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