.device) + out_tensor = self.infer(x, pad_input=pad_input, with_flip_aug=with_flip_aug, **kwargs) + if output_type == "numpy": + return out_tensor.squeeze().cpu().numpy() + elif output_type == "pil": + # uint16 is required for depth pil image + out_16bit_numpy = (out_tensor.squeeze().cpu().numpy()*256).astype(np.uint16) + return Image.fromarray(out_16bit_numpy) + elif output_type == "tensor": + return out_tensor.squeeze().cpu() + else: + raise ValueError(f"output_type {output_type} not supported. Supported values are 'numpy', 'pil' and 'tensor'") + \ No newline at end of file diff --git a/ZoeDepth/zoedepth/models/layers/attractor.py b/ZoeDepth/zoedepth/models/layers/attractor.py new file mode 100644 index 0000000000000000000000000000000000000000..2a8efe645adea1d88a12e2ac5cc6bb2a251eef9d --- /dev/null +++ b/ZoeDepth/zoedepth/models/layers/attractor.py @@ -0,0 +1,208 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn + + +@torch.jit.script +def exp_attractor(dx, alpha: float = 300, gamma: int = 2): + """Exponential attractor: dc = exp(-alpha*|dx|^gamma) * dx , where dx = a - c, a = attractor point, c = bin center, dc = shift in bin centermmary for exp_attractor + + Args: + dx (torch.Tensor): The difference tensor dx = Ai - Cj, where Ai is the attractor point and Cj is the bin center. + alpha (float, optional): Proportional Attractor strength. Determines the absolute strength. Lower alpha = greater attraction. Defaults to 300. + gamma (int, optional): Exponential Attractor strength. Determines the "region of influence" and indirectly number of bin centers affected. Lower gamma = farther reach. Defaults to 2. + + Returns: + torch.Tensor : Delta shifts - dc; New bin centers = Old bin centers + dc + """ + return torch.exp(-alpha*(torch.abs(dx)**gamma)) * (dx) + + +@torch.jit.script +def inv_attractor(dx, alpha: float = 300, gamma: int = 2): + """Inverse attractor: dc = dx / (1 + alpha*dx^gamma), where dx = a - c, a = attractor point, c = bin center, dc = shift in bin center + This is the default one according to the accompanying paper. + + Args: + dx (torch.Tensor): The difference tensor dx = Ai - Cj, where Ai is the attractor point and Cj is the bin center. + alpha (float, optional): Proportional Attractor strength. Determines the absolute strength. Lower alpha = greater attraction. Defaults to 300. + gamma (int, optional): Exponential Attractor strength. Determines the "region of influence" and indirectly number of bin centers affected. Lower gamma = farther reach. Defaults to 2. + + Returns: + torch.Tensor: Delta shifts - dc; New bin centers = Old bin centers + dc + """ + return dx.div(1+alpha*dx.pow(gamma)) + + +class AttractorLayer(nn.Module): + def __init__(self, in_features, n_bins, n_attractors=16, mlp_dim=128, min_depth=1e-3, max_depth=10, + alpha=300, gamma=2, kind='sum', attractor_type='exp', memory_efficient=False): + """ + Attractor layer for bin centers. Bin centers are bounded on the interval (min_depth, max_depth) + """ + super().__init__() + + self.n_attractors = n_attractors + self.n_bins = n_bins + self.min_depth = min_depth + self.max_depth = max_depth + self.alpha = alpha + self.gamma = gamma + self.kind = kind + self.attractor_type = attractor_type + self.memory_efficient = memory_efficient + + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.ReLU(inplace=True), + nn.Conv2d(mlp_dim, n_attractors*2, 1, 1, 0), # x2 for linear norm + nn.ReLU(inplace=True) + ) + + def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False): + """ + Args: + x (torch.Tensor) : feature block; shape - n, c, h, w + b_prev (torch.Tensor) : previous bin centers normed; shape - n, prev_nbins, h, w + + Returns: + tuple(torch.Tensor,torch.Tensor) : new bin centers normed and scaled; shape - n, nbins, h, w + """ + if prev_b_embedding is not None: + if interpolate: + prev_b_embedding = nn.functional.interpolate( + prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True) + x = x + prev_b_embedding + + A = self._net(x) + eps = 1e-3 + A = A + eps + n, c, h, w = A.shape + A = A.view(n, self.n_attractors, 2, h, w) + A_normed = A / A.sum(dim=2, keepdim=True) # n, a, 2, h, w + A_normed = A[:, :, 0, ...] # n, na, h, w + + b_prev = nn.functional.interpolate( + b_prev, (h, w), mode='bilinear', align_corners=True) + b_centers = b_prev + + if self.attractor_type == 'exp': + dist = exp_attractor + else: + dist = inv_attractor + + if not self.memory_efficient: + func = {'mean': torch.mean, 'sum': torch.sum}[self.kind] + # .shape N, nbins, h, w + delta_c = func(dist(A_normed.unsqueeze( + 2) - b_centers.unsqueeze(1)), dim=1) + else: + delta_c = torch.zeros_like(b_centers, device=b_centers.device) + for i in range(self.n_attractors): + # .shape N, nbins, h, w + delta_c += dist(A_normed[:, i, ...].unsqueeze(1) - b_centers) + + if self.kind == 'mean': + delta_c = delta_c / self.n_attractors + + b_new_centers = b_centers + delta_c + B_centers = (self.max_depth - self.min_depth) * \ + b_new_centers + self.min_depth + B_centers, _ = torch.sort(B_centers, dim=1) + B_centers = torch.clip(B_centers, self.min_depth, self.max_depth) + return b_new_centers, B_centers + + +class AttractorLayerUnnormed(nn.Module): + def __init__(self, in_features, n_bins, n_attractors=16, mlp_dim=128, min_depth=1e-3, max_depth=10, + alpha=300, gamma=2, kind='sum', attractor_type='exp', memory_efficient=False): + """ + Attractor layer for bin centers. Bin centers are unbounded + """ + super().__init__() + + self.n_attractors = n_attractors + self.n_bins = n_bins + self.min_depth = min_depth + self.max_depth = max_depth + self.alpha = alpha + self.gamma = gamma + self.kind = kind + self.attractor_type = attractor_type + self.memory_efficient = memory_efficient + + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.ReLU(inplace=True), + nn.Conv2d(mlp_dim, n_attractors, 1, 1, 0), + nn.Softplus() + ) + + def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False): + """ + Args: + x (torch.Tensor) : feature block; shape - n, c, h, w + b_prev (torch.Tensor) : previous bin centers normed; shape - n, prev_nbins, h, w + + Returns: + tuple(torch.Tensor,torch.Tensor) : new bin centers unbounded; shape - n, nbins, h, w. Two outputs just to keep the API consistent with the normed version + """ + if prev_b_embedding is not None: + if interpolate: + prev_b_embedding = nn.functional.interpolate( + prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True) + x = x + prev_b_embedding + + A = self._net(x) + n, c, h, w = A.shape + + b_prev = nn.functional.interpolate( + b_prev, (h, w), mode='bilinear', align_corners=True) + b_centers = b_prev + + if self.attractor_type == 'exp': + dist = exp_attractor + else: + dist = inv_attractor + + if not self.memory_efficient: + func = {'mean': torch.mean, 'sum': torch.sum}[self.kind] + # .shape N, nbins, h, w + delta_c = func( + dist(A.unsqueeze(2) - b_centers.unsqueeze(1)), dim=1) + else: + delta_c = torch.zeros_like(b_centers, device=b_centers.device) + for i in range(self.n_attractors): + delta_c += dist(A[:, i, ...].unsqueeze(1) - + b_centers) # .shape N, nbins, h, w + + if self.kind == 'mean': + delta_c = delta_c / self.n_attractors + + b_new_centers = b_centers + delta_c + B_centers = b_new_centers + + return b_new_centers, B_centers diff --git a/ZoeDepth/zoedepth/models/layers/dist_layers.py b/ZoeDepth/zoedepth/models/layers/dist_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..3208405dfb78fdfc28d5765e5a6d5dbe31967a23 --- /dev/null +++ b/ZoeDepth/zoedepth/models/layers/dist_layers.py @@ -0,0 +1,121 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn + + +def log_binom(n, k, eps=1e-7): + """ log(nCk) using stirling approximation """ + n = n + eps + k = k + eps + return n * torch.log(n) - k * torch.log(k) - (n-k) * torch.log(n-k+eps) + + +class LogBinomial(nn.Module): + def __init__(self, n_classes=256, act=torch.softmax): + """Compute log binomial distribution for n_classes + + Args: + n_classes (int, optional): number of output classes. Defaults to 256. + """ + super().__init__() + self.K = n_classes + self.act = act + self.register_buffer('k_idx', torch.arange( + 0, n_classes).view(1, -1, 1, 1)) + self.register_buffer('K_minus_1', torch.Tensor( + [self.K-1]).view(1, -1, 1, 1)) + + def forward(self, x, t=1., eps=1e-4): + """Compute log binomial distribution for x + + Args: + x (torch.Tensor - NCHW): probabilities + t (float, torch.Tensor - NCHW, optional): Temperature of distribution. Defaults to 1.. + eps (float, optional): Small number for numerical stability. Defaults to 1e-4. + + Returns: + torch.Tensor -NCHW: log binomial distribution logbinomial(p;t) + """ + if x.ndim == 3: + x = x.unsqueeze(1) # make it nchw + + one_minus_x = torch.clamp(1 - x, eps, 1) + x = torch.clamp(x, eps, 1) + y = log_binom(self.K_minus_1, self.k_idx) + self.k_idx * \ + torch.log(x) + (self.K - 1 - self.k_idx) * torch.log(one_minus_x) + return self.act(y/t, dim=1) + + +class ConditionalLogBinomial(nn.Module): + def __init__(self, in_features, condition_dim, n_classes=256, bottleneck_factor=2, p_eps=1e-4, max_temp=50, min_temp=1e-7, act=torch.softmax): + """Conditional Log Binomial distribution + + Args: + in_features (int): number of input channels in main feature + condition_dim (int): number of input channels in condition feature + n_classes (int, optional): Number of classes. Defaults to 256. + bottleneck_factor (int, optional): Hidden dim factor. Defaults to 2. + p_eps (float, optional): small eps value. Defaults to 1e-4. + max_temp (float, optional): Maximum temperature of output distribution. Defaults to 50. + min_temp (float, optional): Minimum temperature of output distribution. Defaults to 1e-7. + """ + super().__init__() + self.p_eps = p_eps + self.max_temp = max_temp + self.min_temp = min_temp + self.log_binomial_transform = LogBinomial(n_classes, act=act) + bottleneck = (in_features + condition_dim) // bottleneck_factor + self.mlp = nn.Sequential( + nn.Conv2d(in_features + condition_dim, bottleneck, + kernel_size=1, stride=1, padding=0), + nn.GELU(), + # 2 for p linear norm, 2 for t linear norm + nn.Conv2d(bottleneck, 2+2, kernel_size=1, stride=1, padding=0), + nn.Softplus() + ) + + def forward(self, x, cond): + """Forward pass + + Args: + x (torch.Tensor - NCHW): Main feature + cond (torch.Tensor - NCHW): condition feature + + Returns: + torch.Tensor: Output log binomial distribution + """ + pt = self.mlp(torch.concat((x, cond), dim=1)) + p, t = pt[:, :2, ...], pt[:, 2:, ...] + + p = p + self.p_eps + p = p[:, 0, ...] / (p[:, 0, ...] + p[:, 1, ...]) + + t = t + self.p_eps + t = t[:, 0, ...] / (t[:, 0, ...] + t[:, 1, ...]) + t = t.unsqueeze(1) + t = (self.max_temp - self.min_temp) * t + self.min_temp + + return self.log_binomial_transform(p, t) diff --git a/ZoeDepth/zoedepth/models/layers/localbins_layers.py b/ZoeDepth/zoedepth/models/layers/localbins_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..f94481605c3e6958ce50e73b2eb31d9f0c07dc67 --- /dev/null +++ b/ZoeDepth/zoedepth/models/layers/localbins_layers.py @@ -0,0 +1,169 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn + + +class SeedBinRegressor(nn.Module): + def __init__(self, in_features, n_bins=16, mlp_dim=256, min_depth=1e-3, max_depth=10): + """Bin center regressor network. Bin centers are bounded on (min_depth, max_depth) interval. + + Args: + in_features (int): input channels + n_bins (int, optional): Number of bin centers. Defaults to 16. + mlp_dim (int, optional): Hidden dimension. Defaults to 256. + min_depth (float, optional): Min depth value. Defaults to 1e-3. + max_depth (float, optional): Max depth value. Defaults to 10. + """ + super().__init__() + self.version = "1_1" + self.min_depth = min_depth + self.max_depth = max_depth + + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.ReLU(inplace=True), + nn.Conv2d(mlp_dim, n_bins, 1, 1, 0), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + """ + Returns tensor of bin_width vectors (centers). One vector b for every pixel + """ + B = self._net(x) + eps = 1e-3 + B = B + eps + B_widths_normed = B / B.sum(dim=1, keepdim=True) + B_widths = (self.max_depth - self.min_depth) * \ + B_widths_normed # .shape NCHW + # pad has the form (left, right, top, bottom, front, back) + B_widths = nn.functional.pad( + B_widths, (0, 0, 0, 0, 1, 0), mode='constant', value=self.min_depth) + B_edges = torch.cumsum(B_widths, dim=1) # .shape NCHW + + B_centers = 0.5 * (B_edges[:, :-1, ...] + B_edges[:, 1:, ...]) + return B_widths_normed, B_centers + + +class SeedBinRegressorUnnormed(nn.Module): + def __init__(self, in_features, n_bins=16, mlp_dim=256, min_depth=1e-3, max_depth=10): + """Bin center regressor network. Bin centers are unbounded + + Args: + in_features (int): input channels + n_bins (int, optional): Number of bin centers. Defaults to 16. + mlp_dim (int, optional): Hidden dimension. Defaults to 256. + min_depth (float, optional): Not used. (for compatibility with SeedBinRegressor) + max_depth (float, optional): Not used. (for compatibility with SeedBinRegressor) + """ + super().__init__() + self.version = "1_1" + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.ReLU(inplace=True), + nn.Conv2d(mlp_dim, n_bins, 1, 1, 0), + nn.Softplus() + ) + + def forward(self, x): + """ + Returns tensor of bin_width vectors (centers). One vector b for every pixel + """ + B_centers = self._net(x) + return B_centers, B_centers + + +class Projector(nn.Module): + def __init__(self, in_features, out_features, mlp_dim=128): + """Projector MLP + + Args: + in_features (int): input channels + out_features (int): output channels + mlp_dim (int, optional): hidden dimension. Defaults to 128. + """ + super().__init__() + + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.ReLU(inplace=True), + nn.Conv2d(mlp_dim, out_features, 1, 1, 0), + ) + + def forward(self, x): + return self._net(x) + + + +class LinearSplitter(nn.Module): + def __init__(self, in_features, prev_nbins, split_factor=2, mlp_dim=128, min_depth=1e-3, max_depth=10): + super().__init__() + + self.prev_nbins = prev_nbins + self.split_factor = split_factor + self.min_depth = min_depth + self.max_depth = max_depth + + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.GELU(), + nn.Conv2d(mlp_dim, prev_nbins * split_factor, 1, 1, 0), + nn.ReLU() + ) + + def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False): + """ + x : feature block; shape - n, c, h, w + b_prev : previous bin widths normed; shape - n, prev_nbins, h, w + """ + if prev_b_embedding is not None: + if interpolate: + prev_b_embedding = nn.functional.interpolate(prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True) + x = x + prev_b_embedding + S = self._net(x) + eps = 1e-3 + S = S + eps + n, c, h, w = S.shape + S = S.view(n, self.prev_nbins, self.split_factor, h, w) + S_normed = S / S.sum(dim=2, keepdim=True) # fractional splits + + b_prev = nn.functional.interpolate(b_prev, (h,w), mode='bilinear', align_corners=True) + + + b_prev = b_prev / b_prev.sum(dim=1, keepdim=True) # renormalize for gurantees + # print(b_prev.shape, S_normed.shape) + # if is_for_query:(1).expand(-1, b_prev.size(0)//n, -1, -1, -1, -1).flatten(0,1) # TODO ? can replace all this with a single torch.repeat? + b = b_prev.unsqueeze(2) * S_normed + b = b.flatten(1,2) # .shape n, prev_nbins * split_factor, h, w + + # calculate bin centers for loss calculation + B_widths = (self.max_depth - self.min_depth) * b # .shape N, nprev * splitfactor, H, W + # pad has the form (left, right, top, bottom, front, back) + B_widths = nn.functional.pad(B_widths, (0,0,0,0,1,0), mode='constant', value=self.min_depth) + B_edges = torch.cumsum(B_widths, dim=1) # .shape NCHW + + B_centers = 0.5 * (B_edges[:, :-1, ...] + B_edges[:,1:,...]) + return b, B_centers \ No newline at end of file diff --git a/ZoeDepth/zoedepth/models/layers/patch_transformer.py b/ZoeDepth/zoedepth/models/layers/patch_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..99d9e51a06b981bae45ce7dd64eaef19a4121991 --- /dev/null +++ b/ZoeDepth/zoedepth/models/layers/patch_transformer.py @@ -0,0 +1,91 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn + + +class PatchTransformerEncoder(nn.Module): + def __init__(self, in_channels, patch_size=10, embedding_dim=128, num_heads=4, use_class_token=False): + """ViT-like transformer block + + Args: + in_channels (int): Input channels + patch_size (int, optional): patch size. Defaults to 10. + embedding_dim (int, optional): Embedding dimension in transformer model. Defaults to 128. + num_heads (int, optional): number of attention heads. Defaults to 4. + use_class_token (bool, optional): Whether to use extra token at the start for global accumulation (called as "class token"). Defaults to False. + """ + super(PatchTransformerEncoder, self).__init__() + self.use_class_token = use_class_token + encoder_layers = nn.TransformerEncoderLayer( + embedding_dim, num_heads, dim_feedforward=1024) + self.transformer_encoder = nn.TransformerEncoder( + encoder_layers, num_layers=4) # takes shape S,N,E + + self.embedding_convPxP = nn.Conv2d(in_channels, embedding_dim, + kernel_size=patch_size, stride=patch_size, padding=0) + + def positional_encoding_1d(self, sequence_length, batch_size, embedding_dim, device='cpu'): + """Generate positional encodings + + Args: + sequence_length (int): Sequence length + embedding_dim (int): Embedding dimension + + Returns: + torch.Tensor SBE: Positional encodings + """ + position = torch.arange( + 0, sequence_length, dtype=torch.float32, device=device).unsqueeze(1) + index = torch.arange( + 0, embedding_dim, 2, dtype=torch.float32, device=device).unsqueeze(0) + div_term = torch.exp(index * (-torch.log(torch.tensor(10000.0, device=device)) / embedding_dim)) + pos_encoding = position * div_term + pos_encoding = torch.cat([torch.sin(pos_encoding), torch.cos(pos_encoding)], dim=1) + pos_encoding = pos_encoding.unsqueeze(1).repeat(1, batch_size, 1) + return pos_encoding + + + def forward(self, x): + """Forward pass + + Args: + x (torch.Tensor - NCHW): Input feature tensor + + Returns: + torch.Tensor - SNE: Transformer output embeddings. S - sequence length (=HW/patch_size^2), N - batch size, E - embedding dim + """ + embeddings = self.embedding_convPxP(x).flatten( + 2) # .shape = n,c,s = n, embedding_dim, s + if self.use_class_token: + # extra special token at start ? + embeddings = nn.functional.pad(embeddings, (1, 0)) + + # change to S,N,E format required by transformer + embeddings = embeddings.permute(2, 0, 1) + S, N, E = embeddings.shape + embeddings = embeddings + self.positional_encoding_1d(S, N, E, device=embeddings.device) + x = self.transformer_encoder(embeddings) # .shape = S, N, E + return x diff --git a/ZoeDepth/zoedepth/models/model_io.py b/ZoeDepth/zoedepth/models/model_io.py new file mode 100644 index 0000000000000000000000000000000000000000..78b6579631dd847ac76651238cb5a948b5a66286 --- /dev/null +++ b/ZoeDepth/zoedepth/models/model_io.py @@ -0,0 +1,92 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch + +def load_state_dict(model, state_dict): + """Load state_dict into model, handling DataParallel and DistributedDataParallel. Also checks for "model" key in state_dict. + + DataParallel prefixes state_dict keys with 'module.' when saving. + If the model is not a DataParallel model but the state_dict is, then prefixes are removed. + If the model is a DataParallel model but the state_dict is not, then prefixes are added. + """ + state_dict = state_dict.get('model', state_dict) + # if model is a DataParallel model, then state_dict keys are prefixed with 'module.' + + do_prefix = isinstance( + model, (torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel)) + state = {} + for k, v in state_dict.items(): + if k.startswith('module.') and not do_prefix: + k = k[7:] + + if not k.startswith('module.') and do_prefix: + k = 'module.' + k + + state[k] = v + + model.load_state_dict(state) + print("Loaded successfully") + return model + + +def load_wts(model, checkpoint_path): + ckpt = torch.load(checkpoint_path, map_location='cpu') + return load_state_dict(model, ckpt) + + +def load_state_dict_from_url(model, url, **kwargs): + state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu', **kwargs) + return load_state_dict(model, state_dict) + + +def load_state_from_resource(model, resource: str): + """Loads weights to the model from a given resource. A resource can be of following types: + 1. URL. Prefixed with "url::" + e.g. url::http(s)://url.resource.com/ckpt.pt + + 2. Local path. Prefixed with "local::" + e.g. local::/path/to/ckpt.pt + + + Args: + model (torch.nn.Module): Model + resource (str): resource string + + Returns: + torch.nn.Module: Model with loaded weights + """ + print(f"Using pretrained resource {resource}") + + if resource.startswith('url::'): + url = resource.split('url::')[1] + return load_state_dict_from_url(model, url, progress=True) + + elif resource.startswith('local::'): + path = resource.split('local::')[1] + return load_wts(model, path) + + else: + raise ValueError("Invalid resource type, only url:: and local:: are supported") + \ No newline at end of file diff --git a/ZoeDepth/zoedepth/models/zoedepth/__init__.py b/ZoeDepth/zoedepth/models/zoedepth/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cc33f737d238766559f0e3a8def3c0b568f23b7f --- /dev/null +++ b/ZoeDepth/zoedepth/models/zoedepth/__init__.py @@ -0,0 +1,31 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +from .zoedepth_v1 import ZoeDepth + +all_versions = { + "v1": ZoeDepth, +} + +get_version = lambda v : all_versions[v] \ No newline at end of file diff --git a/ZoeDepth/zoedepth/models/zoedepth/config_zoedepth.json b/ZoeDepth/zoedepth/models/zoedepth/config_zoedepth.json new file mode 100644 index 0000000000000000000000000000000000000000..99beb2dcd886006ba87805bddbe408b6d5fdff78 --- /dev/null +++ b/ZoeDepth/zoedepth/models/zoedepth/config_zoedepth.json @@ -0,0 +1,58 @@ +{ + "model": { + "name": "ZoeDepth", + "version_name": "v1", + "n_bins": 64, + "bin_embedding_dim": 128, + "bin_centers_type": "softplus", + "n_attractors":[16, 8, 4, 1], + "attractor_alpha": 1000, + "attractor_gamma": 2, + "attractor_kind" : "mean", + "attractor_type" : "inv", + "midas_model_type" : "DPT_BEiT_L_384", + "min_temp": 0.0212, + "max_temp": 50.0, + "output_distribution": "logbinomial", + "memory_efficient": true, + "inverse_midas": false, + "img_size": [384, 512] + }, + + "train": { + "train_midas": true, + "use_pretrained_midas": true, + "trainer": "zoedepth", + "epochs": 5, + "bs": 16, + "optim_kwargs": {"lr": 0.000161, "wd": 0.01}, + "sched_kwargs": {"div_factor": 1, "final_div_factor": 10000, "pct_start": 0.7, "three_phase":false, "cycle_momentum": true}, + "same_lr": false, + "w_si": 1, + "w_domain": 0.2, + "w_reg": 0, + "w_grad": 0, + "avoid_boundary": false, + "random_crop": false, + "input_width": 640, + "input_height": 480, + "midas_lr_factor": 1, + "encoder_lr_factor":10, + "pos_enc_lr_factor":10, + "freeze_midas_bn": true + + }, + + "infer":{ + "train_midas": false, + "use_pretrained_midas": false, + "pretrained_resource" : "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_N.pt", + "force_keep_ar": true + }, + + "eval":{ + "train_midas": false, + "use_pretrained_midas": false, + "pretrained_resource" : "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_N.pt" + } +} \ No newline at end of file diff --git a/ZoeDepth/zoedepth/models/zoedepth/config_zoedepth_kitti.json b/ZoeDepth/zoedepth/models/zoedepth/config_zoedepth_kitti.json new file mode 100644 index 0000000000000000000000000000000000000000..b51802aa44b91c39e15aacaac4b5ab6bec884414 --- /dev/null +++ b/ZoeDepth/zoedepth/models/zoedepth/config_zoedepth_kitti.json @@ -0,0 +1,22 @@ +{ + "model": { + "bin_centers_type": "normed", + "img_size": [384, 768] + }, + + "train": { + }, + + "infer":{ + "train_midas": false, + "use_pretrained_midas": false, + "pretrained_resource" : "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_K.pt", + "force_keep_ar": true + }, + + "eval":{ + "train_midas": false, + "use_pretrained_midas": false, + "pretrained_resource" : "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_K.pt" + } +} \ No newline at end of file diff --git a/ZoeDepth/zoedepth/models/zoedepth/zoedepth_v1.py b/ZoeDepth/zoedepth/models/zoedepth/zoedepth_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..5600cb57dfd9b88f1cccf886fe14e0900856a57f --- /dev/null +++ b/ZoeDepth/zoedepth/models/zoedepth/zoedepth_v1.py @@ -0,0 +1,250 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import itertools + +import torch +import torch.nn as nn +from zoedepth.models.depth_model import DepthModel +from zoedepth.models.base_models.midas import MidasCore +from zoedepth.models.layers.attractor import AttractorLayer, AttractorLayerUnnormed +from zoedepth.models.layers.dist_layers import ConditionalLogBinomial +from zoedepth.models.layers.localbins_layers import (Projector, SeedBinRegressor, + SeedBinRegressorUnnormed) +from zoedepth.models.model_io import load_state_from_resource + + +class ZoeDepth(DepthModel): + def __init__(self, core, n_bins=64, bin_centers_type="softplus", bin_embedding_dim=128, min_depth=1e-3, max_depth=10, + n_attractors=[16, 8, 4, 1], attractor_alpha=300, attractor_gamma=2, attractor_kind='sum', attractor_type='exp', min_temp=5, max_temp=50, train_midas=True, + midas_lr_factor=10, encoder_lr_factor=10, pos_enc_lr_factor=10, inverse_midas=False, **kwargs): + """ZoeDepth model. This is the version of ZoeDepth that has a single metric head + + Args: + core (models.base_models.midas.MidasCore): The base midas model that is used for extraction of "relative" features + n_bins (int, optional): Number of bin centers. Defaults to 64. + bin_centers_type (str, optional): "normed" or "softplus". Activation type used for bin centers. For "normed" bin centers, linear normalization trick is applied. This results in bounded bin centers. + For "softplus", softplus activation is used and thus are unbounded. Defaults to "softplus". + bin_embedding_dim (int, optional): bin embedding dimension. Defaults to 128. + min_depth (float, optional): Lower bound for normed bin centers. Defaults to 1e-3. + max_depth (float, optional): Upper bound for normed bin centers. Defaults to 10. + n_attractors (List[int], optional): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1]. + attractor_alpha (int, optional): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 300. + attractor_gamma (int, optional): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2. + attractor_kind (str, optional): Attraction aggregation "sum" or "mean". Defaults to 'sum'. + attractor_type (str, optional): Type of attractor to use; "inv" (Inverse attractor) or "exp" (Exponential attractor). Defaults to 'exp'. + min_temp (int, optional): Lower bound for temperature of output probability distribution. Defaults to 5. + max_temp (int, optional): Upper bound for temperature of output probability distribution. Defaults to 50. + train_midas (bool, optional): Whether to train "core", the base midas model. Defaults to True. + midas_lr_factor (int, optional): Learning rate reduction factor for base midas model except its encoder and positional encodings. Defaults to 10. + encoder_lr_factor (int, optional): Learning rate reduction factor for the encoder in midas model. Defaults to 10. + pos_enc_lr_factor (int, optional): Learning rate reduction factor for positional encodings in the base midas model. Defaults to 10. + """ + super().__init__() + + self.core = core + self.max_depth = max_depth + self.min_depth = min_depth + self.min_temp = min_temp + self.bin_centers_type = bin_centers_type + + self.midas_lr_factor = midas_lr_factor + self.encoder_lr_factor = encoder_lr_factor + self.pos_enc_lr_factor = pos_enc_lr_factor + self.train_midas = train_midas + self.inverse_midas = inverse_midas + + if self.encoder_lr_factor <= 0: + self.core.freeze_encoder( + freeze_rel_pos=self.pos_enc_lr_factor <= 0) + + N_MIDAS_OUT = 32 + btlnck_features = self.core.output_channels[0] + num_out_features = self.core.output_channels[1:] + + self.conv2 = nn.Conv2d(btlnck_features, btlnck_features, + kernel_size=1, stride=1, padding=0) # btlnck conv + + if bin_centers_type == "normed": + SeedBinRegressorLayer = SeedBinRegressor + Attractor = AttractorLayer + elif bin_centers_type == "softplus": + SeedBinRegressorLayer = SeedBinRegressorUnnormed + Attractor = AttractorLayerUnnormed + elif bin_centers_type == "hybrid1": + SeedBinRegressorLayer = SeedBinRegressor + Attractor = AttractorLayerUnnormed + elif bin_centers_type == "hybrid2": + SeedBinRegressorLayer = SeedBinRegressorUnnormed + Attractor = AttractorLayer + else: + raise ValueError( + "bin_centers_type should be one of 'normed', 'softplus', 'hybrid1', 'hybrid2'") + + self.seed_bin_regressor = SeedBinRegressorLayer( + btlnck_features, n_bins=n_bins, min_depth=min_depth, max_depth=max_depth) + self.seed_projector = Projector(btlnck_features, bin_embedding_dim) + self.projectors = nn.ModuleList([ + Projector(num_out, bin_embedding_dim) + for num_out in num_out_features + ]) + self.attractors = nn.ModuleList([ + Attractor(bin_embedding_dim, n_bins, n_attractors=n_attractors[i], min_depth=min_depth, max_depth=max_depth, + alpha=attractor_alpha, gamma=attractor_gamma, kind=attractor_kind, attractor_type=attractor_type) + for i in range(len(num_out_features)) + ]) + + last_in = N_MIDAS_OUT + 1 # +1 for relative depth + + # use log binomial instead of softmax + self.conditional_log_binomial = ConditionalLogBinomial( + last_in, bin_embedding_dim, n_classes=n_bins, min_temp=min_temp, max_temp=max_temp) + + def forward(self, x, return_final_centers=False, denorm=False, return_probs=False, **kwargs): + """ + Args: + x (torch.Tensor): Input image tensor of shape (B, C, H, W) + return_final_centers (bool, optional): Whether to return the final bin centers. Defaults to False. + denorm (bool, optional): Whether to denormalize the input image. This reverses ImageNet normalization as midas normalization is different. Defaults to False. + return_probs (bool, optional): Whether to return the output probability distribution. Defaults to False. + + Returns: + dict: Dictionary containing the following keys: + - rel_depth (torch.Tensor): Relative depth map of shape (B, H, W) + - metric_depth (torch.Tensor): Metric depth map of shape (B, 1, H, W) + - bin_centers (torch.Tensor): Bin centers of shape (B, n_bins). Present only if return_final_centers is True + - probs (torch.Tensor): Output probability distribution of shape (B, n_bins, H, W). Present only if return_probs is True + + """ + b, c, h, w = x.shape + # print("input shape ", x.shape) + self.orig_input_width = w + self.orig_input_height = h + rel_depth, out = self.core(x, denorm=denorm, return_rel_depth=True) + # print("output shapes", rel_depth.shape, out.shape) + + outconv_activation = out[0] + btlnck = out[1] + x_blocks = out[2:] + + x_d0 = self.conv2(btlnck) + x = x_d0 + _, seed_b_centers = self.seed_bin_regressor(x) + + if self.bin_centers_type == 'normed' or self.bin_centers_type == 'hybrid2': + b_prev = (seed_b_centers - self.min_depth) / \ + (self.max_depth - self.min_depth) + else: + b_prev = seed_b_centers + + prev_b_embedding = self.seed_projector(x) + + # unroll this loop for better performance + for projector, attractor, x in zip(self.projectors, self.attractors, x_blocks): + b_embedding = projector(x) + b, b_centers = attractor( + b_embedding, b_prev, prev_b_embedding, interpolate=True) + b_prev = b.clone() + prev_b_embedding = b_embedding.clone() + + last = outconv_activation + + if self.inverse_midas: + # invert depth followed by normalization + rel_depth = 1.0 / (rel_depth + 1e-6) + rel_depth = (rel_depth - rel_depth.min()) / \ + (rel_depth.max() - rel_depth.min()) + # concat rel depth with last. First interpolate rel depth to last size + rel_cond = rel_depth.unsqueeze(1) + rel_cond = nn.functional.interpolate( + rel_cond, size=last.shape[2:], mode='bilinear', align_corners=True) + last = torch.cat([last, rel_cond], dim=1) + + b_embedding = nn.functional.interpolate( + b_embedding, last.shape[-2:], mode='bilinear', align_corners=True) + x = self.conditional_log_binomial(last, b_embedding) + + # Now depth value is Sum px * cx , where cx are bin_centers from the last bin tensor + # print(x.shape, b_centers.shape) + b_centers = nn.functional.interpolate( + b_centers, x.shape[-2:], mode='bilinear', align_corners=True) + out = torch.sum(x * b_centers, dim=1, keepdim=True) + + # Structure output dict + output = dict(metric_depth=out) + if return_final_centers or return_probs: + output['bin_centers'] = b_centers + + if return_probs: + output['probs'] = x + + return output + + def get_lr_params(self, lr): + """ + Learning rate configuration for different layers of the model + Args: + lr (float) : Base learning rate + Returns: + list : list of parameters to optimize and their learning rates, in the format required by torch optimizers. + """ + param_conf = [] + if self.train_midas: + if self.encoder_lr_factor > 0: + param_conf.append({'params': self.core.get_enc_params_except_rel_pos( + ), 'lr': lr / self.encoder_lr_factor}) + + if self.pos_enc_lr_factor > 0: + param_conf.append( + {'params': self.core.get_rel_pos_params(), 'lr': lr / self.pos_enc_lr_factor}) + + midas_params = self.core.core.scratch.parameters() + midas_lr_factor = self.midas_lr_factor + param_conf.append( + {'params': midas_params, 'lr': lr / midas_lr_factor}) + + remaining_modules = [] + for name, child in self.named_children(): + if name != 'core': + remaining_modules.append(child) + remaining_params = itertools.chain( + *[child.parameters() for child in remaining_modules]) + + param_conf.append({'params': remaining_params, 'lr': lr}) + + return param_conf + + @staticmethod + def build(midas_model_type="DPT_BEiT_L_384", pretrained_resource=None, use_pretrained_midas=False, train_midas=False, freeze_midas_bn=True, **kwargs): + core = MidasCore.build(midas_model_type=midas_model_type, use_pretrained_midas=use_pretrained_midas, + train_midas=train_midas, fetch_features=True, freeze_bn=freeze_midas_bn, **kwargs) + model = ZoeDepth(core, **kwargs) + if pretrained_resource: + assert isinstance(pretrained_resource, str), "pretrained_resource must be a string" + model = load_state_from_resource(model, pretrained_resource) + return model + + @staticmethod + def build_from_config(config): + return ZoeDepth.build(**config) diff --git a/ZoeDepth/zoedepth/models/zoedepth_nk/__init__.py b/ZoeDepth/zoedepth/models/zoedepth_nk/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..513a278b939c10c010e3c0250ec73544d5663886 --- /dev/null +++ b/ZoeDepth/zoedepth/models/zoedepth_nk/__init__.py @@ -0,0 +1,31 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +from .zoedepth_nk_v1 import ZoeDepthNK + +all_versions = { + "v1": ZoeDepthNK, +} + +get_version = lambda v : all_versions[v] \ No newline at end of file diff --git a/ZoeDepth/zoedepth/models/zoedepth_nk/config_zoedepth_nk.json b/ZoeDepth/zoedepth/models/zoedepth_nk/config_zoedepth_nk.json new file mode 100644 index 0000000000000000000000000000000000000000..42bab2a3ad159a09599a5aba270c491021a3cf1a --- /dev/null +++ b/ZoeDepth/zoedepth/models/zoedepth_nk/config_zoedepth_nk.json @@ -0,0 +1,67 @@ +{ + "model": { + "name": "ZoeDepthNK", + "version_name": "v1", + "bin_conf" : [ + { + "name": "nyu", + "n_bins": 64, + "min_depth": 1e-3, + "max_depth": 10.0 + }, + { + "name": "kitti", + "n_bins": 64, + "min_depth": 1e-3, + "max_depth": 80.0 + } + ], + "bin_embedding_dim": 128, + "bin_centers_type": "softplus", + "n_attractors":[16, 8, 4, 1], + "attractor_alpha": 1000, + "attractor_gamma": 2, + "attractor_kind" : "mean", + "attractor_type" : "inv", + "min_temp": 0.0212, + "max_temp": 50.0, + "memory_efficient": true, + "midas_model_type" : "DPT_BEiT_L_384", + "img_size": [384, 512] + }, + + "train": { + "train_midas": true, + "use_pretrained_midas": true, + "trainer": "zoedepth_nk", + "epochs": 5, + "bs": 16, + "optim_kwargs": {"lr": 0.0002512, "wd": 0.01}, + "sched_kwargs": {"div_factor": 1, "final_div_factor": 10000, "pct_start": 0.7, "three_phase":false, "cycle_momentum": true}, + "same_lr": false, + "w_si": 1, + "w_domain": 100, + "avoid_boundary": false, + "random_crop": false, + "input_width": 640, + "input_height": 480, + "w_grad": 0, + "w_reg": 0, + "midas_lr_factor": 10, + "encoder_lr_factor":10, + "pos_enc_lr_factor":10 + }, + + "infer": { + "train_midas": false, + "pretrained_resource": "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_NK.pt", + "use_pretrained_midas": false, + "force_keep_ar": true + }, + + "eval": { + "train_midas": false, + "pretrained_resource": "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_NK.pt", + "use_pretrained_midas": false + } +} \ No newline at end of file diff --git a/ZoeDepth/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py b/ZoeDepth/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..7368ae8031188a9f946d9d3f29633c96e791e68e --- /dev/null +++ b/ZoeDepth/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py @@ -0,0 +1,333 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import itertools + +import torch +import torch.nn as nn + +from zoedepth.models.depth_model import DepthModel +from zoedepth.models.base_models.midas import MidasCore +from zoedepth.models.layers.attractor import AttractorLayer, AttractorLayerUnnormed +from zoedepth.models.layers.dist_layers import ConditionalLogBinomial +from zoedepth.models.layers.localbins_layers import (Projector, SeedBinRegressor, + SeedBinRegressorUnnormed) +from zoedepth.models.layers.patch_transformer import PatchTransformerEncoder +from zoedepth.models.model_io import load_state_from_resource + + +class ZoeDepthNK(DepthModel): + def __init__(self, core, bin_conf, bin_centers_type="softplus", bin_embedding_dim=128, + n_attractors=[16, 8, 4, 1], attractor_alpha=300, attractor_gamma=2, attractor_kind='sum', attractor_type='exp', + min_temp=5, max_temp=50, + memory_efficient=False, train_midas=True, + is_midas_pretrained=True, midas_lr_factor=1, encoder_lr_factor=10, pos_enc_lr_factor=10, inverse_midas=False, **kwargs): + """ZoeDepthNK model. This is the version of ZoeDepth that has two metric heads and uses a learned router to route to experts. + + Args: + core (models.base_models.midas.MidasCore): The base midas model that is used for extraction of "relative" features + + bin_conf (List[dict]): A list of dictionaries that contain the bin configuration for each metric head. Each dictionary should contain the following keys: + "name" (str, typically same as the dataset name), "n_bins" (int), "min_depth" (float), "max_depth" (float) + + The length of this list determines the number of metric heads. + bin_centers_type (str, optional): "normed" or "softplus". Activation type used for bin centers. For "normed" bin centers, linear normalization trick is applied. This results in bounded bin centers. + For "softplus", softplus activation is used and thus are unbounded. Defaults to "normed". + bin_embedding_dim (int, optional): bin embedding dimension. Defaults to 128. + + n_attractors (List[int], optional): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1]. + attractor_alpha (int, optional): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 300. + attractor_gamma (int, optional): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2. + attractor_kind (str, optional): Attraction aggregation "sum" or "mean". Defaults to 'sum'. + attractor_type (str, optional): Type of attractor to use; "inv" (Inverse attractor) or "exp" (Exponential attractor). Defaults to 'exp'. + + min_temp (int, optional): Lower bound for temperature of output probability distribution. Defaults to 5. + max_temp (int, optional): Upper bound for temperature of output probability distribution. Defaults to 50. + + memory_efficient (bool, optional): Whether to use memory efficient version of attractor layers. Memory efficient version is slower but is recommended incase of multiple metric heads in order save GPU memory. Defaults to False. + + train_midas (bool, optional): Whether to train "core", the base midas model. Defaults to True. + is_midas_pretrained (bool, optional): Is "core" pretrained? Defaults to True. + midas_lr_factor (int, optional): Learning rate reduction factor for base midas model except its encoder and positional encodings. Defaults to 10. + encoder_lr_factor (int, optional): Learning rate reduction factor for the encoder in midas model. Defaults to 10. + pos_enc_lr_factor (int, optional): Learning rate reduction factor for positional encodings in the base midas model. Defaults to 10. + + """ + + super().