def get_item(self, idx):
label_url = self.get_label_url(idx)
ori_url = self.get_original_url(idx)
src_url = self.get_src_url(idx)
gt = cv2.imread(label_url, 0)
ori = cv2.imread(ori_url, 0)
src = cv2.imread(src_url)
if src.shape[0] != self.resize or src.shape[1] != self.resize:
src = imutils.resize(src, width=self.resize)
src = src[:self.resize, :self.resize] # ensure size
if gt.shape[0] != self.resize or gt.shape[1] != self.resize:
gt = imutils.resize(gt, width=self.resize)
gt = gt[:self.resize, :self.resize] # ensure size
gt = ((gt > 0) * 255).astype(np.uint8)
if ori.shape[0] != self.resize or ori.shape[1] != self.resize:
ori = imutils.resize(ori, width=self.resize)
ori = ori[:self.resize, :self.resize] # ensure size
gt = self.one2three(gt)
obj = ObjectImageAndMaskTransform(src, gt.astype(np.float32) / 255)
if self.transform:
obj = self.transform(obj)
obj = obj.to_dict()
obj['ori'] = ori.astype(np.float32) / 255
return obj
def __getitem__(self, idx):
image, label = self.data[idx]
image_t = utility.to_channels(image, ch=self.num_channels)
label_t = np.zeros((label.shape[0], label.shape[1], 2))
label_t[:, :, 0] = (label < 1)
label_t[:, :, 1] = (label >= 1)
obj = ObjectImageAndMaskTransform(image_t, label_t)
if self.transform:
obj = self.transform(obj)
return obj.to_dict()
def __getitem__(self, idx):
idx = idx % len(self.data)
image, label = self.data[idx]
image_t = utility.to_channels(image, ch=self.num_channels)
label_t = (label > 127).astype(np.uint8)
obj = ObjectImageAndMaskTransform(image_t, label_t)
if self.transform:
obj = self.transform(obj)
return obj.to_dict()
def __getitem__(self, idx):
idx = idx % len(self.data)
image, label = self.data[idx]
image_t = utility.to_channels(image, ch=self.num_channels)
label_t = np.zeros_like(label)
label_t[:, :, 0] = (label[..., 0] == 0)
label_t[:, :, 1] = (label[..., 0] == 1)
label_t[:, :, 2] = (label[..., 0] >= 2)
obj = ObjectImageAndMaskTransform(image_t, label_t)
if self.transform:
obj = self.transform(obj)
return obj.to_dict()
def __getitem__(self, idx):
idx = idx%len(self.data)
image, mask = self.data[idx]
image_t = utility.to_channels(image, ch=self.num_channels )
label_t = np.zeros( (mask.shape[0], mask.shape[1], 2) )
label_t[:,:,0] = (mask < 0)
label_t[:,:,1] = (mask > 0)
obj = ObjectImageAndMaskTransform( image_t, label_t )
if self.transform:
sample = self.transform( obj )
return obj.to_dict()
def __getitem__(self, idx):
idx = idx % len(self.data)
image, label, contours = self.data[idx]
image_t = utility.to_channels(image, ch=self.num_channels)
label_t = np.zeros((label.shape[0], label.shape[1], 3))
label_t[:, :, 0] = (label < 128)
label_t[:, :, 1] = (label > 128)
label_t[:, :, 2] = (contours > 128)
obj = ObjectImageAndMaskTransform(image_t, label_t)
if self.transform:
obj = self.transform(obj)
return obj.to_dict()
def __getitem__(self, idx):
idx = idx % len(self.data)
data = self.data[idx]
if self.use_weight:
image, label, weight = data
elif self.load_segments:
image, label, segs = data
if self.shuffle_segments:
segs = segs[..., np.random.permutation(segs.shape[-1])]
else:
image, label = data
image_t = utility.to_channels(image, ch=self.num_channels)
label = to_one_hot(label, self.num_classes)
if self.use_weight:
obj = ObjectImageMaskAndWeightTransform(image_t, label, weight)
elif self.load_segments:
obj = ObjectImageMaskAndSegmentationsTransform(
image_t, label, segs)
else:
obj = ObjectImageAndMaskTransform(image_t, label)
if self.transform:
obj = self.transform(obj)
obj = obj.to_dict()
obj['segment'] = preprocessing.apply_preprocessing(
obj['segment'], self.middle_proc)
if self.load_segments: ## Warring!
axis = np.argmin(obj['segment'].shape)
if self.use_ori:
if self.transform:
inputs = torch.cat((obj['image'], obj['segment']),
dim=axis)
else:
inputs = np.concatenate((obj['image'], obj['segment']),
axis=axis)
obj['image'] = inputs
else:
obj['image'] = obj['segment']
obj.pop('segment')
return obj
def __getitem__(self, idx):
idx = idx % len(self.data)
data = self.data[idx]
if self.use_weight:
image, label, weight = data
else:
image, label = data
label = (label == 255).astype(float) # 1024, 1024, 3, max= 1
# image: 1024, 1024, 3, max = 255
image_t = utility.to_channels(image, ch=self.num_channels)
# image_t: 1024, 1024, 3, max = 255
if self.use_weight:
obj = ObjectImageMaskAndWeightTransform(image_t, label, weight)
else:
obj = ObjectImageAndMaskTransform(image_t, label)
if self.transform:
obj = self.transform(obj)
return obj.to_dict()