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import os
import json
import torch
import torchvision.transforms as transforms
import os.path
import numpy as np
import cv2
from torch.utils.data import Dataset
import random
from .__base_dataset__ import BaseDataset
class NYUDataset(BaseDataset):
def __init__(self, cfg, phase, **kwargs):
super(NYUDataset, self).__init__(
cfg=cfg,
phase=phase,
**kwargs)
self.metric_scale = cfg.metric_scale
def get_data_for_trainval(self, idx: int):
anno = self.annotations['files'][idx]
meta_data = self.load_meta_data(anno)
data_path = self.load_data_path(meta_data)
data_batch = self.load_batch(meta_data, data_path)
# if data_path['sem_path'] is not None:
# print(self.data_name)
curr_rgb, curr_depth, curr_normal, curr_sem, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_sem'], data_batch['curr_cam_model']
#curr_stereo_depth = data_batch['curr_stereo_depth']
new_rgb = np.zeros_like(curr_rgb)
new_rgb[6:-6, 6:-6, :] = curr_rgb[6:-6, 6:-6, :]
curr_rgb = new_rgb
# A patch for stereo depth dataloader (no need to modify specific datasets)
if 'curr_stereo_depth' in data_batch.keys():
curr_stereo_depth = data_batch['curr_stereo_depth']
else:
curr_stereo_depth = self.load_stereo_depth_label(None, H=curr_rgb.shape[0], W=curr_rgb.shape[1])
curr_intrinsic = meta_data['cam_in']
# data augmentation
transform_paras = dict(random_crop_size = self.random_crop_size) # dict()
assert curr_rgb.shape[:2] == curr_depth.shape == curr_normal.shape[:2] == curr_sem.shape
rgbs, depths, intrinsics, cam_models, normals, other_labels, transform_paras = self.img_transforms(
images=[curr_rgb, ],
labels=[curr_depth, ],
intrinsics=[curr_intrinsic,],
cam_models=[curr_cam_model, ],
normals = [curr_normal, ],
other_labels=[curr_sem, curr_stereo_depth],
transform_paras=transform_paras)
# process sky masks
sem_mask = other_labels[0].int()
# clip depth map
depth_out = self.normalize_depth(depths[0])
# set the depth of sky region to the invalid
depth_out[sem_mask==142] = -1 # self.depth_normalize[1] - 1e-6
# get inverse depth
inv_depth = self.depth2invdepth(depth_out, sem_mask==142)
filename = os.path.basename(meta_data['rgb'])[:-4] + '.jpg'
curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
cam_models_stacks = [
torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
for i in [2, 4, 8, 16, 32]
]
# stereo_depth
stereo_depth_pre_trans = other_labels[1] * (other_labels[1] > 0.3) * (other_labels[1] < 200)
stereo_depth = stereo_depth_pre_trans * transform_paras['label_scale_factor']
stereo_depth = self.normalize_depth(stereo_depth)
pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
data = dict(input=rgbs[0],
target=depth_out,
intrinsic=curr_intrinsic_mat,
filename=filename,
dataset=self.data_name,
cam_model=cam_models_stacks,
pad=torch.tensor(pad),
data_type=[self.data_type, ],
sem_mask=sem_mask.int(),
stereo_depth= stereo_depth,
normal=normals[0],
inv_depth=inv_depth,
scale=transform_paras['label_scale_factor'])
return data
def get_data_for_test(self, idx: int):
anno = self.annotations['files'][idx]
meta_data = self.load_meta_data(anno)
curr_rgb_path = os.path.join(self.data_root, meta_data['rgb'])
curr_depth_path = os.path.join(self.depth_root, meta_data['depth'])
