小编sha*_*keb的帖子

我正在尝试删除图像中存在的所有线条。我能够检测到这些线条，但是当我尝试删除这些线条时，我仍然在最终图像中看到一些小线条。我曾经cv2.getStructuringElement获得过水平线和垂直线。在某些情况下，最终图像完全扭曲，我无法继续前进

图片取自google

    res = verticle_lines_img + horizontal_lines_img 
    res = cv2.bitwise_not(res)
    fin=cv2.bitwise_or(img_bin, res,mask =cv2.bitwise_not(res))
    fin= cv2.bitwise_not(fin)
    exp =255-res
    final = cv2.bitwise_and(exp,img_bin)
    final = cv2.bitwise_not(final)
    exp = ~exp
    finalised = cv2.bitwise_and(img_bin,final)
    finalised = cv2.bitwise_not(finalised)

请帮忙！谢谢

阅读一些类似的问题，其中大部分都提到您不应该尝试序列化不可序列化的对象。我无法理解这个问题。我能够将模型保存为 .h5 文件，但这不符合我的目的。请帮忙！

    def image_generator(train_data_dir, test_data_dir):
        train_datagen = ImageDataGenerator(rescale=1/255,
                                          rotation_range = 30,  
                                          zoom_range = 0.2, 
                                          width_shift_range=0.1,  
                                          height_shift_range=0.1,
                                          validation_split = 0.15)
      
        test_datagen = ImageDataGenerator(rescale=1/255)
        
        train_generator = train_datagen.flow_from_directory(train_data_dir,
                                      target_size = (160,160),
                                      batch_size = 32,
                                      class_mode = 'categorical',
                                      subset='training')
        
        val_generator = train_datagen.flow_from_directory(train_data_dir,
                                      target_size = (160,160),
                                      batch_size = 32,
                                      class_mode = 'categorical',
                                      subset = 'validation')
        
        test_generator = test_datagen.flow_from_directory(test_data_dir,
                                     target_size=(160,160),
                                     batch_size = 32,
                                     class_mode = 'categorical')
        return train_generator, val_generator, test_generator
    
    def model_output_for_TL (pre_trained_model, last_output):    
        x = Flatten()(last_output)
        
        # Dense hidden layer
        x = Dense(512, activation='relu')(x) …