def __getitem__(self, idx): image_path = self.image_paths[idx] image = image_read(image_path) imgdata = image.pixel_array * image.RescaleSlope + image.RescaleIntercept if self.Dataset_name is not "train": imgname = os.path.splitext(os.path.split(image_path)[1])[0] random_list = [] if self.Dataset_name is not "test": if self.trf_op is not None: keys = np.random.randint(2, size=len(self.trf_op)) for i, key in enumerate(keys): random_list.append(self.trf_op[i]) if key == 1 else None transform_v = transforms.Compose(self.fix_list + random_list) imgdata = transform_v(imgdata).numpy() image_temp = imgdata # imgdata = Image.fromarray(imgdata) # imgdata = imgdata.resize((128, 128), Image.NEAREST) # imgdata = np.array(imgdata) # print(imgdata.shape) if self.Dataset_name is "train": return imgdata, image_temp else: return imgdata, imgname, image_temp
def __getitem__(self, idx): image_path = self.image_paths[idx] image = image_read(image_path) imgdata = image.pixel_array * image.RescaleSlope + image.RescaleIntercept imgname = os.path.splitext(os.path.split(image_path)[1])[0] transform = transforms.Compose(self.fix_list) imgdata = transform(imgdata).numpy() return imgdata, imgname