def __getitem__(self, index):
img_path = os.path.join(self.data_dir, self.data.iloc[index, 0])
img = utils.bgr2rgb(cv2.imread(str(img_path)))
try:
img_path_2 = os.path.join(self.data_dir, self.data.iloc[index, 1])
target = utils.bgr2rgb(cv2.imread(str(img_path_2)))
except:
target = utils.bgr2rgb(cv2.imread(str(img_path)))
if self.input_transforms:
img = self.input_transforms(image=img)['image']
if self.target_transforms:
target = self.target_transforms(image=target)['image']
return img, target
def __getitem__(self, index):
img_path = os.path.join(self.data_dir, self.data.iloc[index, 0])
if self.channels == 3:
img = utils.bgr2rgb(cv2.imread(str(img_path)))
else:
img = cv2.imread(str(img_path), 0)
# img = Image.open(img_path)
# if self.channels == 3:
# img = img.convert('RGB')
# else:
# img = img.convert('L')
y = self.data.iloc[index, 1]
if self.minorities and self.bal_tfms:
if y in self.minorities:
if hasattr(self.bal_tfms, 'transforms'):
for tr in self.bal_tfms.transforms:
tr.p = self.diffs[y]
l = [self.bal_tfms]
l.extend(self.transforms_)
self.tfms = albu.Compose(l)
else:
for t in self.bal_tfms:
t.p = self.diffs[y]
self.transforms_[1:1] = self.bal_tfms
# self.tfms = transforms.Compose(self.transforms_)
self.tfms = albu.Compose(self.transforms_)
# print(self.tfms)
else:
# self.tfms = transforms.Compose(self.transforms_)
self.tfms = albu.Compose(self.transforms_)
else:
# self.tfms = transforms.Compose(self.transforms_)
self.tfms = albu.Compose(self.transforms_)
# x = self.tfms(img)
x = self.tfms(image=img)['image']
if self.channels == 1:
x = x.unsqueeze(0)
if self.seg:
mask = Image.open(self.data.iloc[index, 1])
seg_tfms = albu.Compose([self.tfms.transforms[0]])
y = torch.from_numpy(np.array(seg_tfms(mask))).long().squeeze(0)
# if self.obj:
# s = x.size()[1]
# if isinstance(s,tuple):
# s = s[0]
# row_scale = s/img.size[0]
# col_scale = s/img.size[1]
# y = rescale_bbox(y,row_scale,col_scale)
# y.squeeze_()
# y2 = self.data.iloc[index, 2]
# y = (y,y2)
return (x, y, self.data.iloc[index, 0])
def __getitem__(self, index):
frames = utils.path_list(os.path.join(self.data_dir,
self.data.iloc[index, 0]),
sort=True)
img_path = frames[self.n_frames - 1]
try:
img_ = utils.bgr2rgb(cv2.imread(str(img_path)))
except:
print(img_path)
if len(self.pre_transforms.transforms.transforms) > 0:
img_ = self.pre_transforms(image=img_)['image']
target = self.target_transforms(image=img_)['image']
if len(self.pre_input_transforms.transforms.transforms) > 0:
img_ = self.pre_input_transforms(image=img_)['image']
# img_ = self.downscale_transforms(image=img_)['image']
img = self.input_transforms(image=img_)['image']
resized_target = self.resized_target_transforms(image=img_)['image']
seq = list(reversed(frames[:-1]))
neighbours = []
for img_path in seq:
try:
img_ = utils.bgr2rgb(cv2.imread(str(img_path)))
except:
print(img_path)
if len(self.pre_transforms.transforms.transforms) > 0:
img_ = self.pre_transforms(image=img_)['image']
if len(self.pre_input_transforms.transforms.transforms) > 0:
img_ = self.pre_input_transforms(image=img_)['image']
neighbours.append(self.input_transforms(image=img_)['image'])
flow = [
utils.to_tensor(
self.get_flow(utils.tensor_to_img(img),
utils.tensor_to_img(j))) for j in neighbours
]
return img, target, neighbours, flow, resized_target
def __getitem__(self, index):
img_path = os.path.join(self.data_dir, self.data.iloc[index, 0])
try:
img_ = utils.bgr2rgb(cv2.imread(str(img_path)))
except:
print(img_path)
if len(self.pre_transforms.transforms.transforms) > 0:
img_ = self.pre_transforms(image=img_)['image']
target = self.target_transforms(image=img_)['image']
if len(self.pre_input_transforms.transforms.transforms) > 0:
img_ = self.pre_input_transforms(image=img_)['image']
# img_ = self.downscale_transforms(image=img_)['image']
img = self.input_transforms(image=img_)['image']
resized_target = self.resized_target_transforms(image=img_)['image']
return img, target, resized_target
def __getitem__(self, index):
img_path = os.path.join(self.data_dir, self.data.iloc[index, 0])
if self.channels == 3:
img = utils.bgr2rgb(cv2.imread(str(img_path)))
else:
img = cv2.imread(str(img_path), 0)
# img = Image.open(img_path)
# if self.channels == 3:
# img = img.convert('RGB')
# else:
# img = img.convert('L')
y1, y2 = self.data.iloc[index, 1], self.data.iloc[index, 2]
self.tfms = albu.Compose(self.transforms_)
x = self.tfms(image=img)['image'].unsqueeze(0)
# self.tfms = transforms.Compose(self.transforms_)
# x = self.tfms(img)
return (x, y1, y2)