def __getitem__(self, index):
left = self.left[index]
right = self.right[index]
disp_L = self.disp_L[index]
left_img = self.loader(left)
right_img = self.loader(right)
dataL = self.dploader(disp_L)
if self.training:
w, h = left_img.size
th, tw = 256, 512
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
left_img = left_img.crop((x1, y1, x1 + tw, y1 + th))
right_img = right_img.crop((x1, y1, x1 + tw, y1 + th))
dataL = dataL[y1:y1 + th, x1:x1 + tw]
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
right_img = processed(right_img)
else:
w, h = left_img.size
# left_img = left_img.crop((w - 1232, h - 368, w, h))
# right_img = right_img.crop((w - 1232, h - 368, w, h))
left_img = left_img.crop((w - 1200, h - 352, w, h))
right_img = right_img.crop((w - 1200, h - 352, w, h))
w1, h1 = left_img.size
# dataL1 = dataL[h - 368:h, w - 1232:w]
dataL = dataL[h - 352:h, w - 1200:w]
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
right_img = processed(right_img)
dataL = torch.from_numpy(dataL).float()
return left_img, right_img, dataL
def __getitem__(self, index):
left = self.left[index]
right = self.right[index]
disp_L = self.disp_L[index]
left_img = self.loader(left)
right_img = self.loader(right)
dataL = self.dploader(disp_L)
if self.training:
w, h = left_img.size
th, tw = 256, 512
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
left_img = left_img.crop((x1, y1, x1 + tw, y1 + th))
right_img = right_img.crop((x1, y1, x1 + tw, y1 + th))
dataL = np.ascontiguousarray(dataL, dtype=np.float32) / 256
dataL = dataL[y1:y1 + th, x1:x1 + tw]
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
right_img = processed(right_img)
return left_img, right_img, dataL
else:
w, h = left_img.size
left_img = left_img.crop((w - 1232, h - 368, w, h))
right_img = right_img.crop((w - 1232, h - 368, w, h))
w1, h1 = left_img.size
dataL = dataL.crop((w - 1232, h - 368, w, h))
dataL = np.ascontiguousarray(dataL, dtype=np.float32) / 256
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
right_img = processed(right_img)
return left_img, right_img, dataL
def __getitem__(self, index):
left = self.left[index]
right = self.right[index]
disp_L = self.disp_L[index]
left_img = self.loader(left)
right_img = self.loader(right)
dataL = self.dploader(disp_L)
if self.training:
w, h = left_img.size
th, tw = 256, 768
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
left_img = left_img.crop((x1, y1, x1 + tw, y1 + th))
right_img = right_img.crop((x1, y1, x1 + tw, y1 + th))
dataL = dataL[y1:y1 + th, x1:x1 + tw]
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
right_img = processed(right_img)
else:
w, h = left_img.size
th, tw = 512, 1024
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
left_img = left_img.crop((x1, y1, x1 + tw, y1 + th))
right_img = right_img.crop((x1, y1, x1 + tw, y1 + th))
dataL = dataL[y1:y1 + th, x1:x1 + tw]
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
right_img = processed(right_img)
dataL = torch.from_numpy(dataL).float()
return left_img, right_img, dataL