__init__() + + self.core = core + self.bin_conf = bin_conf + self.min_temp = min_temp + self.max_temp = max_temp + self.memory_efficient = memory_efficient + self.train_midas = train_midas + self.is_midas_pretrained = is_midas_pretrained + self.midas_lr_factor = midas_lr_factor + self.encoder_lr_factor = encoder_lr_factor + self.pos_enc_lr_factor = pos_enc_lr_factor + self.inverse_midas = inverse_midas + + N_MIDAS_OUT = 32 + btlnck_features = self.core.output_channels[0] + num_out_features = self.core.output_channels[1:] + # self.scales = [16, 8, 4, 2] # spatial scale factors + + self.conv2 = nn.Conv2d( + btlnck_features, btlnck_features, kernel_size=1, stride=1, padding=0) + + # Transformer classifier on the bottleneck + self.patch_transformer = PatchTransformerEncoder( + btlnck_features, 1, 128, use_class_token=True) + self.mlp_classifier = nn.Sequential( + nn.Linear(128, 128), + nn.ReLU(), + nn.Linear(128, 2) + ) + + if bin_centers_type == "normed": + SeedBinRegressorLayer = SeedBinRegressor + Attractor = AttractorLayer + elif bin_centers_type == "softplus": + SeedBinRegressorLayer = SeedBinRegressorUnnormed + Attractor = AttractorLayerUnnormed + elif bin_centers_type == "hybrid1": + SeedBinRegressorLayer = SeedBinRegressor + Attractor = AttractorLayerUnnormed + elif bin_centers_type == "hybrid2": + SeedBinRegressorLayer = SeedBinRegressorUnnormed + Attractor = AttractorLayer + else: + raise ValueError( + "bin_centers_type should be one of 'normed', 'softplus', 'hybrid1', 'hybrid2'") + self.bin_centers_type = bin_centers_type + # We have bins for each bin conf. + # Create a map (ModuleDict) of 'name' -> seed_bin_regressor + self.seed_bin_regressors = nn.ModuleDict( + {conf['name']: SeedBinRegressorLayer(btlnck_features, conf["n_bins"], mlp_dim=bin_embedding_dim//2, min_depth=conf["min_depth"], max_depth=conf["max_depth"]) + for conf in bin_conf} + ) + + self.seed_projector = Projector( + btlnck_features, bin_embedding_dim, mlp_dim=bin_embedding_dim//2) + self.projectors = nn.ModuleList([ + Projector(num_out, bin_embedding_dim, mlp_dim=bin_embedding_dim//2) + for num_out in num_out_features + ]) + + # Create a map (ModuleDict) of 'name' -> attractors (ModuleList) + self.attractors = nn.ModuleDict( + {conf['name']: nn.ModuleList([ + Attractor(bin_embedding_dim, n_attractors[i], + mlp_dim=bin_embedding_dim, alpha=attractor_alpha, + gamma=attractor_gamma, kind=attractor_kind, + attractor_type=attractor_type, memory_efficient=memory_efficient, + min_depth=conf["min_depth"], max_depth=conf["max_depth"]) + for i in range(len(n_attractors)) + ]) + for conf in bin_conf} + ) + + last_in = N_MIDAS_OUT + # conditional log binomial for each bin conf + self.conditional_log_binomial = nn.ModuleDict( + {conf['name']: ConditionalLogBinomial(last_in, bin_embedding_dim, conf['n_bins'], bottleneck_factor=4, min_temp=self.min_temp, max_temp=self.max_temp) + for conf in bin_conf} + ) + + def forward(self, x, return_final_centers=False, denorm=False, return_probs=False, **kwargs): + """ + Args: + x (torch.Tensor): Input image tensor of shape (B, C, H, W). Assumes all images are from the same domain. + return_final_centers (bool, optional): Whether to return the final centers of the attractors. Defaults to False. + denorm (bool, optional): Whether to denormalize the input image. Defaults to False. + return_probs (bool, optional): Whether to return the probabilities of the bins. Defaults to False. + + Returns: + dict: Dictionary of outputs with keys: + - "rel_depth": Relative depth map of shape (B, 1, H, W) + - "metric_depth": Metric depth map of shape (B, 1, H, W) + - "domain_logits": Domain logits of shape (B, 2) + - "bin_centers": Bin centers of shape (B, N, H, W). Present only if return_final_centers is True + - "probs": Bin probabilities of shape (B, N, H, W). Present only if return_probs is True + """ + b, c, h, w = x.shape + self.orig_input_width = w + self.orig_input_height = h + rel_depth, out = self.core(x, denorm=denorm, return_rel_depth=True) + + outconv_activation = out[0] + btlnck = out[1] + x_blocks = out[2:] + + x_d0 = self.conv2(btlnck) + x = x_d0 + + # Predict which path to take + embedding = self.patch_transformer(x)[0] # N, E + domain_logits = self.mlp_classifier(embedding) # N, 2 + domain_vote = torch.softmax(domain_logits.sum( + dim=0, keepdim=True), dim=-1) # 1, 2 + + # Get the path + bin_conf_name = ["nyu", "kitti"][torch.argmax( + domain_vote, dim=-1).squeeze().item()] + + try: + conf = [c for c in self.bin_conf if c.name == bin_conf_name][0] + except IndexError: + raise ValueError( + f"bin_conf_name {bin_conf_name} not found in bin_confs") + + min_depth = conf['min_depth'] + max_depth = conf['max_depth'] + + seed_bin_regressor = self.seed_bin_regressors[bin_conf_name] + _, seed_b_centers = seed_bin_regressor(x) + if self.bin_centers_type == 'normed' or self.bin_centers_type == 'hybrid2': + b_prev = (seed_b_centers - min_depth)/(max_depth - min_depth) + else: + b_prev = seed_b_centers + prev_b_embedding = self.seed_projector(x) + + attractors = self.attractors[bin_conf_name] + for projector, attractor, x in zip(self.projectors, attractors, x_blocks): + b_embedding = projector(x) + b, b_centers = attractor( + b_embedding, b_prev, prev_b_embedding, interpolate=True) + b_prev = b + prev_b_embedding = b_embedding + + last = outconv_activation + + b_centers = nn.functional.interpolate( + b_centers, last.shape[-2:], mode='bilinear', align_corners=True) + b_embedding = nn.functional.interpolate( + b_embedding, last.shape[-2:], mode='bilinear', align_corners=True) + + clb = self.conditional_log_binomial[bin_conf_name] + x = clb(last, b_embedding) + + # Now depth value is Sum px * cx , where cx are bin_centers from the last bin tensor + # print(x.shape, b_centers.shape) + # b_centers = nn.functional.interpolate(b_centers, x.shape[-2:], mode='bilinear', align_corners=True) + out = torch.sum(x * b_centers, dim=1, keepdim=True) + + output = dict(domain_logits=domain_logits, metric_depth=out) + if return_final_centers or return_probs: + output['bin_centers'] = b_centers + + if return_probs: + output['probs'] = x + return output + + def get_lr_params(self, lr): + """ + Learning rate configuration for different layers of the model + + Args: + lr (float) : Base learning rate + Returns: + list : list of parameters to optimize and their learning rates, in the format required by torch optimizers. + """ + param_conf = [] + if self.train_midas: + def get_rel_pos_params(): + for name, p in self.core.core.pretrained.named_parameters(): + if "relative_position" in name: + yield p + + def get_enc_params_except_rel_pos(): + for name, p in self.core.core.pretrained.named_parameters(): + if "relative_position" not in name: + yield p + + encoder_params = get_enc_params_except_rel_pos() + rel_pos_params = get_rel_pos_params() + midas_params = self.core.core.scratch.parameters() + midas_lr_factor = self.midas_lr_factor if self.is_midas_pretrained else 1.0 + param_conf.extend([ + {'params': encoder_params, 'lr': lr / self.encoder_lr_factor}, + {'params': rel_pos_params, 'lr': lr / self.pos_enc_lr_factor}, + {'params': midas_params, 'lr': lr / midas_lr_factor} + ]) + + remaining_modules = [] + for name, child in self.named_children(): + if name != 'core': + remaining_modules.append(child) + remaining_params = itertools.chain( + *[child.parameters() for child in remaining_modules]) + param_conf.append({'params': remaining_params, 'lr': lr}) + return param_conf + + def get_conf_parameters(self, conf_name): + """ + Returns parameters of all the ModuleDicts children that are exclusively used for the given bin configuration + """ + params = [] + for name, child in self.named_children(): + if isinstance(child, nn.ModuleDict): + for bin_conf_name, module in child.items(): + if bin_conf_name == conf_name: + params += list(module.parameters()) + return params + + def freeze_conf(self, conf_name): + """ + Freezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration + """ + for p in self.get_conf_parameters(conf_name): + p.requires_grad = False + + def unfreeze_conf(self, conf_name): + """ + Unfreezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration + """ + for p in self.get_conf_parameters(conf_name): + p.requires_grad = True + + def freeze_all_confs(self): + """ + Freezes all the parameters of all the ModuleDicts children + """ + for name, child in self.named_children(): + if isinstance(child, nn.ModuleDict): + for bin_conf_name, module in child.items(): + for p in module.parameters(): + p.requires_grad = False + + @staticmethod + def build(midas_model_type="DPT_BEiT_L_384", pretrained_resource=None, use_pretrained_midas=False, train_midas=False, freeze_midas_bn=True, **kwargs): + core = MidasCore.build(midas_model_type=midas_model_type, use_pretrained_midas=use_pretrained_midas, + train_midas=train_midas, fetch_features=True, freeze_bn=freeze_midas_bn, **kwargs) + model = ZoeDepthNK(core, **kwargs) + if pretrained_resource: + assert isinstance(pretrained_resource, str), "pretrained_resource must be a string" + model = load_state_from_resource(model, pretrained_resource) + return model + + @staticmethod + def build_from_config(config): + return ZoeDepthNK.build(**config) diff --git a/ZoeDepth/zoedepth/trainers/base_trainer.py b/ZoeDepth/zoedepth/trainers/base_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..33fbbea3a7d49efe11b005adb5127f441eabfaf6 --- /dev/null +++ b/ZoeDepth/zoedepth/trainers/base_trainer.py @@ -0,0 +1,326 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os +import uuid +import warnings +from datetime import datetime as dt +from typing import Dict + +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.optim as optim +import wandb +from tqdm import tqdm + +from zoedepth.utils.config import flatten +from zoedepth.utils.misc import RunningAverageDict, colorize, colors + + +def is_rank_zero(args): + return args.rank == 0 + + +class BaseTrainer: + def __init__(self, config, model, train_loader, test_loader=None, device=None): + """ Base Trainer class for training a model.""" + + self.config = config + self.metric_criterion = "abs_rel" + if device is None: + device = torch.device( + 'cuda') if torch.cuda.is_available() else torch.device('cpu') + self.device = device + self.model = model + self.train_loader = train_loader + self.test_loader = test_loader + self.optimizer = self.init_optimizer() + self.scheduler = self.init_scheduler() + + def resize_to_target(self, prediction, target): + if prediction.shape[2:] != target.shape[-2:]: + prediction = nn.functional.interpolate( + prediction, size=target.shape[-2:], mode="bilinear", align_corners=True + ) + return prediction + + def load_ckpt(self, checkpoint_dir="./checkpoints", ckpt_type="best"): + import glob + import os + + from zoedepth.models.model_io import load_wts + + if hasattr(self.config, "checkpoint"): + checkpoint = self.config.checkpoint + elif hasattr(self.config, "ckpt_pattern"): + pattern = self.config.ckpt_pattern + matches = glob.glob(os.path.join( + checkpoint_dir, f"*{pattern}*{ckpt_type}*")) + if not (len(matches) > 0): + raise ValueError(f"No matches found for the pattern {pattern}") + checkpoint = matches[0] + else: + return + model = load_wts(self.model, checkpoint) + # TODO : Resuming training is not properly supported in this repo. Implement loading / saving of optimizer and scheduler to support it. + print("Loaded weights from {0}".format(checkpoint)) + warnings.warn( + "Resuming training is not properly supported in this repo. Implement loading / saving of optimizer and scheduler to support it.") + self.model = model + + def init_optimizer(self): + m = self.model.module if self.config.multigpu else self.model + + if self.config.same_lr: + print("Using same LR") + if hasattr(m, 'core'): + m.core.unfreeze() + params = self.model.parameters() + else: + print("Using diff LR") + if not hasattr(m, 'get_lr_params'): + raise NotImplementedError( + f"Model {m.__class__.__name__} does not implement get_lr_params. Please implement it or use the same LR for all parameters.") + + params = m.get_lr_params(self.config.lr) + + return optim.AdamW(params, lr=self.config.lr, weight_decay=self.config.wd) + + def init_scheduler(self): + lrs = [l['lr'] for l in self.optimizer.param_groups] + return optim.lr_scheduler.OneCycleLR(self.optimizer, lrs, epochs=self.config.epochs, steps_per_epoch=len(self.train_loader), + cycle_momentum=self.config.cycle_momentum, + base_momentum=0.85, max_momentum=0.95, div_factor=self.config.div_factor, final_div_factor=self.config.final_div_factor, pct_start=self.config.pct_start, three_phase=self.config.three_phase) + + def train_on_batch(self, batch, train_step): + raise NotImplementedError + + def validate_on_batch(self, batch, val_step): + raise NotImplementedError + + def raise_if_nan(self, losses): + for key, value in losses.items(): + if torch.isnan(value): + raise ValueError(f"{key} is NaN, Stopping training") + + @property + def iters_per_epoch(self): + return len(self.train_loader) + + @property + def total_iters(self): + return self.config.epochs * self.iters_per_epoch + + def should_early_stop(self): + if self.config.get('early_stop', False) and self.step > self.config.early_stop: + return True + + def train(self): + print(f"Training {self.config.name}") + if self.config.uid is None: + self.config.uid = str(uuid.uuid4()).split('-')[-1] + run_id = f"{dt.now().strftime('%d-%h_%H-%M')}-{self.config.uid}" + self.config.run_id = run_id + self.config.experiment_id = f"{self.config.name}{self.config.version_name}_{run_id}" + self.should_write = ((not self.config.distributed) + or self.config.rank == 0) + self.should_log = self.should_write # and logging + if self.should_log: + tags = self.config.tags.split( + ',') if self.config.tags != '' else None + wandb.init(project=self.config.project, name=self.config.experiment_id, config=flatten(self.config), dir=self.config.root, + tags=tags, notes=self.config.notes, settings=wandb.Settings(start_method="fork")) + + self.model.train() + self.step = 0 + best_loss = np.inf + validate_every = int(self.config.validate_every * self.iters_per_epoch) + + + if self.config.prefetch: + + for i, batch in tqdm(enumerate(self.train_loader), desc=f"Prefetching...", + total=self.iters_per_epoch) if is_rank_zero(self.config) else enumerate(self.train_loader): + pass + + losses = {} + def stringify_losses(L): return "; ".join(map( + lambda kv: f"{colors.fg.purple}{kv[0]}{colors.reset}: {round(kv[1].item(),3):.4e}", L.items())) + for epoch in range(self.config.epochs): + if self.should_early_stop(): + break + + self.epoch = epoch + ################################# Train loop ########################################################## + if self.should_log: + wandb.log({"Epoch": epoch}, step=self.step) + pbar = tqdm(enumerate(self.train_loader), desc=f"Epoch: {epoch + 1}/{self.config.epochs}. Loop: Train", + total=self.iters_per_epoch) if is_rank_zero(self.config) else enumerate(self.train_loader) + for i, batch in pbar: + if self.should_early_stop(): + print("Early stopping") + break + # print(f"Batch {self.step+1} on rank {self.config.rank}") + losses = self.train_on_batch(batch, i) + # print(f"trained batch {self.step+1} on rank {self.config.rank}") + + self.raise_if_nan(losses) + if is_rank_zero(self.config) and self.config.print_losses: + pbar.set_description( + f"Epoch: {epoch + 1}/{self.config.epochs}. Loop: Train. Losses: {stringify_losses(losses)}") + self.scheduler.step() + + if self.should_log and self.step % 50 == 0: + wandb.log({f"Train/{name}": loss.item() + for name, loss in losses.items()}, step=self.step) + + self.step += 1 + + ######################################################################################################## + + if self.test_loader: + if (self.step % validate_every) == 0: + self.model.eval() + if self.should_write: + self.save_checkpoint( + f"{self.config.experiment_id}_latest.pt") + + ################################# Validation loop ################################################## + # validate on the entire validation set in every process but save only from rank 0, I know, inefficient, but avoids divergence of processes + metrics, test_losses = self.validate() + # print("Validated: {}".format(metrics)) + if self.should_log: + wandb.log( + {f"Test/{name}": tloss for name, tloss in test_losses.items()}, step=self.step) + + wandb.log({f"Metrics/{k}": v for k, + v in metrics.items()}, step=self.step) + + if (metrics[self.metric_criterion] < best_loss) and self.should_write: + self.save_checkpoint( + f"{self.config.experiment_id}_best.pt") + best_loss = metrics[self.metric_criterion] + + self.model.train() + + if self.config.distributed: + dist.barrier() + # print(f"Validated: {metrics} on device {self.config.rank}") + + # print(f"Finished step {self.step} on device {self.config.rank}") + ################################################################################################# + + # Save / validate at the end + self.step += 1 # log as final point + self.model.eval() + self.save_checkpoint(f"{self.config.experiment_id}_latest.pt") + if self.test_loader: + + ################################# Validation loop ################################################## + metrics, test_losses = self.validate() + # print("Validated: {}".format(metrics)) + if self.should_log: + wandb.log({f"Test/{name}": tloss for name, + tloss in test_losses.items()}, step=self.step) + wandb.log({f"Metrics/{k}": v for k, + v in metrics.items()}, step=self.step) + + if (metrics[self.metric_criterion] < best_loss) and self.should_write: + self.save_checkpoint( + f"{self.config.experiment_id}_best.pt") + best_loss = metrics[self.metric_criterion] + + self.model.train() + + def validate(self): + with torch.no_grad(): + losses_avg = RunningAverageDict() + metrics_avg = RunningAverageDict() + for i, batch in tqdm(enumerate(self.test_loader), desc=f"Epoch: {self.epoch + 1}/{self.config.epochs}. Loop: Validation", total=len(self.test_loader), disable=not is_rank_zero(self.config)): + metrics, losses = self.validate_on_batch(batch, val_step=i) + + if losses: + losses_avg.update(losses) + if metrics: + metrics_avg.update(metrics) + + return metrics_avg.get_value(), losses_avg.get_value() + + def save_checkpoint(self, filename): + if not self.should_write: + return + root = self.config.save_dir + if not os.path.isdir(root): + os.makedirs(root) + + fpath = os.path.join(root, filename) + m = self.model.module if self.config.multigpu else self.model + torch.save( + { + "model": m.state_dict(), + "optimizer": None, # TODO : Change to self.optimizer.state_dict() if resume support is needed, currently None to reduce file size + "epoch": self.epoch + }, fpath) + + def log_images(self, rgb: Dict[str, list] = {}, depth: Dict[str, list] = {}, scalar_field: Dict[str, list] = {}, prefix="", scalar_cmap="jet", min_depth=None, max_depth=None): + if not self.should_log: + return + + if min_depth is None: + try: + min_depth = self.config.min_depth + max_depth = self.config.max_depth + except AttributeError: + min_depth = None + max_depth = None + + depth = {k: colorize(v, vmin=min_depth, vmax=max_depth) + for k, v in depth.items()} + scalar_field = {k: colorize( + v, vmin=None, vmax=None, cmap=scalar_cmap) for k, v in scalar_field.items()} + images = {**rgb, **depth, **scalar_field} + wimages = { + prefix+"Predictions": [wandb.Image(v, caption=k) for k, v in images.items()]} + wandb.log(wimages, step=self.step) + + def log_line_plot(self, data): + if not self.should_log: + return + + plt.plot(data) + plt.ylabel("Scale factors") + wandb.log({"Scale factors": wandb.Image(plt)}, step=self.step) + plt.close() + + def log_bar_plot(self, title, labels, values): + if not self.should_log: + return + + data = [[label, val] for (label, val) in zip(labels, values)] + table = wandb.Table(data=data, columns=["label", "value"]) + wandb.log({title: wandb.plot.bar(table, "label", + "value", title=title)}, step=self.step) diff --git a/ZoeDepth/zoedepth/trainers/builder.py b/ZoeDepth/zoedepth/trainers/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..a663541b08912ebedce21a68c7599ce4c06e85d0 --- /dev/null +++ b/ZoeDepth/zoedepth/trainers/builder.py @@ -0,0 +1,48 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +from importlib import import_module + + +def get_trainer(config): + """Builds and returns a trainer based on the config. + + Args: + config (dict): the config dict (typically constructed using utils.config.get_config) + config.trainer (str): the name of the trainer to use. The module named "{config.trainer}_trainer" must exist in trainers root module + + Raises: + ValueError: If the specified trainer does not exist under trainers/ folder + + Returns: + Trainer (inherited from zoedepth.trainers.BaseTrainer): The Trainer object + """ + assert "trainer" in config and config.trainer is not None and config.trainer != '', "Trainer not specified. Config: {0}".format( + config) + try: + Trainer = getattr(import_module( + f"zoedepth.trainers.{config.trainer}_trainer"), 'Trainer') + except ModuleNotFoundError as e: + raise ValueError(f"Trainer {config.trainer}_trainer not found.") from e + return Trainer diff --git a/ZoeDepth/zoedepth/trainers/loss.py b/ZoeDepth/zoedepth/trainers/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..0c5a1c15cdf5628c1474c566fdc6e58159d7f5ab --- /dev/null +++ b/ZoeDepth/zoedepth/trainers/loss.py @@ -0,0 +1,316 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.cuda.amp as amp +import numpy as np + + +KEY_OUTPUT = 'metric_depth' + + +def extract_key(prediction, key): + if isinstance(prediction, dict): + return prediction[key] + return prediction + + +# Main loss function used for ZoeDepth. Copy/paste from AdaBins repo (https://github.com/shariqfarooq123/AdaBins/blob/0952d91e9e762be310bb4cd055cbfe2448c0ce20/loss.py#L7) +class SILogLoss(nn.Module): + """SILog loss (pixel-wise)""" + def __init__(self, beta=0.15): + super(SILogLoss, self).__init__() + self.name = 'SILog' + self.beta = beta + + def forward(self, input, target, mask=None, interpolate=True, return_interpolated=False): + input = extract_key(input, KEY_OUTPUT) + if input.shape[-1] != target.shape[-1] and interpolate: + input = nn.functional.interpolate( + input, target.shape[-2:], mode='bilinear', align_corners=True) + intr_input = input + else: + intr_input = input + + if target.ndim == 3: + target = target.unsqueeze(1) + + if mask is not None: + if mask.ndim == 3: + mask = mask.unsqueeze(1) + + input = input[mask] + target = target[mask] + + with amp.autocast(enabled=False): # amp causes NaNs in this loss function + alpha = 1e-7 + g = torch.log(input + alpha) - torch.log(target + alpha) + + # n, c, h, w = g.shape + # norm = 1/(h*w) + # Dg = norm * torch.sum(g**2) - (0.85/(norm**2)) * (torch.sum(g))**2 + + Dg = torch.var(g) + self.beta * torch.pow(torch.mean(g), 2) + + loss = 10 * torch.sqrt(Dg) + + if torch.isnan(loss): + print("Nan SILog loss") + print("input:", input.shape) + print("target:", target.shape) + print("G", torch.sum(torch.isnan(g))) + print("Input min max", torch.min(input), torch.max(input)) + print("Target min max", torch.min(target), torch.max(target)) + print("Dg", torch.isnan(Dg)) + print("loss", torch.isnan(loss)) + + if not return_interpolated: + return loss + + return loss, intr_input + + +def grad(x): + # x.shape : n, c, h, w + diff_x = x[..., 1:, 1:] - x[..., 1:, :-1] + diff_y = x[..., 1:, 1:] - x[..., :-1, 1:] + mag = diff_x**2 + diff_y**2 + # angle_ratio + angle = torch.atan(diff_y / (diff_x + 1e-10)) + return mag, angle + + +def grad_mask(mask): + return mask[..., 1:, 1:] & mask[..., 1:, :-1] & mask[..., :-1, 1:] + + +class GradL1Loss(nn.Module): + """Gradient loss""" + def __init__(self): + super(GradL1Loss, self).__init__() + self.name = 'GradL1' + + def forward(self, input, target, mask=None, interpolate=True, return_interpolated=False): + input = extract_key(input, KEY_OUTPUT) + if input.shape[-1] != target.shape[-1] and interpolate: + input = nn.functional.interpolate( + input, target.shape[-2:], mode='bilinear', align_corners=True) + intr_input = input + else: + intr_input = input + + grad_gt = grad(target) + grad_pred = grad(input) + mask_g = grad_mask(mask) + + loss = nn.functional.l1_loss(grad_pred[0][mask_g], grad_gt[0][mask_g]) + loss = loss + \ + nn.functional.l1_loss(grad_pred[1][mask_g], grad_gt[1][mask_g]) + if not return_interpolated: + return loss + return loss, intr_input + + +class OrdinalRegressionLoss(object): + + def __init__(self, ord_num, beta, discretization="SID"): + self.ord_num = ord_num + self.beta = beta + self.discretization = discretization + + def _create_ord_label(self, gt): + N,one, H, W = gt.shape + # print("gt shape:", gt.shape) + + ord_c0 = torch.ones(N, self.ord_num, H, W).to(gt.device) + if self.discretization == "SID": + label = self.ord_num * torch.log(gt) / np.log(self.beta) + else: + label = self.ord_num * (gt - 1.0) / (self.beta - 1.0) + label = label.long() + mask = torch.linspace(0, self.ord_num - 1, self.ord_num, requires_grad=False) \ + .view(1, self.ord_num, 1, 1).to(gt.device) + mask = mask.repeat(N, 1, H, W).contiguous().long() + mask = (mask > label) + ord_c0[mask] = 0 + ord_c1 = 1 - ord_c0 + # implementation according to the paper. + # ord_label = torch.ones(N, self.ord_num * 2, H, W).to(gt.device) + # ord_label[:, 0::2, :, :] = ord_c0 + # ord_label[:, 1::2, :, :] = ord_c1 + # reimplementation for fast speed. + ord_label = torch.cat((ord_c0, ord_c1), dim=1) + return ord_label, mask + + def __call__(self, prob, gt): + """ + :param prob: ordinal regression probability, N x 2*Ord Num x H x W, torch.Tensor + :param gt: depth ground truth, NXHxW, torch.Tensor + :return: loss: loss value, torch.float + """ + # N, C, H, W = prob.shape + valid_mask = gt > 0. + ord_label, mask = self._create_ord_label(gt) + # print("prob shape: {}, ord label shape: {}".format(prob.shape, ord_label.shape)) + entropy = -prob * ord_label + loss = torch.sum(entropy, dim=1)[valid_mask.squeeze(1)] + return loss.mean() + + +class DiscreteNLLLoss(nn.Module): + """Cross entropy loss""" + def __init__(self, min_depth=1e-3, max_depth=10, depth_bins=64): + super(DiscreteNLLLoss, self).__init__() + self.name = 'CrossEntropy' + self.ignore_index = -(depth_bins + 1) + # self._loss_func = nn.NLLLoss(ignore_index=self.ignore_index) + self._loss_func = nn.CrossEntropyLoss(ignore_index=self.ignore_index) + self.min_depth = min_depth + self.max_depth = max_depth + self.depth_bins = depth_bins + self.alpha = 1 + self.zeta = 1 - min_depth + self.beta = max_depth + self.zeta + + def quantize_depth(self, depth): + # depth : N1HW + # output : NCHW + + # Quantize depth log-uniformly on [1, self.beta] into self.depth_bins bins + depth = torch.log(depth / self.alpha) / np.log(self.beta / self.alpha) + depth = depth * (self.depth_bins - 1) + depth = torch.round(depth) + depth = depth.long() + return depth + + + + def _dequantize_depth(self, depth): + """ + Inverse of quantization + depth : NCHW -> N1HW + """ + # Get the center of the bin + + + + + def forward(self, input, target, mask=None, interpolate=True, return_interpolated=False): + input = extract_key(input, KEY_OUTPUT) + # assert torch.all(input <= 0), "Input should be negative" + + if input.shape[-1] != target.shape[-1] and interpolate: + input = nn.functional.interpolate( + input, target.shape[-2:], mode='bilinear', align_corners=True) + intr_input = input + else: + intr_input = input + + # assert torch.all(input)<=1) + if target.ndim == 3: + target = target.unsqueeze(1) + + target = self.quantize_depth(target) + if mask is not None: + if mask.ndim == 3: + mask = mask.unsqueeze(1) + + # Set the mask to ignore_index + mask = mask.long() + input = input * mask + (1 - mask) * self.ignore_index + target = target * mask + (1 - mask) * self.ignore_index + + + + input = input.flatten(2) # N, nbins, H*W + target = target.flatten(1) # N, H*W + loss = self._loss_func(input, target) + + if not return_interpolated: + return loss + return loss, intr_input + + + + +def compute_scale_and_shift(prediction, target, mask): + # system matrix: A = [[a_00, a_01], [a_10, a_11]] + a_00 = torch.sum(mask * prediction * prediction, (1, 2)) + a_01 = torch.sum(mask * prediction, (1, 2)) + a_11 = torch.sum(mask, (1, 2)) + + # right hand side: b = [b_0, b_1] + b_0 = torch.sum(mask * prediction * target, (1, 2)) + b_1 = torch.sum(mask * target, (1, 2)) + + # solution: x = A^-1 . b = [[a_11, -a_01], [-a_10, a_00]] / (a_00 * a_11 - a_01 * a_10) . b + x_0 = torch.zeros_like(b_0) + x_1 = torch.zeros_like(b_1) + + det = a_00 * a_11 - a_01 * a_01 + # A needs to be a positive definite matrix. + valid = det > 0 + + x_0[valid] = (a_11[valid] * b_0[valid] - a_01[valid] * b_1[valid]) / det[valid] + x_1[valid] = (-a_01[valid] * b_0[valid] + a_00[valid] * b_1[valid]) / det[valid] + + return x_0, x_1 +class ScaleAndShiftInvariantLoss(nn.Module): + def __init__(self): + super().__init__() + self.name = "SSILoss" + + def forward(self, prediction, target, mask, interpolate=True, return_interpolated=False): + + if prediction.shape[-1] != target.shape[-1] and interpolate: + prediction = nn.functional.interpolate(prediction, target.shape[-2:], mode='bilinear', align_corners=True) + intr_input = prediction + else: + intr_input = prediction + + + prediction, target, mask = prediction.squeeze(), target.squeeze(), mask.squeeze() + assert prediction.shape == target.shape, f"Shape mismatch: Expected same shape but got {prediction.shape} and {target.shape}." + + scale, shift = compute_scale_and_shift(prediction, target, mask) + + scaled_prediction = scale.view(-1, 1, 1) * prediction + shift.view(-1, 1, 1) + + loss = nn.functional.l1_loss(scaled_prediction[mask], target[mask]) + if not return_interpolated: + return loss + return loss, intr_input + + + + +if __name__ == '__main__': + # Tests for DiscreteNLLLoss + celoss = DiscreteNLLLoss() + print(celoss(torch.rand(4, 64, 26, 32)*10, torch.rand(4, 1, 26, 32)*10, )) + + d = torch.Tensor([6.59, 3.8, 10.0]) + print(celoss.dequantize_depth(celoss.quantize_depth(d))) diff --git a/ZoeDepth/zoedepth/trainers/zoedepth_nk_trainer.py b/ZoeDepth/zoedepth/trainers/zoedepth_nk_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..d528ae126f1c51b2f25fd31f94a39591ceb2f43a --- /dev/null +++ b/ZoeDepth/zoedepth/trainers/zoedepth_nk_trainer.py @@ -0,0 +1,143 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.cuda.amp as amp +import torch.nn as nn + +from zoedepth.trainers.loss import GradL1Loss, SILogLoss +from zoedepth.utils.config import DATASETS_CONFIG +from zoedepth.utils.misc import compute_metrics + +from .base_trainer import BaseTrainer + + +class Trainer(BaseTrainer): + def __init__(self, config, model, train_loader, test_loader=None, device=None): + super().__init__(config, model, train_loader, + test_loader=test_loader, device=device) + self.device = device + self.silog_loss = SILogLoss() + self.grad_loss = GradL1Loss() + self.domain_classifier_loss = nn.CrossEntropyLoss() + + self.scaler = amp.GradScaler(enabled=self.config.use_amp) + + def train_on_batch(self, batch, train_step): + """ + Expects a batch of images and depth as input + batch["image"].shape : batch_size, c, h, w + batch["depth"].shape : batch_size, 1, h, w + + Assumes all images in a batch are from the same dataset + """ + + images, depths_gt = batch['image'].to( + self.device), batch['depth'].to(self.device) + # batch['dataset'] is a tensor strings all valued either 'nyu' or 'kitti'. labels nyu -> 0, kitti -> 1 + dataset = batch['dataset'][0] + # Convert to 0s or 1s + domain_labels = torch.Tensor([dataset == 'kitti' for _ in range( + images.size(0))]).to(torch.long).to(self.device) + + # m = self.model.module if self.config.multigpu else self.model + + b, c, h, w = images.size() + mask = batch["mask"].to(self.device).to(torch.bool) + + losses = {} + + with amp.autocast(enabled=self.config.use_amp): + output = self.model(images) + pred_depths = output['metric_depth'] + domain_logits = output['domain_logits'] + + l_si, pred = self.silog_loss( + pred_depths, depths_gt, mask=mask, interpolate=True, return_interpolated=True) + loss = self.config.w_si * l_si + losses[self.silog_loss.name] = l_si + + if self.config.w_grad > 0: + l_grad = self.grad_loss(pred, depths_gt, mask=mask) + loss = loss + self.config.w_grad * l_grad + losses[self.grad_loss.name] = l_grad + else: + l_grad = torch.Tensor([0]) + + if self.config.w_domain > 0: + l_domain = self.domain_classifier_loss( + domain_logits, domain_labels) + loss = loss + self.config.w_domain * l_domain + losses["DomainLoss"] = l_domain + else: + l_domain = torch.Tensor([0.]) + + self.scaler.scale(loss).backward() + + if self.config.clip_grad > 0: + self.scaler.unscale_(self.optimizer) + nn.utils.clip_grad_norm_( + self.model.parameters(), self.config.clip_grad) + + self.scaler.step(self.optimizer) + + if self.should_log and self.step > 1 and (self.step % int(self.config.log_images_every * self.iters_per_epoch)) == 0: + depths_gt[torch.logical_not(mask)] = -99 + self.log_images(rgb={"Input": images[0, ...]}, depth={"GT": depths_gt[0], "PredictedMono": pred[0]}, prefix="Train", + min_depth=DATASETS_CONFIG[dataset]['min_depth'], max_depth=DATASETS_CONFIG[dataset]['max_depth']) + + self.scaler.update() + self.optimizer.zero_grad(set_to_none=True) + + return losses + + def validate_on_batch(self, batch, val_step): + images = batch['image'].to(self.device) + depths_gt = batch['depth'].to(self.device) + dataset = batch['dataset'][0] + if 'has_valid_depth' in batch: + if not batch['has_valid_depth']: + return None, None + + depths_gt = depths_gt.squeeze().unsqueeze(0).unsqueeze(0) + with amp.autocast(enabled=self.config.use_amp): + m = self.model.module if self.config.multigpu else self.model + pred_depths = m(images)["metric_depth"] + pred_depths = pred_depths.squeeze().unsqueeze(0).unsqueeze(0) + + mask = torch.logical_and( + depths_gt > self.config.min_depth, depths_gt < self.config.max_depth) + with amp.autocast(enabled=self.config.use_amp): + l_depth = self.silog_loss( + pred_depths, depths_gt, mask=mask.to(torch.bool), interpolate=True) + + metrics = compute_metrics(depths_gt, pred_depths, **self.config) + losses = {f"{self.silog_loss.name}": l_depth.item()} + + if val_step == 1 and self.should_log: + depths_gt[torch.logical_not(mask)] = -99 + self.log_images(rgb={"Input": images[0]}, depth={"GT": depths_gt[0], "PredictedMono": pred_depths[0]}, prefix="Test", + min_depth=DATASETS_CONFIG[dataset]['min_depth'], max_depth=DATASETS_CONFIG[dataset]['max_depth']) + + return metrics, losses diff --git a/ZoeDepth/zoedepth/trainers/zoedepth_trainer.py b/ZoeDepth/zoedepth/trainers/zoedepth_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..3ac1c24c0512c1c1b191670a7c24abb4fca47ba1 --- /dev/null +++ b/ZoeDepth/zoedepth/trainers/zoedepth_trainer.py @@ -0,0 +1,177 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.cuda.amp as amp +import torch.nn as nn + +from zoedepth.trainers.loss import GradL1Loss, SILogLoss +from zoedepth.utils.config import DATASETS_CONFIG +from zoedepth.utils.misc import compute_metrics +from zoedepth.data.preprocess import get_black_border + +from .base_trainer import BaseTrainer +from torchvision import transforms +from PIL import Image +import numpy as np + +class Trainer(BaseTrainer): + def __init__(self, config, model, train_loader, test_loader=None, device=None): + super().