img.replace(right_test_dir, label_dir).replace('_R', '').replace(
'_1500_maskImg', '').replace('.png', '.dpt').replace('.tif', '.dpt')
for img in close_right_filelist
]
angle = 5
x_translation = 0
y_translation = 0
scale = 1.0
close_db = myImageloader(left_img_files=close_left_filelist,
right_img_files=close_right_filelist,
label_files=close_label_filelist,
angle=angle,
x_translation=x_translation,
y_translation=y_translation,
scale=scale,
train_patch_w=256,
transform=transforms.Compose([transforms.ToTensor()]),
label_transform=transforms.Compose(
[transforms.ToTensor()]))
train_loader = torch.utils.data.DataLoader(myImageloader(
left_img_files=left_train_filelist,
right_img_files=right_train_filelist,
label_files=train_labels,
angle=angle,
x_translation=x_translation,
y_translation=y_translation,
scale=scale,
train_patch_w=256,
angle = 30
translate = 0.1
scale = 1.1
train_filelist = [
os.path.join(train_dir, img) for img in os.listdir(train_dir)
]
train_labels = [img.replace(train_dir, label_dir) for img in train_filelist]
test_filelist = [os.path.join(train_dir, img) for img in os.listdir(test_dir)]
test_labels = [img.replace(test_dir, label_dir) for img in test_filelist]
train_loader = torch.utils.data.DataLoader(myImageloader(
img_files=train_filelist,
label_files=train_labels,
angle=angle,
translation=translate,
scale=scale,
transform=transforms.Compose([transforms.ToTensor()]),
label_transform=transforms.Compose([transforms.ToTensor()])),
batch_size=1,
shuffle=True,
num_workers=4)
test_loader = torch.utils.data.DataLoader(myImageloader(
img_files=test_filelist,
label_files=test_labels,
angle=angle,
translation=translate,
scale=scale,
transform=transforms.Compose([transforms.ToTensor()]),
label_transform=transforms.Compose([transforms.ToTensor()])),