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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 ScanNetDataset(BaseDataset):
def __init__(self, cfg, phase, **kwargs):
super(ScanNetDataset, self).__init__(
cfg=cfg,
phase=phase,
**kwargs)
self.metric_scale = cfg.metric_scale
# def get_data_for_test(self, idx):
# anno = self.annotations['files'][idx]
# curr_rgb_path = os.path.join(self.data_root, anno['rgb'])
# curr_depth_path = os.path.join(self.depth_root, anno['depth'])
# meta_data = self.load_meta_data(anno)
# ori_curr_intrinsic = meta_data['cam_in']
# curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path)
# # curr_rgb = cv2.resize(curr_rgb, dsize=(640, 480), interpolation=cv2.INTER_LINEAR)
# 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)
# # load tmpl rgb info
# # tmpl_annos = self.load_tmpl_annos(anno, curr_rgb, meta_data)
# # tmpl_rgb = tmpl_annos['tmpl_rgb_list'] # list of reference rgbs
# transform_paras = dict()
# rgbs, depths, intrinsics, cam_models, _, other_labels, transform_paras = self.img_transforms(
# images=[curr_rgb, ],
# labels=[curr_depth, ],
# intrinsics=[ori_curr_intrinsic,],
# cam_models=[curr_cam_model, ],
# transform_paras=transform_paras)
# # depth in original size
# depth_out = self.clip_depth(curr_depth) * self.depth_range[1]
# filename = os.path.basename(anno['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)
# 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=False,
# pad=pad,
# scale=scale_ratio,
# raw_rgb=raw_rgb,
# normal =np.zeros_like(curr_rgb.transpose((2,0,1))),
# )
# return data
def get_data_for_test(self, idx: int, test_mode=True):
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, test_mode)
# load data
curr_rgb, curr_depth, curr_normal, curr_cam_model = data_batch['curr_rgb'], data_batch['curr_depth'], data_batch['curr_normal'], data_batch['curr_cam_model']
ori_curr_intrinsic = meta_data['cam_in']
# get crop size
transform_paras = dict()
rgbs, depths, intrinsics, cam_models, _, 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, ],
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]) #
inv_depth = self.depth2invdepth(depth_out, np.zeros_like(depth_out, dtype=np.bool))
filename = os.path.basename(meta_data['rgb'])[:-4] + '.jpg'
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)
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,
pad=pad,
scale=scale_ratio,
raw_rgb=raw_rgb,
sample_id=idx,
data_path=meta_data['rgb'],
inv_depth=inv_depth,
normal=curr_normal,
)
return data
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, test_mode=False)
# 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']
# 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 load_batch(self, meta_data, data_path, test_mode):
# print('############')
# print(data_path['rgb_path'])
# print(data_path['normal_path'])
# print('############')
curr_intrinsic = meta_data['cam_in']
# load rgb/depth
curr_rgb, curr_depth = self.load_rgb_depth(data_path['rgb_path'], data_path['depth_path'], test_mode)
# get semantic labels
curr_sem = self.load_sem_label(data_path['sem_path'], curr_depth)
# create camera model
curr_cam_model = self.create_cam_model(curr_rgb.shape[0], curr_rgb.shape[1], curr_intrinsic)
# get normal labels
curr_normal = self.load_norm_label(data_path['normal_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1], test_mode=test_mode)
# get depth mask
depth_mask = self.load_depth_valid_mask(data_path['depth_mask_path'])
curr_depth[~depth_mask] = -1
# get stereo depth
curr_stereo_depth = self.load_stereo_depth_label(data_path['disp_path'], H=curr_rgb.shape[0], W=curr_rgb.shape[1])
data_batch = dict(
curr_rgb = curr_rgb,
curr_depth = curr_depth,
curr_sem = curr_sem,
curr_normal = curr_normal,
curr_cam_model=curr_cam_model,
curr_stereo_depth=curr_stereo_depth,
)
return data_batch
def load_rgb_depth(self, rgb_path: str, depth_path: str, test_mode: bool):
"""
Load the rgb and depth map with the paths.
"""
rgb = self.load_data(rgb_path, is_rgb_img=True)
if rgb is None:
self.logger.info(f'>>>>{rgb_path} has errors.')
depth = self.load_data(depth_path)
if depth is None:
self.logger.info(f'{depth_path} has errors.')
# self.check_data(dict(
# rgb_path=rgb,
# depth_path=depth,
# ))
depth = depth.astype(np.float)
# if depth.shape != rgb.shape[:2]:
# print(f'no-equal in {self.data_name}')
# depth = cv2.resize(depth, rgb.shape[::-1][1:])
depth = self.process_depth(depth, rgb, test_mode)
return rgb, depth
def process_depth(self, depth, rgb, test_mode=False):
depth[depth>65500] = 0
depth /= self.metric_scale
h, w, _ = rgb.shape # to rgb size
if test_mode==False:
depth = cv2.resize(depth, (w, h), interpolation=cv2.INTER_NEAREST)
return depth
def load_norm_label(self, norm_path, H, W, test_mode):
if norm_path is None:
norm_gt = np.zeros((H, W, 3)).astype(np.float32)
else:
norm_gt = cv2.imread(norm_path)
norm_gt = cv2.cvtColor(norm_gt, cv2.COLOR_BGR2RGB)
norm_gt = np.array(norm_gt).astype(np.uint8)
mask_path = 'orient-mask'.join(norm_path.rsplit('normal', 1))
mask_gt = cv2.imread(mask_path)
mask_gt = np.array(mask_gt).astype(np.uint8)
valid_mask = np.logical_not(
np.logical_and(
np.logical_and(
mask_gt[:, :, 0] == 0, mask_gt[:, :, 1] == 0),
mask_gt[:, :, 2] == 0))
valid_mask = valid_mask[:, :, np.newaxis]
# 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_valid_mask = (np.linalg.norm(norm_gt, axis=2, keepdims=True) > 0.5) * valid_mask
norm_gt = norm_gt * norm_valid_mask
if test_mode==False:
norm_gt = cv2.resize(norm_gt, (W, H), interpolation=cv2.INTER_NEAREST)
return norm_gt
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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