__init__(config, model, train_loader, + test_loader=test_loader, device=device) + self.device = device + self.silog_loss = SILogLoss() + self.grad_loss = GradL1Loss() + self.scaler = amp.GradScaler(enabled=self.config.use_amp) + + def train_on_batch(self, batch, train_step): + """ + Expects a batch of images and depth as input + batch["image"].shape : batch_size, c, h, w + batch["depth"].shape : batch_size, 1, h, w + """ + + images, depths_gt = batch['image'].to( + self.device), batch['depth'].to(self.device) + dataset = batch['dataset'][0] + + b, c, h, w = images.size() + mask = batch["mask"].to(self.device).to(torch.bool) + + losses = {} + + with amp.autocast(enabled=self.config.use_amp): + + output = self.model(images) + pred_depths = output['metric_depth'] + + l_si, pred = self.silog_loss( + pred_depths, depths_gt, mask=mask, interpolate=True, return_interpolated=True) + loss = self.config.w_si * l_si + losses[self.silog_loss.name] = l_si + + if self.config.w_grad > 0: + l_grad = self.grad_loss(pred, depths_gt, mask=mask) + loss = loss + self.config.w_grad * l_grad + losses[self.grad_loss.name] = l_grad + else: + l_grad = torch.Tensor([0]) + + self.scaler.scale(loss).backward() + + if self.config.clip_grad > 0: + self.scaler.unscale_(self.optimizer) + nn.utils.clip_grad_norm_( + self.model.parameters(), self.config.clip_grad) + + self.scaler.step(self.optimizer) + + if self.should_log and (self.step % int(self.config.log_images_every * self.iters_per_epoch)) == 0: + # -99 is treated as invalid depth in the log_images function and is colored grey. + depths_gt[torch.logical_not(mask)] = -99 + + self.log_images(rgb={"Input": images[0, ...]}, depth={"GT": depths_gt[0], "PredictedMono": pred[0]}, prefix="Train", + min_depth=DATASETS_CONFIG[dataset]['min_depth'], max_depth=DATASETS_CONFIG[dataset]['max_depth']) + + if self.config.get("log_rel", False): + self.log_images( + scalar_field={"RelPred": output["relative_depth"][0]}, prefix="TrainRel") + + self.scaler.update() + self.optimizer.zero_grad() + + return losses + + @torch.no_grad() + def eval_infer(self, x): + with amp.autocast(enabled=self.config.use_amp): + m = self.model.module if self.config.multigpu else self.model + pred_depths = m(x)['metric_depth'] + return pred_depths + + @torch.no_grad() + def crop_aware_infer(self, x): + # if we are not avoiding the black border, we can just use the normal inference + if not self.config.get("avoid_boundary", False): + return self.eval_infer(x) + + # otherwise, we need to crop the image to avoid the black border + # For now, this may be a bit slow due to converting to numpy and back + # We assume no normalization is done on the input image + + # get the black border + assert x.shape[0] == 1, "Only batch size 1 is supported for now" + x_pil = transforms.ToPILImage()(x[0].cpu()) + x_np = np.array(x_pil, dtype=np.uint8) + black_border_params = get_black_border(x_np) + top, bottom, left, right = black_border_params.top, black_border_params.bottom, black_border_params.left, black_border_params.right + x_np_cropped = x_np[top:bottom, left:right, :] + x_cropped = transforms.ToTensor()(Image.fromarray(x_np_cropped)) + + # run inference on the cropped image + pred_depths_cropped = self.eval_infer(x_cropped.unsqueeze(0).to(self.device)) + + # resize the prediction to x_np_cropped's size + pred_depths_cropped = nn.functional.interpolate( + pred_depths_cropped, size=(x_np_cropped.shape[0], x_np_cropped.shape[1]), mode="bilinear", align_corners=False) + + + # pad the prediction back to the original size + pred_depths = torch.zeros((1, 1, x_np.shape[0], x_np.shape[1]), device=pred_depths_cropped.device, dtype=pred_depths_cropped.dtype) + pred_depths[:, :, top:bottom, left:right] = pred_depths_cropped + + return pred_depths + + + + def validate_on_batch(self, batch, val_step): + images = batch['image'].to(self.device) + depths_gt = batch['depth'].to(self.device) + dataset = batch['dataset'][0] + mask = batch["mask"].to(self.device) + if 'has_valid_depth' in batch: + if not batch['has_valid_depth']: + return None, None + + depths_gt = depths_gt.squeeze().unsqueeze(0).unsqueeze(0) + mask = mask.squeeze().unsqueeze(0).unsqueeze(0) + if dataset == 'nyu': + pred_depths = self.crop_aware_infer(images) + else: + pred_depths = self.eval_infer(images) + pred_depths = pred_depths.squeeze().unsqueeze(0).unsqueeze(0) + + with amp.autocast(enabled=self.config.use_amp): + l_depth = self.silog_loss( + pred_depths, depths_gt, mask=mask.to(torch.bool), interpolate=True) + + metrics = compute_metrics(depths_gt, pred_depths, **self.config) + losses = {f"{self.silog_loss.name}": l_depth.item()} + + if val_step == 1 and self.should_log: + depths_gt[torch.logical_not(mask)] = -99 + self.log_images(rgb={"Input": images[0]}, depth={"GT": depths_gt[0], "PredictedMono": pred_depths[0]}, prefix="Test", + min_depth=DATASETS_CONFIG[dataset]['min_depth'], max_depth=DATASETS_CONFIG[dataset]['max_depth']) + + return metrics, losses diff --git a/ZoeDepth/zoedepth/utils/__init__.py b/ZoeDepth/zoedepth/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5f2668792389157609abb2a0846fb620e7d67eb9 --- /dev/null +++ b/ZoeDepth/zoedepth/utils/__init__.py @@ -0,0 +1,24 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + diff --git a/ZoeDepth/zoedepth/utils/arg_utils.py b/ZoeDepth/zoedepth/utils/arg_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3004ec3679c0a40fd8961253733fb4343ad545 --- /dev/null +++ b/ZoeDepth/zoedepth/utils/arg_utils.py @@ -0,0 +1,33 @@ + + +def infer_type(x): # hacky way to infer type from string args + if not isinstance(x, str): + return x + + try: + x = int(x) + return x + except ValueError: + pass + + try: + x = float(x) + return x + except ValueError: + pass + + return x + + +def parse_unknown(unknown_args): + clean = [] + for a in unknown_args: + if "=" in a: + k, v = a.split("=") + clean.extend([k, v]) + else: + clean.append(a) + + keys = clean[::2] + values = clean[1::2] + return {k.replace("--", ""): infer_type(v) for k, v in zip(keys, values)} diff --git a/ZoeDepth/zoedepth/utils/config.py b/ZoeDepth/zoedepth/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..363b0e186cd1247e8f5fc224e6f69f5fc8190c99 --- /dev/null +++ b/ZoeDepth/zoedepth/utils/config.py @@ -0,0 +1,437 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import json +import os + +from zoedepth.utils.easydict import EasyDict as edict + +from zoedepth.utils.arg_utils import infer_type +import pathlib +import platform + +ROOT = pathlib.Path(__file__).parent.parent.resolve() + +HOME_DIR = os.path.expanduser("~") + +COMMON_CONFIG = { + "save_dir": os.path.expanduser("~/shortcuts/monodepth3_checkpoints"), + "project": "ZoeDepth", + "tags": '', + "notes": "", + "gpu": None, + "root": ".", + "uid": None, + "print_losses": False +} + +DATASETS_CONFIG = { + "kitti": { + "dataset": "kitti", + "min_depth": 0.001, + "max_depth": 80, + "data_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"), + "gt_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"), + "filenames_file": "./train_test_inputs/kitti_eigen_train_files_with_gt.txt", + "input_height": 352, + "input_width": 1216, # 704 + "data_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"), + "gt_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"), + "filenames_file_eval": "./train_test_inputs/kitti_eigen_test_files_with_gt.txt", + + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + + "do_random_rotate": True, + "degree": 1.0, + "do_kb_crop": True, + "garg_crop": True, + "eigen_crop": False, + "use_right": False + }, + "kitti_test": { + "dataset": "kitti", + "min_depth": 0.001, + "max_depth": 80, + "data_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"), + "gt_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"), + "filenames_file": "./train_test_inputs/kitti_eigen_train_files_with_gt.txt", + "input_height": 352, + "input_width": 1216, + "data_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"), + "gt_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"), + "filenames_file_eval": "./train_test_inputs/kitti_eigen_test_files_with_gt.txt", + + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + + "do_random_rotate": False, + "degree": 1.0, + "do_kb_crop": True, + "garg_crop": True, + "eigen_crop": False, + "use_right": False + }, + "nyu": { + "dataset": "nyu", + "avoid_boundary": False, + "min_depth": 1e-3, # originally 0.1 + "max_depth": 10, + "data_path": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/sync/"), + "gt_path": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/sync/"), + "filenames_file": "./train_test_inputs/nyudepthv2_train_files_with_gt.txt", + "input_height": 480, + "input_width": 640, + "data_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/official_splits/test/"), + "gt_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/official_splits/test/"), + "filenames_file_eval": "./train_test_inputs/nyudepthv2_test_files_with_gt.txt", + "min_depth_eval": 1e-3, + "max_depth_eval": 10, + "min_depth_diff": -10, + "max_depth_diff": 10, + + "do_random_rotate": True, + "degree": 1.0, + "do_kb_crop": False, + "garg_crop": False, + "eigen_crop": True + }, + "ibims": { + "dataset": "ibims", + "ibims_root": os.path.join(HOME_DIR, "shortcuts/datasets/ibims/ibims1_core_raw/"), + "eigen_crop": True, + "garg_crop": False, + "do_kb_crop": False, + "min_depth_eval": 0, + "max_depth_eval": 10, + "min_depth": 1e-3, + "max_depth": 10 + }, + "sunrgbd": { + "dataset": "sunrgbd", + "sunrgbd_root": os.path.join(HOME_DIR, "shortcuts/datasets/SUNRGBD/test/"), + "eigen_crop": True, + "garg_crop": False, + "do_kb_crop": False, + "min_depth_eval": 0, + "max_depth_eval": 8, + "min_depth": 1e-3, + "max_depth": 10 + }, + "diml_indoor": { + "dataset": "diml_indoor", + "diml_indoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diml_indoor_test/"), + "eigen_crop": True, + "garg_crop": False, + "do_kb_crop": False, + "min_depth_eval": 0, + "max_depth_eval": 10, + "min_depth": 1e-3, + "max_depth": 10 + }, + "diml_outdoor": { + "dataset": "diml_outdoor", + "diml_outdoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diml_outdoor_test/"), + "eigen_crop": False, + "garg_crop": True, + "do_kb_crop": False, + "min_depth_eval": 2, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 80 + }, + "diode_indoor": { + "dataset": "diode_indoor", + "diode_indoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diode_indoor/"), + "eigen_crop": True, + "garg_crop": False, + "do_kb_crop": False, + "min_depth_eval": 1e-3, + "max_depth_eval": 10, + "min_depth": 1e-3, + "max_depth": 10 + }, + "diode_outdoor": { + "dataset": "diode_outdoor", + "diode_outdoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diode_outdoor/"), + "eigen_crop": False, + "garg_crop": True, + "do_kb_crop": False, + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 80 + }, + "hypersim_test": { + "dataset": "hypersim_test", + "hypersim_test_root": os.path.join(HOME_DIR, "shortcuts/datasets/hypersim_test/"), + "eigen_crop": True, + "garg_crop": False, + "do_kb_crop": False, + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 10 + }, + "vkitti": { + "dataset": "vkitti", + "vkitti_root": os.path.join(HOME_DIR, "shortcuts/datasets/vkitti_test/"), + "eigen_crop": False, + "garg_crop": True, + "do_kb_crop": True, + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 80 + }, + "vkitti2": { + "dataset": "vkitti2", + "vkitti2_root": os.path.join(HOME_DIR, "shortcuts/datasets/vkitti2/"), + "eigen_crop": False, + "garg_crop": True, + "do_kb_crop": True, + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 80, + }, + "ddad": { + "dataset": "ddad", + "ddad_root": os.path.join(HOME_DIR, "shortcuts/datasets/ddad/ddad_val/"), + "eigen_crop": False, + "garg_crop": True, + "do_kb_crop": True, + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 80, + }, +} + +ALL_INDOOR = ["nyu", "ibims", "sunrgbd", "diode_indoor", "hypersim_test"] +ALL_OUTDOOR = ["kitti", "diml_outdoor", "diode_outdoor", "vkitti2", "ddad"] +ALL_EVAL_DATASETS = ALL_INDOOR + ALL_OUTDOOR + +COMMON_TRAINING_CONFIG = { + "dataset": "nyu", + "distributed": True, + "workers": 16, + "clip_grad": 0.1, + "use_shared_dict": False, + "shared_dict": None, + "use_amp": False, + + "aug": True, + "random_crop": False, + "random_translate": False, + "translate_prob": 0.2, + "max_translation": 100, + + "validate_every": 0.25, + "log_images_every": 0.1, + "prefetch": False, +} + + +def flatten(config, except_keys=('bin_conf')): + def recurse(inp): + if isinstance(inp, dict): + for key, value in inp.items(): + if key in except_keys: + yield (key, value) + if isinstance(value, dict): + yield from recurse(value) + else: + yield (key, value) + + return dict(list(recurse(config))) + + +def split_combined_args(kwargs): + """Splits the arguments that are combined with '__' into multiple arguments. + Combined arguments should have equal number of keys and values. + Keys are separated by '__' and Values are separated with ';'. + For example, '__n_bins__lr=256;0.001' + + Args: + kwargs (dict): key-value pairs of arguments where key-value is optionally combined according to the above format. + + Returns: + dict: Parsed dict with the combined arguments split into individual key-value pairs. + """ + new_kwargs = dict(kwargs) + for key, value in kwargs.items(): + if key.startswith("__"): + keys = key.split("__")[1:] + values = value.split(";") + assert len(keys) == len( + values), f"Combined arguments should have equal number of keys and values. Keys are separated by '__' and Values are separated with ';'. For example, '__n_bins__lr=256;0.001. Given (keys,values) is ({keys}, {values})" + for k, v in zip(keys, values): + new_kwargs[k] = v + return new_kwargs + + +def parse_list(config, key, dtype=int): + """Parse a list of values for the key if the value is a string. The values are separated by a comma. + Modifies the config in place. + """ + if key in config: + if isinstance(config[key], str): + config[key] = list(map(dtype, config[key].split(','))) + assert isinstance(config[key], list) and all([isinstance(e, dtype) for e in config[key]] + ), f"{key} should be a list of values dtype {dtype}. Given {config[key]} of type {type(config[key])} with values of type {[type(e) for e in config[key]]}." + + +def get_model_config(model_name, model_version=None): + """Find and parse the .json config file for the model. + + Args: + model_name (str): name of the model. The config file should be named config_{model_name}[_{model_version}].json under the models/{model_name} directory. + model_version (str, optional): Specific config version. If specified config_{model_name}_{model_version}.json is searched for and used. Otherwise config_{model_name}.json is used. Defaults to None. + + Returns: + easydict: the config dictionary for the model. + """ + config_fname = f"config_{model_name}_{model_version}.json" if model_version is not None else f"config_{model_name}.json" + config_file = os.path.join(ROOT, "models", model_name, config_fname) + if not os.path.exists(config_file): + return None + + with open(config_file, "r") as f: + config = edict(json.load(f)) + + # handle dictionary inheritance + # only training config is supported for inheritance + if "inherit" in config.train and config.train.inherit is not None: + inherit_config = get_model_config(config.train["inherit"]).train + for key, value in inherit_config.items(): + if key not in config.train: + config.train[key] = value + return edict(config) + + +def update_model_config(config, mode, model_name, model_version=None, strict=False): + model_config = get_model_config(model_name, model_version) + if model_config is not None: + config = {**config, ** + flatten({**model_config.model, **model_config[mode]})} + elif strict: + raise ValueError(f"Config file for model {model_name} not found.") + return config + + +def check_choices(name, value, choices): + # return # No checks in dev branch + if value not in choices: + raise ValueError(f"{name} {value} not in supported choices {choices}") + + +KEYS_TYPE_BOOL = ["use_amp", "distributed", "use_shared_dict", "same_lr", "aug", "three_phase", + "prefetch", "cycle_momentum"] # Casting is not necessary as their int casted values in config are 0 or 1 + + +def get_config(model_name, mode='train', dataset=None, **overwrite_kwargs): + """Main entry point to get the config for the model. + + Args: + model_name (str): name of the desired model. + mode (str, optional): "train" or "infer". Defaults to 'train'. + dataset (str, optional): If specified, the corresponding dataset configuration is loaded as well. Defaults to None. + + Keyword Args: key-value pairs of arguments to overwrite the default config. + + The order of precedence for overwriting the config is (Higher precedence first): + # 1. overwrite_kwargs + # 2. "config_version": Config file version if specified in overwrite_kwargs. The corresponding config loaded is config_{model_name}_{config_version}.json + # 3. "version_name": Default Model version specific config specified in overwrite_kwargs. The corresponding config loaded is config_{model_name}_{version_name}.json + # 4. common_config: Default config for all models specified in COMMON_CONFIG + + Returns: + easydict: The config dictionary for the model. + """ + + + check_choices("Model", model_name, ["zoedepth", "zoedepth_nk"]) + check_choices("Mode", mode, ["train", "infer", "eval"]) + if mode == "train": + check_choices("Dataset", dataset, ["nyu", "kitti", "mix", None]) + + config = flatten({**COMMON_CONFIG, **COMMON_TRAINING_CONFIG}) + config = update_model_config(config, mode, model_name) + + # update with model version specific config + version_name = overwrite_kwargs.get("version_name", config["version_name"]) + config = update_model_config(config, mode, model_name, version_name) + + # update with config version if specified + config_version = overwrite_kwargs.get("config_version", None) + if config_version is not None: + print("Overwriting config with config_version", config_version) + config = update_model_config(config, mode, model_name, config_version) + + # update with overwrite_kwargs + # Combined args are useful for hyperparameter search + overwrite_kwargs = split_combined_args(overwrite_kwargs) + config = {**config, **overwrite_kwargs} + + # Casting to bool # TODO: Not necessary. Remove and test + for key in KEYS_TYPE_BOOL: + if key in config: + config[key] = bool(config[key]) + + # Model specific post processing of config + parse_list(config, "n_attractors") + + # adjust n_bins for each bin configuration if bin_conf is given and n_bins is passed in overwrite_kwargs + if 'bin_conf' in config and 'n_bins' in overwrite_kwargs: + bin_conf = config['bin_conf'] # list of dicts + n_bins = overwrite_kwargs['n_bins'] + new_bin_conf = [] + for conf in bin_conf: + conf['n_bins'] = n_bins + new_bin_conf.append(conf) + config['bin_conf'] = new_bin_conf + + if mode == "train": + orig_dataset = dataset + if dataset == "mix": + dataset = 'nyu' # Use nyu as default for mix. Dataset config is changed accordingly while loading the dataloader + if dataset is not None: + config['project'] = f"MonoDepth3-{orig_dataset}" # Set project for wandb + + if dataset is not None: + config['dataset'] = dataset + config = {**DATASETS_CONFIG[dataset], **config} + + + config['model'] = model_name + typed_config = {k: infer_type(v) for k, v in config.items()} + # add hostname to config + config['hostname'] = platform.node() + return edict(typed_config) + + +def change_dataset(config, new_dataset): + config.update(DATASETS_CONFIG[new_dataset]) + return config diff --git a/ZoeDepth/zoedepth/utils/easydict/__init__.py b/ZoeDepth/zoedepth/utils/easydict/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..15928179b0182c6045d98bc0a7be1c6ca45f675e --- /dev/null +++ b/ZoeDepth/zoedepth/utils/easydict/__init__.py @@ -0,0 +1,158 @@ +""" +EasyDict +Copy/pasted from https://github.com/makinacorpus/easydict +Original author: Mathieu Leplatre +""" + +class EasyDict(dict): + """ + Get attributes + + >>> d = EasyDict({'foo':3}) + >>> d['foo'] + 3 + >>> d.foo + 3 + >>> d.bar + Traceback (most recent call last): + ... + AttributeError: 'EasyDict' object has no attribute 'bar' + + Works recursively + + >>> d = EasyDict({'foo':3, 'bar':{'x':1, 'y':2}}) + >>> isinstance(d.bar, dict) + True + >>> d.bar.x + 1 + + Bullet-proof + + >>> EasyDict({}) + {} + >>> EasyDict(d={}) + {} + >>> EasyDict(None) + {} + >>> d = {'a': 1} + >>> EasyDict(**d) + {'a': 1} + >>> EasyDict((('a', 1), ('b', 2))) + {'a': 1, 'b': 2} + + Set attributes + + >>> d = EasyDict() + >>> d.foo = 3 + >>> d.foo + 3 + >>> d.bar = {'prop': 'value'} + >>> d.bar.prop + 'value' + >>> d + {'foo': 3, 'bar': {'prop': 'value'}} + >>> d.bar.prop = 'newer' + >>> d.bar.prop + 'newer' + + + Values extraction + + >>> d = EasyDict({'foo':0, 'bar':[{'x':1, 'y':2}, {'x':3, 'y':4}]}) + >>> isinstance(d.bar, list) + True + >>> from operator import attrgetter + >>> list(map(attrgetter('x'), d.bar)) + [1, 3] + >>> list(map(attrgetter('y'), d.bar)) + [2, 4] + >>> d = EasyDict() + >>> list(d.keys()) + [] + >>> d = EasyDict(foo=3, bar=dict(x=1, y=2)) + >>> d.foo + 3 + >>> d.bar.x + 1 + + Still like a dict though + + >>> o = EasyDict({'clean':True}) + >>> list(o.items()) + [('clean', True)] + + And like a class + + >>> class Flower(EasyDict): + ... power = 1 + ... + >>> f = Flower() + >>> f.power + 1 + >>> f = Flower({'height': 12}) + >>> f.height + 12 + >>> f['power'] + 1 + >>> sorted(f.keys()) + ['height', 'power'] + + update and pop items + >>> d = EasyDict(a=1, b='2') + >>> e = EasyDict(c=3.0, a=9.0) + >>> d.update(e) + >>> d.c + 3.0 + >>> d['c'] + 3.0 + >>> d.get('c') + 3.0 + >>> d.update(a=4, b=4) + >>> d.b + 4 + >>> d.pop('a') + 4 + >>> d.a + Traceback (most recent call last): + ... + AttributeError: 'EasyDict' object has no attribute 'a' + """ + def __init__(self, d=None, **kwargs): + if d is None: + d = {} + else: + d = dict(d) + if kwargs: + d.update(**kwargs) + for k, v in d.items(): + setattr(self, k, v) + # Class attributes + for k in self.__class__.__dict__.keys(): + if not (k.startswith('__') and k.endswith('__')) and not k in ('update', 'pop'): + setattr(self, k, getattr(self, k)) + + def __setattr__(self, name, value): + if isinstance(value, (list, tuple)): + value = [self.__class__(x) + if isinstance(x, dict) else x for x in value] + elif isinstance(value, dict) and not isinstance(value, self.__class__): + value = self.__class__(value) + super(EasyDict, self).__setattr__(name, value) + super(EasyDict, self).__setitem__(name, value) + + __setitem__ = __setattr__ + + def update(self, e=None, **f): + d = e or dict() + d.update(f) + for k in d: + setattr(self, k, d[k]) + + def pop(self, k, d=None): + delattr(self, k) + return super(EasyDict, self).pop(k, d) + + +if __name__ == "__main__": + import doctest + doctest.testmod() \ No newline at end of file diff --git a/ZoeDepth/zoedepth/utils/geometry.py b/ZoeDepth/zoedepth/utils/geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..e3da8c75b5a8e39b4b58a4dcd827b84d79b9115c --- /dev/null +++ b/ZoeDepth/zoedepth/utils/geometry.py @@ -0,0 +1,98 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import numpy as np + +def get_intrinsics(H,W): + """ + Intrinsics for a pinhole camera model. + Assume fov of 55 degrees and central principal point. + """ + f = 0.5 * W / np.tan(0.5 * 55 * np.pi / 180.0) + cx = 0.5 * W + cy = 0.5 * H + return np.array([[f, 0, cx], + [0, f, cy], + [0, 0, 1]]) + +def depth_to_points(depth, R=None, t=None): + + K = get_intrinsics(depth.shape[1], depth.shape[2]) + Kinv = np.linalg.inv(K) + if R is None: + R = np.eye(3) + if t is None: + t = np.zeros(3) + + # M converts from your coordinate to PyTorch3D's coordinate system + M = np.eye(3) + M[0, 0] = -1.0 + M[1, 1] = -1.0 + + height, width = depth.shape[1:3] + + x = np.arange(width) + y = np.arange(height) + coord = np.stack(np.meshgrid(x, y), -1) + coord = np.concatenate((coord, np.ones_like(coord)[:, :, [0]]), -1) # z=1 + coord = coord.astype(np.float32) + # coord = torch.as_tensor(coord, dtype=torch.float32, device=device) + coord = coord[None] # bs, h, w, 3 + + D = depth[:, :, :, None, None] + # print(D.shape, Kinv[None, None, None, ...].shape, coord[:, :, :, :, None].shape ) + pts3D_1 = D * Kinv[None, None, None, ...] @ coord[:, :, :, :, None] + # pts3D_1 live in your coordinate system. Convert them to Py3D's + pts3D_1 = M[None, None, None, ...] @ pts3D_1 + # from reference to targe tviewpoint + pts3D_2 = R[None, None, None, ...] @ pts3D_1 + t[None, None, None, :, None] + # pts3D_2 = pts3D_1 + # depth_2 = pts3D_2[:, :, :, 2, :] # b,1,h,w + return pts3D_2[:, :, :, :3, 0][0] + + +def create_triangles(h, w, mask=None): + """ + Reference: https://github.com/google-research/google-research/blob/e96197de06613f1b027d20328e06d69829fa5a89/infinite_nature/render_utils.py#L68 + Creates mesh triangle indices from a given pixel grid size. + This function is not and need not be differentiable as triangle indices are + fixed. + Args: + h: (int) denoting the height of the image. + w: (int) denoting the width of the image. + Returns: + triangles: 2D numpy array of indices (int) with shape (2(W-1)(H-1) x 3) + """ + x, y = np.meshgrid(range(w - 1), range(h - 1)) + tl = y * w + x + tr = y * w + x + 1 + bl = (y + 1) * w + x + br = (y + 1) * w + x + 1 + triangles = np.array([tl, bl, tr, br, tr, bl]) + triangles = np.transpose(triangles, (1, 2, 0)).reshape( + ((w - 1) * (h - 1) * 2, 3)) + if mask is not None: + mask = mask.reshape(-1) + triangles = triangles[mask[triangles].all(1)] + return triangles diff --git a/ZoeDepth/zoedepth/utils/misc.py b/ZoeDepth/zoedepth/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..4bbe403d3669829eecdf658458c76aa5e87e2b33 --- /dev/null +++ b/ZoeDepth/zoedepth/utils/misc.py @@ -0,0 +1,368 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +"""Miscellaneous utility functions.""" + +from scipy import ndimage + +import base64 +import math +import re +from io import BytesIO + +import matplotlib +import matplotlib.cm +import numpy as np +import requests +import torch +import torch.distributed as dist +import torch.nn +import torch.nn as nn +import torch.utils.data.distributed +from PIL import Image +from torchvision.transforms import ToTensor + + +class RunningAverage: + def __init__(self): + self.avg = 0 + self.count = 0 + + def append(self, value): + self.avg = (value + self.count * self.avg) / (self.count + 1) + self.count += 1 + + def get_value(self): + return self.avg + + +def denormalize(x): + """Reverses the imagenet normalization applied to the input. + + Args: + x (torch.Tensor - shape(N,3,H,W)): input tensor + + Returns: + torch.Tensor - shape(N,3,H,W): Denormalized input + """ + mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(x.device) + std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(x.device) + return x * std + mean + + +class RunningAverageDict: + """A dictionary of running averages.""" + def __init__(self): + self._dict = None + + def update(self, new_dict): + if new_dict is None: + return + + if self._dict is None: + self._dict = dict() + for key, value in new_dict.items(): + self._dict[key] = RunningAverage() + + for key, value in new_dict.items(): + self._dict[key].append(value) + + def get_value(self): + if self._dict is None: + return None + return {key: value.get_value() for key, value in self._dict.items()} + + +def colorize(value, vmin=None, vmax=None, cmap='gray_r', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None): + """Converts a depth map to a color image. + + Args: + value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed + vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None. + vmax (float, optional): vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None. + cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'. + invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99. + invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None. + background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255). + gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False. + value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None. + + Returns: + numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4) + """ + if isinstance(value, torch.Tensor): + value = value.detach().cpu().numpy() + + value = value.squeeze() + if invalid_mask is None: + invalid_mask = value == invalid_val + mask = np.logical_not(invalid_mask) + + # normalize + vmin = np.percentile(value[mask],2) if vmin is None else vmin + vmax = np.percentile(value[mask],85) if vmax is None else vmax + if vmin != vmax: + value = (value - vmin) / (vmax - vmin) # vmin..vmax + else: + # Avoid 0-division + value = value * 0. + + # squeeze last dim if it exists + # grey out the invalid values + + value[invalid_mask] = np.nan + cmapper = matplotlib.cm.get_cmap(cmap) + if value_transform: + value = value_transform(value) + # value = value / value.max() + value = cmapper(value, bytes=True) # (nxmx4) + + # img = value[:, :, :] + img = value[...] + img[invalid_mask] = background_color + + # return img.transpose((2, 0, 1)) + if gamma_corrected: + # gamma correction + img = img / 255 + img = np.power(img, 2.2) + img = img * 255 + img = img.astype(np.uint8) + return img + + +def count_parameters(model, include_all=False): + return sum(p.numel() for p in model.parameters() if p.requires_grad or include_all) + + +def compute_errors(gt, pred): + """Compute metrics for 'pred' compared to 'gt' + + Args: + gt (numpy.ndarray): Ground truth values + pred (numpy.ndarray): Predicted values + + gt.shape should be equal to pred.shape + + Returns: + dict: Dictionary containing the following metrics: + 'a1': Delta1 accuracy: Fraction of pixels that are within a scale factor of 1.25 + 'a2': Delta2 accuracy: Fraction of pixels that are within a scale factor of 1.25^2 + 'a3': Delta3 accuracy: Fraction of pixels that are within a scale factor of 1.25^3 + 'abs_rel': Absolute relative error + 'rmse': Root mean squared error + 'log_10': Absolute log10 error + 'sq_rel': Squared relative error + 'rmse_log': Root mean squared error on the log scale + 'silog': Scale invariant log error + """ + thresh = np.maximum((gt / pred), (pred / gt)) + a1 = (thresh < 1.25).mean() + a2 = (thresh < 1.25 ** 2).mean() + a3 = (thresh < 1.25 ** 3).mean() + + abs_rel = np.mean(np.abs(gt - pred) / gt) + sq_rel = np.mean(((gt - pred) ** 2) / gt) + + rmse = (gt - pred) ** 2 + rmse = np.sqrt(rmse.mean()) + + rmse_log = (np.log(gt) - np.log(pred)) ** 2 + rmse_log = np.sqrt(rmse_log.mean()) + + err = np.log(pred) - np.log(gt) + silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100 + + log_10 = (np.abs(np.log10(gt) - np.log10(pred))).mean() + return dict(a1=a1, a2=a2, a3=a3, abs_rel=abs_rel, rmse=rmse, log_10=log_10, rmse_log=rmse_log, + silog=silog, sq_rel=sq_rel) + + +def compute_metrics(gt, pred, interpolate=True, garg_crop=False, eigen_crop=True, dataset='nyu', min_depth_eval=0.1, max_depth_eval=10, **kwargs): + """Compute metrics of predicted depth maps. Applies cropping and masking as necessary or specified via arguments. Refer to compute_errors for more details on metrics. + """ + if 'config' in kwargs: + config = kwargs['config'] + garg_crop = config.garg_crop + eigen_crop = config.eigen_crop + min_depth_eval = config.min_depth_eval + max_depth_eval = config.max_depth_eval + + if gt.shape[-2:] != pred.shape[-2:] and interpolate: + pred = nn.functional.interpolate( + pred, gt.shape[-2:], mode='bilinear', align_corners=True) + + pred = pred.squeeze().cpu().numpy() + pred[pred < min_depth_eval] = min_depth_eval + pred[pred > max_depth_eval] = max_depth_eval + pred[np.isinf(pred)] = max_depth_eval + pred[np.isnan(pred)] = min_depth_eval + + gt_depth = gt.squeeze().cpu().numpy() + valid_mask = np.logical_and( + gt_depth > min_depth_eval, gt_depth < max_depth_eval) + + if garg_crop or eigen_crop: + gt_height, gt_width = gt_depth.shape + eval_mask = np.zeros(valid_mask.shape) + + if garg_crop: + eval_mask[int(0.40810811 * gt_height):int(0.99189189 * gt_height), + int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1 + + elif eigen_crop: + # print("-"*10, " EIGEN CROP ", "-"*10) + if dataset == 'kitti': + eval_mask[int(0.3324324 * gt_height):int(0.91351351 * gt_height), + int(0.0359477 * gt_width):int(0.96405229 * gt_width)] = 1 + else: + # assert gt_depth.shape == (480, 640), "Error: Eigen crop is currently only valid for (480, 640) images" + eval_mask[45:471, 41:601] = 1 + else: + eval_mask = np.ones(valid_mask.shape) + valid_mask = np.logical_and(valid_mask, eval_mask) + return compute_errors(gt_depth[valid_mask], pred[valid_mask]) + + +#################################### Model uilts ################################################ + + +def parallelize(config, model, find_unused_parameters=True): + + if config.gpu is not None: + torch.cuda.set_device(config.gpu) + model = model.cuda(config.gpu) + + config.multigpu = False + if config.distributed: + # Use DDP + config.multigpu = True + config.rank = config.rank * config.ngpus_per_node + config.gpu + dist.init_process_group(backend=config.dist_backend, init_method=config.dist_url, + world_size=config.world_size, rank=config.rank) + config.batch_size = int(config.batch_size / config.ngpus_per_node) + # config.batch_size = 8 + config.workers = int( + (config.num_workers + config.ngpus_per_node - 1) / config.ngpus_per_node) + print("Device", config.gpu, "Rank", config.rank, "batch size", + config.batch_size, "Workers", config.workers) + torch.cuda.set_device(config.gpu) + model = nn.SyncBatchNorm.convert_sync_batchnorm(model) + model = model.cuda(config.gpu) + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.gpu], output_device=config.gpu, + find_unused_parameters=find_unused_parameters) + + elif config.gpu is None: + # Use DP + config.multigpu = True + model = model.cuda() + model = torch.nn.DataParallel(model) + + return model + + +################################################################################################# + + +##################################################################################################### + + +class colors: + '''Colors class: + Reset all colors with colors.reset + Two subclasses fg for foreground and bg for background. + Use as colors.subclass.colorname. + i.e. colors.fg.red or colors.bg.green + Also, the generic bold, disable, underline, reverse, strikethrough, + and invisible work with the main class + i.e. colors.bold + ''' + reset = '\033[0m' + bold = '\033[01m' + disable = '\033[02m' + underline = '\033[04m' + reverse = '\033[07m' + strikethrough = '\033[09m' + invisible = '\033[08m' + + class fg: + black = '\033[30m' + red = '\033[31m' + green = '\033[32m' + orange = '\033[33m' + blue = '\033[34m' + purple = '\033[35m' + cyan = '\033[36m' + lightgrey = '\033[37m' + darkgrey = '\033[90m' + lightred = '\033[91m' + lightgreen = '\033[92m' + yellow = '\033[93m' + lightblue = '\033[94m' + pink = '\033[95m' + lightcyan = '\033[96m' + + class bg: + black = '\033[40m' + red = '\033[41m' + green = '\033[42m' + orange = '\033[43m' + blue = '\033[44m' + purple = '\033[45m' + cyan = '\033[46m' + lightgrey = '\033[47m' + + +def printc(text, color): + print(f"{color}{text}{colors.reset}") + +############################################ + +def get_image_from_url(url): + response = requests.get(url) + img = Image.open(BytesIO(response.content)).convert("RGB") + return img + +def url_to_torch(url, size=(384, 384)): + img = get_image_from_url(url) + img = img.resize(size, Image.ANTIALIAS) + img = torch.from_numpy(np.asarray(img)).float() + img = img.permute(2, 0, 1) + img.div_(255) + return img + +def pil_to_batched_tensor(img): + return ToTensor()(img).unsqueeze(0) + +def save_raw_16bit(depth, fpath="raw.png"): + if isinstance(depth, torch.Tensor): + depth = depth.squeeze().cpu().numpy() + + assert isinstance(depth, np.ndarray), "Depth must be a torch tensor or numpy array" + assert depth.ndim == 2, "Depth must be 2D" + depth = depth * 256 # scale for 16-bit png + depth = depth.astype(np.uint16) + depth = Image.fromarray(depth) + depth.save(fpath) + print("Saved raw depth to", fpath) \ No newline at end of file diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..9f5cd3f89578a180ca531cd1dc0d96922e974b63 --- /dev/null +++ b/app.py @@ -0,0 +1,268 @@ +# Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr +# +# Copyright 2023 LucidDreamer Authors +# +# Computer Vision Lab, SNU, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from the Computer Vision Lab, SNU or +# its affiliates is strictly prohibited. +# +# For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com. +import time +import gradio as gr +from luciddreamer import LucidDreamer + + +css = """ +#run-button { + background: coral; + color: white; +} +""" + +ld = LucidDreamer() + + +with gr.Blocks(css=css) as demo: + + gr.HTML( + """ +
+
+

LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes

+
If you like our project, please visit our Github, too! โœจโœจโœจ More features are waiting!
