Spaces:
Runtime error
Runtime error
File size: 5,270 Bytes
24f9881 |
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 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
# MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# File author: Shariq Farooq Bhat
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import os
from PIL import Image
import numpy as np
import cv2
class ToTensor(object):
def __init__(self):
self.normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
# self.resize = transforms.Resize((375, 1242))
def __call__(self, sample):
image, depth = sample['image'], sample['depth']
image = self.to_tensor(image)
image = self.normalize(image)
depth = self.to_tensor(depth)
# image = self.resize(image)
return {'image': image, 'depth': depth, 'dataset': "vkitti"}
def to_tensor(self, pic):
if isinstance(pic, np.ndarray):
img = torch.from_numpy(pic.transpose((2, 0, 1)))
return img
# # handle PIL Image
if pic.mode == 'I':
img = torch.from_numpy(np.array(pic, np.int32, copy=False))
elif pic.mode == 'I;16':
img = torch.from_numpy(np.array(pic, np.int16, copy=False))
else:
img = torch.ByteTensor(
torch.ByteStorage.from_buffer(pic.tobytes()))
# PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
if pic.mode == 'YCbCr':
nchannel = 3
elif pic.mode == 'I;16':
nchannel = 1
else:
nchannel = len(pic.mode)
img = img.view(pic.size[1], pic.size[0], nchannel)
img = img.transpose(0, 1).transpose(0, 2).contiguous()
if isinstance(img, torch.ByteTensor):
return img.float()
else:
return img
class VKITTI(Dataset):
def __init__(self, data_dir_root, do_kb_crop=True):
import glob
# image paths are of the form <data_dir_root>/{HR, LR}/<scene>/{color, depth_filled}/*.png
self.image_files = glob.glob(os.path.join(
data_dir_root, "test_color", '*.png'))
self.depth_files = [r.replace("test_color", "test_depth")
for r in self.image_files]
self.do_kb_crop = True
self.transform = ToTensor()
def __getitem__(self, idx):
image_path = self.image_files[idx]
depth_path = self.depth_files[idx]
image = Image.open(image_path)
depth = Image.open(depth_path)
depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR |
cv2.IMREAD_ANYDEPTH)
print("dpeth min max", depth.min(), depth.max())
# print(np.shape(image))
# print(np.shape(depth))
# depth[depth > 8] = -1
if self.do_kb_crop and False:
height = image.height
width = image.width
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
depth = depth.crop(
(left_margin, top_margin, left_margin + 1216, top_margin + 352))
image = image.crop(
(left_margin, top_margin, left_margin + 1216, top_margin + 352))
# uv = uv[:, top_margin:top_margin + 352, left_margin:left_margin + 1216]
image = np.asarray(image, dtype=np.float32) / 255.0
# depth = np.asarray(depth, dtype=np.uint16) /1.
depth = depth[..., None]
sample = dict(image=image, depth=depth)
# return sample
sample = self.transform(sample)
if idx == 0:
print(sample["image"].shape)
return sample
def __len__(self):
return len(self.image_files)
def get_vkitti_loader(data_dir_root, batch_size=1, **kwargs):
dataset = VKITTI(data_dir_root)
return DataLoader(dataset, batch_size, **kwargs)
if __name__ == "__main__":
loader = get_vkitti_loader(
data_dir_root="/home/bhatsf/shortcuts/datasets/vkitti_test")
print("Total files", len(loader.dataset))
for i, sample in enumerate(loader):
print(sample["image"].shape)
print(sample["depth"].shape)
print(sample["dataset"])
print(sample['depth'].min(), sample['depth'].max())
if i > 5:
break
|