CV 数据增强augment体现在训练集样例的增加上，举例如下

if is_train:
        data_gen2 = ImageDataGenerator(rescale = .3, rotation_range =0.2 , zoom_range = 0.2,  width_shift_range = 0.2,  height_shift_range= 0.2)
        dataflow_generator2 = data_gen2.flow_from_directory(data_dir,target_size=(160, 160),batch_size=1,color_mode='rgb',class_mode='categorical')
        labels_dict = dataflow_generator2.class_indices
        print("labels_dict",labels_dict)
        sample_count = len(dataflow_generator2.filenames)*augment_times
        print('sample_count:',sample_count)
        #filenames = dataflow_generator2.filenames
        #labels = dataflow_generator2.class_indices
        #print(filenames)
        #print(labels)
        for image_data in dataflow_generator:
            # TODO：使用 plt.imshow 和 plt.show() 显示图像
            #print(len(image_data[1]))
            for j in range(0,len(image_data[1])):
                image = image_data[0][j].astype('uint8')
                images.append(image)
                labels.append(image_data[1][j])

            #print(image_data[1])          #label
            #print(image_data[0][0].shape) #image

            sample_count -= 1
            if sample_count <= 0:
                images = np.array(images)
                labels = np.array(labels)
                break