def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is class_index of the target class.
"""
img = Image.open(self.X_train[index])
img = img.resize(self.input_img_resize, Image.ANTIALIAS)
img = np.asarray(img.convert("RGB"), dtype=np.float32)
# Pillow reads gifs
mask = Image.open(self.y_train_masks[index])
mask = mask.resize(self.output_img_resize, Image.ANTIALIAS)
mask = np.asarray(mask.convert("L"), dtype=np.float32) # GrayScale
if self.X_transform:
img, mask = self.X_transform(img, mask)
if self.y_transform:
img, mask = self.y_transform(img, mask)
img = transformer.image_to_tensor(img)
mask = transformer.mask_to_tensor(mask, self.threshold)
return img, mask
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is class_index of the target class.
"""
img_path = self.X_train[index]
img = Image.open(img_path)
img = img.resize(self.img_resize, Image.ANTIALIAS)
img = np.asarray(img.convert("RGB"), dtype=np.float32)
img = transformer.image_to_tensor(img)
return img, img_path.split("/")[-1]
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is class_index of the target class.
"""
img = Image.open(self.X_train[index])
#img = img.resize(self.img_resize, Image.ANTIALIAS)
img = transformer.center_cropping_resize(img, self.img_resize)
img = np.asarray(img.convert("L"), dtype=np.float32) # Greyscale
# Pillow reads tifs
mask = Image.open(self.y_train_masks[index])
#mask = mask.resize(self.img_resize, Image.ANTIALIAS)
mask = transformer.center_cropping_resize(mask, self.img_resize)
mask = np.asarray(mask.convert("L"), dtype=np.float32) # GreyScale
maskout = Image.open(self.z_train_masks[index])
# maskout = maskout.resize(self.img_resize, Image.ANTIALIAS)
maskout = transformer.center_cropping_resize(maskout, self.img_resize)
maskout = np.asarray(maskout.convert("L"),
dtype=np.float32) # GreyScale
if self.X_transform:
img, mask, maskout = self.X_transform(img, mask, maskout)
if self.y_transform:
img, mask, maskout = self.y_transform(img, mask, maskout)
img = transformer.image_to_tensor(img)
mask = transformer.mask_to_tensor(mask, self.threshold)
maskout = transformer.mask_to_tensor(maskout, 0.5)
#print("UNIQUE of mask is", np.unique(mask))
#print("UNIQUE of maskout is", np.unique(maskout))
return img, mask, maskout