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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 PIL import Image | |
| from torch.utils.data import Dataset | |
| import random | |
| from .__base_dataset__ import BaseDataset | |
| import pickle | |
| class TaskonomyDataset(BaseDataset): | |
| def __init__(self, cfg, phase, **kwargs): | |
| super(TaskonomyDataset, self).__init__( | |
| cfg=cfg, | |
| phase=phase, | |
| **kwargs) | |
| self.metric_scale = cfg.metric_scale | |
| #self.cap_range = self.depth_range # in meter | |
| def __getitem__(self, idx: int) -> dict: | |
| if self.phase == 'test': | |
| return self.get_data_for_test(idx) | |
| else: | |
| return self.get_data_for_trainval(idx) | |
| def load_meta_data(self, anno: dict) -> dict: | |
| """ | |
| Load meta data information. | |
| """ | |
| if self.meta_data_root is not None and ('meta_data' in anno or 'meta' in anno): | |
| meta_data_path = os.path.join(self.meta_data_root, anno['meta_data']) if 'meta_data' in anno else os.path.join(self.meta_data_root, anno['meta']) | |
| with open(meta_data_path, 'rb') as f: | |
| meta_data = pickle.load(f) | |
| meta_data.update(anno) | |
| else: | |
| meta_data = anno | |
| u0, v0, fx, fy = meta_data['cam_in'] | |
| meta_data['cam_in'] = [fx, fy, u0, v0] # fix data bugs | |
| return meta_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) | |
| 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'] | |
| curr_intrinsic = meta_data['cam_in'] | |
| ins_planes_path = os.path.join(self.data_root, meta_data['ins_planes']) if ('ins_planes' in meta_data) and (meta_data['ins_planes'] is not None) else None | |
| # get instance planes | |
| ins_planes = self.load_ins_planes(curr_depth, ins_planes_path) | |
| # load data | |
| # u0, v0, fx, fy = meta_data['cam_in'] # this is | |
| # ori_curr_intrinsic = [fx, fy, u0, v0] | |
| # curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path) | |
| # get crop size | |
| # transform_paras = dict() | |
| transform_paras = dict(random_crop_size = self.random_crop_size) | |
| 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=[ins_planes, ], | |
| transform_paras=transform_paras) | |
| # process instance planes | |
| ins_planes = other_labels[0].int() | |
| # clip depth map | |
| depth_out = self.normalize_depth(depths[0]) | |
| # get inverse depth | |
| inv_depth = self.depth2invdepth(depth_out, torch.zeros_like(depth_out, dtype=torch.bool)) | |
| filename = os.path.basename(meta_data['rgb']) | |
| 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] | |
| ] | |
| 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=ins_planes, | |
| normal=normals[0], | |
| inv_depth=inv_depth, | |
| stereo_depth=torch.zeros_like(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) | |
| data_path = self.load_data_path(meta_data) | |
| data_batch = self.load_batch(meta_data, data_path) | |
| 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'] | |
| # curr_rgb_path = os.path.join(self.data_root, meta_data['rgb']) | |
| # curr_depth_path = os.path.join(self.depth_root, meta_data['depth']) | |
| # curr_rgb, curr_depth = self.load_rgb_depth(curr_rgb_path, curr_depth_path) | |
| # 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_image_pose(curr_rgb, meta_data) | |
| # tmpl_rgbs = 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,], # + 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] | |
| inv_depth = self.depth2invdepth(depth_out, np.zeros_like(depth_out, dtype=np.bool)) | |
| 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) | |
| 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 load_norm_label(self, norm_path, H, W): | |
| with open(norm_path, 'rb') as f: | |
| normal = Image.open(f) | |
| normal = np.array(normal.convert(normal.mode), dtype=np.uint8) | |
| invalid_mask = np.all(normal == 128, axis=2) | |
| normal = normal.astype(np.float64) / 255.0 * 2 - 1 | |
| normal[invalid_mask, :] = 0 | |
| return normal | |
| def process_depth(self, depth: np.array, rgb: np.array) -> np.array: | |
| depth[depth>60000] = 0 | |
| depth = depth / self.metric_scale | |
| return depth | |
| def load_ins_planes(self, depth: np.array, ins_planes_path: str) -> np.array: | |
| if ins_planes_path is not None: | |
| ins_planes = cv2.imread(ins_planes_path, -1) | |
| else: | |
| ins_planes = np.zeros_like(depth) | |
| return ins_planes | |
| if __name__ == '__main__': | |
| from mmcv.utils import Config | |
| cfg = Config.fromfile('mono/configs/Apolloscape_DDAD/convnext_base.cascade.1m.sgd.mae.py') | |
| dataset_i = ApolloscapeDataset(cfg['Apolloscape'], 'train', **cfg.data_basic) | |
| print(dataset_i) | |