+
+
+ + + +   + + Project Page + +   + + + +   + + + +
+
+
+ """ + ) + + with gr.Row(): + + result_gallery = gr.Video(label='RGB Video', show_label=True) + + result_depth = gr.Video(label='Depth Video', show_label=True) + + result_ply_file = gr.File(label='Gaussian splatting PLY', show_label=True) + + + with gr.Row(): + + input_image = gr.Image( + label='Image prompt', + sources='upload', + type='pil', + ) + + with gr.Column(): + model_name = gr.Radio( + label='SD checkpoint', + choices=['SD1.5 (default)', 'Blazing Drive V11m', 'Realistic Vision V5.1', 'RealCartoon-Pixar V5',], + ) + + prompt = gr.Textbox( + label='Text prompt', + value='A cozy livingroom', + ) + n_prompt = gr.Textbox( + label='Negative prompt', + value='photo frame, frame, boarder, simple color, inconsistent, humans, people', + ) + gen_camerapath = gr.Radio( + label='Camera trajectory for generation (STEP 1)', + choices=['rotate360', 'lookaround', 'lookdown'], + ) + + with gr.Row(): + seed = gr.Slider( + label='Seed', + minimum=1, + maximum=2147483647, + step=1, + randomize=True, + ) + diff_steps = gr.Slider( + label='SD inpainting steps', + minimum=1, + maximum=50, + step=1, + value=30, + ) + + render_camerapath = gr.Radio( + label='Camera trajectory for rendering (STEP 2)', + choices=['back_and_forth', 'llff', 'headbanging'], + ) + + with gr.Column(): + run_button = gr.Button(value='Run! (it may take a while)', elem_id='run-button') + + gr.HTML( + """ +
+
+

...or you can run in two steps

+
(hint: press STEP 2 if you have already baked Gaussians in STEP 1).
+
+
+ """ + ) + + with gr.Row(): + gaussian_button = gr.Button(value='STEP 1: Generate Gaussians') + render_button = gr.Button(value='STEP 2: Render A Video') + + gr.HTML( + """ +
+
+
...or you can just watch a quick preload we have baked already.
+
+
+ """ + ) + + example_name = gr.Radio( + label='Quick load', + choices=['girl', 'ruin', 'doge', 'christmas', 'elf', 'animelake', 'fantasy', 'kitchen', 'DON\'T'], + value='DON\'T', + ) + + ips = [example_name, input_image, prompt, n_prompt, gen_camerapath, seed, diff_steps, render_camerapath, model_name] + + run_button.click(fn=ld.run, inputs=ips[1:] + ips[:1], outputs=[result_ply_file, result_gallery, result_depth]) + gaussian_button.click(fn=ld.create, inputs=ips[1:-1] + ips[:1], outputs=[result_ply_file]) + render_button.click(fn=ld.render_video, inputs=ips[-2:-1] + ips[:1], outputs=[result_gallery, result_depth]) + + gr.Examples( + examples=[ + [ + 'girl', + 'examples/girl.jpg', + 'dark messy room, noir style, indoors, bottle, shoe soles, jacket, cup, window, blurry, black footwear, depth of field, box, couch, table, gun, chair, foreshortening', + 'photo frame, frame, boarder, simple color, inconsistent', + 'lookaround', + 1, + 25, + 'back_and_forth', + 'Blazing Drive V11m', + ], + [ + 'ruin', + 'examples/ruin.png', + 'Postapocalyptic city in desert', + 'photo frame, frame, boarder, simple color, inconsistent, humans, people', + 'lookaround', + 1, + 25, + 'back_and_forth', + 'Blazing Drive V11m', + ], + [ + 'doge', + 'examples/doge.png', + 'a cozy livingroom', + 'photo frame, frame, boarder, simple color, inconsistent, humans, people', + 'lookaround', + 1, + 25, + 'back_and_forth', + 'Realistic Vision V5.1', + ], + [ + 'christmas', + 'examples/christmas.png', + 'Cozy livingroom in christmas', + 'photo frame, frame, boarder, simple color, inconsistent, humans, people', + 'lookaround', + 1, + 25, + 'back_and_forth', + 'Realistic Vision V5.1', + ], + [ + 'elf', + 'examples/elf.png', + 'serene deep forest', + 'photo frame, frame, boarder, simple color, inconsistent, humans, people', + 'lookaround', + 1, + 25, + 'back_and_forth', + 'RealCartoon-Pixar V5', + ], + [ + 'animelake', + 'examples/Image015_animelakehouse.jpg', + 'anime style, animation, best quality, a boat on lake, trees and rocks near the lake. a house and port in front of a house', + 'photo frame, frame, boarder, simple color, inconsistent', + 'lookdown', + 1, + 50, + 'back_and_forth', + 'SD1.5 (default)', + ], + [ + 'fantasy', + 'examples/Image003_fantasy.jpg', + 'A vibrant, colorful floating community city, clouds above a beautiful, enchanted landscape filled with whimsical flora, enchanted forest landscape, Magical and dreamy woodland with vibrant green foliage and sparkling flowers, Landscape with twisted trees and vines, natural lighting and dark shadows, unique fantastical elements like floating islands and floating orbs, Highly detailed vegetation and foliage, deep contrast and color vibrancy,', # texture and intricate details in a floating element', + 'photo frame, frame, boarder, simple color, inconsistent', + 'lookaround', + 30, + 50, + 'back_and_forth', + 'SD1.5 (default)', + ], + [ + 'kitchen', + 'examples/image020.png', + 'High-resolution photography kitchen design, wooden floor, small windows opening onto the garden, Bauhaus furniture and decoration, high ceiling, beige blue salmon pastel palette, interior design magazine, cozy atmosphere; 8k, intricate detail, photorealistic, realistic light, wide angle, kinfolk photography, A+D architecture, Kitchen Sink, Basket of fruits and vegetables, a bottle of ', #drinking water, walls painted magazine style photo, looking towards a sink under a window, with a door on the left of the sink with a 25 cm distance from the kitchen, the kitchen is an L shaped starting from the right corner, on the far right a fridge nest to it a stove, next the dishwasher then the sink, a smokey grey kitchen with modern touches, taupe walls, a taup ceiling with spotlights inside the ceiling with 90 cm distance, wooden parquet floor', + 'photo frame, frame, boarder, simple color, inconsistent', + 'rotate360', + 1, + 50, + 'headbanging', + 'SD1.5 (default)', + ], + ], + inputs=ips, + outputs=[result_ply_file, result_gallery, result_depth], + fn=ld.run, + cache_examples=False, + ) + + gr.HTML( + """ +
+
+
+
Acknowledgement and Disclaimer
+
+

We deeply thank br_d, 7whitefire7, and SG161222 for their awesome Stable Diffusion models. We also appreciate ai_pictures21 and recatm for the beautiful illustrations used in the examples. Please note that the authors of this work do not own the model checkpoints and the illustrations in this demo. LucidDreamer algorithm cannot be used for commercial purpose. Please contact the authors for permission requests.

+
+
+ """ + ) + + +if __name__ == '__main__': + demo.queue(max_size=20).launch(server_port=16011) \ No newline at end of file diff --git a/arguments.py b/arguments.py new file mode 100644 index 0000000000000000000000000000000000000000..9f14ed38002102ca0aed25cf52723c8528825344 --- /dev/null +++ b/arguments.py @@ -0,0 +1,51 @@ +### +# Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr +# For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com. +# All rights reserved. +### +import numpy as np + + +class GSParams: + def __init__(self): + self.sh_degree = 3 + self.images = "images" + self.resolution = -1 + self.white_background = False + self.data_device = "cuda" + self.eval = False + self.use_depth = False + + self.iterations = 2990#3_000 + self.position_lr_init = 0.00016 + self.position_lr_final = 0.0000016 + self.position_lr_delay_mult = 0.01 + self.position_lr_max_steps = 2990#3_000 + self.feature_lr = 0.0025 + self.opacity_lr = 0.05 + self.scaling_lr = 0.005 + self.rotation_lr = 0.001 + self.percent_dense = 0.01 + self.lambda_dssim = 0.2 + self.densification_interval = 100 + self.opacity_reset_interval = 3001 # To prevent from saving right after reset opacity + self.densify_from_iter = 500 + self.densify_until_iter = 15_000 + self.densify_grad_threshold = 0.0002 + + self.convert_SHs_python = False + self.compute_cov3D_python = False + self.debug = False + + +class CameraParams: + def __init__(self, H: int = 512, W: int = 512): + self.H = H + self.W = W + self.focal = (5.8269e+02, 5.8269e+02) + self.fov = (2*np.arctan(self.W / (2*self.focal[0])), 2*np.arctan(self.H / (2*self.focal[1]))) + self.K = np.array([ + [self.focal[0], 0., self.W/2], + [0., self.focal[1], self.H/2], + [0., 0., 1.], + ]).astype(np.float32) \ No newline at end of file diff --git a/cameras/back_and_forth.json b/cameras/back_and_forth.json new file mode 100644 index 0000000000000000000000000000000000000000..10dc34373cc5bed50e9635ee35e3f766a8cc7bcb --- /dev/null +++ b/cameras/back_and_forth.json @@ -0,0 +1,4427 @@ +{ + "camera_angle_x": 0.8279103882874479, + "frames": [ + { + "transform_matrix": [ + [ + 1.0, + 0.0, + 0.0, + -0.0 + ], + [ + 0.0, + -1.0, + 0.0, + -0.0 + ], + [ + 0.0, + 0.0, + -1.0, + -0.0 + ] + ] + }, + { + "transform_matrix": [ + [ + 1.0, + 0.0, + 0.0, + -0.0 + ], + [ + 0.0, + -1.0, + 0.0, + -0.0 + ], + [ + 0.0, + 0.0, + -1.0, + -0.05 + ] + ] + }, + { + "transform_matrix": [ + [ + 1.0, + 0.0, + 0.0, + -0.0 + ], + [ + 0.0, + -1.0, + 0.0, + -0.0 + ], + [ + 0.0, + 0.0, + -1.0, + -0.1 + ] + ] + }, + { + "transform_matrix": [ + [ + 1.0, + 0.0, + 0.0, + -0.0 + ], + [ + 0.0, + -1.0, + 0.0, + -0.0 + ], + [ + 0.0, + 0.0, + -1.0, + -0.15000000000000002 + ] + ] + }, + { + "transform_matrix": [ + [ + 1.0, + 0.0, + 0.0, + -0.0 + ], + [ + 0.0, + -1.0, + 0.0, + -0.0 + ], + [ + 0.0, + 0.0, + -1.0, + -0.2 + ] + ] + }, + { + "transform_matrix": [ + [ 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Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from the Computer Vision Lab, SNU or +# its affiliates is strictly prohibited. +# +# For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com. +import os +import glob +import json +import time +import datetime +import warnings +import shutil +from random import randint +from argparse import ArgumentParser + +warnings.filterwarnings(action='ignore') + +import pickle +import imageio +import numpy as np +import open3d as o3d +from PIL import Image +from tqdm import tqdm +from scipy.interpolate import griddata as interp_grid +from scipy.ndimage import minimum_filter, maximum_filter + +import torch +import torch.nn.functional as F +import gradio as gr +from diffusers import ( + StableDiffusionInpaintPipeline, StableDiffusionPipeline, ControlNetModel, StableDiffusionControlNetInpaintPipeline) + +from arguments import GSParams, CameraParams +from gaussian_renderer import render +from scene import Scene, GaussianModel +from scene.dataset_readers import loadCameraPreset +from utils.loss import l1_loss, ssim +from utils.camera import load_json +from utils.depth import colorize +from utils.lama import LaMa +from utils.trajectory import get_camerapaths, get_pcdGenPoses + + +get_kernel = lambda p: torch.ones(1, 1, p * 2 + 1, p * 2 + 1).to('cuda') +t2np = lambda x: (x[0].permute(1, 2, 0).clamp_(0, 1) * 255.0).to(torch.uint8).detach().cpu().numpy() +np2t = lambda x: (torch.as_tensor(x).to(torch.float32).permute(2, 0, 1) / 255.0)[None, ...].to('cuda') +pad_mask = lambda x, padamount=1: t2np( + F.conv2d(np2t(x[..., None]), get_kernel(padamount), padding=padamount))[..., 0].astype(bool) + + +class LucidDreamer: + def __init__(self): + self.opt = GSParams() + self.cam = CameraParams() + self.root = 'outputs' + self.default_model = 'SD1.5 (default)' + self.timestamp = datetime.datetime.now().strftime('%y%m%d_%H%M%S') + + self.gaussians = GaussianModel(self.opt.sh_degree) + + bg_color = [1, 1, 1] if self.opt.white_background else [0, 0, 0] + self.background = torch.tensor(bg_color, dtype=torch.float32, device='cuda') + + self.rgb_model = StableDiffusionInpaintPipeline.from_pretrained( + 'runwayml/stable-diffusion-inpainting', revision='fp16', torch_dtype=torch.float16).to('cuda') + self.d_model = torch.hub.load('./ZoeDepth', 'ZoeD_N', source='local', pretrained=True).to('cuda') + self.controlnet = None + self.lama = None + self.current_model = self.default_model + + def load_model(self, model_name, use_lama=False): + if model_name is None: + model_name = self.default_model + if self.current_model == model_name: + return + if model_name == self.default_model: + self.controlnet = None + self.lama = None + self.rgb_model = StableDiffusionInpaintPipeline.from_pretrained( + 'runwayml/stable-diffusion-inpainting', + revision='fp16', + torch_dtype=torch.float16, + safety_checker=None, + ).to('cuda') + else: + if self.controlnet is None: + self.controlnet = ControlNetModel.from_pretrained( + 'lllyasviel/control_v11p_sd15_inpaint', torch_dtype=torch.float16) + if self.lama is None and use_lama: + self.lama = LaMa('cuda') + self.rgb_model = StableDiffusionControlNetInpaintPipeline.from_pretrained( + f'stablediffusion/{model_name}', + controlnet=self.controlnet, + revision='fp16', + torch_dtype=torch.float16, + safety_checker=None, + ).to('cuda') + # self.rgb_model.enable_model_cpu_offload() + torch.cuda.empty_cache() + self.current_model = model_name + + def rgb(self, prompt, image, negative_prompt='', generator=None, num_inference_steps=50, mask_image=None): + if self.current_model == self.default_model: + return self.rgb_model( + prompt=prompt, + negative_prompt=negative_prompt, + generator=generator, + num_inference_steps=num_inference_steps, + image=image, + mask_image=mask_image, + ).images[0] + + kwargs = { + 'negative_prompt': negative_prompt, + 'generator': generator, + 'strength': 0.8, + 'num_inference_steps': num_inference_steps, + 'height': self.cam.H, + 'width': self.cam.W, + } + + image_np = np.array(image).astype(float) / 255.0 + mask_np = np.array(mask_image) / 255.0 + mask_sum = np.clip((image_np.prod(axis=-1) == 0) + (1 - mask_np), 0, 1) + mask_padded = pad_mask(mask_sum, 3) + masked = image_np * np.logical_not(mask_padded[..., None]) + + if self.lama is not None: + lama_image = Image.fromarray(lama(masked, mask_padded).astype(np.uint8)) + else: + lama_image = image + + mask_image = Image.fromarray(mask_padded.astype(np.uint8) * 255) + control_image = self.make_controlnet_inpaint_condition(lama_image, mask_image) + + return self.rgb_model( + prompt=prompt, + image=lama_image, + control_image=control_image, + mask_image=mask_image, + **kwargs, + ).images[0] + + def d(self, im): + return self.d_model.infer_pil(im) + + def make_controlnet_inpaint_condition(self, image, image_mask): + image = np.array(image.convert("RGB")).astype(np.float32) / 255.0 + image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0 + + assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size" + image[image_mask > 0.5] = -1.0 # set as masked pixel + image = np.expand_dims(image, 0).transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + return image + + def run(self, rgb_cond, txt_cond, neg_txt_cond, pcdgenpath, seed, diff_steps, render_camerapath, model_name=None, example_name=None): + # gaussians, default_gallery = self.create( + gaussians = self.create( + rgb_cond, txt_cond, neg_txt_cond, pcdgenpath, seed, diff_steps, model_name, example_name) + gallery, depth = self.render_video(render_camerapath, example_name=example_name) + return (gaussians, gallery, depth) + # return (gaussians, default_gallery, gallery) + + def create(self, rgb_cond, txt_cond, neg_txt_cond, pcdgenpath, seed, diff_steps, model_name=None, example_name=None): + self.cleaner() + self.load_model(model_name) + if example_name and example_name != 'DON\'T': + outfile = os.path.join('examples', f'{example_name}.ply') + if not os.path.exists(outfile): + self.traindata = self.generate_pcd(rgb_cond, txt_cond, neg_txt_cond, pcdgenpath, seed, diff_steps) + self.scene = Scene(self.traindata, self.gaussians, self.opt) + self.training() + outfile = self.save_ply(outfile) + else: + self.traindata = self.generate_pcd(rgb_cond, txt_cond, neg_txt_cond, pcdgenpath, seed, diff_steps) + self.scene = Scene(self.traindata, self.gaussians, self.opt) + self.training() + self.timestamp = datetime.datetime.now().strftime('%y%m%d_%H%M%S') + outfile = self.save_ply() + # default_gallery = self.render_video('llff', example_name=example_name) + return outfile #, default_gallery + + def save_ply(self, fpath=None): + if fpath is None: + dpath = os.path.join(self.root, self.timestamp) + fpath = os.path.join(dpath, 'gsplat.ply') + os.makedirs(dpath, exist_ok=True) + if not os.path.exists(fpath): + self.gaussians.save_ply(fpath) + else: + self.gaussians.load_ply(fpath) + return fpath + + def cleaner(self): + # Remove the temporary file created yesterday. + for dpath in glob.glob(os.path.join(self.root, '*')): + timestamp = datetime.datetime.strptime(os.path.basename(dpath), '%y%m%d_%H%M%S') + if timestamp < datetime.datetime.now() - datetime.timedelta(days=1): + try: + shutil.rmtree(dpath) + except OSError as e: + print("Error: %s - %s." % (e.filename, e.strerror)) + + def render_video(self, preset, example_name=None): + if example_name and example_name != 'DON\'T': + videopath = os.path.join('examples', f'{example_name}_{preset}.mp4') + depthpath = os.path.join('examples', f'depth_{example_name}_{preset}.mp4') + else: + videopath = os.path.join(self.root, self.timestamp, f'{preset}.mp4') + depthpath = os.path.join(self.root, self.timestamp, f'depth_{preset}.mp4') + if os.path.exists(videopath) and os.path.exists(depthpath): + return videopath, depthpath + + if not hasattr(self, 'scene'): + views = load_json(os.path.join('cameras', f'{preset}.json'), self.cam.H, self.cam.W) + else: + views = self.scene.getPresetCameras(preset) + + framelist = [] + depthlist = [] + dmin, dmax = 1e8, -1e8 + for view in views: + results = render(view, self.gaussians, self.opt, self.background, render_only=True) + frame, depth = results['render'], results['depth'] + framelist.append( + np.round(frame.permute(1,2,0).detach().cpu().numpy().clip(0,1)*255.).astype(np.uint8)) + depth = -(depth * (depth > 0)).detach().cpu().numpy() + dmin_local = depth.min().item() + dmax_local = depth.max().item() + if dmin_local < dmin: + dmin = dmin_local + if dmax_local > dmax: + dmax = dmax_local + depthlist.append(depth) + # depthlist = [colorize(depth, vmin=dmin, vmax=dmax) for depth in depthlist] + depthlist = [colorize(depth) for depth in depthlist] + if not os.path.exists(videopath): + imageio.mimwrite(videopath, framelist, fps=60, quality=8) + if not os.path.exists(depthpath): + imageio.mimwrite(depthpath, depthlist, fps=60, quality=8) + return videopath, depthpath + + def training(self): + if not self.scene: + raise('Build 3D Scene First!') + + for iteration in tqdm(range(1, self.opt.iterations + 1)): + self.gaussians.update_learning_rate(iteration) + + # Every 1000 its we increase the levels of SH up to a maximum degree + if iteration % 1000 == 0: + self.gaussians.oneupSHdegree() + + # Pick a random Camera + viewpoint_stack = self.scene.getTrainCameras().copy() + viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1)) + + # import pdb; pdb.set_trace() + # Render + render_pkg = render(viewpoint_cam, self.gaussians, self.opt, self.background) + image, viewspace_point_tensor, visibility_filter, radii = ( + render_pkg['render'], render_pkg['viewspace_points'], render_pkg['visibility_filter'], render_pkg['radii']) + + # Loss + gt_image = viewpoint_cam.original_image.cuda() + Ll1 = l1_loss(image, gt_image) + loss = (1.0 - self.opt.lambda_dssim) * Ll1 + self.opt.lambda_dssim * (1.0 - ssim(image, gt_image)) + loss.backward() + + with torch.no_grad(): + # Densification + if iteration < self.opt.densify_until_iter: + # Keep track of max radii in image-space for pruning + self.gaussians.max_radii2D[visibility_filter] = torch.max( + self.gaussians.max_radii2D[visibility_filter], radii[visibility_filter]) + self.gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter) + + if iteration > self.opt.densify_from_iter and iteration % self.opt.densification_interval == 0: + size_threshold = 20 if iteration > self.opt.opacity_reset_interval else None + self.gaussians.densify_and_prune( + self.opt.densify_grad_threshold, 0.005, self.scene.cameras_extent, size_threshold) + + if (iteration % self.opt.opacity_reset_interval == 0 + or (self.opt.white_background and iteration == self.opt.densify_from_iter) + ): + self.gaussians.reset_opacity() + + # Optimizer step + if iteration < self.opt.iterations: + self.gaussians.optimizer.step() + self.gaussians.optimizer.zero_grad(set_to_none = True) + + def generate_pcd(self, rgb_cond, prompt, negative_prompt, pcdgenpath, seed, diff_steps, progress=gr.Progress()): + ## processing inputs + generator=torch.Generator(device='cuda').manual_seed(seed) + + w_in, h_in = rgb_cond.size + if w_in/h_in > 1.1 or h_in/w_in > 1.1: # if height and width are similar, do center crop + in_res = max(w_in, h_in) + image_in, mask_in = np.zeros((in_res, in_res, 3), dtype=np.uint8), 255*np.ones((in_res, in_res, 3), dtype=np.uint8) + image_in[int(in_res/2-h_in/2):int(in_res/2+h_in/2), int(in_res/2-w_in/2):int(in_res/2+w_in/2)] = np.array(rgb_cond) + mask_in[int(in_res/2-h_in/2):int(in_res/2+h_in/2), int(in_res/2-w_in/2):int(in_res/2+w_in/2)] = 0 + image_curr = self.rgb( + prompt=prompt, image=Image.fromarray(image_in).resize((self.cam.W, self.cam.H)), + negative_prompt=negative_prompt, generator=generator, + mask_image=Image.fromarray(mask_in).resize((self.cam.W, self.cam.H))) + + else: # if there is a large gap between height and width, do inpainting + if w_in > h_in: + image_curr = rgb_cond.crop((int(w_in/2-h_in/2), 0, int(w_in/2+h_in/2), h_in)).resize((self.cam.W, self.cam.H)) + else: # w <= h + image_curr = rgb_cond.crop((0, int(h_in/2-w_in/2), w_in, int(h_in/2+w_in/2))).resize((self.cam.W, self.cam.H)) + + render_poses = get_pcdGenPoses(pcdgenpath) + depth_curr = self.d(image_curr) + center_depth = np.mean(depth_curr[h_in//2-10:h_in//2+10, w_in//2-10:w_in//2+10]) + + ########################################################################################################################### + # Iterative scene generation + H, W, K = self.cam.H, self.cam.W, self.cam.K + + x, y = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy') # pixels + edgeN = 2 + edgemask = np.ones((H-2*edgeN, W-2*edgeN)) + edgemask = np.pad(edgemask, ((edgeN,edgeN),(edgeN,edgeN))) + + ### initialize + R0, T0 = render_poses[0,:3,:3], render_poses[0,:3,3:4] + pts_coord_cam = np.matmul(np.linalg.inv(K), np.stack((x*depth_curr, y*depth_curr, 1*depth_curr), axis=0).reshape(3,-1)) + new_pts_coord_world2 = (np.linalg.inv(R0).dot(pts_coord_cam) - np.linalg.inv(R0).dot(T0)).astype(np.float32) ## new_pts_coord_world2 + new_pts_colors2 = (np.array(image_curr).reshape(-1,3).astype(np.float32)/255.) ## new_pts_colors2 + + pts_coord_world, pts_colors = new_pts_coord_world2.copy(), new_pts_colors2.copy() + + progress(0, desc='Dreaming...') + # time.sleep(0.5) + + for i in progress.tqdm(range(1, len(render_poses)), desc='Dreaming'): + R, T = render_poses[i,:3,:3], render_poses[i,:3,3:4] + + ### Transform world to pixel + pts_coord_cam2 = R.dot(pts_coord_world) + T ### Same with c2w*world_coord (in homogeneous space) + pixel_coord_cam2 = np.matmul(K, pts_coord_cam2) #.reshape(3,H,W).transpose(1,2,0).astype(np.float32) + + valid_idx = np.where(np.logical_and.reduce((pixel_coord_cam2[2]>0, + pixel_coord_cam2[0]/pixel_coord_cam2[2]>=0, + pixel_coord_cam2[0]/pixel_coord_cam2[2]<=W-1, + pixel_coord_cam2[1]/pixel_coord_cam2[2]>=0, + pixel_coord_cam2[1]/pixel_coord_cam2[2]<=H-1)))[0] + pixel_coord_cam2 = pixel_coord_cam2[:2, valid_idx]/pixel_coord_cam2[-1:, valid_idx] + round_coord_cam2 = np.round(pixel_coord_cam2).astype(np.int32) + + x, y = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy') + grid = np.stack((x,y), axis=-1).reshape(-1,2) + image2 = interp_grid(pixel_coord_cam2.transpose(1,0), pts_colors[valid_idx], grid, method='linear', fill_value=0).reshape(H,W,3) + image2 = edgemask[...,None]*image2 + (1-edgemask[...,None])*np.pad(image2[1:-1,1:-1], ((1,1),(1,1),(0,0)), mode='edge') + + round_mask2 = np.zeros((H,W), dtype=np.float32) + round_mask2[round_coord_cam2[1], round_coord_cam2[0]] = 1 + + round_mask2 = maximum_filter(round_mask2, size=(9,9), axes=(0,1)) + image2 = round_mask2[...,None]*image2 + (1-round_mask2[...,None])*(-1) + + mask2 = minimum_filter((image2.sum(-1)!=-3)*1, size=(11,11), axes=(0,1)) + image2 = mask2[...,None]*image2 + (1-mask2[...,None])*0 + + mask_hf = np.abs(mask2[:H-1, :W-1] - mask2[1:, :W-1]) + np.abs(mask2[:H-1, :W-1] - mask2[:H-1, 1:]) + mask_hf = np.pad(mask_hf, ((0,1), (0,1)), 'edge') + mask_hf = np.where(mask_hf < 0.3, 0, 1) + border_valid_idx = np.where(mask_hf[round_coord_cam2[1], round_coord_cam2[0]] == 1)[0] # use valid_idx[border_valid_idx] for world1 + + image_curr = self.rgb( + prompt=prompt, image=Image.fromarray(np.round(image2*255.).astype(np.uint8)), + negative_prompt=negative_prompt, generator=generator, num_inference_steps=diff_steps, + mask_image=Image.fromarray(np.round((1-mask2[:,:])*255.).astype(np.uint8))) + depth_curr = self.d(image_curr) + + + ### depth optimize + t_z2 = torch.tensor(depth_curr) + sc = torch.ones(1).float().requires_grad_(True) + optimizer = torch.optim.Adam(params=[sc], lr=0.001) + + for idx in range(100): + trans3d = torch.tensor([[sc,0,0,0], [0,sc,0,0], [0,0,sc,0], [0,0,0,1]]).requires_grad_(True) + coord_cam2 = torch.matmul(torch.tensor(np.linalg.inv(K)), torch.stack((torch.tensor(x)*t_z2, torch.tensor(y)*t_z2, 1*t_z2), axis=0)[:,round_coord_cam2[1], round_coord_cam2[0]].reshape(3,-1)) + coord_world2 = (torch.tensor(np.linalg.inv(R)).float().matmul(coord_cam2) - torch.tensor(np.linalg.inv(R)).float().matmul(torch.tensor(T).float())) + coord_world2_warp = torch.cat((coord_world2, torch.ones((1,valid_idx.shape[0]))), dim=0) + coord_world2_trans = torch.matmul(trans3d, coord_world2_warp) + coord_world2_trans = coord_world2_trans[:3] / coord_world2_trans[-1] + loss = torch.mean((torch.tensor(pts_coord_world[:,valid_idx]).float() - coord_world2_trans)**2) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + with torch.no_grad(): + coord_cam2 = torch.matmul(torch.tensor(np.linalg.inv(K)), torch.stack((torch.tensor(x)*t_z2, torch.tensor(y)*t_z2, 1*t_z2), axis=0)[:,round_coord_cam2[1, border_valid_idx], round_coord_cam2[0, border_valid_idx]].reshape(3,-1)) + coord_world2 = (torch.tensor(np.linalg.inv(R)).float().matmul(coord_cam2) - torch.tensor(np.linalg.inv(R)).float().matmul(torch.tensor(T).float())) + coord_world2_warp = torch.cat((coord_world2, torch.ones((1, border_valid_idx.shape[0]))), dim=0) + coord_world2_trans = torch.matmul(trans3d, coord_world2_warp) + coord_world2_trans = coord_world2_trans[:3] / coord_world2_trans[-1] + + trans3d = trans3d.detach().numpy() + + pts_coord_cam2 = np.matmul(np.linalg.inv(K), np.stack((x*depth_curr, y*depth_curr, 1*depth_curr), axis=0).reshape(3,-1))[:,np.where(1-mask2.reshape(-1))[0]] + camera_origin_coord_world2 = - np.linalg.inv(R).dot(T).astype(np.float32) # 3, 1 + new_pts_coord_world2 = (np.linalg.inv(R).dot(pts_coord_cam2) - np.linalg.inv(R).dot(T)).astype(np.float32) + new_pts_coord_world2_warp = np.concatenate((new_pts_coord_world2, np.ones((1, new_pts_coord_world2.shape[1]))), axis=0) + new_pts_coord_world2 = np.matmul(trans3d, new_pts_coord_world2_warp) + new_pts_coord_world2 = new_pts_coord_world2[:3] / new_pts_coord_world2[-1] + new_pts_colors2 = (np.array(image_curr).reshape(-1,3).astype(np.float32)/255.)[np.where(1-mask2.reshape(-1))[0]] + + vector_camorigin_to_campixels = coord_world2_trans.detach().numpy() - camera_origin_coord_world2 + vector_camorigin_to_pcdpixels = pts_coord_world[:,valid_idx[border_valid_idx]] - camera_origin_coord_world2 + + compensate_depth_coeff = np.sum(vector_camorigin_to_pcdpixels * vector_camorigin_to_campixels, axis=0) / np.sum(vector_camorigin_to_campixels * vector_camorigin_to_campixels, axis=0) # N_correspond + compensate_pts_coord_world2_correspond = camera_origin_coord_world2 + vector_camorigin_to_campixels * compensate_depth_coeff.reshape(1,-1) + + compensate_coord_cam2_correspond = R.dot(compensate_pts_coord_world2_correspond) + T + homography_coord_cam2_correspond = R.dot(coord_world2_trans.detach().numpy()) + T + + compensate_depth_correspond = compensate_coord_cam2_correspond[-1] - homography_coord_cam2_correspond[-1] # N_correspond + compensate_depth_zero = np.zeros(4) + compensate_depth = np.concatenate((compensate_depth_correspond, compensate_depth_zero), axis=0) # N_correspond+4 + + pixel_cam2_correspond = pixel_coord_cam2[:, border_valid_idx] # 2, N_correspond (xy) + pixel_cam2_zero = np.array([[0,0,W-1,W-1],[0,H-1,0,H-1]]) + pixel_cam2 = np.concatenate((pixel_cam2_correspond, pixel_cam2_zero), axis=1).transpose(1,0) # N+H, 2 + + # Calculate for masked pixels + masked_pixels_xy = np.stack(np.where(1-mask2), axis=1)[:, [1,0]] + new_depth_linear, new_depth_nearest = interp_grid(pixel_cam2, compensate_depth, masked_pixels_xy), interp_grid(pixel_cam2, compensate_depth, masked_pixels_xy, method='nearest') + new_depth = np.where(np.isnan(new_depth_linear), new_depth_nearest, new_depth_linear) + + pts_coord_cam2 = np.matmul(np.linalg.inv(K), np.stack((x*depth_curr, y*depth_curr, 1*depth_curr), axis=0).reshape(3,-1))[:,np.where(1-mask2.reshape(-1))[0]] + x_nonmask, y_nonmask = x.reshape(-1)[np.where(1-mask2.reshape(-1))[0]], y.reshape(-1)[np.where(1-mask2.reshape(-1))[0]] + compensate_pts_coord_cam2 = np.matmul(np.linalg.inv(K), np.stack((x_nonmask*new_depth, y_nonmask*new_depth, 1*new_depth), axis=0)) + new_warp_pts_coord_cam2 = pts_coord_cam2 + compensate_pts_coord_cam2 + + new_pts_coord_world2 = (np.linalg.inv(R).dot(new_warp_pts_coord_cam2) - np.linalg.inv(R).dot(T)).astype(np.float32) + new_pts_coord_world2_warp = np.concatenate((new_pts_coord_world2, np.ones((1, new_pts_coord_world2.shape[1]))), axis=0) + new_pts_coord_world2 = np.matmul(trans3d, new_pts_coord_world2_warp) + new_pts_coord_world2 = new_pts_coord_world2[:3] / new_pts_coord_world2[-1] + new_pts_colors2 = (np.array(image_curr).reshape(-1,3).astype(np.float32)/255.)[np.where(1-mask2.reshape(-1))[0]] + + pts_coord_world = np.concatenate((pts_coord_world, new_pts_coord_world2), axis=-1) ### Same with inv(c2w) * cam_coord (in homogeneous space) + pts_colors = np.concatenate((pts_colors, new_pts_colors2), axis=0) + + ################################################################################################# + + yz_reverse = np.array([[1,0,0], [0,-1,0], [0,0,-1]]) + traindata = { + 'camera_angle_x': self.cam.fov[0], + 'W': W, + 'H': H, + 'pcd_points': pts_coord_world, + 'pcd_colors': pts_colors, + 'frames': [], + } + + # render_poses = get_pcdGenPoses(pcdgenpath) + internel_render_poses = get_pcdGenPoses('hemisphere', {'center_depth': center_depth}) + + progress(0, desc='Aligning...') + # time.sleep(0.5) + + for i in progress.tqdm(range(len(render_poses)), desc='Aligning'): + for j in range(len(internel_render_poses)): + idx = i * len(internel_render_poses) + j + print(f'{idx+1} / {len(render_poses)*len(internel_render_poses)}') + + ### Transform world to pixel + Rw2i = render_poses[i,:3,:3] + Tw2i = render_poses[i,:3,3:4] + Ri2j = internel_render_poses[j,:3,:3] + Ti2j = internel_render_poses[j,:3,3:4] + + Rw2j = np.matmul(Ri2j, Rw2i) + Tw2j = np.matmul(Ri2j, Tw2i) + Ti2j + + # Transfrom cam2 to world + change sign of yz axis + Rj2w = np.matmul(yz_reverse, Rw2j).T + Tj2w = -np.matmul(Rj2w, np.matmul(yz_reverse, Tw2j)) + Pc2w = np.concatenate((Rj2w, Tj2w), axis=1) + Pc2w = np.concatenate((Pc2w, np.array([[0,0,0,1]])), axis=0) + + pts_coord_camj = Rw2j.dot(pts_coord_world) + Tw2j + pixel_coord_camj = np.matmul(K, pts_coord_camj) + + valid_idxj = np.where(np.logical_and.reduce((pixel_coord_camj[2]>0, + pixel_coord_camj[0]/pixel_coord_camj[2]>=0, + pixel_coord_camj[0]/pixel_coord_camj[2]<=W-1, + pixel_coord_camj[1]/pixel_coord_camj[2]>=0, + pixel_coord_camj[1]/pixel_coord_camj[2]<=H-1)))[0] + pts_depthsj = pixel_coord_camj[-1:, valid_idxj] + pixel_coord_camj = pixel_coord_camj[:2, valid_idxj]/pixel_coord_camj[-1:, valid_idxj] + round_coord_camj = np.round(pixel_coord_camj).astype(np.int32) + + + x, y = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy') # pixels + grid = np.stack((x,y), axis=-1).reshape(-1,2) + imagej = interp_grid(pixel_coord_camj.transpose(1,0), pts_colors[valid_idxj], grid, method='linear', fill_value=0).reshape(H,W,3) + imagej = edgemask[...,None]*imagej + (1-edgemask[...,None])*np.pad(imagej[1:-1,1:-1], ((1,1),(1,1),(0,0)), mode='edge') + + depthj = interp_grid(pixel_coord_camj.transpose(1,0), pts_depthsj.T, grid, method='linear', fill_value=0).reshape(H,W) + depthj = edgemask*depthj + (1-edgemask)*np.pad(depthj[1:-1,1:-1], ((1,1),(1,1)), mode='edge') + + maskj = np.zeros((H,W), dtype=np.float32) + maskj[round_coord_camj[1], round_coord_camj[0]] = 1 + maskj = maximum_filter(maskj, size=(9,9), axes=(0,1)) + imagej = maskj[...,None]*imagej + (1-maskj[...,None])*(-1) + + maskj = minimum_filter((imagej.sum(-1)!=-3)*1, size=(11,11), axes=(0,1)) + imagej = maskj[...,None]*imagej + (1-maskj[...,None])*0 + + traindata['frames'].append({ + 'image': Image.fromarray(np.round(imagej*255.).astype(np.uint8)), + 'transform_matrix': Pc2w.tolist(), + }) + + progress(1, desc='Baking Gaussians...') + return traindata \ No newline at end of file diff --git a/scene/.ipynb_checkpoints/__init__-checkpoint.py b/scene/.ipynb_checkpoints/__init__-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..52b44779caf93a1303469e2f3409b2ca46fce713 --- /dev/null +++ b/scene/.ipynb_checkpoints/__init__-checkpoint.py @@ -0,0 +1,41 @@ +### +# Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr +# For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com. +# All rights reserved. +### +import os +import random + +from arguments import GSParams +from utils.system import searchForMaxIteration +from scene.dataset_readers import readDataInfo +from scene.gaussian_model import GaussianModel + + +class Scene: + gaussians: GaussianModel + + def __init__(self, traindata, gaussians: GaussianModel, opt: GSParams): + self.traindata = traindata + self.gaussians = gaussians + + info = readDataInfo(traindata, opt.white_background) + random.shuffle(info.train_cameras) # Multi-res consistent random shuffling + self.cameras_extent = info.nerf_normalization["radius"] + + print("Loading Training Cameras") + self.train_cameras = info.train_cameras + print("Loading Preset Cameras") + self.preset_cameras = {} + for campath in info.preset_cameras.keys(): + self.preset_cameras[campath] = info.preset_cameras[campath] + + self.gaussians.create_from_pcd(info.point_cloud, self.cameras_extent) + self.gaussians.training_setup(opt) + + def getTrainCameras(self): + return self.train_cameras + + def getPresetCameras(self, preset): + assert preset in self.preset_cameras + return self.preset_cameras[preset] \ No newline at end of file diff --git a/scene/.ipynb_checkpoints/cameras-checkpoint.py b/scene/.ipynb_checkpoints/cameras-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..651ad6c35f247e034b9b36c7635baf0eee48509f --- /dev/null +++ b/scene/.ipynb_checkpoints/cameras-checkpoint.py @@ -0,0 +1,76 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import numpy as np + +import torch +from torch import nn + +from utils.graphics import getWorld2View2, getProjectionMatrix +from utils.loss import image2canny + + +class Camera(nn.Module): + def __init__(self, colmap_id, R, T, FoVx, FoVy, image, gt_alpha_mask, + image_name, uid, + trans=np.array([0.0, 0.0, 0.0]), scale=1.0, data_device = "cuda" + ): + super(Camera, self).