# load data
ori_curr_intrinsic = meta_data['cam_in']
curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path)
# crop rgb/depth
new_rgb = np.zeros_like(curr_rgb)
new_rgb[6:-6, 6:-6, :] = curr_rgb[6:-6, 6:-6, :]
curr_rgb = new_rgb
ori_h, ori_w, _ = curr_rgb.shape
# create camera model
curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], ori_curr_intrinsic)
if 'normal' in meta_data.keys():
normal_path = os.path.join(self.data_root, meta_data['normal'])
else:
normal_path = None
curr_normal = self.load_norm_label(normal_path, H=curr_rgb.shape[0], W=curr_rgb.shape[1])
# load tmpl rgb info
# tmpl_annos = self.load_tmpl_image_pose(curr_rgb, meta_data)
# tmpl_rgbs = tmpl_annos['tmpl_rgb_list'] # list of reference rgbs
# get crop size
transform_paras = dict()
rgbs, depths, intrinsics, cam_models, normals, other_labels, transform_paras = self.img_transforms(
images=[curr_rgb,], #+ tmpl_rgbs,
labels=[curr_depth, ],
intrinsics=[ori_curr_intrinsic, ], # * (len(tmpl_rgbs) + 1),
cam_models=[curr_cam_model, ],
normals = [curr_normal, ],
transform_paras=transform_paras)
# depth in original size and orignial metric***
depth_out = self.clip_depth(curr_depth) * self.depth_range[1] # self.clip_depth(depths[0]) #
filename = os.path.basename(meta_data['rgb'])
curr_intrinsic_mat = self.intrinsics_list2mat(intrinsics[0])
pad = transform_paras['pad'] if 'pad' in transform_paras else [0,0,0,0]
scale_ratio = transform_paras['label_scale_factor'] if 'label_scale_factor' in transform_paras else 1.0
cam_models_stacks = [
torch.nn.functional.interpolate(cam_models[0][None, :, :, :], size=(cam_models[0].shape[1]//i, cam_models[0].shape[2]//i), mode='bilinear', align_corners=False).squeeze()
for i in [2, 4, 8, 16, 32]
]
raw_rgb = torch.from_numpy(curr_rgb)
# rel_pose = torch.from_numpy(tmpl_annos['tmpl_pose_list'][0])
curr_normal = torch.from_numpy(curr_normal.transpose((2,0,1)))
data = dict(input=rgbs[0],
target=depth_out,
intrinsic=curr_intrinsic_mat,
filename=filename,
dataset=self.data_name,
cam_model=cam_models_stacks,
# ref_input=rgbs[1:],
# tmpl_flg=tmpl_annos['w_tmpl'],
pad=pad,
scale=scale_ratio,
raw_rgb=raw_rgb,
# rel_pose=rel_pose,
normal=curr_normal
#normal=np.zeros_like(curr_rgb.transpose((2,0,1))),
)
return data
def load_norm_label(self, norm_path, H, W):
if norm_path is None:
norm_gt = np.zeros((H, W, 3)).astype(np.float32)
else:
norm_gt = cv2.imread(norm_path)
norm_gt = np.array(norm_gt).astype(np.uint8)
norm_valid_mask = np.logical_not(
np.logical_and(
np.logical_and(
norm_gt[:, :, 0] == 0, norm_gt[:, :, 1] == 0),
norm_gt[:, :, 2] == 0))
norm_valid_mask = norm_valid_mask[:, :, np.newaxis]
norm_gt = ((norm_gt.astype(np.float32) / 255.0) * 2.0) - 1.0
norm_gt = norm_gt * norm_valid_mask * -1
return norm_gt
def process_depth(self, depth, rgb):
# eign crop
new_depth = np.zeros_like(depth)
new_depth[45:471, 41:601] = depth[45:471, 41:601]
new_depth[new_depth>65500] = 0
new_depth /= self.metric_scale
return new_depth
if __name__ == '__main__':
from mmcv.utils import Config
cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py')
dataset_i = NYUDataset(cfg['Apolloscape'], 'train', **cfg.data_basic)
print(dataset_i)
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