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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)
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