def predictGenerator(test_path, batch_size=2, percent=1, dim=(256, 256), n_channels=1, save_path='test_result/'): if not os.path.exists(save_path): os.makedirs(save_path) files = random.sample(test_path, int(np.ceil(len(test_path) * percent))) for i, item in enumerate(files): img_array = img_load(os.path.join(item[0]), shape=dim, norm=True) lab_array = lab_load(os.path.join(item[1]), shape=dim, norm=False, binary=True) img_save_name = str(i) + "_source_img_" + os.path.splitext( os.path.split(item[0])[1])[0] + '.png' lab_save_name = str(i) + "_source_label_" + os.path.split(item[1])[1] imgdata = img_array * 255 labdata = lab_array * 255 io.imsave(os.path.join(save_path, img_save_name), imgdata.clip(0, 255, imgdata).astype(np.uint8)) io.imsave(os.path.join(save_path, lab_save_name), labdata.clip(0, 255, labdata).astype(np.uint8)) img_array = np.reshape(img_array, img_array.shape + (1, )) img_array = np.reshape(img_array, (1, ) + img_array.shape) yield img_array
def testGenerator(test_path, num_image=50, target_size=(256, 256), result_path='test_result/'): filelist = travel_testfiles(test_path) files = random.sample(filelist, num_image) for i, item in enumerate(files): img_array = img_load(os.path.join(test_path, item), shape=target_size, norm=True) io.imsave(os.path.join(result_path, "%d_source.png" % i), img_array) img_array = np.reshape(img_array, img_array.shape + (1,)) img_array = np.reshape(img_array, (1,) + img_array.shape) yield img_array
def __data_generation(self, list_IDs_temp): X = np.empty((self.batch_size, *self.dim, self.n_channels)) # print('X.shape', X.shape) y1 = np.empty((self.batch_size, *self.dim, 1)) # print('y1.shape', y1.shape) for i, item in enumerate(list_IDs_temp): img_array = img_load(item[0], shape=(256, 256), norm=True) lab_array = lab_load(item[1], shape=(256, 256), norm=False, binary=True) img_array = img_array.reshape(*img_array.shape, self.n_channels) lab_array = lab_array.reshape(*lab_array.shape, self.n_channels) if self.datagen: seed = random.randint(1, 10000) img_array = self.datagen.random_transform(img_array, seed) lab_array = self.datagen.random_transform(lab_array, seed) # print('each_img_shape', img_array.shape) # print('each_lab_shape', lab_array.shape) X[i, ] = img_array y1[i, ] = lab_array return X, y1