#!/usr/bin/env python3 ################################################################### # Use this script to polygonize binary mask detection in COCO format (for example frome the Open Solution from the CrowdAI challenge: # https://github.com/neptune-ai/open-solution-mapping-challenge) # using the frame field polygonization method and save the output in COCO format. # Example use: # python polygonize_coco.py --run_dirpath "/home/lydorn/repos/lydorn/Polygonization-by-Frame-Field-Learning/frame_field_learning/runs/mapping_dataset.unet_resnet101_pretrained.train_val | 2020-09-07 11:28:51" --images_dirpath "/home/lydorn/data/mapping_challenge_dataset/raw/val/images" --gt_filepath /home/lydorn/data/mapping_challenge_dataset/raw/val/annotation.json --in_filepath "/home/lydorn/data/mapping_challenge_dataset/eval_runs/mapping_dataset.open_solution | 0000-00-00 00:00:00/test.annotation.seg.json" --out_filepath "/home/lydorn/data/mapping_challenge_dataset/eval_runs/mapping_dataset.open_solution | 0000-00-00 00:00:00/test.annotation.poly.json" ################################################################### import argparse from pycocotools.coco import COCO from pycocotools import mask as cocomask from pycocotools.cocoeval import COCOeval import numpy as np import skimage.io import matplotlib.pyplot as plt import pylab import random import os import json import sys from tqdm import tqdm import torch from frame_field_learning.model import FrameFieldModel from frame_field_learning import inference, polygonize_acm, data_transforms, save_utils, polygonize_utils from lydorn_utils import run_utils, print_utils, python_utils from backbone import get_backbone import torch_lydorn pylab.rcParams['figure.figsize'] = (8.0, 10.0) # Image stats for the open challenge dataset: image_mean = [0.30483739, 0.35143595, 0.3973895] image_std = [0.16362707, 0.15187606, 0.14273278] polygonize_config = { "steps": 500, "data_level": 0.5, "data_coef": 0.1, "length_coef": 0.4, "crossfield_coef": 0.5, "poly_lr": 0.001, "warmup_iters": 499, "warmup_factor": 0.1, "device": "cuda", "tolerance": 0.125, "seg_threshold": 0.5, "min_area": 10 } def get_args(): argparser = argparse.ArgumentParser(description=__doc__) argparser.add_argument( '--run_dirpath', required=True, type=str, help='Full path to the run directory to use for frame field prediction (needed for frame field polygonization).') argparser.add_argument( '--images_dirpath', required=True, type=str, help='Path to the images directory to use for frame field prediction (needed for frame field polygonization).') argparser.add_argument( '--gt_filepath', required=True, type=str, help='Filepath of the ground truth annotations in COCO format (.json file).') argparser.add_argument( '--in_filepath', required=True, type=str, help='Filepath of the input mask annotations in COCO format (.json file).') argparser.add_argument( '--out_filepath', required=True, type=str, help='Filepath of the output polygon annotations in COCO format (.json file).') argparser.add_argument( '--batch_size', default=16, type=int, help='Batch size for running inference on the model.') argparser.add_argument( '--batch_size_mult', default=64, type=int, help='Multiply batch_size by this factor for polygonization.') args = argparser.parse_args() return args def list_to_batch(sample_data_list): tile_data = {} for key in sample_data_list[0].keys(): if isinstance(sample_data_list[0][key], list): tile_data[key] = [item for _tile_data in sample_data_list for item in _tile_data[key]] elif isinstance(sample_data_list[0][key], torch.Tensor): tile_data[key] = torch.cat([_tile_data[key] for _tile_data in sample_data_list], dim=0) else: raise TypeError(f"Type {type(sample_data_list[0][key])} is not handled!") return tile_data def run_model(config, model, sample_data_list): tile_data = list_to_batch(sample_data_list) tile_data = inference.inference(config, model, tile_data, compute_polygonization=False) return tile_data def run_polygonization(sample_data_list): tile_data = list_to_batch(sample_data_list) # Polygonize input mask with predicted frame field seg_batch = tile_data["mask_image"] crossfield_batch = tile_data["crossfield"] polygons_batch, _ = polygonize_acm.polygonize(seg_batch, crossfield_batch, polygonize_config) # Discard the probs computed by polygonize(). They will be computed next using the score_image # Convert to COCO format coco_ann_list = [] for polygons, img_id, score_image in zip(polygons_batch, tile_data["img_id"], tile_data["score_image"]): scores = polygonize_utils.compute_geom_prob(polygons, score_image[0, :, :].numpy()) coco_ann = save_utils.poly_coco(polygons, scores, image_id=img_id) coco_ann_list.extend(coco_ann) return coco_ann_list def polygonize_masks(run_dirpath, images_dirpath, gt_filepath, in_filepath, out_filepath, batch_size, batch_size_mult): coco_gt = COCO(gt_filepath) coco_dt = coco_gt.loadRes(in_filepath) # --- Load model --- # # Load run's config file: config = run_utils.load_config(config_dirpath=run_dirpath) if config is None: print_utils.print_error( "ERROR: cannot continue without a config file. Exiting now...") sys.exit() config["backbone_params"]["pretrained"] = False # Don't load pretrained model backbone = get_backbone(config["backbone_params"]) eval_online_cuda_transform = data_transforms.get_eval_online_cuda_transform(config) model = FrameFieldModel(config, backbone=backbone, eval_transform=eval_online_cuda_transform) model.to(config["device"]) checkpoints_dirpath = run_utils.setup_run_subdir(run_dirpath, config["optim_params"]["checkpoints_dirname"]) model = inference.load_checkpoint(model, checkpoints_dirpath, config["device"]) model.eval() # --- Polygonize input COCO mask detections --- # img_ids = coco_dt.getImgIds() # img_ids = sorted(img_ids)[:1] # TODO: rm limit output_annotations = [] model_data_list = [] # Used to accumulate inputs and run model inference on it. poly_data_list = [] # Used to accumulate inputs and run polygonization on it. for img_id in tqdm(img_ids, desc="Polygonizing"): # Load image img = coco_gt.loadImgs(img_id)[0] image = skimage.io.imread(os.path.join(images_dirpath, img["file_name"])) # Draw mask from input COCO mask annotations mask_image = np.zeros((img["height"], img["width"])) score_image = np.zeros((img["height"], img["width"])) dts = coco_dt.loadAnns(coco_dt.getAnnIds(imgIds=img_id)) for dt in dts: dt_mask = cocomask.decode(dt["segmentation"]) mask_image = np.maximum(mask_image, dt_mask) score_image = np.maximum(score_image, dt_mask * dt["score"]) # Accumulate inputs into the current batch sample_data = { "img_id": [img_id], "mask_image": torch_lydorn.torchvision.transforms.functional.to_tensor(mask_image)[None, ...].float(), "score_image": torch_lydorn.torchvision.transforms.functional.to_tensor(score_image)[None, ...].float(), "image": torch_lydorn.torchvision.transforms.functional.to_tensor(image)[None, ...], "image_mean": torch.tensor(image_mean)[None, ...], "image_std": torch.tensor(image_std)[None, ...] } # Accumulate batch for running the model model_data_list.append(sample_data) if len(model_data_list) == batch_size: # Run model tile_data = run_model(config, model, model_data_list) model_data_list = [] # Empty model batch # Accumulate batch for running the polygonization poly_data_list.append(tile_data) if len(poly_data_list) == batch_size_mult: coco_ann = run_polygonization(poly_data_list) output_annotations.extend(coco_ann) poly_data_list = [] # Finish with incomplete batches if len(model_data_list): tile_data = run_model(config, model, model_data_list) poly_data_list.append(tile_data) if len(poly_data_list): coco_ann = run_polygonization(poly_data_list) output_annotations.extend(coco_ann) print("Saving output...") with open(out_filepath, 'w') as outfile: json.dump(output_annotations, outfile) if __name__ == "__main__": args = get_args() polygonize_masks(args.run_dirpath, args.images_dirpath, args.gt_filepath, args.in_filepath, args.out_filepath, args.batch_size, args.batch_size_mult)