def __getitem__(self, index):
img1 = Image.open(self.__left[index])
params = get_params(self.opt, img1.size)
img1 = Image.open(self.__left[index]).convert('RGB')
img2 = Image.open(self.__right[index]).convert('RGB')
arg = random.random() > 0.5
if arg:
img1, img2 = self.augument_image_pair(img1, img2)
# img1.show()
# img2.show()
transform = get_transform(self.opt, params)
# imgp = transform(img1)
# imgq = transform(img2)
# imgp.show()
# imgq.show()
img1 = transform(img1)
img2 = transform(img2)
input_dict = {'left_img': img1.cuda(), 'right_img': img2.cuda()}
return input_dict
def __getitem__(self, index):
stereo_img = Image.open(self.__stereo[index]).convert('RGB')
s = stereo_img.size
img1 = stereo_img.crop((0, 0, s[0] / 2, s[1]))
params = get_params(self.opt, img1.size)
# img2 = Image.open(self.__right[index]).convert('RGB')
transform = get_transform(self.opt, params)
img1 = transform(img1)
# img2 = transform(img2)
# print('size(img1),size(img1)')
# print(np.array(img1).shape,np.array(img1).shape)
# img1 = torch.from_numpy(np.asarray(img1))
# img2 = torch.from_numpy(np.asarray(img2))
# print('type(img1),type(img1)')
# print(type(img1),type(img1))
# img1 = self.transforms(img1)
# img2 = self.transforms(img2)
input_dict = {'test_img': img1.cuda()}
return input_dict
def __getitem__(self, index):
stereo_img = Image.open(self.__stereo[index]).convert('RGB')
s = stereo_img.size
img1 = stereo_img.crop((0, 0, s[0] / 2, s[1]))
img2 = stereo_img.crop((0, 0, s[0] / 2, s[1]))
params = get_params(self.opt, img1.size)
arg = random.random() > 0.5
if arg:
img1, img2 = self.augument_image_pair(img1, img2)
transform = get_transform(self.opt, params)
img1 = transform(img1)
img2 = transform(img2)
input_dict = {'left_img': img1.cuda(), 'right_img': img2.cuda()}
return input_dict