__init__() + + self.uid = uid + self.colmap_id = colmap_id + self.R = R + self.T = T + self.FoVx = FoVx + self.FoVy = FoVy + self.image_name = image_name + + try: + self.data_device = torch.device(data_device) + except Exception as e: + print(e) + print(f"[Warning] Custom device {data_device} failed, fallback to default cuda device" ) + self.data_device = torch.device("cuda") + + self.original_image = image.clamp(0.0, 1.0).to(self.data_device) + self.canny_mask = image2canny(self.original_image.permute(1,2,0), 50, 150, isEdge1=False).detach().to(self.data_device) + self.image_width = self.original_image.shape[2] + self.image_height = self.original_image.shape[1] + + if gt_alpha_mask is not None: + self.original_image *= gt_alpha_mask.to(self.data_device) + else: + self.original_image *= torch.ones((1, self.image_height, self.image_width), device=self.data_device) + + self.zfar = 100.0 + self.znear = 0.01 + + self.trans = trans + self.scale = scale + + self.world_view_transform = torch.tensor(getWorld2View2(R, T, trans, scale)).transpose(0, 1).cuda() + self.projection_matrix = getProjectionMatrix(znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy).transpose(0,1).cuda() + self.full_proj_transform = (self.world_view_transform.unsqueeze(0).bmm(self.projection_matrix.unsqueeze(0))).squeeze(0) + self.camera_center = self.world_view_transform.inverse()[3, :3] + + +class MiniCam: + def __init__(self, width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform): + self.image_width = width + self.image_height = height + self.FoVy = fovy + self.FoVx = fovx + self.znear = znear + self.zfar = zfar + self.world_view_transform = world_view_transform + self.full_proj_transform = full_proj_transform + view_inv = torch.inverse(self.world_view_transform) + self.camera_center = view_inv[3][:3] + diff --git a/scene/.ipynb_checkpoints/colmap_loader-checkpoint.py b/scene/.ipynb_checkpoints/colmap_loader-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..a3944fe73e1ed1d4be1ba98f66c66531b82b9537 --- /dev/null +++ b/scene/.ipynb_checkpoints/colmap_loader-checkpoint.py @@ -0,0 +1,301 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import numpy as np +import collections +import struct + + +CameraModel = collections.namedtuple( + "CameraModel", ["model_id", "model_name", "num_params"]) +Camera = collections.namedtuple( + "Camera", ["id", "model", "width", "height", "params"]) +BaseImage = collections.namedtuple( + "Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"]) +Point3D = collections.namedtuple( + "Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"]) +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12) +} +CAMERA_MODEL_IDS = dict([(camera_model.model_id, camera_model) + for camera_model in CAMERA_MODELS]) +CAMERA_MODEL_NAMES = dict([(camera_model.model_name, camera_model) + for camera_model in CAMERA_MODELS]) + + +def qvec2rotmat(qvec): + return np.array([ + [1 - 2 * qvec[2]**2 - 2 * qvec[3]**2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]], + [2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1]**2 - 2 * qvec[3]**2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]], + [2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]]) + + +def rotmat2qvec(R): + Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat + K = np.array([ + [Rxx - Ryy - Rzz, 0, 0, 0], + [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0], + [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0], + [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz]]) / 3.0 + eigvals, eigvecs = np.linalg.eigh(K) + qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)] + if qvec[0] < 0: + qvec *= -1 + return qvec + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_points3D_text(path): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + xyzs = None + rgbs = None + errors = None + num_points = 0 + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + num_points += 1 + + + xyzs = np.empty((num_points, 3)) + rgbs = np.empty((num_points, 3)) + errors = np.empty((num_points, 1)) + count = 0 + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = np.array(float(elems[7])) + xyzs[count] = xyz + rgbs[count] = rgb + errors[count] = error + count += 1 + + return xyzs, rgbs, errors + + +def read_points3D_binary(path_to_model_file): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + + + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + + xyzs = np.empty((num_points, 3)) + rgbs = np.empty((num_points, 3)) + errors = np.empty((num_points, 1)) + + for p_id in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd") + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes( + fid, num_bytes=8, format_char_sequence="Q")[0] + track_elems = read_next_bytes( + fid, num_bytes=8*track_length, + format_char_sequence="ii"*track_length) + xyzs[p_id] = xyz + rgbs[p_id] = rgb + errors[p_id] = error + return xyzs, rgbs, errors + + +def read_intrinsics_text(path): + """ + Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + assert model == "PINHOLE", "While the loader support other types, the rest of the code assumes PINHOLE" + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera(id=camera_id, model=model, + width=width, height=height, + params=params) + return cameras + + +def read_extrinsics_binary(path_to_model_file): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for _ in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi") + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, + format_char_sequence="Q")[0] + x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D, + format_char_sequence="ddq"*num_points2D) + xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])), + tuple(map(float, x_y_id_s[1::3]))]) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, qvec=qvec, tvec=tvec, + camera_id=camera_id, name=image_name, + xys=xys, point3D_ids=point3D_ids) + return images + + +def read_intrinsics_binary(path_to_model_file): + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for _ in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ") + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes(fid, num_bytes=8*num_params, + format_char_sequence="d"*num_params) + cameras[camera_id] = Camera(id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params)) + assert len(cameras) == num_cameras + return cameras + + +def read_extrinsics_text(path): + """ + Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack([tuple(map(float, elems[0::3])), + tuple(map(float, elems[1::3]))]) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, qvec=qvec, tvec=tvec, + camera_id=camera_id, name=image_name, + xys=xys, point3D_ids=point3D_ids) + return images + + +def read_colmap_bin_array(path): + """ + Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_dense.py + + :param path: path to the colmap binary file. + :return: nd array with the floating point values in the value + """ + with open(path, "rb") as fid: + width, height, channels = np.genfromtxt(fid, delimiter="&", max_rows=1, + usecols=(0, 1, 2), dtype=int) + fid.seek(0) + num_delimiter = 0 + byte = fid.read(1) + while True: + if byte == b"&": + num_delimiter += 1 + if num_delimiter >= 3: + break + byte = fid.read(1) + array = np.fromfile(fid, np.float32) + array = array.reshape((width, height, channels), order="F") + return np.transpose(array, (1, 0, 2)).squeeze() diff --git a/scene/.ipynb_checkpoints/dataset_readers-checkpoint.py b/scene/.ipynb_checkpoints/dataset_readers-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..289047f46b403d16c12a4bb948ef32cf32efd2ac --- /dev/null +++ b/scene/.ipynb_checkpoints/dataset_readers-checkpoint.py @@ -0,0 +1,434 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import os +import sys +import json +from typing import NamedTuple +from pathlib import Path + +import imageio +import torch +import numpy as np +from PIL import Image +from plyfile import PlyData, PlyElement + +from scene.gaussian_model import BasicPointCloud +from scene.cameras import MiniCam, Camera +from scene.colmap_loader import read_extrinsics_text, read_intrinsics_text, qvec2rotmat, \ + read_extrinsics_binary, read_intrinsics_binary, read_points3D_binary, read_points3D_text +from utils.graphics import getWorld2View2, focal2fov, fov2focal +from utils.graphics import getProjectionMatrix +from utils.trajectory import get_camerapaths +from utils.sh import SH2RGB + + +class CameraInfo(NamedTuple): + uid: int + R: np.array + T: np.array + FovY: np.array + FovX: np.array + image: np.array + image_path: str + image_name: str + width: int + height: int + + +class SceneInfo(NamedTuple): + point_cloud: BasicPointCloud + train_cameras: list + test_cameras: list + preset_cameras: list + nerf_normalization: dict + ply_path: str + + +def getNerfppNorm(cam_info): + def get_center_and_diag(cam_centers): + cam_centers = np.hstack(cam_centers) + avg_cam_center = np.mean(cam_centers, axis=1, keepdims=True) + center = avg_cam_center + dist = np.linalg.norm(cam_centers - center, axis=0, keepdims=True) + diagonal = np.max(dist) + return center.flatten(), diagonal + + cam_centers = [] + + for cam in cam_info: + W2C = getWorld2View2(cam.R, cam.T) + C2W = np.linalg.inv(W2C) + cam_centers.append(C2W[:3, 3:4]) + + center, diagonal = get_center_and_diag(cam_centers) + radius = diagonal * 1.1 + + translate = -center + + return {"translate": translate, "radius": radius} + + +def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder): + cam_infos = [] + for idx, key in enumerate(cam_extrinsics): + sys.stdout.write('\r') + # the exact output you're looking for: + sys.stdout.write("Reading camera {}/{}".format(idx+1, len(cam_extrinsics))) + sys.stdout.flush() + + extr = cam_extrinsics[key] + intr = cam_intrinsics[extr.camera_id] + height = intr.height + width = intr.width + + uid = intr.id + R = np.transpose(qvec2rotmat(extr.qvec)) + T = np.array(extr.tvec) + + if intr.model=="SIMPLE_PINHOLE": + focal_length_x = intr.params[0] + FovY = focal2fov(focal_length_x, height) + FovX = focal2fov(focal_length_x, width) + elif intr.model=="PINHOLE": + focal_length_x = intr.params[0] + focal_length_y = intr.params[1] + FovY = focal2fov(focal_length_y, height) + FovX = focal2fov(focal_length_x, width) + else: + assert False, "Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!" + + image_path = os.path.join(images_folder, os.path.basename(extr.name)) + image_name = os.path.basename(image_path).split(".")[0] + image = Image.open(image_path) + + cam_info = CameraInfo(uid=uid, R=R, T=T, FovY=FovY, FovX=FovX, image=image, + image_path=image_path, image_name=image_name, width=width, height=height) + cam_infos.append(cam_info) + sys.stdout.write('\n') + return cam_infos + + +def fetchPly(path): + plydata = PlyData.read(path) + vertices = plydata['vertex'] + idx = np.random.choice(len(vertices['x']),size=(min(len(vertices['x']), 100_000),),replace=False) + positions = np.vstack([vertices['x'][idx], vertices['y'][idx], vertices['z'][idx]]).T if 'x' in vertices else None + colors = np.vstack([vertices['red'][idx], vertices['green'][idx], vertices['blue'][idx]]).T / 255.0 if 'red' in vertices else None + normals = np.vstack([vertices['nx'][idx], vertices['ny'][idx], vertices['nz'][idx]]).T if 'nx' in vertices else None + return BasicPointCloud(points=positions, colors=colors, normals=normals) + + +def storePly(path, xyz, rgb): + # Define the dtype for the structured array + dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'), + ('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'), + ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')] + + normals = np.zeros_like(xyz) + + elements = np.empty(xyz.shape[0], dtype=dtype) + attributes = np.concatenate((xyz, normals, rgb), axis=1) + elements[:] = list(map(tuple, attributes)) + + # Create the PlyData object and write to file + vertex_element = PlyElement.describe(elements, 'vertex') + ply_data = PlyData([vertex_element]) + ply_data.write(path) + + +def readColmapSceneInfo(path, images, eval, preset=None, llffhold=8): + try: + cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.bin") + cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.bin") + cam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file) + cam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file) + except: + cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.txt") + cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.txt") + cam_extrinsics = read_extrinsics_text(cameras_extrinsic_file) + cam_intrinsics = read_intrinsics_text(cameras_intrinsic_file) + + reading_dir = "images" if images == None else images + cam_infos_unsorted = readColmapCameras(cam_extrinsics=cam_extrinsics, cam_intrinsics=cam_intrinsics, images_folder=os.path.join(path, reading_dir)) + cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : x.image_name) + + if eval: + # train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0] + # test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0] + train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % 5 == 2 or idx % 5 == 0] + test_cam_infos = [c for idx, c in enumerate(cam_infos) if not (idx % 5 == 2 or idx % 5 == 0)] + else: + train_cam_infos = cam_infos + test_cam_infos = [] + + nerf_normalization = getNerfppNorm(train_cam_infos) + + ply_path = os.path.join(path, "sparse/0/points3D.ply") + bin_path = os.path.join(path, "sparse/0/points3D.bin") + txt_path = os.path.join(path, "sparse/0/points3D.txt") + if not os.path.exists(ply_path): + print("Converting point3d.bin to .ply, will happen only the first time you open the scene.") + try: + xyz, rgb, _ = read_points3D_binary(bin_path) + except: + xyz, rgb, _ = read_points3D_text(txt_path) + storePly(ply_path, xyz, rgb) + try: + pcd = fetchPly(ply_path) + except: + pcd = None + + if preset: + preset_cam_infos = readCamerasFromPreset('/home/chung/workspace/gaussian-splatting/poses_supplementary', f"{preset}.json") + else: + preset_cam_infos = None + + scene_info = SceneInfo(point_cloud=pcd, + train_cameras=train_cam_infos, + test_cameras=test_cam_infos, + preset_cameras=preset_cam_infos, + nerf_normalization=nerf_normalization, + ply_path=ply_path) + return scene_info + + +def readCamerasFromTransforms(path, transformsfile, white_background, extension=".png"): + cam_infos = [] + + with open(os.path.join(path, transformsfile)) as json_file: + contents = json.load(json_file) + fovx = contents["camera_angle_x"] + + frames = contents["frames"] + for idx, frame in enumerate(frames): + cam_name = os.path.join(path, frame["file_path"] + extension) + + # NeRF 'transform_matrix' is a camera-to-world transform + c2w = np.array(frame["transform_matrix"]) + # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) + c2w[:3, 1:3] *= -1 + + # get the world-to-camera transform and set R, T + w2c = np.linalg.inv(c2w) + R = np.transpose(w2c[:3,:3]) # R is stored transposed due to 'glm' in CUDA code + T = w2c[:3, 3] + + image_path = os.path.join(path, cam_name) + image_name = Path(cam_name).stem + image = Image.open(image_path) + + # if os.path.exists(os.path.join(path, frame["file_path"].replace("/train/", "/depths_train/")+'.npy')): + # depth = np.load(os.path.join(path, frame["file_path"].replace("/train/", "/depths_train/")+'.npy')) + # if os.path.exists(os.path.join(path, frame["file_path"].replace("/train/", "/masks_train/")+'.png')): + # mask = imageio.v3.imread(os.path.join(path, frame["file_path"].replace("/train/", "/masks_train/")+'.png'))[:,:,0]/255. + # else: + # mask = np.ones_like(depth) + # final_depth = depth*mask + # else: + # final_depth = None + + im_data = np.array(image.convert("RGBA")) + + bg = np.array([1,1,1]) if white_background else np.array([0, 0, 0]) + + norm_data = im_data / 255.0 + arr = norm_data[:,:,:3] * norm_data[:, :, 3:4] + bg * (1 - norm_data[:, :, 3:4]) + image = Image.fromarray(np.array(arr*255.0, dtype=np.byte), "RGB") + + fovy = focal2fov(fov2focal(fovx, image.size[1]), image.size[0]) + FovY = fovy + FovX = fovx + + cam_infos.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image, + image_path=image_path, image_name=image_name, width=image.size[0], height=image.size[1])) + + return cam_infos + + +def readCamerasFromPreset(path, transformsfile): + cam_infos = [] + + with open(os.path.join(path, transformsfile)) as json_file: + contents = json.load(json_file) + FOV = contents["camera_angle_x"]*1.2 + + frames = contents["frames"] + for idx, frame in enumerate(frames): + # NeRF 'transform_matrix' is a camera-to-world transform + c2w = np.array(frame["transform_matrix"]) + # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) + c2w[:3, 1:3] *= -1 + + # get the world-to-camera transform and set R, T + w2c = np.linalg.inv(np.concatenate((c2w, np.array([0,0,0,1]).reshape(1,4)), axis=0)) + R = np.transpose(w2c[:3,:3]) # R is stored transposed due to 'glm' in CUDA code + T = w2c[:3, 3] + # R = c2w[:3,:3] + # T = - np.transpose(R).dot(c2w[:3,3]) + + image = Image.fromarray(np.zeros((512,512)), "RGB") + FovY = focal2fov(fov2focal(FOV, 512), image.size[0]) + FovX = focal2fov(fov2focal(FOV, 512), image.size[1]) + # FovX, FovY = contents["camera_angle_x"], contents["camera_angle_x"] + + cam_infos.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image, + image_path='None', image_name='None', width=image.size[1], height=image.size[0])) + + return cam_infos + + +def readNerfSyntheticInfo(path, white_background, eval, preset=None, extension=".png"): + print("Reading Training Transforms") + train_cam_infos = readCamerasFromTransforms(path, "transforms_train.json", white_background, extension) + print("Reading Test Transforms") + test_cam_infos = readCamerasFromTransforms(path, "transforms_test.json", white_background, extension) + + if preset: + preset_cam_infos = readCamerasFromPreset('/home/chung/workspace/gaussian-splatting/poses_supplementary', f"{preset}.json") + else: + preset_cam_infos = None + + if not eval: + train_cam_infos.extend(test_cam_infos) + test_cam_infos = [] + + nerf_normalization = getNerfppNorm(train_cam_infos) + + ply_path = os.path.join(path, "points3d.ply") + if not os.path.exists(ply_path): + # Since this data set has no colmap data, we start with random points + num_pts = 100_000 + print(f"Generating random point cloud ({num_pts})...") + + # We create random points inside the bounds of the synthetic Blender scenes + xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3 + shs = np.random.random((num_pts, 3)) / 255.0 + pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3))) + + storePly(ply_path, xyz, SH2RGB(shs) * 255) + + try: + pcd = fetchPly(ply_path) + except: + pcd = None + + scene_info = SceneInfo(point_cloud=pcd, + train_cameras=train_cam_infos, + test_cameras=test_cam_infos, + preset_cameras=preset_cam_infos, + nerf_normalization=nerf_normalization, + ply_path=ply_path) + return scene_info + + +def loadCamerasFromData(traindata, white_background): + cameras = [] + + fovx = traindata["camera_angle_x"] + frames = traindata["frames"] + for idx, frame in enumerate(frames): + # NeRF 'transform_matrix' is a camera-to-world transform + c2w = np.array(frame["transform_matrix"]) + # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) + c2w[:3, 1:3] *= -1 + + # get the world-to-camera transform and set R, T + w2c = np.linalg.inv(c2w) + R = np.transpose(w2c[:3,:3]) # R is stored transposed due to 'glm' in CUDA code + T = w2c[:3, 3] + + image = frame["image"] if "image" in frame else None + im_data = np.array(image.convert("RGBA")) + + bg = np.array([1,1,1]) if white_background else np.array([0, 0, 0]) + + norm_data = im_data / 255.0 + arr = norm_data[:,:,:3] * norm_data[:, :, 3:4] + bg * (1 - norm_data[:, :, 3:4]) + image = Image.fromarray(np.array(arr*255.0, dtype=np.byte), "RGB") + loaded_mask = np.ones_like(norm_data[:, :, 3:4]) + + fovy = focal2fov(fov2focal(fovx, image.size[1]), image.size[0]) + FovY = fovy + FovX = fovx + + image = torch.Tensor(arr).permute(2,0,1) + loaded_mask = None #torch.Tensor(loaded_mask).permute(2,0,1) + + ### torch๋กœ ๋ฐ”๊ฟ”์•ผํ•จ + cameras.append(Camera(colmap_id=idx, R=R, T=T, FoVx=FovX, FoVy=FovY, image=image, + gt_alpha_mask=loaded_mask, image_name='', uid=idx, data_device='cuda')) + + return cameras + + +def loadCameraPreset(traindata, presetdata): + cam_infos = {} + ## camera setting (for H, W and focal) + fovx = traindata["camera_angle_x"] * 1.2 + W, H = traindata["frames"][0]["image"].size + # W, H = traindata["W"], traindata["H"] + + for camkey in presetdata: + cam_infos[camkey] = [] + for idx, frame in enumerate(presetdata[camkey]["frames"]): + # NeRF 'transform_matrix' is a camera-to-world transform + c2w = np.array(frame["transform_matrix"]) + # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) + c2w[:3, 1:3] *= -1 + + # get the world-to-camera transform and set R, T + w2c = np.linalg.inv(c2w) + R = np.transpose(w2c[:3,:3]) # R is stored transposed due to 'glm' in CUDA code + T = w2c[:3, 3] + + fovy = focal2fov(fov2focal(fovx, W), H) + FovY = fovy + FovX = fovx + + znear, zfar = 0.01, 100 + world_view_transform = torch.tensor(getWorld2View2(R, T, np.array([0.0, 0.0, 0.0]), 1.0)).transpose(0, 1).cuda() + projection_matrix = getProjectionMatrix(znear=znear, zfar=zfar, fovX=FovX, fovY=FovY).transpose(0,1).cuda() + full_proj_transform = (world_view_transform.unsqueeze(0).bmm(projection_matrix.unsqueeze(0))).squeeze(0) + + cam_infos[camkey].append(MiniCam(width=W, height=H, fovy=FovY, fovx=FovX, znear=znear, zfar=zfar, + world_view_transform=world_view_transform, full_proj_transform=full_proj_transform)) + + return cam_infos + + +def readDataInfo(traindata, white_background): + print("Reading Training Transforms") + + train_cameras = loadCamerasFromData(traindata, white_background) + preset_minicams = loadCameraPreset(traindata, presetdata=get_camerapaths()) + + # if not eval: + # train_cam_infos.extend(test_cam_infos) + # test_cam_infos = [] + + nerf_normalization = getNerfppNorm(train_cameras) + + pcd = BasicPointCloud(points=traindata['pcd_points'].T, colors=traindata['pcd_colors'], normals=None) + + + scene_info = SceneInfo(point_cloud=pcd, + train_cameras=train_cameras, + test_cameras=[], + preset_cameras=preset_minicams, + nerf_normalization=nerf_normalization, + ply_path='') + return scene_info + + +sceneLoadTypeCallbacks = { + "Colmap": readColmapSceneInfo, + "Blender" : readNerfSyntheticInfo +} \ No newline at end of file diff --git a/scene/.ipynb_checkpoints/gaussian_model-checkpoint.py b/scene/.ipynb_checkpoints/gaussian_model-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..3aed4a27234f60b281feaa822f0dbe0955bf8a82 --- /dev/null +++ b/scene/.ipynb_checkpoints/gaussian_model-checkpoint.py @@ -0,0 +1,407 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import os + +import numpy as np +from plyfile import PlyData, PlyElement + +import torch +from torch import nn + +from simple_knn._C import distCUDA2 +from utils.general import inverse_sigmoid, get_expon_lr_func, build_rotation +from utils.system import mkdir_p +from utils.sh import RGB2SH +from utils.graphics import BasicPointCloud +from utils.general import strip_symmetric, build_scaling_rotation + + +class GaussianModel: + def setup_functions(self): + def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation): + L = build_scaling_rotation(scaling_modifier * scaling, rotation) + actual_covariance = L @ L.transpose(1, 2) + symm = strip_symmetric(actual_covariance) + return symm + + self.scaling_activation = torch.exp + self.scaling_inverse_activation = torch.log + + self.covariance_activation = build_covariance_from_scaling_rotation + + self.opacity_activation = torch.sigmoid + self.inverse_opacity_activation = inverse_sigmoid + + self.rotation_activation = torch.nn.functional.normalize + + + def __init__(self, sh_degree : int): + self.active_sh_degree = 0 + self.max_sh_degree = sh_degree + self._xyz = torch.empty(0) + self._features_dc = torch.empty(0) + self._features_rest = torch.empty(0) + self._scaling = torch.empty(0) + self._rotation = torch.empty(0) + self._opacity = torch.empty(0) + self.max_radii2D = torch.empty(0) + self.xyz_gradient_accum = torch.empty(0) + self.denom = torch.empty(0) + self.optimizer = None + self.percent_dense = 0 + self.spatial_lr_scale = 0 + self.setup_functions() + + def capture(self): + return ( + self.active_sh_degree, + self._xyz, + self._features_dc, + self._features_rest, + self._scaling, + self._rotation, + self._opacity, + self.max_radii2D, + self.xyz_gradient_accum, + self.denom, + self.optimizer.state_dict(), + self.spatial_lr_scale, + ) + + def restore(self, model_args, training_args): + (self.active_sh_degree, + self._xyz, + self._features_dc, + self._features_rest, + self._scaling, + self._rotation, + self._opacity, + self.max_radii2D, + xyz_gradient_accum, + denom, + opt_dict, + self.spatial_lr_scale) = model_args + self.training_setup(training_args) + self.xyz_gradient_accum = xyz_gradient_accum + self.denom = denom + self.optimizer.load_state_dict(opt_dict) + + @property + def get_scaling(self): + return self.scaling_activation(self._scaling) + + @property + def get_rotation(self): + return self.rotation_activation(self._rotation) + + @property + def get_xyz(self): + return self._xyz + + @property + def get_features(self): + features_dc = self._features_dc + features_rest = self._features_rest + return torch.cat((features_dc, features_rest), dim=1) + + @property + def get_opacity(self): + return self.opacity_activation(self._opacity) + + def get_covariance(self, scaling_modifier = 1): + return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation) + + def oneupSHdegree(self): + if self.active_sh_degree < self.max_sh_degree: + self.active_sh_degree += 1 + + def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float): + self.spatial_lr_scale = spatial_lr_scale + fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda() + fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda()) + features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda() + features[:, :3, 0 ] = fused_color + features[:, 3:, 1:] = 0.0 + + print("Number of points at initialisation : ", fused_point_cloud.shape[0]) + + dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001) + scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3) + rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda") + rots[:, 0] = 1 + + opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda")) + + self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True)) + self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True)) + self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True)) + self._scaling = nn.Parameter(scales.requires_grad_(True)) + self._rotation = nn.Parameter(rots.requires_grad_(True)) + self._opacity = nn.Parameter(opacities.requires_grad_(True)) + self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") + + def training_setup(self, training_args): + self.percent_dense = training_args.percent_dense + self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + + l = [ + {'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"}, + {'params': [self._features_dc], 'lr': training_args.feature_lr, "name": "f_dc"}, + {'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, "name": "f_rest"}, + {'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"}, + {'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"}, + {'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"} + ] + + self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15) + self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale, + lr_final=training_args.position_lr_final*self.spatial_lr_scale, + lr_delay_mult=training_args.position_lr_delay_mult, + max_steps=training_args.position_lr_max_steps) + + def update_learning_rate(self, iteration): + ''' Learning rate scheduling per step ''' + for param_group in self.optimizer.param_groups: + if param_group["name"] == "xyz": + lr = self.xyz_scheduler_args(iteration) + param_group['lr'] = lr + return lr + + def construct_list_of_attributes(self): + l = ['x', 'y', 'z', 'nx', 'ny', 'nz'] + # All channels except the 3 DC + for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]): + l.append('f_dc_{}'.format(i)) + for i in range(self._features_rest.shape[1]*self._features_rest.shape[2]): + l.append('f_rest_{}'.format(i)) + l.append('opacity') + for i in range(self._scaling.shape[1]): + l.append('scale_{}'.format(i)) + for i in range(self._rotation.shape[1]): + l.append('rot_{}'.format(i)) + return l + + def save_ply(self, filepath): + xyz = self._xyz.detach().cpu().numpy() + normals = np.zeros_like(xyz) + f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() + f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() + opacities = self._opacity.detach().cpu().numpy() + scale = self._scaling.detach().cpu().numpy() + rotation = self._rotation.detach().cpu().numpy() + + dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()] + + elements = np.empty(xyz.shape[0], dtype=dtype_full) + attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1) + elements[:] = list(map(tuple, attributes)) + el = PlyElement.describe(elements, 'vertex') + PlyData([el]).write(filepath) + + def reset_opacity(self): + opacities_new = inverse_sigmoid(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01)) + optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity") + self._opacity = optimizable_tensors["opacity"] + + def load_ply(self, path): + plydata = PlyData.read(path) + + xyz = np.stack((np.asarray(plydata.elements[0]["x"]), + np.asarray(plydata.elements[0]["y"]), + np.asarray(plydata.elements[0]["z"])), axis=1) + opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis] + + features_dc = np.zeros((xyz.shape[0], 3, 1)) + features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"]) + features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"]) + features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"]) + + extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")] + extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1])) + assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3 + features_extra = np.zeros((xyz.shape[0], len(extra_f_names))) + for idx, attr_name in enumerate(extra_f_names): + features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name]) + # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC) + features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1)) + + scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")] + scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1])) + scales = np.zeros((xyz.shape[0], len(scale_names))) + for idx, attr_name in enumerate(scale_names): + scales[:, idx] = np.asarray(plydata.elements[0][attr_name]) + + rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")] + rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1])) + rots = np.zeros((xyz.shape[0], len(rot_names))) + for idx, attr_name in enumerate(rot_names): + rots[:, idx] = np.asarray(plydata.elements[0][attr_name]) + + self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True)) + self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True)) + self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True)) + self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True)) + self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True)) + self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True)) + + self.active_sh_degree = self.max_sh_degree + + def replace_tensor_to_optimizer(self, tensor, name): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + if group["name"] == name: + stored_state = self.optimizer.state.get(group['params'][0], None) + stored_state["exp_avg"] = torch.zeros_like(tensor) + stored_state["exp_avg_sq"] = torch.zeros_like(tensor) + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter(tensor.requires_grad_(True)) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + return optimizable_tensors + + def _prune_optimizer(self, mask): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + stored_state = self.optimizer.state.get(group['params'][0], None) + if stored_state is not None: + stored_state["exp_avg"] = stored_state["exp_avg"][mask] + stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask] + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True))) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + else: + group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True)) + optimizable_tensors[group["name"]] = group["params"][0] + return optimizable_tensors + + def prune_points(self, mask): + valid_points_mask = ~mask + optimizable_tensors = self._prune_optimizer(valid_points_mask) + + self._xyz = optimizable_tensors["xyz"] + self._features_dc = optimizable_tensors["f_dc"] + self._features_rest = optimizable_tensors["f_rest"] + self._opacity = optimizable_tensors["opacity"] + self._scaling = optimizable_tensors["scaling"] + self._rotation = optimizable_tensors["rotation"] + + self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask] + + self.denom = self.denom[valid_points_mask] + self.max_radii2D = self.max_radii2D[valid_points_mask] + + def cat_tensors_to_optimizer(self, tensors_dict): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + assert len(group["params"]) == 1 + extension_tensor = tensors_dict[group["name"]] + stored_state = self.optimizer.state.get(group['params'][0], None) + if stored_state is not None: + + stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0) + stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0) + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True)) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + else: + group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True)) + optimizable_tensors[group["name"]] = group["params"][0] + + return optimizable_tensors + + def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation): + d = {"xyz": new_xyz, + "f_dc": new_features_dc, + "f_rest": new_features_rest, + "opacity": new_opacities, + "scaling" : new_scaling, + "rotation" : new_rotation} + + optimizable_tensors = self.cat_tensors_to_optimizer(d) + self._xyz = optimizable_tensors["xyz"] + self._features_dc = optimizable_tensors["f_dc"] + self._features_rest = optimizable_tensors["f_rest"] + self._opacity = optimizable_tensors["opacity"] + self._scaling = optimizable_tensors["scaling"] + self._rotation = optimizable_tensors["rotation"] + + self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") + + def densify_and_split(self, grads, grad_threshold, scene_extent, N=2): + n_init_points = self.get_xyz.shape[0] + # Extract points that satisfy the gradient condition + padded_grad = torch.zeros((n_init_points), device="cuda") + padded_grad[:grads.shape[0]] = grads.squeeze() + selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False) + selected_pts_mask = torch.logical_and(selected_pts_mask, + torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent) + + stds = self.get_scaling[selected_pts_mask].repeat(N,1) + means =torch.zeros((stds.size(0), 3),device="cuda") + samples = torch.normal(mean=means, std=stds) + rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N,1,1) + new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask].repeat(N, 1) + new_scaling = self.scaling_inverse_activation(self.get_scaling[selected_pts_mask].repeat(N,1) / (0.8*N)) + new_rotation = self._rotation[selected_pts_mask].repeat(N,1) + new_features_dc = self._features_dc[selected_pts_mask].repeat(N,1,1) + new_features_rest = self._features_rest[selected_pts_mask].repeat(N,1,1) + new_opacity = self._opacity[selected_pts_mask].repeat(N,1) + + self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation) + + prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool))) + self.prune_points(prune_filter) + + def densify_and_clone(self, grads, grad_threshold, scene_extent): + # Extract points that satisfy the gradient condition + selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False) + selected_pts_mask = torch.logical_and(selected_pts_mask, + torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent) + + new_xyz = self._xyz[selected_pts_mask] + new_features_dc = self._features_dc[selected_pts_mask] + new_features_rest = self._features_rest[selected_pts_mask] + new_opacities = self._opacity[selected_pts_mask] + new_scaling = self._scaling[selected_pts_mask] + new_rotation = self._rotation[selected_pts_mask] + + self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation) + + def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size): + grads = self.xyz_gradient_accum / self.denom + grads[grads.isnan()] = 0.0 + + self.densify_and_clone(grads, max_grad, extent) + self.densify_and_split(grads, max_grad, extent) + + prune_mask = (self.get_opacity < min_opacity).squeeze() + if max_screen_size: + big_points_vs = self.max_radii2D > max_screen_size + big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent + prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws) + self.prune_points(prune_mask) + + torch.cuda.empty_cache() + + def add_densification_stats(self, viewspace_point_tensor, update_filter): + self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True) + self.denom[update_filter] += 1 \ No newline at end of file diff --git a/scene/__init__.py b/scene/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..52b44779caf93a1303469e2f3409b2ca46fce713 --- /dev/null +++ b/scene/__init__.py @@ -0,0 +1,41 @@ +### +# Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr +# For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com. +# All rights reserved. +### +import os +import random + +from arguments import GSParams +from utils.system import searchForMaxIteration +from scene.dataset_readers import readDataInfo +from scene.gaussian_model import GaussianModel + + +class Scene: + gaussians: GaussianModel + + def __init__(self, traindata, gaussians: GaussianModel, opt: GSParams): + self.traindata = traindata + self.gaussians = gaussians + + info = readDataInfo(traindata, opt.white_background) + random.shuffle(info.train_cameras) # Multi-res consistent random shuffling + self.cameras_extent = info.nerf_normalization["radius"] + + print("Loading Training Cameras") + self.train_cameras = info.train_cameras + print("Loading Preset Cameras") + self.preset_cameras = {} + for campath in info.preset_cameras.keys(): + self.preset_cameras[campath] = info.preset_cameras[campath] + + self.gaussians.create_from_pcd(info.point_cloud, self.cameras_extent) + self.gaussians.training_setup(opt) + + def getTrainCameras(self): + return self.train_cameras + + def getPresetCameras(self, preset): + assert preset in self.preset_cameras + return self.preset_cameras[preset] \ No newline at end of file diff --git a/scene/cameras.py b/scene/cameras.py new file mode 100644 index 0000000000000000000000000000000000000000..651ad6c35f247e034b9b36c7635baf0eee48509f --- /dev/null +++ b/scene/cameras.py @@ -0,0 +1,76 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import numpy as np + +import torch +from torch import nn + +from utils.graphics import getWorld2View2, getProjectionMatrix +from utils.loss import image2canny + + +class Camera(nn.Module): + def __init__(self, colmap_id, R, T, FoVx, FoVy, image, gt_alpha_mask, + image_name, uid, + trans=np.array([0.0, 0.0, 0.0]), scale=1.0, data_device = "cuda" + ): + super(Camera, self).