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