File size: 3,263 Bytes
436b829 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | from ppd.data.depth_estimation import Dataset as BaseDataset
from ppd.data.depth_estimation import *
from os.path import join
import os
from torchvision.transforms import Compose
import json
import h5py
from PIL import Image
import torchvision.transforms.functional as TF
class Dataset(BaseDataset):
def build_metas(self):
self.dataset_name = 'kitti'
splits = open(self.cfg.split_path, 'r').readlines()
self.rgb_files = []
self.depth_files = []
for split in splits:
rgb_file, depth_file, _ = split.strip().split(' ')
if depth_file != 'None':
self.rgb_files.append(join(self.cfg.data_root, rgb_file))
self.depth_files.append(join(self.cfg.data_root, depth_file))
def read_rgb(self, index):
img_path = self.rgb_files[index]
start_time = time.time()
rgb = cv2.imread(img_path)
end_time = time.time()
if end_time - start_time > 1:
Log.warn(f'Long time to read {img_path}: {end_time - start_time}')
rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)
rgb = np.asarray(rgb / 255.).astype(np.float32)
######## benchmark crop
KB_CROP_HEIGHT = 352
KB_CROP_WIDTH = 1216
height, width = rgb.shape[:2]
top_margin = int(height - KB_CROP_HEIGHT)
left_margin = int((width - KB_CROP_WIDTH) / 2)
rgb = rgb[
top_margin : top_margin + KB_CROP_HEIGHT,
left_margin : left_margin + KB_CROP_WIDTH,
:
]
return rgb
def read_depth(self, index):
depth = imageio.imread(self.depth_files[index]) / 256.
######## benchmark crop
KB_CROP_HEIGHT = 352
KB_CROP_WIDTH = 1216
height, width = depth.shape
top_margin = int(height - KB_CROP_HEIGHT)
left_margin = int((width - KB_CROP_WIDTH) / 2)
depth = depth[
top_margin : top_margin + KB_CROP_HEIGHT,
left_margin : left_margin + KB_CROP_WIDTH,
]
valid_mask = np.logical_and(
depth > 0.1, ~np.isnan(depth)) & (~np.isinf(depth))
valid_mask = np.logical_and(valid_mask, depth < 80.)
if valid_mask.sum() == 0:
Log.warn('No valid mask in the depth map of {}'.format(
self.depth_files[index]))
if valid_mask.sum() != 0 and np.isnan(depth).sum() != 0:
depth[np.isnan(depth)] = depth[valid_mask].max()
if valid_mask.sum() != 0 and np.isinf(depth).sum() != 0:
depth[np.isinf(depth)] = depth[valid_mask].max()
####### benchmark crop
eval_mask = np.zeros_like(valid_mask, dtype=bool)
gt_height, gt_width = eval_mask.shape
eval_mask[
int(0.3324324 * gt_height) : int(0.91351351 * gt_height),
int(0.0359477 * gt_width) : int(0.96405229 * gt_width),
] = 1
valid_mask = np.logical_and(valid_mask, eval_mask)
####### benchmark crop
depth[valid_mask == 0] = 0
return depth, valid_mask.astype(np.uint8)
def read_rgb_name(self, index):
return '__'.join(self.rgb_files[index].split('/')[-4:]) |