__init__() + + self.uid = uid + self.colmap_id = colmap_id + self.R = R + self.T = T + self.FoVx = FoVx + self.FoVy = FoVy + self.image_name = image_name + + try: + self.data_device = torch.device(data_device) + except Exception as e: + print(e) + print(f"[Warning] Custom device {data_device} failed, fallback to default cuda device" ) + self.data_device = torch.device("cuda") + + self.original_image = image.clamp(0.0, 1.0).to(self.data_device) + self.canny_mask = image2canny(self.original_image.permute(1,2,0), 50, 150, isEdge1=False).detach().to(self.data_device) + self.image_width = self.original_image.shape[2] + self.image_height = self.original_image.shape[1] + + if gt_alpha_mask is not None: + self.original_image *= gt_alpha_mask.to(self.data_device) + else: + self.original_image *= torch.ones((1, self.image_height, self.image_width), device=self.data_device) + + self.zfar = 100.0 + self.znear = 0.01 + + self.trans = trans + self.scale = scale + + self.world_view_transform = torch.tensor(getWorld2View2(R, T, trans, scale)).transpose(0, 1).cuda() + self.projection_matrix = getProjectionMatrix(znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy).transpose(0,1).cuda() + self.full_proj_transform = (self.world_view_transform.unsqueeze(0).bmm(self.projection_matrix.unsqueeze(0))).squeeze(0) + self.camera_center = self.world_view_transform.inverse()[3, :3] + + +class MiniCam: + def __init__(self, width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform): + self.image_width = width + self.image_height = height + self.FoVy = fovy + self.FoVx = fovx + self.znear = znear + self.zfar = zfar + self.world_view_transform = world_view_transform + self.full_proj_transform = full_proj_transform + view_inv = torch.inverse(self.world_view_transform) + self.camera_center = view_inv[3][:3] + diff --git a/scene/colmap_loader.py b/scene/colmap_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..a3944fe73e1ed1d4be1ba98f66c66531b82b9537 --- /dev/null +++ b/scene/colmap_loader.py @@ -0,0 +1,301 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import numpy as np +import collections +import struct + + +CameraModel = collections.namedtuple( + "CameraModel", ["model_id", "model_name", "num_params"]) +Camera = collections.namedtuple( + "Camera", ["id", "model", "width", "height", "params"]) +BaseImage = collections.namedtuple( + "Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"]) +Point3D = collections.namedtuple( + "Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"]) +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12) +} +CAMERA_MODEL_IDS = dict([(camera_model.model_id, camera_model) + for camera_model in CAMERA_MODELS]) +CAMERA_MODEL_NAMES = dict([(camera_model.model_name, camera_model) + for camera_model in CAMERA_MODELS]) + + +def qvec2rotmat(qvec): + return np.array([ + [1 - 2 * qvec[2]**2 - 2 * qvec[3]**2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]], + [2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1]**2 - 2 * qvec[3]**2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]], + [2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]]) + + +def rotmat2qvec(R): + Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat + K = np.array([ + [Rxx - Ryy - Rzz, 0, 0, 0], + [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0], + [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0], + [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz]]) / 3.0 + eigvals, eigvecs = np.linalg.eigh(K) + qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)] + if qvec[0] < 0: + qvec *= -1 + return qvec + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_points3D_text(path): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + xyzs = None + rgbs = None + errors = None + num_points = 0 + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + num_points += 1 + + + xyzs = np.empty((num_points, 3)) + rgbs = np.empty((num_points, 3)) + errors = np.empty((num_points, 1)) + count = 0 + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = np.array(float(elems[7])) + xyzs[count] = xyz + rgbs[count] = rgb + errors[count] = error + count += 1 + + return xyzs, rgbs, errors + + +def read_points3D_binary(path_to_model_file): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + + + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + + xyzs = np.empty((num_points, 3)) + rgbs = np.empty((num_points, 3)) + errors = np.empty((num_points, 1)) + + for p_id in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd") + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes( + fid, num_bytes=8, format_char_sequence="Q")[0] + track_elems = read_next_bytes( + fid, num_bytes=8*track_length, + format_char_sequence="ii"*track_length) + xyzs[p_id] = xyz + rgbs[p_id] = rgb + errors[p_id] = error + return xyzs, rgbs, errors + + +def read_intrinsics_text(path): + """ + Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + assert model == "PINHOLE", "While the loader support other types, the rest of the code assumes PINHOLE" + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera(id=camera_id, model=model, + width=width, height=height, + params=params) + return cameras + + +def read_extrinsics_binary(path_to_model_file): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for _ in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi") + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, + format_char_sequence="Q")[0] + x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D, + format_char_sequence="ddq"*num_points2D) + xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])), + tuple(map(float, x_y_id_s[1::3]))]) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, qvec=qvec, tvec=tvec, + camera_id=camera_id, name=image_name, + xys=xys, point3D_ids=point3D_ids) + return images + + +def read_intrinsics_binary(path_to_model_file): + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for _ in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ") + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes(fid, num_bytes=8*num_params, + format_char_sequence="d"*num_params) + cameras[camera_id] = Camera(id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params)) + assert len(cameras) == num_cameras + return cameras + + +def read_extrinsics_text(path): + """ + Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack([tuple(map(float, elems[0::3])), + tuple(map(float, elems[1::3]))]) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, qvec=qvec, tvec=tvec, + camera_id=camera_id, name=image_name, + xys=xys, point3D_ids=point3D_ids) + return images + + +def read_colmap_bin_array(path): + """ + Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_dense.py + + :param path: path to the colmap binary file. + :return: nd array with the floating point values in the value + """ + with open(path, "rb") as fid: + width, height, channels = np.genfromtxt(fid, delimiter="&", max_rows=1, + usecols=(0, 1, 2), dtype=int) + fid.seek(0) + num_delimiter = 0 + byte = fid.read(1) + while True: + if byte == b"&": + num_delimiter += 1 + if num_delimiter >= 3: + break + byte = fid.read(1) + array = np.fromfile(fid, np.float32) + array = array.reshape((width, height, channels), order="F") + return np.transpose(array, (1, 0, 2)).squeeze() diff --git a/scene/dataset_readers.py b/scene/dataset_readers.py new file mode 100644 index 0000000000000000000000000000000000000000..289047f46b403d16c12a4bb948ef32cf32efd2ac --- /dev/null +++ b/scene/dataset_readers.py @@ -0,0 +1,434 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import os +import sys +import json +from typing import NamedTuple +from pathlib import Path + +import imageio +import torch +import numpy as np +from PIL import Image +from plyfile import PlyData, PlyElement + +from scene.gaussian_model import BasicPointCloud +from scene.cameras import MiniCam, Camera +from scene.colmap_loader import read_extrinsics_text, read_intrinsics_text, qvec2rotmat, \ + read_extrinsics_binary, read_intrinsics_binary, read_points3D_binary, read_points3D_text +from utils.graphics import getWorld2View2, focal2fov, fov2focal +from utils.graphics import getProjectionMatrix +from utils.trajectory import get_camerapaths +from utils.sh import SH2RGB + + +class CameraInfo(NamedTuple): + uid: int + R: np.array + T: np.array + FovY: np.array + FovX: np.array + image: np.array + image_path: str + image_name: str + width: int + height: int + + +class SceneInfo(NamedTuple): + point_cloud: BasicPointCloud + train_cameras: list + test_cameras: list + preset_cameras: list + nerf_normalization: dict + ply_path: str + + +def getNerfppNorm(cam_info): + def get_center_and_diag(cam_centers): + cam_centers = np.hstack(cam_centers) + avg_cam_center = np.mean(cam_centers, axis=1, keepdims=True) + center = avg_cam_center + dist = np.linalg.norm(cam_centers - center, axis=0, keepdims=True) + diagonal = np.max(dist) + return center.flatten(), diagonal + + cam_centers = [] + + for cam in cam_info: + W2C = getWorld2View2(cam.R, cam.T) + C2W = np.linalg.inv(W2C) + cam_centers.append(C2W[:3, 3:4]) + + center, diagonal = get_center_and_diag(cam_centers) + radius = diagonal * 1.1 + + translate = -center + + return {"translate": translate, "radius": radius} + + +def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder): + cam_infos = [] + for idx, key in enumerate(cam_extrinsics): + sys.stdout.write('\r') + # the exact output you're looking for: + sys.stdout.write("Reading camera {}/{}".format(idx+1, len(cam_extrinsics))) + sys.stdout.flush() + + extr = cam_extrinsics[key] + intr = cam_intrinsics[extr.camera_id] + height = intr.height + width = intr.width + + uid = intr.id + R = np.transpose(qvec2rotmat(extr.qvec)) + T = np.array(extr.tvec) + + if intr.model=="SIMPLE_PINHOLE": + focal_length_x = intr.params[0] + FovY = focal2fov(focal_length_x, height) + FovX = focal2fov(focal_length_x, width) + elif intr.model=="PINHOLE": + focal_length_x = intr.params[0] + focal_length_y = intr.params[1] + FovY = focal2fov(focal_length_y, height) + FovX = focal2fov(focal_length_x, width) + else: + assert False, "Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!" + + image_path = os.path.join(images_folder, os.path.basename(extr.name)) + image_name = os.path.basename(image_path).split(".")[0] + image = Image.open(image_path) + + cam_info = CameraInfo(uid=uid, R=R, T=T, FovY=FovY, FovX=FovX, image=image, + image_path=image_path, image_name=image_name, width=width, height=height) + cam_infos.append(cam_info) + sys.stdout.write('\n') + return cam_infos + + +def fetchPly(path): + plydata = PlyData.read(path) + vertices = plydata['vertex'] + idx = np.random.choice(len(vertices['x']),size=(min(len(vertices['x']), 100_000),),replace=False) + positions = np.vstack([vertices['x'][idx], vertices['y'][idx], vertices['z'][idx]]).T if 'x' in vertices else None + colors = np.vstack([vertices['red'][idx], vertices['green'][idx], vertices['blue'][idx]]).T / 255.0 if 'red' in vertices else None + normals = np.vstack([vertices['nx'][idx], vertices['ny'][idx], vertices['nz'][idx]]).T if 'nx' in vertices else None + return BasicPointCloud(points=positions, colors=colors, normals=normals) + + +def storePly(path, xyz, rgb): + # Define the dtype for the structured array + dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'), + ('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'), + ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')] + + normals = np.zeros_like(xyz) + + elements = np.empty(xyz.shape[0], dtype=dtype) + attributes = np.concatenate((xyz, normals, rgb), axis=1) + elements[:] = list(map(tuple, attributes)) + + # Create the PlyData object and write to file + vertex_element = PlyElement.describe(elements, 'vertex') + ply_data = PlyData([vertex_element]) + ply_data.write(path) + + +def readColmapSceneInfo(path, images, eval, preset=None, llffhold=8): + try: + cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.bin") + cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.bin") + cam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file) + cam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file) + except: + cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.txt") + cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.txt") + cam_extrinsics = read_extrinsics_text(cameras_extrinsic_file) + cam_intrinsics = read_intrinsics_text(cameras_intrinsic_file) + + reading_dir = "images" if images == None else images + cam_infos_unsorted = readColmapCameras(cam_extrinsics=cam_extrinsics, cam_intrinsics=cam_intrinsics, images_folder=os.path.join(path, reading_dir)) + cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : x.image_name) + + if eval: + # train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0] + # test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0] + train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % 5 == 2 or idx % 5 == 0] + test_cam_infos = [c for idx, c in enumerate(cam_infos) if not (idx % 5 == 2 or idx % 5 == 0)] + else: + train_cam_infos = cam_infos + test_cam_infos = [] + + nerf_normalization = getNerfppNorm(train_cam_infos) + + ply_path = os.path.join(path, "sparse/0/points3D.ply") + bin_path = os.path.join(path, "sparse/0/points3D.bin") + txt_path = os.path.join(path, "sparse/0/points3D.txt") + if not os.path.exists(ply_path): + print("Converting point3d.bin to .ply, will happen only the first time you open the scene.") + try: + xyz, rgb, _ = read_points3D_binary(bin_path) + except: + xyz, rgb, _ = read_points3D_text(txt_path) + storePly(ply_path, xyz, rgb) + try: + pcd = fetchPly(ply_path) + except: + pcd = None + + if preset: + preset_cam_infos = readCamerasFromPreset('/home/chung/workspace/gaussian-splatting/poses_supplementary', f"{preset}.json") + else: + preset_cam_infos = None + + scene_info = SceneInfo(point_cloud=pcd, + train_cameras=train_cam_infos, + test_cameras=test_cam_infos, + preset_cameras=preset_cam_infos, + nerf_normalization=nerf_normalization, + ply_path=ply_path) + return scene_info + + +def readCamerasFromTransforms(path, transformsfile, white_background, extension=".png"): + cam_infos = [] + + with open(os.path.join(path, transformsfile)) as json_file: + contents = json.load(json_file) + fovx = contents["camera_angle_x"] + + frames = contents["frames"] + for idx, frame in enumerate(frames): + cam_name = os.path.join(path, frame["file_path"] + extension) + + # NeRF 'transform_matrix' is a camera-to-world transform + c2w = np.array(frame["transform_matrix"]) + # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) + c2w[:3, 1:3] *= -1 + + # get the world-to-camera transform and set R, T + w2c = np.linalg.inv(c2w) + R = np.transpose(w2c[:3,:3]) # R is stored transposed due to 'glm' in CUDA code + T = w2c[:3, 3] + + image_path = os.path.join(path, cam_name) + image_name = Path(cam_name).stem + image = Image.open(image_path) + + # if os.path.exists(os.path.join(path, frame["file_path"].replace("/train/", "/depths_train/")+'.npy')): + # depth = np.load(os.path.join(path, frame["file_path"].replace("/train/", "/depths_train/")+'.npy')) + # if os.path.exists(os.path.join(path, frame["file_path"].replace("/train/", "/masks_train/")+'.png')): + # mask = imageio.v3.imread(os.path.join(path, frame["file_path"].replace("/train/", "/masks_train/")+'.png'))[:,:,0]/255. + # else: + # mask = np.ones_like(depth) + # final_depth = depth*mask + # else: + # final_depth = None + + im_data = np.array(image.convert("RGBA")) + + bg = np.array([1,1,1]) if white_background else np.array([0, 0, 0]) + + norm_data = im_data / 255.0 + arr = norm_data[:,:,:3] * norm_data[:, :, 3:4] + bg * (1 - norm_data[:, :, 3:4]) + image = Image.fromarray(np.array(arr*255.0, dtype=np.byte), "RGB") + + fovy = focal2fov(fov2focal(fovx, image.size[1]), image.size[0]) + FovY = fovy + FovX = fovx + + cam_infos.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image, + image_path=image_path, image_name=image_name, width=image.size[0], height=image.size[1])) + + return cam_infos + + +def readCamerasFromPreset(path, transformsfile): + cam_infos = [] + + with open(os.path.join(path, transformsfile)) as json_file: + contents = json.load(json_file) + FOV = contents["camera_angle_x"]*1.2 + + frames = contents["frames"] + for idx, frame in enumerate(frames): + # NeRF 'transform_matrix' is a camera-to-world transform + c2w = np.array(frame["transform_matrix"]) + # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) + c2w[:3, 1:3] *= -1 + + # get the world-to-camera transform and set R, T + w2c = np.linalg.inv(np.concatenate((c2w, np.array([0,0,0,1]).reshape(1,4)), axis=0)) + R = np.transpose(w2c[:3,:3]) # R is stored transposed due to 'glm' in CUDA code + T = w2c[:3, 3] + # R = c2w[:3,:3] + # T = - np.transpose(R).dot(c2w[:3,3]) + + image = Image.fromarray(np.zeros((512,512)), "RGB") + FovY = focal2fov(fov2focal(FOV, 512), image.size[0]) + FovX = focal2fov(fov2focal(FOV, 512), image.size[1]) + # FovX, FovY = contents["camera_angle_x"], contents["camera_angle_x"] + + cam_infos.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image, + image_path='None', image_name='None', width=image.size[1], height=image.size[0])) + + return cam_infos + + +def readNerfSyntheticInfo(path, white_background, eval, preset=None, extension=".png"): + print("Reading Training Transforms") + train_cam_infos = readCamerasFromTransforms(path, "transforms_train.json", white_background, extension) + print("Reading Test Transforms") + test_cam_infos = readCamerasFromTransforms(path, "transforms_test.json", white_background, extension) + + if preset: + preset_cam_infos = readCamerasFromPreset('/home/chung/workspace/gaussian-splatting/poses_supplementary', f"{preset}.json") + else: + preset_cam_infos = None + + if not eval: + train_cam_infos.extend(test_cam_infos) + test_cam_infos = [] + + nerf_normalization = getNerfppNorm(train_cam_infos) + + ply_path = os.path.join(path, "points3d.ply") + if not os.path.exists(ply_path): + # Since this data set has no colmap data, we start with random points + num_pts = 100_000 + print(f"Generating random point cloud ({num_pts})...") + + # We create random points inside the bounds of the synthetic Blender scenes + xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3 + shs = np.random.random((num_pts, 3)) / 255.0 + pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3))) + + storePly(ply_path, xyz, SH2RGB(shs) * 255) + + try: + pcd = fetchPly(ply_path) + except: + pcd = None + + scene_info = SceneInfo(point_cloud=pcd, + train_cameras=train_cam_infos, + test_cameras=test_cam_infos, + preset_cameras=preset_cam_infos, + nerf_normalization=nerf_normalization, + ply_path=ply_path) + return scene_info + + +def loadCamerasFromData(traindata, white_background): + cameras = [] + + fovx = traindata["camera_angle_x"] + frames = traindata["frames"] + for idx, frame in enumerate(frames): + # NeRF 'transform_matrix' is a camera-to-world transform + c2w = np.array(frame["transform_matrix"]) + # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) + c2w[:3, 1:3] *= -1 + + # get the world-to-camera transform and set R, T + w2c = np.linalg.inv(c2w) + R = np.transpose(w2c[:3,:3]) # R is stored transposed due to 'glm' in CUDA code + T = w2c[:3, 3] + + image = frame["image"] if "image" in frame else None + im_data = np.array(image.convert("RGBA")) + + bg = np.array([1,1,1]) if white_background else np.array([0, 0, 0]) + + norm_data = im_data / 255.0 + arr = norm_data[:,:,:3] * norm_data[:, :, 3:4] + bg * (1 - norm_data[:, :, 3:4]) + image = Image.fromarray(np.array(arr*255.0, dtype=np.byte), "RGB") + loaded_mask = np.ones_like(norm_data[:, :, 3:4]) + + fovy = focal2fov(fov2focal(fovx, image.size[1]), image.size[0]) + FovY = fovy + FovX = fovx + + image = torch.Tensor(arr).permute(2,0,1) + loaded_mask = None #torch.Tensor(loaded_mask).permute(2,0,1) + + ### torch๋กœ ๋ฐ”๊ฟ”์•ผํ•จ + cameras.append(Camera(colmap_id=idx, R=R, T=T, FoVx=FovX, FoVy=FovY, image=image, + gt_alpha_mask=loaded_mask, image_name='', uid=idx, data_device='cuda')) + + return cameras + + +def loadCameraPreset(traindata, presetdata): + cam_infos = {} + ## camera setting (for H, W and focal) + fovx = traindata["camera_angle_x"] * 1.2 + W, H = traindata["frames"][0]["image"].size + # W, H = traindata["W"], traindata["H"] + + for camkey in presetdata: + cam_infos[camkey] = [] + for idx, frame in enumerate(presetdata[camkey]["frames"]): + # NeRF 'transform_matrix' is a camera-to-world transform + c2w = np.array(frame["transform_matrix"]) + # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) + c2w[:3, 1:3] *= -1 + + # get the world-to-camera transform and set R, T + w2c = np.linalg.inv(c2w) + R = np.transpose(w2c[:3,:3]) # R is stored transposed due to 'glm' in CUDA code + T = w2c[:3, 3] + + fovy = focal2fov(fov2focal(fovx, W), H) + FovY = fovy + FovX = fovx + + znear, zfar = 0.01, 100 + world_view_transform = torch.tensor(getWorld2View2(R, T, np.array([0.0, 0.0, 0.0]), 1.0)).transpose(0, 1).cuda() + projection_matrix = getProjectionMatrix(znear=znear, zfar=zfar, fovX=FovX, fovY=FovY).transpose(0,1).cuda() + full_proj_transform = (world_view_transform.unsqueeze(0).bmm(projection_matrix.unsqueeze(0))).squeeze(0) + + cam_infos[camkey].append(MiniCam(width=W, height=H, fovy=FovY, fovx=FovX, znear=znear, zfar=zfar, + world_view_transform=world_view_transform, full_proj_transform=full_proj_transform)) + + return cam_infos + + +def readDataInfo(traindata, white_background): + print("Reading Training Transforms") + + train_cameras = loadCamerasFromData(traindata, white_background) + preset_minicams = loadCameraPreset(traindata, presetdata=get_camerapaths()) + + # if not eval: + # train_cam_infos.extend(test_cam_infos) + # test_cam_infos = [] + + nerf_normalization = getNerfppNorm(train_cameras) + + pcd = BasicPointCloud(points=traindata['pcd_points'].T, colors=traindata['pcd_colors'], normals=None) + + + scene_info = SceneInfo(point_cloud=pcd, + train_cameras=train_cameras, + test_cameras=[], + preset_cameras=preset_minicams, + nerf_normalization=nerf_normalization, + ply_path='') + return scene_info + + +sceneLoadTypeCallbacks = { + "Colmap": readColmapSceneInfo, + "Blender" : readNerfSyntheticInfo +} \ No newline at end of file diff --git a/scene/gaussian_model.py b/scene/gaussian_model.py new file mode 100644 index 0000000000000000000000000000000000000000..3aed4a27234f60b281feaa822f0dbe0955bf8a82 --- /dev/null +++ b/scene/gaussian_model.py @@ -0,0 +1,407 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import os + +import numpy as np +from plyfile import PlyData, PlyElement + +import torch +from torch import nn + +from simple_knn._C import distCUDA2 +from utils.general import inverse_sigmoid, get_expon_lr_func, build_rotation +from utils.system import mkdir_p +from utils.sh import RGB2SH +from utils.graphics import BasicPointCloud +from utils.general import strip_symmetric, build_scaling_rotation + + +class GaussianModel: + def setup_functions(self): + def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation): + L = build_scaling_rotation(scaling_modifier * scaling, rotation) + actual_covariance = L @ L.transpose(1, 2) + symm = strip_symmetric(actual_covariance) + return symm + + self.scaling_activation = torch.exp + self.scaling_inverse_activation = torch.log + + self.covariance_activation = build_covariance_from_scaling_rotation + + self.opacity_activation = torch.sigmoid + self.inverse_opacity_activation = inverse_sigmoid + + self.rotation_activation = torch.nn.functional.normalize + + + def __init__(self, sh_degree : int): + self.active_sh_degree = 0 + self.max_sh_degree = sh_degree + self._xyz = torch.empty(0) + self._features_dc = torch.empty(0) + self._features_rest = torch.empty(0) + self._scaling = torch.empty(0) + self._rotation = torch.empty(0) + self._opacity = torch.empty(0) + self.max_radii2D = torch.empty(0) + self.xyz_gradient_accum = torch.empty(0) + self.denom = torch.empty(0) + self.optimizer = None + self.percent_dense = 0 + self.spatial_lr_scale = 0 + self.setup_functions() + + def capture(self): + return ( + self.active_sh_degree, + self._xyz, + self._features_dc, + self._features_rest, + self._scaling, + self._rotation, + self._opacity, + self.max_radii2D, + self.xyz_gradient_accum, + self.denom, + self.optimizer.state_dict(), + self.spatial_lr_scale, + ) + + def restore(self, model_args, training_args): + (self.active_sh_degree, + self._xyz, + self._features_dc, + self._features_rest, + self._scaling, + self._rotation, + self._opacity, + self.max_radii2D, + xyz_gradient_accum, + denom, + opt_dict, + self.spatial_lr_scale) = model_args + self.training_setup(training_args) + self.xyz_gradient_accum = xyz_gradient_accum + self.denom = denom + self.optimizer.load_state_dict(opt_dict) + + @property + def get_scaling(self): + return self.scaling_activation(self._scaling) + + @property + def get_rotation(self): + return self.rotation_activation(self._rotation) + + @property + def get_xyz(self): + return self._xyz + + @property + def get_features(self): + features_dc = self._features_dc + features_rest = self._features_rest + return torch.cat((features_dc, features_rest), dim=1) + + @property + def get_opacity(self): + return self.opacity_activation(self._opacity) + + def get_covariance(self, scaling_modifier = 1): + return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation) + + def oneupSHdegree(self): + if self.active_sh_degree < self.max_sh_degree: + self.active_sh_degree += 1 + + def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float): + self.spatial_lr_scale = spatial_lr_scale + fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda() + fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda()) + features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda() + features[:, :3, 0 ] = fused_color + features[:, 3:, 1:] = 0.0 + + print("Number of points at initialisation : ", fused_point_cloud.shape[0]) + + dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001) + scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3) + rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda") + rots[:, 0] = 1 + + opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda")) + + self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True)) + self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True)) + self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True)) + self._scaling = nn.Parameter(scales.requires_grad_(True)) + self._rotation = nn.Parameter(rots.requires_grad_(True)) + self._opacity = nn.Parameter(opacities.requires_grad_(True)) + self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") + + def training_setup(self, training_args): + self.percent_dense = training_args.percent_dense + self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + + l = [ + {'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"}, + {'params': [self._features_dc], 'lr': training_args.feature_lr, "name": "f_dc"}, + {'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, "name": "f_rest"}, + {'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"}, + {'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"}, + {'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"} + ] + + self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15) + self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale, + lr_final=training_args.position_lr_final*self.spatial_lr_scale, + lr_delay_mult=training_args.position_lr_delay_mult, + max_steps=training_args.position_lr_max_steps) + + def update_learning_rate(self, iteration): + ''' Learning rate scheduling per step ''' + for param_group in self.optimizer.param_groups: + if param_group["name"] == "xyz": + lr = self.xyz_scheduler_args(iteration) + param_group['lr'] = lr + return lr + + def construct_list_of_attributes(self): + l = ['x', 'y', 'z', 'nx', 'ny', 'nz'] + # All channels except the 3 DC + for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]): + l.append('f_dc_{}'.format(i)) + for i in range(self._features_rest.shape[1]*self._features_rest.shape[2]): + l.append('f_rest_{}'.format(i)) + l.append('opacity') + for i in range(self._scaling.shape[1]): + l.append('scale_{}'.format(i)) + for i in range(self._rotation.shape[1]): + l.append('rot_{}'.format(i)) + return l + + def save_ply(self, filepath): + xyz = self._xyz.detach().cpu().numpy() + normals = np.zeros_like(xyz) + f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() + f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() + opacities = self._opacity.detach().cpu().numpy() + scale = self._scaling.detach().cpu().numpy() + rotation = self._rotation.detach().cpu().numpy() + + dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()] + + elements = np.empty(xyz.shape[0], dtype=dtype_full) + attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1) + elements[:] = list(map(tuple, attributes)) + el = PlyElement.describe(elements, 'vertex') + PlyData([el]).write(filepath) + + def reset_opacity(self): + opacities_new = inverse_sigmoid(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01)) + optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity") + self._opacity = optimizable_tensors["opacity"] + + def load_ply(self, path): + plydata = PlyData.read(path) + + xyz = np.stack((np.asarray(plydata.elements[0]["x"]), + np.asarray(plydata.elements[0]["y"]), + np.asarray(plydata.elements[0]["z"])), axis=1) + opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis] + + features_dc = np.zeros((xyz.shape[0], 3, 1)) + features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"]) + features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"]) + features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"]) + + extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")] + extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1])) + assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3 + features_extra = np.zeros((xyz.shape[0], len(extra_f_names))) + for idx, attr_name in enumerate(extra_f_names): + features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name]) + # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC) + features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1)) + + scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")] + scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1])) + scales = np.zeros((xyz.shape[0], len(scale_names))) + for idx, attr_name in enumerate(scale_names): + scales[:, idx] = np.asarray(plydata.elements[0][attr_name]) + + rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")] + rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1])) + rots = np.zeros((xyz.shape[0], len(rot_names))) + for idx, attr_name in enumerate(rot_names): + rots[:, idx] = np.asarray(plydata.elements[0][attr_name]) + + self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True)) + self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True)) + self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True)) + self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True)) + self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True)) + self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True)) + + self.active_sh_degree = self.max_sh_degree + + def replace_tensor_to_optimizer(self, tensor, name): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + if group["name"] == name: + stored_state = self.optimizer.state.get(group['params'][0], None) + stored_state["exp_avg"] = torch.zeros_like(tensor) + stored_state["exp_avg_sq"] = torch.zeros_like(tensor) + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter(tensor.requires_grad_(True)) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + return optimizable_tensors + + def _prune_optimizer(self, mask): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + stored_state = self.optimizer.state.get(group['params'][0], None) + if stored_state is not None: + stored_state["exp_avg"] = stored_state["exp_avg"][mask] + stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask] + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True))) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + else: + group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True)) + optimizable_tensors[group["name"]] = group["params"][0] + return optimizable_tensors + + def prune_points(self, mask): + valid_points_mask = ~mask + optimizable_tensors = self._prune_optimizer(valid_points_mask) + + self._xyz = optimizable_tensors["xyz"] + self._features_dc = optimizable_tensors["f_dc"] + self._features_rest = optimizable_tensors["f_rest"] + self._opacity = optimizable_tensors["opacity"] + self._scaling = optimizable_tensors["scaling"] + self._rotation = optimizable_tensors["rotation"] + + self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask] + + self.denom = self.denom[valid_points_mask] + self.max_radii2D = self.max_radii2D[valid_points_mask] + + def cat_tensors_to_optimizer(self, tensors_dict): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + assert len(group["params"]) == 1 + extension_tensor = tensors_dict[group["name"]] + stored_state = self.optimizer.state.get(group['params'][0], None) + if stored_state is not None: + + stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0) + stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0) + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True)) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + else: + group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True)) + optimizable_tensors[group["name"]] = group["params"][0] + + return optimizable_tensors + + def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation): + d = {"xyz": new_xyz, + "f_dc": new_features_dc, + "f_rest": new_features_rest, + "opacity": new_opacities, + "scaling" : new_scaling, + "rotation" : new_rotation} + + optimizable_tensors = self.cat_tensors_to_optimizer(d) + self._xyz = optimizable_tensors["xyz"] + self._features_dc = optimizable_tensors["f_dc"] + self._features_rest = optimizable_tensors["f_rest"] + self._opacity = optimizable_tensors["opacity"] + self._scaling = optimizable_tensors["scaling"] + self._rotation = optimizable_tensors["rotation"] + + self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") + + def densify_and_split(self, grads, grad_threshold, scene_extent, N=2): + n_init_points = self.get_xyz.shape[0] + # Extract points that satisfy the gradient condition + padded_grad = torch.zeros((n_init_points), device="cuda") + padded_grad[:grads.shape[0]] = grads.squeeze() + selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False) + selected_pts_mask = torch.logical_and(selected_pts_mask, + torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent) + + stds = self.get_scaling[selected_pts_mask].repeat(N,1) + means =torch.zeros((stds.size(0), 3),device="cuda") + samples = torch.normal(mean=means, std=stds) + rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N,1,1) + new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask].repeat(N, 1) + new_scaling = self.scaling_inverse_activation(self.get_scaling[selected_pts_mask].repeat(N,1) / (0.8*N)) + new_rotation = self._rotation[selected_pts_mask].repeat(N,1) + new_features_dc = self._features_dc[selected_pts_mask].repeat(N,1,1) + new_features_rest = self._features_rest[selected_pts_mask].repeat(N,1,1) + new_opacity = self._opacity[selected_pts_mask].repeat(N,1) + + self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation) + + prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool))) + self.prune_points(prune_filter) + + def densify_and_clone(self, grads, grad_threshold, scene_extent): + # Extract points that satisfy the gradient condition + selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False) + selected_pts_mask = torch.logical_and(selected_pts_mask, + torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent) + + new_xyz = self._xyz[selected_pts_mask] + new_features_dc = self._features_dc[selected_pts_mask] + new_features_rest = self._features_rest[selected_pts_mask] + new_opacities = self._opacity[selected_pts_mask] + new_scaling = self._scaling[selected_pts_mask] + new_rotation = self._rotation[selected_pts_mask] + + self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation) + + def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size): + grads = self.xyz_gradient_accum / self.denom + grads[grads.isnan()] = 0.0 + + self.densify_and_clone(grads, max_grad, extent) + self.densify_and_split(grads, max_grad, extent) + + prune_mask = (self.get_opacity < min_opacity).squeeze() + if max_screen_size: + big_points_vs = self.max_radii2D > max_screen_size + big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent + prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws) + self.prune_points(prune_mask) + + torch.cuda.empty_cache() + + def add_densification_stats(self, viewspace_point_tensor, update_filter): + self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True) + self.denom[update_filter] += 1 \ No newline at end of file diff --git a/utils/.ipynb_checkpoints/__init__-checkpoint.py b/utils/.ipynb_checkpoints/__init__-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/utils/.ipynb_checkpoints/camera-checkpoint.py b/utils/.ipynb_checkpoints/camera-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..9dc34a7047dbb37698e8df4800c0766e43c100cf --- /dev/null +++ b/utils/.ipynb_checkpoints/camera-checkpoint.py @@ -0,0 +1,120 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import json + +import numpy as np +import torch + +from scene.cameras import Camera, MiniCam +from utils.general import PILtoTorch +from utils.graphics import fov2focal, focal2fov, getWorld2View, getProjectionMatrix + + +WARNED = False + + +def load_json(path, H, W): + cams = [] + with open(path) as json_file: + contents = json.load(json_file) + FoVx = contents["camera_angle_x"] + FoVy = focal2fov(fov2focal(FoVx, W), H) + zfar = 100.0 + znear = 0.01 + + frames = contents["frames"] + for idx, frame in enumerate(frames): + # NeRF 'transform_matrix' is a camera-to-world transform + c2w = np.array(frame["transform_matrix"]) + # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) + c2w[:3, 1:3] *= -1 + if c2w.shape[0] == 3: + one = np.zeros((1, 4)) + one[0, -1] = 1 + c2w = np.concatenate((c2w, one), axis=0) + + # get the world-to-camera transform and set R, T + w2c = np.linalg.inv(c2w) + R = np.transpose(w2c[:3, :3]) # R is stored transposed due to 'glm' in CUDA code + T = w2c[:3, 3] + + w2c = torch.as_tensor(getWorld2View(R, T)).T.cuda() + proj = getProjectionMatrix(znear, zfar, FoVx, FoVy).T.cuda() + cams.append(MiniCam(W, H, FoVx, FoVy, znear, zfar, w2c, w2c @ proj)) + return cams + + +def loadCam(args, id, cam_info, resolution_scale): + orig_w, orig_h = cam_info.image.size + + if args.resolution in [1, 2, 4, 8]: + resolution = round(orig_w/(resolution_scale * args.resolution)), round(orig_h/(resolution_scale * args.resolution)) + else: # should be a type that converts to float + if args.resolution == -1: + if orig_w > 1600: + global WARNED + if not WARNED: + print("[ INFO ] Encountered quite large input images (>1.6K pixels width), rescaling to 1.6K.\n " + "If this is not desired, please explicitly specify '--resolution/-r' as 1") + WARNED = True + global_down = orig_w / 1600 + else: + global_down = 1 + else: + global_down = orig_w / args.resolution + + scale = float(global_down) * float(resolution_scale) + resolution = (int(orig_w / scale), int(orig_h / scale)) + + resized_image_rgb = PILtoTorch(cam_info.image, resolution) + + gt_image = resized_image_rgb[:3, ...] + loaded_mask = None + + if resized_image_rgb.shape[1] == 4: + loaded_mask = resized_image_rgb[3:4, ...] + + return Camera(colmap_id=cam_info.uid, R=cam_info.R, T=cam_info.T, + FoVx=cam_info.FovX, FoVy=cam_info.FovY, + image=gt_image, gt_alpha_mask=loaded_mask, + image_name=cam_info.image_name, uid=id, data_device=args.data_device) + + +def cameraList_from_camInfos(cam_infos, resolution_scale, args): + camera_list = [] + + for id, c in enumerate(cam_infos): + camera_list.append(loadCam(args, id, c, resolution_scale)) + + return camera_list + + +def camera_to_JSON(id, camera : Camera): + Rt = np.zeros((4, 4)) + Rt[:3, :3] = camera.R.transpose() + Rt[:3, 3] = camera.T + Rt[3, 3] = 1.0 + + W2C = np.linalg.inv(Rt) + pos = W2C[:3, 3] + rot = W2C[:3, :3] + serializable_array_2d = [x.tolist() for x in rot] + camera_entry = { + 'id' : id, + 'img_name' : camera.image_name, + 'width' : camera.width, + 'height' : camera.height, + 'position': pos.tolist(), + 'rotation': serializable_array_2d, + 'fy' : fov2focal(camera.FovY, camera.height), + 'fx' : fov2focal(camera.FovX, camera.width) + } + return camera_entry diff --git a/utils/.ipynb_checkpoints/depth-checkpoint.py b/utils/.ipynb_checkpoints/depth-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..242fa05ce5dac721be5ac10209e638369477f46c --- /dev/null +++ b/utils/.ipynb_checkpoints/depth-checkpoint.py @@ -0,0 +1,62 @@ +import matplotlib +import matplotlib.cm +import numpy as np +import torch + + +def colorize(value, vmin=None, vmax=None, cmap='jet', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None): + """Converts a depth map to a color image. + + Args: + value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed + vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None. + vmax (float, optional): vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None. + cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'. + invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99. + invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None. + background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255). + gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False. + value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None. + + Returns: + numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4) + """ + if isinstance(value, torch.Tensor): + value = value.detach().cpu().numpy() + + value = value.squeeze() + if invalid_mask is None: + invalid_mask = value == invalid_val + mask = np.logical_not(invalid_mask) + + # normalize + vmin = np.percentile(value[mask],2) if vmin is None else vmin + vmax = np.percentile(value[mask],98) if vmax is None else vmax + if vmin != vmax: + value = (value - vmin) / (vmax - vmin) # vmin..vmax + else: + # Avoid 0-division + value = value * 0. + + # squeeze last dim if it exists + # grey out the invalid values + + value[invalid_mask] = np.nan + cmapper = matplotlib.cm.get_cmap(cmap) + if value_transform: + value = value_transform(value) + # value = value / value.max() + value = cmapper(value, bytes=True) # (nxmx4) + + # img = value[:, :, :] + img = value[...] + img[invalid_mask] = background_color + + # return img.transpose((2, 0, 1)) + if gamma_corrected: + # gamma correction + img = img / 255 + img = np.power(img, 2.2) + img = img * 255 + img = img.astype(np.uint8) + return img \ No newline at end of file diff --git a/utils/.ipynb_checkpoints/general-checkpoint.py b/utils/.ipynb_checkpoints/general-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..e14edf106c655a717b6a4227297503277746c3cf --- /dev/null +++ b/utils/.ipynb_checkpoints/general-checkpoint.py @@ -0,0 +1,140 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import sys +import random +from datetime import datetime +import numpy as np +import torch + + +def inverse_sigmoid(x): + return torch.log(x/(1-x)) + + +def PILtoTorch(pil_image, resolution): + resized_image_PIL = pil_image.resize(resolution) + resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0 + if len(resized_image.shape) == 3: + return resized_image.permute(2, 0, 1) + else: + return resized_image.unsqueeze(dim=-1).permute(2, 0, 1) + + +def get_expon_lr_func( + lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000 +): + """ + Copied from Plenoxels + + Continuous learning rate decay function. Adapted from JaxNeRF + The returned rate is lr_init when step=0 and lr_final when step=max_steps, and + is log-linearly interpolated elsewhere (equivalent to exponential decay). + If lr_delay_steps>0 then the learning rate will be scaled by some smooth + function of lr_delay_mult, such that the initial learning rate is + lr_init*lr_delay_mult at the beginning of optimization but will be eased back + to the normal learning rate when steps>lr_delay_steps. + :param conf: config subtree 'lr' or similar + :param max_steps: int, the number of steps during optimization. + :return HoF which takes step as input + """ + + def helper(step): + if step < 0 or (lr_init == 0.0 and lr_final == 0.0): + # Disable this parameter + return 0.0 + if lr_delay_steps > 0: + # A kind of reverse cosine decay. + delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin( + 0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1) + ) + else: + delay_rate = 1.0 + t = np.clip(step / max_steps, 0, 1) + log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return delay_rate * log_lerp + + return helper + + +def strip_lowerdiag(L): + uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda") + + uncertainty[:, 0] = L[:, 0, 0] + uncertainty[:, 1] = L[:, 0, 1] + uncertainty[:, 2] = L[:, 0, 2] + uncertainty[:, 3] = L[:, 1, 1] + uncertainty[:, 4] = L[:, 1, 2] + uncertainty[:, 5] = L[:, 2, 2] + return uncertainty + + +def strip_symmetric(sym): + return strip_lowerdiag(sym) + + +def build_rotation(r): + norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3]) + + q = r / norm[:, None] + + R = torch.zeros((q.size(0), 3, 3), device='cuda') + + r = q[:, 0] + x = q[:, 1] + y = q[:, 2] + z = q[:, 3] + + R[:, 0, 0] = 1 - 2 * (y*y + z*z) + R[:, 0, 1] = 2 * (x*y - r*z) + R[:, 0, 2] = 2 * (x*z + r*y) + R[:, 1, 0] = 2 * (x*y + r*z) + R[:, 1, 1] = 1 - 2 * (x*x + z*z) + R[:, 1, 2] = 2 * (y*z - r*x) + R[:, 2, 0] = 2 * (x*z - r*y) + R[:, 2, 1] = 2 * (y*z + r*x) + R[:, 2, 2] = 1 - 2 * (x*x + y*y) + return R + + +def build_scaling_rotation(s, r): + L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda") + R = build_rotation(r) + + L[:,0,0] = s[:,0] + L[:,1,1] = s[:,1] + L[:,2,2] = s[:,2] + + L = R @ L + return L + + +def safe_state(silent): + old_f = sys.stdout + class F: + def __init__(self, silent): + self.silent = silent + + def write(self, x): + if not self.silent: + if x.endswith("\n"): + old_f.write(x.replace("\n", " [{}]\n".format(str(datetime.now().strftime("%d/%m %H:%M:%S"))))) + else: + old_f.write(x) + + def flush(self): + old_f.flush() + + sys.stdout = F(silent) + + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + torch.cuda.set_device(torch.device("cuda:0")) diff --git a/utils/.ipynb_checkpoints/graphics-checkpoint.py b/utils/.ipynb_checkpoints/graphics-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..a40ac192cd7c50242b9c86fedd2d5cce11030996 --- /dev/null +++ b/utils/.ipynb_checkpoints/graphics-checkpoint.py @@ -0,0 +1,83 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import math +from typing import NamedTuple +import numpy as np +import torch + + +class BasicPointCloud(NamedTuple): + points : np.array + colors : np.array + normals : np.array + + +def geom_transform_points(points, transf_matrix): + P, _ = points.shape + ones = torch.ones(P, 1, dtype=points.dtype, device=points.device) + points_hom = torch.cat([points, ones], dim=1) + points_out = torch.matmul(points_hom, transf_matrix.unsqueeze(0)) + + denom = points_out[..., 3:] + 0.0000001 + return (points_out[..., :3] / denom).squeeze(dim=0) + + +def getWorld2View(R, t): + Rt = np.zeros((4, 4)) + Rt[:3, :3] = R.transpose() + Rt[:3, 3] = t + Rt[3, 3] = 1.0 + return np.float32(Rt) + + +def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0): + Rt = np.zeros((4, 4)) + Rt[:3, :3] = R.transpose() + Rt[:3, 3] = t + Rt[3, 3] = 1.0 + + C2W = np.linalg.inv(Rt) + cam_center = C2W[:3, 3] + cam_center = (cam_center + translate) * scale + C2W[:3, 3] = cam_center + Rt = np.linalg.inv(C2W) + return np.float32(Rt) + + +def getProjectionMatrix(znear, zfar, fovX, fovY): + tanHalfFovY = math.tan((fovY / 2)) + tanHalfFovX = math.tan((fovX / 2)) + + top = tanHalfFovY * znear + bottom = -top + right = tanHalfFovX * znear + left = -right + + P = torch.zeros(4, 4) + + z_sign = 1.0 + + P[0, 0] = 2.0 * znear / (right - left) + P[1, 1] = 2.0 * znear / (top - bottom) + P[0, 2] = (right + left) / (right - left) + P[1, 2] = (top + bottom) / (top - bottom) + P[3, 2] = z_sign + P[2, 2] = z_sign * zfar / (zfar - znear) + P[2, 3] = -(zfar * znear) / (zfar - znear) + return P + + +def fov2focal(fov, pixels): + return pixels / (2 * math.tan(fov / 2)) + + +def focal2fov(focal, pixels): + return 2*math.atan(pixels/(2*focal)) \ No newline at end of file diff --git a/utils/.ipynb_checkpoints/image-checkpoint.py b/utils/.ipynb_checkpoints/image-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..0b105203c4cd57355534ab81f0915b604fc68aee --- /dev/null +++ b/utils/.ipynb_checkpoints/image-checkpoint.py @@ -0,0 +1,20 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import torch + + +def mse(img1, img2): + return (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True) + + +def psnr(img1, img2): + mse = (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True) + return 20 * torch.log10(1.0 / torch.sqrt(mse)) diff --git a/utils/.ipynb_checkpoints/loss-checkpoint.py b/utils/.ipynb_checkpoints/loss-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..e1a5e9f08c0c10fc6ec12e63445cd3f9dd3eb8af --- /dev/null +++ b/utils/.ipynb_checkpoints/loss-checkpoint.py @@ -0,0 +1,99 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +from math import exp + +import torch +import torch.nn.functional as F +from torch.autograd import Variable + + +def l1_loss(network_output, gt): + return torch.abs((network_output - gt)).mean() + + +def l2_loss(network_output, gt): + return ((network_output - gt) ** 2).mean() + + +def gaussian(window_size, sigma): + gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) + return gauss / gauss.sum() + + +def create_window(window_size, channel): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) + window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) + return window + + +def ssim(img1, img2, window_size=11, size_average=True): + channel = img1.size(-3) + window = create_window(window_size, channel) + + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + + return _ssim(img1, img2, window, window_size, channel, size_average) + + +def _ssim(img1, img2, window, window_size, channel, size_average=True): + mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) + mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq + sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq + sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 + + C1 = 0.01 ** 2 + C2 = 0.03 ** 2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) + + if size_average: + return ssim_map.mean() + else: + return ssim_map.mean(1).mean(1).mean(1) + + +import numpy as np +import cv2 +def image2canny(image, thres1, thres2, isEdge1=True): + """ image: (H, W, 3)""" + canny_mask = torch.from_numpy(cv2.Canny((image.detach().cpu().numpy()*255.).astype(np.uint8), thres1, thres2)/255.) + if not isEdge1: + canny_mask = 1. - canny_mask + return canny_mask.float() + +with torch.no_grad(): + kernelsize=3 + conv = torch.nn.Conv2d(1, 1, kernel_size=kernelsize, padding=(kernelsize//2)) + kernel = torch.tensor([[0.,1.,0.],[1.,0.,1.],[0.,1.,0.]]).reshape(1,1,kernelsize,kernelsize) + conv.weight.data = kernel #torch.ones((1,1,kernelsize,kernelsize)) + conv.bias.data = torch.tensor([0.]) + conv.requires_grad_(False) + conv = conv.cuda() + + +def nearMean_map(array, mask, kernelsize=3): + """ array: (H,W) / mask: (H,W) """ + cnt_map = torch.ones_like(array) + + nearMean_map = conv((array * mask)[None,None]) + cnt_map = conv((cnt_map * mask)[None,None]) + nearMean_map = (nearMean_map / (cnt_map+1e-8)).squeeze() + + return nearMean_map \ No newline at end of file diff --git a/utils/.ipynb_checkpoints/sh-checkpoint.py b/utils/.ipynb_checkpoints/sh-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..e00d11a6c63cfb5a119348db1b16a7fb81edb7cb --- /dev/null +++ b/utils/.ipynb_checkpoints/sh-checkpoint.py @@ -0,0 +1,120 @@ +# Copyright 2021 The PlenOctree Authors. +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. +import torch + + +C0 = 0.28209479177387814 +C1 = 0.4886025119029199 +C2 = [ + 1.0925484305920792, + -1.0925484305920792, + 0.31539156525252005, + -1.0925484305920792, + 0.5462742152960396 +] +C3 = [ + -0.5900435899266435, + 2.890611442640554, + -0.4570457994644658, + 0.3731763325901154, + -0.4570457994644658, + 1.445305721320277, + -0.5900435899266435 +] +C4 = [ + 2.5033429417967046, + -1.7701307697799304, + 0.9461746957575601, + -0.6690465435572892, + 0.10578554691520431, + -0.6690465435572892, + 0.47308734787878004, + -1.7701307697799304, + 0.6258357354491761, +] + + +def eval_sh(deg, sh, dirs): + """ + Evaluate spherical harmonics at unit directions + using hardcoded SH polynomials. + Works with torch/np/jnp. + ... Can be 0 or more batch dimensions. + Args: + deg: int SH deg. Currently, 0-3 supported + sh: jnp.ndarray SH coeffs [..., C, (deg + 1) ** 2] + dirs: jnp.ndarray unit directions [..., 3] + Returns: + [..., C] + """ + assert deg <= 4 and deg >= 0 + coeff = (deg + 1) ** 2 + assert sh.shape[-1] >= coeff + + result = C0 * sh[..., 0] + if deg > 0: + x, y, z = dirs[..., 0:1], dirs[..., 1:2], dirs[..., 2:3] + result = (result - + C1 * y * sh[..., 1] + + C1 * z * sh[..., 2] - + C1 * x * sh[..., 3]) + + if deg > 1: + xx, yy, zz = x * x, y * y, z * z + xy, yz, xz = x * y, y * z, x * z + result = (result + + C2[0] * xy * sh[..., 4] + + C2[1] * yz * sh[..., 5] + + C2[2] * (2.0 * zz - xx - yy) * sh[..., 6] + + C2[3] * xz * sh[..., 7] + + C2[4] * (xx - yy) * sh[..., 8]) + + if deg > 2: + result = (result + + C3[0] * y * (3 * xx - yy) * sh[..., 9] + + C3[1] * xy * z * sh[..., 10] + + C3[2] * y * (4 * zz - xx - yy)* sh[..., 11] + + C3[3] * z * (2 * zz - 3 * xx - 3 * yy) * sh[..., 12] + + C3[4] * x * (4 * zz - xx - yy) * sh[..., 13] + + C3[5] * z * (xx - yy) * sh[..., 14] + + C3[6] * x * (xx - 3 * yy) * sh[..., 15]) + + if deg > 3: + result = (result + C4[0] * xy * (xx - yy) * sh[..., 16] + + C4[1] * yz * (3 * xx - yy) * sh[..., 17] + + C4[2] * xy * (7 * zz - 1) * sh[..., 18] + + C4[3] * yz * (7 * zz - 3) * sh[..., 19] + + C4[4] * (zz * (35 * zz - 30) + 3) * sh[..., 20] + + C4[5] * xz * (7 * zz - 3) * sh[..., 21] + + C4[6] * (xx - yy) * (7 * zz - 1) * sh[..., 22] + + C4[7] * xz * (xx - 3 * yy) * sh[..., 23] + + C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) * sh[..., 24]) + return result + + +def RGB2SH(rgb): + return (rgb - 0.5) / C0 + + +def SH2RGB(sh): + return sh * C0 + 0.5 \ No newline at end of file diff --git a/utils/.ipynb_checkpoints/system-checkpoint.py b/utils/.ipynb_checkpoints/system-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..d62c148cbe48e5eabb6666aa581bd4f5f7c8cd93 --- /dev/null +++ b/utils/.ipynb_checkpoints/system-checkpoint.py @@ -0,0 +1,29 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +from errno import EEXIST +from os import makedirs, path +import os + + +def mkdir_p(folder_path): + # Creates a directory. equivalent to using mkdir -p on the command line + try: + makedirs(folder_path) + except OSError as exc: # Python >2.5 + if exc.errno == EEXIST and path.isdir(folder_path): + pass + else: + raise + + +def searchForMaxIteration(folder): + saved_iters = [int(fname.split("_")[-1]) for fname in os.listdir(folder)] + return max(saved_iters) diff --git a/utils/.ipynb_checkpoints/trajectory-checkpoint.py b/utils/.ipynb_checkpoints/trajectory-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..79d30015c2a0e1499ea11ae13ad5b42e5246d031 --- /dev/null +++ b/utils/.ipynb_checkpoints/trajectory-checkpoint.py @@ -0,0 +1,621 @@ +# Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr +# +# Copyright 2023 LucidDreamer Authors +# +# Computer Vision Lab, SNU, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from the Computer Vision Lab, SNU or +# its affiliates is strictly prohibited. +# +# For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com. +import os +import numpy as np +import torch + + +def generate_seed(scale, viewangle): + # World 2 Camera + #### rotate x,y + render_poses = [np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0]])] + ang = 5 + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.eye(3), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) # Turn left + posetemp[:3,3:4] = np.array([0,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(-3*ang/180*np.pi), 0, np.sin(-3*ang/180*np.pi)], [0, 1, 0], [-np.sin(-3*ang/180*np.pi), 0, np.cos(-3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + posetemp[:3,3:4] = np.array([1,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(3*ang/180*np.pi), 0, np.sin(3*ang/180*np.pi)], [0, 1, 0], [-np.sin(3*ang/180*np.pi), 0, np.cos(3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + posetemp[:3,3:4] = np.array([-1,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + # for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + # th, phi = i/180*np.pi, j/180*np.pi + # posetemp = np.zeros((3, 4)) + # posetemp[:3,:3] = np.matmul(np.eye(3), + # np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + # posetemp[:3,3:4] = np.array([0,0,1]).reshape(3,1) # * scale # Transition vector + # render_poses.append(posetemp) + + + rot_cam=viewangle/3 + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) # Turn left + posetemp[:3,3:4] = np.array([0,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(-3*ang/180*np.pi), 0, np.sin(-3*ang/180*np.pi)], [0, 1, 0], [-np.sin(-3*ang/180*np.pi), 0, np.cos(-3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([0,0,1]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(3*ang/180*np.pi), 0, np.sin(3*ang/180*np.pi)], [0, 1, 0], [-np.sin(3*ang/180*np.pi), 0, np.cos(3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([0,0,-1]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + # for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + # th, phi = i/180*np.pi, j/180*np.pi + # posetemp = np.zeros((3, 4)) + # posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + # np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + # posetemp[:3,3:4] = np.array([1,0,0]).reshape(3,1) # * scale # Transition vector + # render_poses.append(posetemp) + + + rot_cam=viewangle*2/3 + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) # Turn left + posetemp[:3,3:4] = np.array([0,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(-3*ang/180*np.pi), 0, np.sin(-3*ang/180*np.pi)], [0, 1, 0], [-np.sin(-3*ang/180*np.pi), 0, np.cos(-3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([-1,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(3*ang/180*np.pi), 0, np.sin(3*ang/180*np.pi)], [0, 1, 0], [-np.sin(3*ang/180*np.pi), 0, np.cos(3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([1,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + # for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + # th, phi = i/180*np.pi, j/180*np.pi + # posetemp = np.zeros((3, 4)) + # posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + # np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + # posetemp[:3,3:4] = np.array([0,0,-1]).reshape(3,1) # * scale # Transition vector + # render_poses.append(posetemp) + + rot_cam=viewangle + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) # Turn left + posetemp[:3,3:4] = np.array([0,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(-3*ang/180*np.pi), 0, np.sin(-3*ang/180*np.pi)], [0, 1, 0], [-np.sin(-3*ang/180*np.pi), 0, np.cos(-3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([0,0,-1]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(3*ang/180*np.pi), 0, np.sin(3*ang/180*np.pi)], [0, 1, 0], [-np.sin(3*ang/180*np.pi), 0, np.cos(3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([0,0,1]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + # for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + # th, phi = i/180*np.pi, j/180*np.pi + # posetemp = np.zeros((3, 4)) + # posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + # np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + # posetemp[:3,3:4] = np.array([-1,0,0]).reshape(3,1) # * scale # Transition vector + # render_poses.append(posetemp) + + render_poses.append(np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0]])) + render_poses = np.stack(render_poses, axis=0) + + return render_poses + + +def generate_seed_360(viewangle, n_views): + N = n_views + render_poses = np.zeros((N, 3, 4)) + for i in range(N): + th = (viewangle/N)*i/180*np.pi + render_poses[i,:3,:3] = np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]) + render_poses[i,:3,3:4] = np.random.randn(3,1)*0.0 # Transition vector + + return render_poses + + +def generate_seed_360_half(viewangle, n_views): + N = n_views // 2 + halfangle = viewangle / 2 + render_poses = np.zeros((N*2, 3, 4)) + for i in range(N): + th = (halfangle/N)*i/180*np.pi + render_poses[i,:3,:3] = np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]) + render_poses[i,:3,3:4] = np.random.randn(3,1)*0.0 # Transition vector + for i in range(N): + th = -(halfangle/N)*i/180*np.pi + render_poses[i+N,:3,:3] = np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]) + render_poses[i+N,:3,3:4] = np.random.randn(3,1)*0.0 # Transition vector + return render_poses + + +def generate_seed_preset(): + degsum = 60 + thlist = np.concatenate((np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:])) + philist = np.concatenate((np.linspace(0,0,7), np.linspace(-22.5,-22.5,7), np.linspace(22.5,22.5,7))) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_newpreset(): + degsum = 60 + thlist = np.concatenate((np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:])) + philist = np.concatenate((np.linspace(0,0,7), np.linspace(22.5,22.5,7))) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_horizon(): + movement = np.linspace(0, 5, 11) + render_poses = np.zeros((len(movement), 3, 4)) + for i in range(len(movement)): + + render_poses[i,:3,:3] = np.eye(3) + render_poses[i,:3,3:4] = np.array([[-movement[i]], [0], [0]]) + + return render_poses + + +def generate_seed_backward(): + movement = np.linspace(0, 5, 11) + render_poses = np.zeros((len(movement), 3, 4)) + for i in range(len(movement)): + render_poses[i,:3,:3] = np.eye(3) + render_poses[i,:3,3:4] = np.array([[0], [0], [movement[i]]]) + return render_poses + + +def generate_seed_arc(): + degree = 5 + # thlist = np.array([degree, 0, 0, 0, -degree]) + thlist = np.arange(0, degree, 5) + np.arange(0, -degree, 5)[1:] + phi = 0 + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + d = 4.3 # ์–˜๋ฅผ ์กฐ์ ˆํ•˜๋ฉด ์ดˆ๊ธฐ ์ž์„ธ ๊ธฐ์ค€์œผ๋กœ ์•ž์œผ๋กœ d๋งŒํผ ๋–จ์–ด์ง„ ์ ์„ ๊ธฐ์ค€์œผ๋กœ ๋„๋Š” ์ž์„ธ๊ฐ€ ๋งŒ๋“ค์–ด์ง + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[0,:3,3:4] = np.array([d*np.sin(th/180*np.pi), 0, d-d*np.cos(th/180*np.pi)]).reshape(3,1) + np.array([0, d*np.sin(phi/180*np.pi), d-d*np.cos(phi/180*np.pi)]).reshape(3,1)# Transition vector + # render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_hemisphere(center_depth, degree=5): + degree = 5 + thlist = np.array([degree, 0, 0, 0, -degree]) + philist = np.array([0, -degree, 0, degree, 0]) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + # curr_pose = np.zeros((1, 3, 4)) + d = center_depth # ์–˜๋ฅผ ์กฐ์ ˆํ•˜๋ฉด ์ดˆ๊ธฐ ์ž์„ธ ๊ธฐ์ค€์œผ๋กœ ์•ž์œผ๋กœ d๋งŒํผ ๋–จ์–ด์ง„ ์ ์„ ๊ธฐ์ค€์œผ๋กœ ๋„๋Š” ์ž์„ธ๊ฐ€ ๋งŒ๋“ค์–ด์ง + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[0,:3,3:4] = np.array([d*np.sin(th/180*np.pi), 0, d-d*np.cos(th/180*np.pi)]).reshape(3,1) + np.array([0, d*np.sin(phi/180*np.pi), d-d*np.cos(phi/180*np.pi)]).reshape(3,1)# Transition vector + # render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_hemisphere_(degree, nviews): + # thlist = np.array([degree, 0, 0, 0, -degree]) + # philist = np.array([0, -degree, 0, degree, 0]) + thlist = degree * np.sin(np.linspace(0, 2*np.pi, nviews)) + philist = degree * np.cos(np.linspace(0, 2*np.pi, nviews)) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + # curr_pose = np.zeros((1, 3, 4)) + d = 4.3 # ์–˜๋ฅผ ์กฐ์ ˆํ•˜๋ฉด ์ดˆ๊ธฐ ์ž์„ธ ๊ธฐ์ค€์œผ๋กœ ์•ž์œผ๋กœ d๋งŒํผ ๋–จ์–ด์ง„ ์ ์„ ๊ธฐ์ค€์œผ๋กœ ๋„๋Š” ์ž์„ธ๊ฐ€ ๋งŒ๋“ค์–ด์ง + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[0,:3,3:4] = np.array([d*np.sin(th/180*np.pi), 0, d-d*np.cos(th/180*np.pi)]).reshape(3,1) + np.array([0, d*np.sin(phi/180*np.pi), d-d*np.cos(phi/180*np.pi)]).reshape(3,1)# Transition vector + return render_poses + + +def generate_seed_nothing(): + degree = 5 + thlist = np.array([0]) + philist = np.array([0]) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + # curr_pose = np.zeros((1, 3, 4)) + d = 4.3 # ์–˜๋ฅผ ์กฐ์ ˆํ•˜๋ฉด ์ดˆ๊ธฐ ์ž์„ธ ๊ธฐ์ค€์œผ๋กœ ์•ž์œผ๋กœ d๋งŒํผ ๋–จ์–ด์ง„ ์ ์„ ๊ธฐ์ค€์œผ๋กœ ๋„๋Š” ์ž์„ธ๊ฐ€ ๋งŒ๋“ค์–ด์ง + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[0,:3,3:4] = np.array([d*np.sin(th/180*np.pi), 0, d-d*np.cos(th/180*np.pi)]).reshape(3,1) + np.array([0, d*np.sin(phi/180*np.pi), d-d*np.cos(phi/180*np.pi)]).reshape(3,1)# Transition vector + # render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_lookaround(): + degsum = 60 + thlist = np.concatenate((np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:])) + philist = np.concatenate((np.linspace(0,0,7), np.linspace(22.5,22.5,7), np.linspace(-22.5,-22.5,7))) + assert len(thlist) == len(philist) + + render_poses = [] + # up / left --> right + thlist = np.linspace(-degsum, degsum, 2*degsum+1) + for i in range(len(thlist)): + render_pose = np.zeros((3,4)) + th = thlist[i] + phi = 22.5 + + render_pose[:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_pose[:3,3:4] = np.zeros((3,1)) + render_poses.append(render_pose) + + # right / up --> center + phlist = np.linspace(22.5, 0, 23) + # Exclude first frame (same as last frame before) + phlist = phlist[1:] + for i in range(len(phlist)): + render_pose = np.zeros((3,4)) + th = degsum + phi = phlist[i] + + render_pose[:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_pose[:3,3:4] = np.zeros((3,1)) + render_poses.append(render_pose) + + # center / right --> left + thlist = np.linspace(degsum, -degsum, 2*degsum+1) + thlist = thlist[1:] + for i in range(len(thlist)): + render_pose = np.zeros((3,4)) + th = thlist[i] + phi = 0 + + render_pose[:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_pose[:3,3:4] = np.zeros((3,1)) + render_poses.append(render_pose) + + # left / center --> down + phlist = np.linspace(0, -22.5, 23) + phlist = phlist[1:] + for i in range(len(phlist)): + render_pose = np.zeros((3,4)) + th = -degsum + phi = phlist[i] + + render_pose[:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_pose[:3,3:4] = np.zeros((3,1)) + render_poses.append(render_pose) + + + thlist = np.linspace(-degsum, degsum, 2*degsum+1) + for i in range(len(thlist)): + render_pose = np.zeros((3,4)) + th = thlist[i] + phi = -22.5 + + render_pose[:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_pose[:3,3:4] = np.zeros((3,1)) + render_poses.append(render_pose) + + return render_poses + + +def generate_seed_lookdown(): + degsum = 60 + thlist = np.concatenate((np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:])) + philist = np.concatenate((np.linspace(0,0,7), np.linspace(-22.5,-22.5,7))) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_back(): + movement = np.linspace(0, 5, 101) + render_poses = [] # np.zeros((len(movement), 3, 4)) + for i in range(len(movement)): + render_pose = np.zeros((3,4)) + render_pose[:3,:3] = np.eye(3) + render_pose[:3,3:4] = np.array([[0], [0], [movement[i]]]) + render_poses.append(render_pose) + + movement = np.linspace(5, 0, 101) + movement = movement[1:] + for i in range(len(movement)): + render_pose = np.zeros((3,4)) + render_pose[:3,:3] = np.eye(3) + render_pose[:3,3:4] = np.array([[0], [0], [movement[i]]]) + render_poses.append(render_pose) + + return render_poses + + +def generate_seed_llff(degree, nviews, round=4, d=2.3): + assert round%4==0 + # thlist = np.array([degree, 0, 0, 0, -degree]) + # philist = np.array([0, -degree, 0, degree, 0]) + # d = 2.3 + thlist = degree * np.sin(np.linspace(0, 2*np.pi*round, nviews)) + philist = degree * np.cos(np.linspace(0, 2*np.pi*round, nviews)) + zlist = d/15 * np.sin(np.linspace(0, 2*np.pi*round//4, nviews)) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + z = zlist[i] + # curr_pose = np.zeros((1, 3, 4)) + # d = 4.3 # ์–˜๋ฅผ ์กฐ์ ˆํ•˜๋ฉด ์ดˆ๊ธฐ ์ž์„ธ ๊ธฐ์ค€์œผ๋กœ ์•ž์œผ๋กœ d๋งŒํผ ๋–จ์–ด์ง„ ์ ์„ ๊ธฐ์ค€์œผ๋กœ ๋„๋Š” ์ž์„ธ๊ฐ€ ๋งŒ๋“ค์–ด์ง + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.array([d*np.sin(th/180*np.pi), 0, -z+d-d*np.cos(th/180*np.pi)]).reshape(3,1) + np.array([0, d*np.sin(phi/180*np.pi), -z+d-d*np.cos(phi/180*np.pi)]).reshape(3,1)# Transition vector + return render_poses + + +def generate_seed_headbanging(maxdeg, nviews_per_round, round=3, fullround=1): + radius = np.concatenate((np.linspace(0, maxdeg, nviews_per_round*round), maxdeg*np.ones(nviews_per_round*fullround), np.linspace(maxdeg, 0, nviews_per_round*round))) + thlist = 2.66*radius * np.sin(np.linspace(0, 2*np.pi*(round+fullround+round), nviews_per_round*(round+fullround+round))) + philist = radius * np.cos(np.linspace(0, 2*np.pi*(round+fullround+round), nviews_per_round*(round+fullround+round))) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_headbanging_circle(maxdeg, nviews_per_round, round=3, fullround=1): + radius = np.concatenate((np.linspace(0, maxdeg, nviews_per_round*round), maxdeg*np.ones(nviews_per_round*fullround), np.linspace(maxdeg, 0, nviews_per_round*round))) + thlist = 2.66*radius * np.sin(np.linspace(0, 2*np.pi*(round+fullround+round), nviews_per_round*(round+fullround+round))) + philist = radius * np.cos(np.linspace(0, 2*np.pi*(round+fullround+round), nviews_per_round*(round+fullround+round))) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def get_pcdGenPoses(pcdgenpath, argdict={}): + if pcdgenpath == 'rotate360': + render_poses = generate_seed_360(360, 10) + elif pcdgenpath == 'lookaround': + render_poses = generate_seed_preset() + elif pcdgenpath == 'moveright': + render_poses = generate_seed_horizon() + elif pcdgenpath == 'moveback': + render_poses = generate_seed_backward() + elif pcdgenpath == 'arc': + render_poses = generate_seed_arc() + elif pcdgenpath == 'lookdown': + render_poses = generate_seed_newpreset() + elif pcdgenpath == 'hemisphere': + render_poses = generate_seed_hemisphere(argdict['center_depth']) + else: + raise("Invalid pcdgenpath") + return render_poses + + +def get_camerapaths(): + preset_json = {} + for cam_path in ["back_and_forth", "llff", "headbanging"]: + if cam_path == 'back_and_forth': + render_poses = generate_seed_back() + elif cam_path == 'llff': + render_poses = generate_seed_llff(5, 400, round=4, d=2) + elif cam_path == 'headbanging': + render_poses = generate_seed_headbanging(maxdeg=15, nviews_per_round=180, round=2, fullround=0) + else: + raise("Unknown pass") + + yz_reverse = np.array([[1,0,0], [0,-1,0], [0,0,-1]]) + blender_train_json = {"frames": []} + for render_pose in render_poses: + curr_frame = {} + ### Transform world to pixel + Rw2i = render_pose[:3,:3] + Tw2i = render_pose[:3,3:4] + + # Transfrom cam2 to world + change sign of yz axis + Ri2w = np.matmul(yz_reverse, Rw2i).T + Ti2w = -np.matmul(Ri2w, np.matmul(yz_reverse, Tw2i)) + Pc2w = np.concatenate((Ri2w, Ti2w), axis=1) + Pc2w = np.concatenate((Pc2w, np.array([0,0,0,1]).reshape((1,4))), axis=0) + + curr_frame["transform_matrix"] = Pc2w.tolist() + blender_train_json["frames"].append(curr_frame) + + preset_json[cam_path] = blender_train_json + + return preset_json + + +def main(): + cam_path = 'headbanging_circle' + os.makedirs("poses_supplementary", exist_ok=True) + + if cam_path == 'lookaround': + render_poses = generate_seed_lookaround() + elif cam_path == 'back': + render_poses = generate_seed_back() + elif cam_path == '360': + render_poses = generate_seed_360(360, 360) + elif cam_path == '1440': + render_poses = generate_seed_360(360, 1440) + elif cam_path == 'llff': + d = 8 + render_poses = generate_seed_llff(5, 400, round=4, d=d) + elif cam_path == 'headbanging': + round=3 + render_poses = generate_seed_headbanging_(maxdeg=15, nviews_per_round=180, round=round, fullround=0) + elif cam_path == 'headbanging_circle': + round=2 + render_poses = generate_seed_headbanging_circle(maxdeg=5, nviews_per_round=180, round=round, fullround=0) + + + yz_reverse = np.array([[1,0,0], [0,-1,0], [0,0,-1]]) + + c2w_poses = [] + for render_pose in render_poses: + ### Transform world to pixel + Rw2i = render_pose[:3,:3] + Tw2i = render_pose[:3,3:4] + + # Transfrom cam2 to world + change sign of yz axis + Ri2w = np.matmul(yz_reverse, Rw2i).T + Ti2w = -np.matmul(Ri2w, np.matmul(yz_reverse, Tw2i)) + Pc2w = np.concatenate((Ri2w, Ti2w), axis=1) + # Pc2w = np.concatenate((Pc2w, np.array([[0,0,0,1]])), axis=0) + + c2w_poses.append(Pc2w) + + c2w_poses = np.stack(c2w_poses, axis=0) + + # np.save(f'poses_supplementary/{cam_path}.npy', c2w_poses) + + FX = 5.8269e+02 + W = 512 + fov_x = 2*np.arctan(W / (2*FX)) + if cam_path in ['360', '1440', 'llff', 'headbanging']: + fov_x = fov_x * 1.2 + blender_train_json = {} + blender_train_json["camera_angle_x"] = fov_x + blender_train_json["frames"] = [] + + for render_pose in render_poses: + curr_frame = {} + ### Transform world to pixel + Rw2i = render_pose[:3,:3] + Tw2i = render_pose[:3,3:4] + + # Transfrom cam2 to world + change sign of yz axis + Ri2w = np.matmul(yz_reverse, Rw2i).T + Ti2w = -np.matmul(Ri2w, np.matmul(yz_reverse, Tw2i)) + Pc2w = np.concatenate((Ri2w, Ti2w), axis=1) + + curr_frame["transform_matrix"] = Pc2w.tolist() + (blender_train_json["frames"]).append(curr_frame) + + import json + if cam_path=='llff': + train_json_path = f"poses_supplementary/{cam_path}_d{d}.json" + elif cam_path=='headbanging': + train_json_path = f"poses_supplementary/{cam_path}_r{round}.json" + else: + train_json_path = f"poses_supplementary/{cam_path}.json" + + with open(train_json_path, 'w') as outfile: + json.dump(blender_train_json, outfile, indent=4) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/utils/camera.py b/utils/camera.py new file mode 100644 index 0000000000000000000000000000000000000000..9dc34a7047dbb37698e8df4800c0766e43c100cf --- /dev/null +++ b/utils/camera.py @@ -0,0 +1,120 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import json + +import numpy as np +import torch + +from scene.cameras import Camera, MiniCam +from utils.general import PILtoTorch +from utils.graphics import fov2focal, focal2fov, getWorld2View, getProjectionMatrix + + +WARNED = False + + +def load_json(path, H, W): + cams = [] + with open(path) as json_file: + contents = json.load(json_file) + FoVx = contents["camera_angle_x"] + FoVy = focal2fov(fov2focal(FoVx, W), H) + zfar = 100.0 + znear = 0.01 + + frames = contents["frames"] + for idx, frame in enumerate(frames): + # NeRF 'transform_matrix' is a camera-to-world transform + c2w = np.array(frame["transform_matrix"]) + # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) + c2w[:3, 1:3] *= -1 + if c2w.shape[0] == 3: + one = np.zeros((1, 4)) + one[0, -1] = 1 + c2w = np.concatenate((c2w, one), axis=0) + + # get the world-to-camera transform and set R, T + w2c = np.linalg.inv(c2w) + R = np.transpose(w2c[:3, :3]) # R is stored transposed due to 'glm' in CUDA code + T = w2c[:3, 3] + + w2c = torch.as_tensor(getWorld2View(R, T)).T.cuda() + proj = getProjectionMatrix(znear, zfar, FoVx, FoVy).T.cuda() + cams.append(MiniCam(W, H, FoVx, FoVy, znear, zfar, w2c, w2c @ proj)) + return cams + + +def loadCam(args, id, cam_info, resolution_scale): + orig_w, orig_h = cam_info.image.size + + if args.resolution in [1, 2, 4, 8]: + resolution = round(orig_w/(resolution_scale * args.resolution)), round(orig_h/(resolution_scale * args.resolution)) + else: # should be a type that converts to float + if args.resolution == -1: + if orig_w > 1600: + global WARNED + if not WARNED: + print("[ INFO ] Encountered quite large input images (>1.6K pixels width), rescaling to 1.6K.\n " + "If this is not desired, please explicitly specify '--resolution/-r' as 1") + WARNED = True + global_down = orig_w / 1600 + else: + global_down = 1 + else: + global_down = orig_w / args.resolution + + scale = float(global_down) * float(resolution_scale) + resolution = (int(orig_w / scale), int(orig_h / scale)) + + resized_image_rgb = PILtoTorch(cam_info.image, resolution) + + gt_image = resized_image_rgb[:3, ...] + loaded_mask = None + + if resized_image_rgb.shape[1] == 4: + loaded_mask = resized_image_rgb[3:4, ...] + + return Camera(colmap_id=cam_info.uid, R=cam_info.R, T=cam_info.T, + FoVx=cam_info.FovX, FoVy=cam_info.FovY, + image=gt_image, gt_alpha_mask=loaded_mask, + image_name=cam_info.image_name, uid=id, data_device=args.data_device) + + +def cameraList_from_camInfos(cam_infos, resolution_scale, args): + camera_list = [] + + for id, c in enumerate(cam_infos): + camera_list.append(loadCam(args, id, c, resolution_scale)) + + return camera_list + + +def camera_to_JSON(id, camera : Camera): + Rt = np.zeros((4, 4)) + Rt[:3, :3] = camera.R.transpose() + Rt[:3, 3] = camera.T + Rt[3, 3] = 1.0 + + W2C = np.linalg.inv(Rt) + pos = W2C[:3, 3] + rot = W2C[:3, :3] + serializable_array_2d = [x.tolist() for x in rot] + camera_entry = { + 'id' : id, + 'img_name' : camera.image_name, + 'width' : camera.width, + 'height' : camera.height, + 'position': pos.tolist(), + 'rotation': serializable_array_2d, + 'fy' : fov2focal(camera.FovY, camera.height), + 'fx' : fov2focal(camera.FovX, camera.width) + } + return camera_entry diff --git a/utils/depth.py b/utils/depth.py new file mode 100644 index 0000000000000000000000000000000000000000..242fa05ce5dac721be5ac10209e638369477f46c --- /dev/null +++ b/utils/depth.py @@ -0,0 +1,62 @@ +import matplotlib +import matplotlib.cm +import numpy as np +import torch + + +def colorize(value, vmin=None, vmax=None, cmap='jet', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None): + """Converts a depth map to a color image. + + Args: + value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed + vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None. + vmax (float, optional): vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None. + cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'. + invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99. + invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None. + background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255). + gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False. + value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None. + + Returns: + numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4) + """ + if isinstance(value, torch.Tensor): + value = value.detach().cpu().numpy() + + value = value.squeeze() + if invalid_mask is None: + invalid_mask = value == invalid_val + mask = np.logical_not(invalid_mask) + + # normalize + vmin = np.percentile(value[mask],2) if vmin is None else vmin + vmax = np.percentile(value[mask],98) if vmax is None else vmax + if vmin != vmax: + value = (value - vmin) / (vmax - vmin) # vmin..vmax + else: + # Avoid 0-division + value = value * 0. + + # squeeze last dim if it exists + # grey out the invalid values + + value[invalid_mask] = np.nan + cmapper = matplotlib.cm.get_cmap(cmap) + if value_transform: + value = value_transform(value) + # value = value / value.max() + value = cmapper(value, bytes=True) # (nxmx4) + + # img = value[:, :, :] + img = value[...] + img[invalid_mask] = background_color + + # return img.transpose((2, 0, 1)) + if gamma_corrected: + # gamma correction + img = img / 255 + img = np.power(img, 2.2) + img = img * 255 + img = img.astype(np.uint8) + return img \ No newline at end of file diff --git a/utils/general.py b/utils/general.py new file mode 100644 index 0000000000000000000000000000000000000000..e14edf106c655a717b6a4227297503277746c3cf --- /dev/null +++ b/utils/general.py @@ -0,0 +1,140 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import sys +import random +from datetime import datetime +import numpy as np +import torch + + +def inverse_sigmoid(x): + return torch.log(x/(1-x)) + + +def PILtoTorch(pil_image, resolution): + resized_image_PIL = pil_image.resize(resolution) + resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0 + if len(resized_image.shape) == 3: + return resized_image.permute(2, 0, 1) + else: + return resized_image.unsqueeze(dim=-1).permute(2, 0, 1) + + +def get_expon_lr_func( + lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000 +): + """ + Copied from Plenoxels + + Continuous learning rate decay function. Adapted from JaxNeRF + The returned rate is lr_init when step=0 and lr_final when step=max_steps, and + is log-linearly interpolated elsewhere (equivalent to exponential decay). + If lr_delay_steps>0 then the learning rate will be scaled by some smooth + function of lr_delay_mult, such that the initial learning rate is + lr_init*lr_delay_mult at the beginning of optimization but will be eased back + to the normal learning rate when steps>lr_delay_steps. + :param conf: config subtree 'lr' or similar + :param max_steps: int, the number of steps during optimization. + :return HoF which takes step as input + """ + + def helper(step): + if step < 0 or (lr_init == 0.0 and lr_final == 0.0): + # Disable this parameter + return 0.0 + if lr_delay_steps > 0: + # A kind of reverse cosine decay. + delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin( + 0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1) + ) + else: + delay_rate = 1.0 + t = np.clip(step / max_steps, 0, 1) + log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return delay_rate * log_lerp + + return helper + + +def strip_lowerdiag(L): + uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda") + + uncertainty[:, 0] = L[:, 0, 0] + uncertainty[:, 1] = L[:, 0, 1] + uncertainty[:, 2] = L[:, 0, 2] + uncertainty[:, 3] = L[:, 1, 1] + uncertainty[:, 4] = L[:, 1, 2] + uncertainty[:, 5] = L[:, 2, 2] + return uncertainty + + +def strip_symmetric(sym): + return strip_lowerdiag(sym) + + +def build_rotation(r): + norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3]) + + q = r / norm[:, None] + + R = torch.zeros((q.size(0), 3, 3), device='cuda') + + r = q[:, 0] + x = q[:, 1] + y = q[:, 2] + z = q[:, 3] + + R[:, 0, 0] = 1 - 2 * (y*y + z*z) + R[:, 0, 1] = 2 * (x*y - r*z) + R[:, 0, 2] = 2 * (x*z + r*y) + R[:, 1, 0] = 2 * (x*y + r*z) + R[:, 1, 1] = 1 - 2 * (x*x + z*z) + R[:, 1, 2] = 2 * (y*z - r*x) + R[:, 2, 0] = 2 * (x*z - r*y) + R[:, 2, 1] = 2 * (y*z + r*x) + R[:, 2, 2] = 1 - 2 * (x*x + y*y) + return R + + +def build_scaling_rotation(s, r): + L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda") + R = build_rotation(r) + + L[:,0,0] = s[:,0] + L[:,1,1] = s[:,1] + L[:,2,2] = s[:,2] + + L = R @ L + return L + + +def safe_state(silent): + old_f = sys.stdout + class F: + def __init__(self, silent): + self.silent = silent + + def write(self, x): + if not self.silent: + if x.endswith("\n"): + old_f.write(x.replace("\n", " [{}]\n".format(str(datetime.now().strftime("%d/%m %H:%M:%S"))))) + else: + old_f.write(x) + + def flush(self): + old_f.flush() + + sys.stdout = F(silent) + + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + torch.cuda.set_device(torch.device("cuda:0")) diff --git a/utils/graphics.py b/utils/graphics.py new file mode 100644 index 0000000000000000000000000000000000000000..a40ac192cd7c50242b9c86fedd2d5cce11030996 --- /dev/null +++ b/utils/graphics.py @@ -0,0 +1,83 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import math +from typing import NamedTuple +import numpy as np +import torch + + +class BasicPointCloud(NamedTuple): + points : np.array + colors : np.array + normals : np.array + + +def geom_transform_points(points, transf_matrix): + P, _ = points.shape + ones = torch.ones(P, 1, dtype=points.dtype, device=points.device) + points_hom = torch.cat([points, ones], dim=1) + points_out = torch.matmul(points_hom, transf_matrix.unsqueeze(0)) + + denom = points_out[..., 3:] + 0.0000001 + return (points_out[..., :3] / denom).squeeze(dim=0) + + +def getWorld2View(R, t): + Rt = np.zeros((4, 4)) + Rt[:3, :3] = R.transpose() + Rt[:3, 3] = t + Rt[3, 3] = 1.0 + return np.float32(Rt) + + +def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0): + Rt = np.zeros((4, 4)) + Rt[:3, :3] = R.transpose() + Rt[:3, 3] = t + Rt[3, 3] = 1.0 + + C2W = np.linalg.inv(Rt) + cam_center = C2W[:3, 3] + cam_center = (cam_center + translate) * scale + C2W[:3, 3] = cam_center + Rt = np.linalg.inv(C2W) + return np.float32(Rt) + + +def getProjectionMatrix(znear, zfar, fovX, fovY): + tanHalfFovY = math.tan((fovY / 2)) + tanHalfFovX = math.tan((fovX / 2)) + + top = tanHalfFovY * znear + bottom = -top + right = tanHalfFovX * znear + left = -right + + P = torch.zeros(4, 4) + + z_sign = 1.0 + + P[0, 0] = 2.0 * znear / (right - left) + P[1, 1] = 2.0 * znear / (top - bottom) + P[0, 2] = (right + left) / (right - left) + P[1, 2] = (top + bottom) / (top - bottom) + P[3, 2] = z_sign + P[2, 2] = z_sign * zfar / (zfar - znear) + P[2, 3] = -(zfar * znear) / (zfar - znear) + return P + + +def fov2focal(fov, pixels): + return pixels / (2 * math.tan(fov / 2)) + + +def focal2fov(focal, pixels): + return 2*math.atan(pixels/(2*focal)) \ No newline at end of file diff --git a/utils/image.py b/utils/image.py new file mode 100644 index 0000000000000000000000000000000000000000..0b105203c4cd57355534ab81f0915b604fc68aee --- /dev/null +++ b/utils/image.py @@ -0,0 +1,20 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +import torch + + +def mse(img1, img2): + return (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True) + + +def psnr(img1, img2): + mse = (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True) + return 20 * torch.log10(1.0 / torch.sqrt(mse)) diff --git a/utils/lama.py b/utils/lama.py new file mode 100644 index 0000000000000000000000000000000000000000..d0486758d61251b2d90fcc5c12d6c10e29f64c99 --- /dev/null +++ b/utils/lama.py @@ -0,0 +1,188 @@ +import os +import sys +import hashlib +import logging +from typing import Union +from urllib.parse import urlparse + +import numpy as np +import torch +from torch.hub import download_url_to_file, get_dir + + +LAMA_MODEL_URL = os.environ.get( + "LAMA_MODEL_URL", + "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",) +LAMA_MODEL_MD5 = os.environ.get( + "LAMA_MODEL_MD5", + "e3aa4aaa15225a33ec84f9f4bc47e500") + + +def md5sum(filename: str) -> str: + md5 = hashlib.md5() + with open(filename, "rb") as f: + for chunk in iter(lambda: f.read(128 * md5.block_size), b""): + md5.update(chunk) + return md5.hexdigest() + + +def handle_error(model_path: str, model_md5: str, e: str) -> None: + _md5 = md5sum(model_path) + if _md5 != model_md5: + try: + os.remove(model_path) + logging.error( + f"Model md5: {_md5}, expected md5: {model_md5}, wrong model " + f"deleted. Please restart lama-cleaner. If you still have " + f"errors, please try download model manually first https://" + f"lama-cleaner-docs.vercel.app/install/download_model_" + f"manually.\n") + except: + logging.error( + f"Model md5: {_md5}, expected md5: {model_md5}, please delete" + f" {model_path} and restart lama-cleaner.") + else: + logging.error( + f"Failed to load model {model_path}, please submit an issue at " + f"https://github.com/ironjr/simple-lama/issues and include a " + f"screenshot of the error:\n{e}") + exit(-1) + + +def get_cache_path_by_url(url: str) -> str: + parts = urlparse(url) + hub_dir = get_dir() + model_dir = os.path.join(hub_dir, "checkpoints") + if not os.path.isdir(model_dir): + os.makedirs(model_dir) + filename = os.path.basename(parts.path) + cached_file = os.path.join(model_dir, filename) + return cached_file + + +def download_model(url: str, model_md5: str = None) -> str: + cached_file = get_cache_path_by_url(url) + if not os.path.exists(cached_file): + sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file)) + hash_prefix = None + download_url_to_file(url, cached_file, hash_prefix, progress=True) + if model_md5: + _md5 = md5sum(cached_file) + if model_md5 == _md5: + logging.info(f"Download model success, md5: {_md5}") + else: + try: + os.remove(cached_file) + logging.error( + f"Model md5: {_md5}, expected md5: {model_md5}, wrong" + f" model deleted. Please restart lama-cleaner. If you" + f" still have errors, please try download model " + f"manually first https://lama-cleaner-docs.vercel" + f".app/install/download_model_manually.\n") + except: + logging.error( + f"Model md5: {_md5}, expected md5: {model_md5}, " + f"please delete {cached_file} and restart lama-" + f"cleaner.") + exit(-1) + return cached_file + + +def load_jit_model( + url_or_path: str, + device: Union[torch.device, str], + model_md5: str, +) -> torch.jit._script.RecursiveScriptModule: + if os.path.exists(url_or_path): + model_path = url_or_path + else: + model_path = download_model(url_or_path, model_md5) + + logging.info(f"Loading model from: {model_path}") + try: + model = torch.jit.load(model_path, map_location="cpu").to(device) + except Exception as e: + handle_error(model_path, model_md5, e) + model.eval() + return model + + +def norm_img(np_img: np.ndarray) -> np.ndarray: + if len(np_img.shape) == 2: + np_img = np_img[:, :, np.newaxis] + np_img = np.transpose(np_img, (2, 0, 1)) + np_img = np_img.astype("float32") / 255 + return np_img + + +def ceil_modulo(x: int, mod: int) -> int: + if x % mod == 0: + return x + return (x // mod + 1) * mod + + +def pad_img_to_modulo(img: np.ndarray, mod: int) -> np.ndarray: + if len(img.shape) == 2: + img = img[:, :, np.newaxis] + height, width = img.shape[:2] + out_height = ceil_modulo(height, mod) + out_width = ceil_modulo(width, mod) + return np.pad( + img, + ((0, out_height - height), (0, out_width - width), (0, 0)), + mode="symmetric", + ) + + +class LaMa: + name = "lama" + pad_mod = 8 + + def __init__(self, device: Union[torch.device, str], **kwargs) -> None: + self.device = device + self.model = load_jit_model( + LAMA_MODEL_URL, device, LAMA_MODEL_MD5).eval() + + @staticmethod + def is_downloaded() -> bool: + return os.path.exists(get_cache_path_by_url(LAMA_MODEL_URL)) + + def forward(self, image: np.ndarray, mask: np.ndarray) -> np.ndarray: + """Input image and output image have same size + image: [H, W, C] RGB + mask: [H, W] + return: RGB IMAGE + """ + dtype = image.dtype + image = norm_img(image) + mask = norm_img(mask if np.max(mask) > 1.0 else mask * 2) + + mask = (mask > 0) * 1 + image = torch.from_numpy(image).unsqueeze(0).to(self.device) + mask = torch.from_numpy(mask).unsqueeze(0).to(self.device) + + inpainted_image = self.model(image, mask) + + cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy() + cur_res = np.clip(cur_res * 255, 0, 255) + return cur_res.astype(dtype) + + @torch.no_grad() + def __call__(self, image: np.ndarray, mask: np.ndarray) -> np.ndarray: + """ + images: [H, W, C] RGB, not normalized + masks: [H, W] + return: RGB IMAGE + """ + dtype = image.dtype + origin_height, origin_width = image.shape[:2] + pad_image = pad_img_to_modulo(image, mod=self.pad_mod) + pad_mask = pad_img_to_modulo(mask, mod=self.pad_mod) + + result = self.forward(pad_image, pad_mask) + result = result[0:origin_height, 0:origin_width, :] + + mask = mask[:, :, np.newaxis] + mask = mask / 255 if np.max(mask) > 1.0 else mask + result = result * mask + image * (1 - mask) + return result.astype(dtype) diff --git a/utils/loss.py b/utils/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..e1a5e9f08c0c10fc6ec12e63445cd3f9dd3eb8af --- /dev/null +++ b/utils/loss.py @@ -0,0 +1,99 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +from math import exp + +import torch +import torch.nn.functional as F +from torch.autograd import Variable + + +def l1_loss(network_output, gt): + return torch.abs((network_output - gt)).mean() + + +def l2_loss(network_output, gt): + return ((network_output - gt) ** 2).mean() + + +def gaussian(window_size, sigma): + gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) + return gauss / gauss.sum() + + +def create_window(window_size, channel): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) + window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) + return window + + +def ssim(img1, img2, window_size=11, size_average=True): + channel = img1.size(-3) + window = create_window(window_size, channel) + + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + + return _ssim(img1, img2, window, window_size, channel, size_average) + + +def _ssim(img1, img2, window, window_size, channel, size_average=True): + mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) + mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq + sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq + sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 + + C1 = 0.01 ** 2 + C2 = 0.03 ** 2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) + + if size_average: + return ssim_map.mean() + else: + return ssim_map.mean(1).mean(1).mean(1) + + +import numpy as np +import cv2 +def image2canny(image, thres1, thres2, isEdge1=True): + """ image: (H, W, 3)""" + canny_mask = torch.from_numpy(cv2.Canny((image.detach().cpu().numpy()*255.).astype(np.uint8), thres1, thres2)/255.) + if not isEdge1: + canny_mask = 1. - canny_mask + return canny_mask.float() + +with torch.no_grad(): + kernelsize=3 + conv = torch.nn.Conv2d(1, 1, kernel_size=kernelsize, padding=(kernelsize//2)) + kernel = torch.tensor([[0.,1.,0.],[1.,0.,1.],[0.,1.,0.]]).reshape(1,1,kernelsize,kernelsize) + conv.weight.data = kernel #torch.ones((1,1,kernelsize,kernelsize)) + conv.bias.data = torch.tensor([0.]) + conv.requires_grad_(False) + conv = conv.cuda() + + +def nearMean_map(array, mask, kernelsize=3): + """ array: (H,W) / mask: (H,W) """ + cnt_map = torch.ones_like(array) + + nearMean_map = conv((array * mask)[None,None]) + cnt_map = conv((cnt_map * mask)[None,None]) + nearMean_map = (nearMean_map / (cnt_map+1e-8)).squeeze() + + return nearMean_map \ No newline at end of file diff --git a/utils/sh.py b/utils/sh.py new file mode 100644 index 0000000000000000000000000000000000000000..e00d11a6c63cfb5a119348db1b16a7fb81edb7cb --- /dev/null +++ b/utils/sh.py @@ -0,0 +1,120 @@ +# Copyright 2021 The PlenOctree Authors. +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. +import torch + + +C0 = 0.28209479177387814 +C1 = 0.4886025119029199 +C2 = [ + 1.0925484305920792, + -1.0925484305920792, + 0.31539156525252005, + -1.0925484305920792, + 0.5462742152960396 +] +C3 = [ + -0.5900435899266435, + 2.890611442640554, + -0.4570457994644658, + 0.3731763325901154, + -0.4570457994644658, + 1.445305721320277, + -0.5900435899266435 +] +C4 = [ + 2.5033429417967046, + -1.7701307697799304, + 0.9461746957575601, + -0.6690465435572892, + 0.10578554691520431, + -0.6690465435572892, + 0.47308734787878004, + -1.7701307697799304, + 0.6258357354491761, +] + + +def eval_sh(deg, sh, dirs): + """ + Evaluate spherical harmonics at unit directions + using hardcoded SH polynomials. + Works with torch/np/jnp. + ... Can be 0 or more batch dimensions. + Args: + deg: int SH deg. Currently, 0-3 supported + sh: jnp.ndarray SH coeffs [..., C, (deg + 1) ** 2] + dirs: jnp.ndarray unit directions [..., 3] + Returns: + [..., C] + """ + assert deg <= 4 and deg >= 0 + coeff = (deg + 1) ** 2 + assert sh.shape[-1] >= coeff + + result = C0 * sh[..., 0] + if deg > 0: + x, y, z = dirs[..., 0:1], dirs[..., 1:2], dirs[..., 2:3] + result = (result - + C1 * y * sh[..., 1] + + C1 * z * sh[..., 2] - + C1 * x * sh[..., 3]) + + if deg > 1: + xx, yy, zz = x * x, y * y, z * z + xy, yz, xz = x * y, y * z, x * z + result = (result + + C2[0] * xy * sh[..., 4] + + C2[1] * yz * sh[..., 5] + + C2[2] * (2.0 * zz - xx - yy) * sh[..., 6] + + C2[3] * xz * sh[..., 7] + + C2[4] * (xx - yy) * sh[..., 8]) + + if deg > 2: + result = (result + + C3[0] * y * (3 * xx - yy) * sh[..., 9] + + C3[1] * xy * z * sh[..., 10] + + C3[2] * y * (4 * zz - xx - yy)* sh[..., 11] + + C3[3] * z * (2 * zz - 3 * xx - 3 * yy) * sh[..., 12] + + C3[4] * x * (4 * zz - xx - yy) * sh[..., 13] + + C3[5] * z * (xx - yy) * sh[..., 14] + + C3[6] * x * (xx - 3 * yy) * sh[..., 15]) + + if deg > 3: + result = (result + C4[0] * xy * (xx - yy) * sh[..., 16] + + C4[1] * yz * (3 * xx - yy) * sh[..., 17] + + C4[2] * xy * (7 * zz - 1) * sh[..., 18] + + C4[3] * yz * (7 * zz - 3) * sh[..., 19] + + C4[4] * (zz * (35 * zz - 30) + 3) * sh[..., 20] + + C4[5] * xz * (7 * zz - 3) * sh[..., 21] + + C4[6] * (xx - yy) * (7 * zz - 1) * sh[..., 22] + + C4[7] * xz * (xx - 3 * yy) * sh[..., 23] + + C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) * sh[..., 24]) + return result + + +def RGB2SH(rgb): + return (rgb - 0.5) / C0 + + +def SH2RGB(sh): + return sh * C0 + 0.5 \ No newline at end of file diff --git a/utils/system.py b/utils/system.py new file mode 100644 index 0000000000000000000000000000000000000000..d62c148cbe48e5eabb6666aa581bd4f5f7c8cd93 --- /dev/null +++ b/utils/system.py @@ -0,0 +1,29 @@ +# +# Copyright (C) 2023, Inria +# GRAPHDECO research group, https://team.inria.fr/graphdeco +# All rights reserved. +# +# This software is free for non-commercial, research and evaluation use +# under the terms of the LICENSE.md file. +# +# For inquiries contact george.drettakis@inria.fr +# +from errno import EEXIST +from os import makedirs, path +import os + + +def mkdir_p(folder_path): + # Creates a directory. equivalent to using mkdir -p on the command line + try: + makedirs(folder_path) + except OSError as exc: # Python >2.5 + if exc.errno == EEXIST and path.isdir(folder_path): + pass + else: + raise + + +def searchForMaxIteration(folder): + saved_iters = [int(fname.split("_")[-1]) for fname in os.listdir(folder)] + return max(saved_iters) diff --git a/utils/trajectory.py b/utils/trajectory.py new file mode 100644 index 0000000000000000000000000000000000000000..79d30015c2a0e1499ea11ae13ad5b42e5246d031 --- /dev/null +++ b/utils/trajectory.py @@ -0,0 +1,621 @@ +# Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr +# +# Copyright 2023 LucidDreamer Authors +# +# Computer Vision Lab, SNU, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from the Computer Vision Lab, SNU or +# its affiliates is strictly prohibited. +# +# For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com. +import os +import numpy as np +import torch + + +def generate_seed(scale, viewangle): + # World 2 Camera + #### rotate x,y + render_poses = [np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0]])] + ang = 5 + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.eye(3), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) # Turn left + posetemp[:3,3:4] = np.array([0,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(-3*ang/180*np.pi), 0, np.sin(-3*ang/180*np.pi)], [0, 1, 0], [-np.sin(-3*ang/180*np.pi), 0, np.cos(-3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + posetemp[:3,3:4] = np.array([1,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(3*ang/180*np.pi), 0, np.sin(3*ang/180*np.pi)], [0, 1, 0], [-np.sin(3*ang/180*np.pi), 0, np.cos(3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + posetemp[:3,3:4] = np.array([-1,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + # for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + # th, phi = i/180*np.pi, j/180*np.pi + # posetemp = np.zeros((3, 4)) + # posetemp[:3,:3] = np.matmul(np.eye(3), + # np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + # posetemp[:3,3:4] = np.array([0,0,1]).reshape(3,1) # * scale # Transition vector + # render_poses.append(posetemp) + + + rot_cam=viewangle/3 + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) # Turn left + posetemp[:3,3:4] = np.array([0,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(-3*ang/180*np.pi), 0, np.sin(-3*ang/180*np.pi)], [0, 1, 0], [-np.sin(-3*ang/180*np.pi), 0, np.cos(-3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([0,0,1]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(3*ang/180*np.pi), 0, np.sin(3*ang/180*np.pi)], [0, 1, 0], [-np.sin(3*ang/180*np.pi), 0, np.cos(3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([0,0,-1]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + # for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + # th, phi = i/180*np.pi, j/180*np.pi + # posetemp = np.zeros((3, 4)) + # posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + # np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + # posetemp[:3,3:4] = np.array([1,0,0]).reshape(3,1) # * scale # Transition vector + # render_poses.append(posetemp) + + + rot_cam=viewangle*2/3 + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) # Turn left + posetemp[:3,3:4] = np.array([0,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(-3*ang/180*np.pi), 0, np.sin(-3*ang/180*np.pi)], [0, 1, 0], [-np.sin(-3*ang/180*np.pi), 0, np.cos(-3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([-1,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(3*ang/180*np.pi), 0, np.sin(3*ang/180*np.pi)], [0, 1, 0], [-np.sin(3*ang/180*np.pi), 0, np.cos(3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([1,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + # for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + # th, phi = i/180*np.pi, j/180*np.pi + # posetemp = np.zeros((3, 4)) + # posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + # np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + # posetemp[:3,3:4] = np.array([0,0,-1]).reshape(3,1) # * scale # Transition vector + # render_poses.append(posetemp) + + rot_cam=viewangle + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) # Turn left + posetemp[:3,3:4] = np.array([0,0,0]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(-3*ang/180*np.pi), 0, np.sin(-3*ang/180*np.pi)], [0, 1, 0], [-np.sin(-3*ang/180*np.pi), 0, np.cos(-3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([0,0,-1]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,ang,ang,ang,ang,0,-ang,-ang,-ang,-ang,-ang,0,0,0,0]): + th, phi = i/180*np.pi, j/180*np.pi + posetemp = np.zeros((3, 4)) + posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + np.matmul(np.array([[np.cos(3*ang/180*np.pi), 0, np.sin(3*ang/180*np.pi)], [0, 1, 0], [-np.sin(3*ang/180*np.pi), 0, np.cos(3*ang/180*np.pi)]]), + np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]])))) + posetemp[:3,3:4] = np.array([0,0,1]).reshape(3,1) # * scale # Transition vector + render_poses.append(posetemp) + + # for i,j in zip([ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,ang,2*ang,3*ang,2*ang,ang,0], [0,0,0,ang,2*ang,3*ang,2*ang,ang,0,-ang,-2*ang,-3*ang,-2*ang,-ang,0,0,0,0]): + # th, phi = i/180*np.pi, j/180*np.pi + # posetemp = np.zeros((3, 4)) + # posetemp[:3,:3] = np.matmul(np.array([[np.cos(rot_cam/180*np.pi), 0, np.sin(rot_cam/180*np.pi)], [0, 1, 0], [-np.sin(rot_cam/180*np.pi), 0, np.cos(rot_cam/180*np.pi)]]), + # np.matmul(np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]), np.array([[1, 0, 0], [0, np.cos(phi), -np.sin(phi)], [0, np.sin(phi), np.cos(phi)]]))) + # posetemp[:3,3:4] = np.array([-1,0,0]).reshape(3,1) # * scale # Transition vector + # render_poses.append(posetemp) + + render_poses.append(np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0]])) + render_poses = np.stack(render_poses, axis=0) + + return render_poses + + +def generate_seed_360(viewangle, n_views): + N = n_views + render_poses = np.zeros((N, 3, 4)) + for i in range(N): + th = (viewangle/N)*i/180*np.pi + render_poses[i,:3,:3] = np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]) + render_poses[i,:3,3:4] = np.random.randn(3,1)*0.0 # Transition vector + + return render_poses + + +def generate_seed_360_half(viewangle, n_views): + N = n_views // 2 + halfangle = viewangle / 2 + render_poses = np.zeros((N*2, 3, 4)) + for i in range(N): + th = (halfangle/N)*i/180*np.pi + render_poses[i,:3,:3] = np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]) + render_poses[i,:3,3:4] = np.random.randn(3,1)*0.0 # Transition vector + for i in range(N): + th = -(halfangle/N)*i/180*np.pi + render_poses[i+N,:3,:3] = np.array([[np.cos(th), 0, np.sin(th)], [0, 1, 0], [-np.sin(th), 0, np.cos(th)]]) + render_poses[i+N,:3,3:4] = np.random.randn(3,1)*0.0 # Transition vector + return render_poses + + +def generate_seed_preset(): + degsum = 60 + thlist = np.concatenate((np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:])) + philist = np.concatenate((np.linspace(0,0,7), np.linspace(-22.5,-22.5,7), np.linspace(22.5,22.5,7))) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_newpreset(): + degsum = 60 + thlist = np.concatenate((np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:])) + philist = np.concatenate((np.linspace(0,0,7), np.linspace(22.5,22.5,7))) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_horizon(): + movement = np.linspace(0, 5, 11) + render_poses = np.zeros((len(movement), 3, 4)) + for i in range(len(movement)): + + render_poses[i,:3,:3] = np.eye(3) + render_poses[i,:3,3:4] = np.array([[-movement[i]], [0], [0]]) + + return render_poses + + +def generate_seed_backward(): + movement = np.linspace(0, 5, 11) + render_poses = np.zeros((len(movement), 3, 4)) + for i in range(len(movement)): + render_poses[i,:3,:3] = np.eye(3) + render_poses[i,:3,3:4] = np.array([[0], [0], [movement[i]]]) + return render_poses + + +def generate_seed_arc(): + degree = 5 + # thlist = np.array([degree, 0, 0, 0, -degree]) + thlist = np.arange(0, degree, 5) + np.arange(0, -degree, 5)[1:] + phi = 0 + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + d = 4.3 # ์–˜๋ฅผ ์กฐ์ ˆํ•˜๋ฉด ์ดˆ๊ธฐ ์ž์„ธ ๊ธฐ์ค€์œผ๋กœ ์•ž์œผ๋กœ d๋งŒํผ ๋–จ์–ด์ง„ ์ ์„ ๊ธฐ์ค€์œผ๋กœ ๋„๋Š” ์ž์„ธ๊ฐ€ ๋งŒ๋“ค์–ด์ง + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[0,:3,3:4] = np.array([d*np.sin(th/180*np.pi), 0, d-d*np.cos(th/180*np.pi)]).reshape(3,1) + np.array([0, d*np.sin(phi/180*np.pi), d-d*np.cos(phi/180*np.pi)]).reshape(3,1)# Transition vector + # render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_hemisphere(center_depth, degree=5): + degree = 5 + thlist = np.array([degree, 0, 0, 0, -degree]) + philist = np.array([0, -degree, 0, degree, 0]) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + # curr_pose = np.zeros((1, 3, 4)) + d = center_depth # ์–˜๋ฅผ ์กฐ์ ˆํ•˜๋ฉด ์ดˆ๊ธฐ ์ž์„ธ ๊ธฐ์ค€์œผ๋กœ ์•ž์œผ๋กœ d๋งŒํผ ๋–จ์–ด์ง„ ์ ์„ ๊ธฐ์ค€์œผ๋กœ ๋„๋Š” ์ž์„ธ๊ฐ€ ๋งŒ๋“ค์–ด์ง + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[0,:3,3:4] = np.array([d*np.sin(th/180*np.pi), 0, d-d*np.cos(th/180*np.pi)]).reshape(3,1) + np.array([0, d*np.sin(phi/180*np.pi), d-d*np.cos(phi/180*np.pi)]).reshape(3,1)# Transition vector + # render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_hemisphere_(degree, nviews): + # thlist = np.array([degree, 0, 0, 0, -degree]) + # philist = np.array([0, -degree, 0, degree, 0]) + thlist = degree * np.sin(np.linspace(0, 2*np.pi, nviews)) + philist = degree * np.cos(np.linspace(0, 2*np.pi, nviews)) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + # curr_pose = np.zeros((1, 3, 4)) + d = 4.3 # ์–˜๋ฅผ ์กฐ์ ˆํ•˜๋ฉด ์ดˆ๊ธฐ ์ž์„ธ ๊ธฐ์ค€์œผ๋กœ ์•ž์œผ๋กœ d๋งŒํผ ๋–จ์–ด์ง„ ์ ์„ ๊ธฐ์ค€์œผ๋กœ ๋„๋Š” ์ž์„ธ๊ฐ€ ๋งŒ๋“ค์–ด์ง + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[0,:3,3:4] = np.array([d*np.sin(th/180*np.pi), 0, d-d*np.cos(th/180*np.pi)]).reshape(3,1) + np.array([0, d*np.sin(phi/180*np.pi), d-d*np.cos(phi/180*np.pi)]).reshape(3,1)# Transition vector + return render_poses + + +def generate_seed_nothing(): + degree = 5 + thlist = np.array([0]) + philist = np.array([0]) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + # curr_pose = np.zeros((1, 3, 4)) + d = 4.3 # ์–˜๋ฅผ ์กฐ์ ˆํ•˜๋ฉด ์ดˆ๊ธฐ ์ž์„ธ ๊ธฐ์ค€์œผ๋กœ ์•ž์œผ๋กœ d๋งŒํผ ๋–จ์–ด์ง„ ์ ์„ ๊ธฐ์ค€์œผ๋กœ ๋„๋Š” ์ž์„ธ๊ฐ€ ๋งŒ๋“ค์–ด์ง + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[0,:3,3:4] = np.array([d*np.sin(th/180*np.pi), 0, d-d*np.cos(th/180*np.pi)]).reshape(3,1) + np.array([0, d*np.sin(phi/180*np.pi), d-d*np.cos(phi/180*np.pi)]).reshape(3,1)# Transition vector + # render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_lookaround(): + degsum = 60 + thlist = np.concatenate((np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:])) + philist = np.concatenate((np.linspace(0,0,7), np.linspace(22.5,22.5,7), np.linspace(-22.5,-22.5,7))) + assert len(thlist) == len(philist) + + render_poses = [] + # up / left --> right + thlist = np.linspace(-degsum, degsum, 2*degsum+1) + for i in range(len(thlist)): + render_pose = np.zeros((3,4)) + th = thlist[i] + phi = 22.5 + + render_pose[:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_pose[:3,3:4] = np.zeros((3,1)) + render_poses.append(render_pose) + + # right / up --> center + phlist = np.linspace(22.5, 0, 23) + # Exclude first frame (same as last frame before) + phlist = phlist[1:] + for i in range(len(phlist)): + render_pose = np.zeros((3,4)) + th = degsum + phi = phlist[i] + + render_pose[:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_pose[:3,3:4] = np.zeros((3,1)) + render_poses.append(render_pose) + + # center / right --> left + thlist = np.linspace(degsum, -degsum, 2*degsum+1) + thlist = thlist[1:] + for i in range(len(thlist)): + render_pose = np.zeros((3,4)) + th = thlist[i] + phi = 0 + + render_pose[:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_pose[:3,3:4] = np.zeros((3,1)) + render_poses.append(render_pose) + + # left / center --> down + phlist = np.linspace(0, -22.5, 23) + phlist = phlist[1:] + for i in range(len(phlist)): + render_pose = np.zeros((3,4)) + th = -degsum + phi = phlist[i] + + render_pose[:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_pose[:3,3:4] = np.zeros((3,1)) + render_poses.append(render_pose) + + + thlist = np.linspace(-degsum, degsum, 2*degsum+1) + for i in range(len(thlist)): + render_pose = np.zeros((3,4)) + th = thlist[i] + phi = -22.5 + + render_pose[:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_pose[:3,3:4] = np.zeros((3,1)) + render_poses.append(render_pose) + + return render_poses + + +def generate_seed_lookdown(): + degsum = 60 + thlist = np.concatenate((np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:], np.linspace(0, degsum, 4), np.linspace(0, -degsum, 4)[1:])) + philist = np.concatenate((np.linspace(0,0,7), np.linspace(-22.5,-22.5,7))) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_back(): + movement = np.linspace(0, 5, 101) + render_poses = [] # np.zeros((len(movement), 3, 4)) + for i in range(len(movement)): + render_pose = np.zeros((3,4)) + render_pose[:3,:3] = np.eye(3) + render_pose[:3,3:4] = np.array([[0], [0], [movement[i]]]) + render_poses.append(render_pose) + + movement = np.linspace(5, 0, 101) + movement = movement[1:] + for i in range(len(movement)): + render_pose = np.zeros((3,4)) + render_pose[:3,:3] = np.eye(3) + render_pose[:3,3:4] = np.array([[0], [0], [movement[i]]]) + render_poses.append(render_pose) + + return render_poses + + +def generate_seed_llff(degree, nviews, round=4, d=2.3): + assert round%4==0 + # thlist = np.array([degree, 0, 0, 0, -degree]) + # philist = np.array([0, -degree, 0, degree, 0]) + # d = 2.3 + thlist = degree * np.sin(np.linspace(0, 2*np.pi*round, nviews)) + philist = degree * np.cos(np.linspace(0, 2*np.pi*round, nviews)) + zlist = d/15 * np.sin(np.linspace(0, 2*np.pi*round//4, nviews)) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + z = zlist[i] + # curr_pose = np.zeros((1, 3, 4)) + # d = 4.3 # ์–˜๋ฅผ ์กฐ์ ˆํ•˜๋ฉด ์ดˆ๊ธฐ ์ž์„ธ ๊ธฐ์ค€์œผ๋กœ ์•ž์œผ๋กœ d๋งŒํผ ๋–จ์–ด์ง„ ์ ์„ ๊ธฐ์ค€์œผ๋กœ ๋„๋Š” ์ž์„ธ๊ฐ€ ๋งŒ๋“ค์–ด์ง + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.array([d*np.sin(th/180*np.pi), 0, -z+d-d*np.cos(th/180*np.pi)]).reshape(3,1) + np.array([0, d*np.sin(phi/180*np.pi), -z+d-d*np.cos(phi/180*np.pi)]).reshape(3,1)# Transition vector + return render_poses + + +def generate_seed_headbanging(maxdeg, nviews_per_round, round=3, fullround=1): + radius = np.concatenate((np.linspace(0, maxdeg, nviews_per_round*round), maxdeg*np.ones(nviews_per_round*fullround), np.linspace(maxdeg, 0, nviews_per_round*round))) + thlist = 2.66*radius * np.sin(np.linspace(0, 2*np.pi*(round+fullround+round), nviews_per_round*(round+fullround+round))) + philist = radius * np.cos(np.linspace(0, 2*np.pi*(round+fullround+round), nviews_per_round*(round+fullround+round))) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def generate_seed_headbanging_circle(maxdeg, nviews_per_round, round=3, fullround=1): + radius = np.concatenate((np.linspace(0, maxdeg, nviews_per_round*round), maxdeg*np.ones(nviews_per_round*fullround), np.linspace(maxdeg, 0, nviews_per_round*round))) + thlist = 2.66*radius * np.sin(np.linspace(0, 2*np.pi*(round+fullround+round), nviews_per_round*(round+fullround+round))) + philist = radius * np.cos(np.linspace(0, 2*np.pi*(round+fullround+round), nviews_per_round*(round+fullround+round))) + assert len(thlist) == len(philist) + + render_poses = np.zeros((len(thlist), 3, 4)) + for i in range(len(thlist)): + th = thlist[i] + phi = philist[i] + + render_poses[i,:3,:3] = np.matmul(np.array([[np.cos(th/180*np.pi), 0, -np.sin(th/180*np.pi)], [0, 1, 0], [np.sin(th/180*np.pi), 0, np.cos(th/180*np.pi)]]), np.array([[1, 0, 0], [0, np.cos(phi/180*np.pi), -np.sin(phi/180*np.pi)], [0, np.sin(phi/180*np.pi), np.cos(phi/180*np.pi)]])) + render_poses[i,:3,3:4] = np.zeros((3,1)) + + return render_poses + + +def get_pcdGenPoses(pcdgenpath, argdict={}): + if pcdgenpath == 'rotate360': + render_poses = generate_seed_360(360, 10) + elif pcdgenpath == 'lookaround': + render_poses = generate_seed_preset() + elif pcdgenpath == 'moveright': + render_poses = generate_seed_horizon() + elif pcdgenpath == 'moveback': + render_poses = generate_seed_backward() + elif pcdgenpath == 'arc': + render_poses = generate_seed_arc() + elif pcdgenpath == 'lookdown': + render_poses = generate_seed_newpreset() + elif pcdgenpath == 'hemisphere': + render_poses = generate_seed_hemisphere(argdict['center_depth']) + else: + raise("Invalid pcdgenpath") + return render_poses + + +def get_camerapaths(): + preset_json = {} + for cam_path in ["back_and_forth", "llff", "headbanging"]: + if cam_path == 'back_and_forth': + render_poses = generate_seed_back() + elif cam_path == 'llff': + render_poses = generate_seed_llff(5, 400, round=4, d=2) + elif cam_path == 'headbanging': + render_poses = generate_seed_headbanging(maxdeg=15, nviews_per_round=180, round=2, fullround=0) + else: + raise("Unknown pass") + + yz_reverse = np.array([[1,0,0], [0,-1,0], [0,0,-1]]) + blender_train_json = {"frames": []} + for render_pose in render_poses: + curr_frame = {} + ### Transform world to pixel + Rw2i = render_pose[:3,:3] + Tw2i = render_pose[:3,3:4] + + # Transfrom cam2 to world + change sign of yz axis + Ri2w = np.matmul(yz_reverse, Rw2i).T + Ti2w = -np.matmul(Ri2w, np.matmul(yz_reverse, Tw2i)) + Pc2w = np.concatenate((Ri2w, Ti2w), axis=1) + Pc2w = np.concatenate((Pc2w, np.array([0,0,0,1]).reshape((1,4))), axis=0) + + curr_frame["transform_matrix"] = Pc2w.tolist() + blender_train_json["frames"].append(curr_frame) + + preset_json[cam_path] = blender_train_json + + return preset_json + + +def main(): + cam_path = 'headbanging_circle' + os.makedirs("poses_supplementary", exist_ok=True) + + if cam_path == 'lookaround': + render_poses = generate_seed_lookaround() + elif cam_path == 'back': + render_poses = generate_seed_back() + elif cam_path == '360': + render_poses = generate_seed_360(360, 360) + elif cam_path == '1440': + render_poses = generate_seed_360(360, 1440) + elif cam_path == 'llff': + d = 8 + render_poses = generate_seed_llff(5, 400, round=4, d=d) + elif cam_path == 'headbanging': + round=3 + render_poses = generate_seed_headbanging_(maxdeg=15, nviews_per_round=180, round=round, fullround=0) + elif cam_path == 'headbanging_circle': + round=2 + render_poses = generate_seed_headbanging_circle(maxdeg=5, nviews_per_round=180, round=round, fullround=0) + + + yz_reverse = np.array([[1,0,0], [0,-1,0], [0,0,-1]]) + + c2w_poses = [] + for render_pose in render_poses: + ### Transform world to pixel + Rw2i = render_pose[:3,:3] + Tw2i = render_pose[:3,3:4] + + # Transfrom cam2 to world + change sign of yz axis + Ri2w = np.matmul(yz_reverse, Rw2i).T + Ti2w = -np.matmul(Ri2w, np.matmul(yz_reverse, Tw2i)) + Pc2w = np.concatenate((Ri2w, Ti2w), axis=1) + # Pc2w = np.concatenate((Pc2w, np.array([[0,0,0,1]])), axis=0) + + c2w_poses.append(Pc2w) + + c2w_poses = np.stack(c2w_poses, axis=0) + + # np.save(f'poses_supplementary/{cam_path}.npy', c2w_poses) + + FX = 5.8269e+02 + W = 512 + fov_x = 2*np.arctan(W / (2*FX)) + if cam_path in ['360', '1440', 'llff', 'headbanging']: + fov_x = fov_x * 1.2 + blender_train_json = {} + blender_train_json["camera_angle_x"] = fov_x + blender_train_json["frames"] = [] + + for render_pose in render_poses: + curr_frame = {} + ### Transform world to pixel + Rw2i = render_pose[:3,:3] + Tw2i = render_pose[:3,3:4] + + # Transfrom cam2 to world + change sign of yz axis + Ri2w = np.matmul(yz_reverse, Rw2i).T + Ti2w = -np.matmul(Ri2w, np.matmul(yz_reverse, Tw2i)) + Pc2w = np.concatenate((Ri2w, Ti2w), axis=1) + + curr_frame["transform_matrix"] = Pc2w.tolist() + (blender_train_json["frames"]).append(curr_frame) + + import json + if cam_path=='llff': + train_json_path = f"poses_supplementary/{cam_path}_d{d}.json" + elif cam_path=='headbanging': + train_json_path = f"poses_supplementary/{cam_path}_r{round}.json" + else: + train_json_path = f"poses_supplementary/{cam_path}.json" + + with open(train_json_path, 'w') as outfile: + json.dump(blender_train_json, outfile, indent=4) + + +if __name__ == '__main__': + main() \ No newline at end of file