Python 在Keras中编写用于图像预处理的自定义函数

我是一个初学者，使用来自数据帧的keras\u flow\u课程，培训关于糖尿病视网膜病变的图像数据集。但我的模特一直不合身。所以我尝试了预处理，通过编写一个自定义的预处理函数，在我的图像数据生成器类中传递，使用OpenCV的自适应阈值实现。当我在Keras之外使用该函数时，它工作得非常好，但当我将其添加到图像数据生成器类并拟合模型时，它会在第一个历元开始之前返回一个类型错误，即

内置操作的参数类型错误

以下是预处理代码：

def preprocess(im):

    im = cv2.imread(im, 1)
    im= cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    im=cv2.resize(im, (300,300))
    im.resize(300, 300, 1)
    block_size = 73 
    constant = 2    
# ADAPTIVE GAUSSIAN THRESHOLDING

    thr2 = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, block_size, constant)
    return thr2

当我用数据帧中的图像测试它时，它在Keras之外运行良好，但当我将它添加到图像数据生成器类时，它抛出了一个错误

train_datagen = ImageDataGenerator(
    rotation_range=30,
    width_shift_range=0.4,
    height_shift_range=0.4,
    shear_range=0.3,
    zoom_range=0.3,
    horizontal_flip = True,
    fill_mode='nearest',
    preprocessing_function = preprocess)

valid_datagen = ImageDataGenerator(preprocessing_function = preprocess)

然后，我从dataframe加载数据集：

from keras.preprocessing.image import ImageDataGenerator

traingen = train_datagen.flow_from_dataframe(x_train, x_col='path', y_col='level',class_mode='other', 
                                             target_size=(300,300), color_mode='grayscale', batch_size=16)

validgen = valid_datagen.flow_from_dataframe(valid, x_col='path', y_col='level',class_mode='other',
                                            target_size=(300,300), color_mode='grayscale', batch_size=16)

然后，我使用
model.fit_generator
拟合模型，这会抛出类型错误：
内置操作的参数类型不正确

TypeError                                 Traceback (most recent call last)
<ipython-input-126-30ceb84a2574> in <module>()
      2 
      3 history = model.fit_generator(traingen, validation_data = validgen, epochs=100, steps_per_epoch=10,
----> 4                                   validation_steps=10, verbose=1, callbacks=[lr_reduction])
      5 
      6 

~/var/python/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~/var/python/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1416             use_multiprocessing=use_multiprocessing,
   1417             shuffle=shuffle,
-> 1418             initial_epoch=initial_epoch)
   1419 
   1420     @interfaces.legacy_generator_methods_support

~/var/python/lib/python3.6/site-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    179             batch_index = 0
    180             while steps_done < steps_per_epoch:
--> 181                 generator_output = next(output_generator)
    182 
    183                 if not hasattr(generator_output, '__len__'):

~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
    599         except Exception as e:
    600             self.stop()
--> 601             six.reraise(*sys.exc_info())
    602 
    603 

~/var/python/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
    691             if value.__traceback__ is not tb:
    692                 raise value.with_traceback(tb)
--> 693             raise value
    694         finally:
    695             value = None

~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
    593         try:
    594             while self.is_running():
--> 595                 inputs = self.queue.get(block=True).get()
    596                 self.queue.task_done()
    597                 if inputs is not None:

~/var/python/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
    642             return self._value
    643         else:
--> 644             raise self._value
    645 
    646     def _set(self, i, obj):

~/var/python/lib/python3.6/multiprocessing/pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
    117         job, i, func, args, kwds = task
    118         try:
--> 119             result = (True, func(*args, **kwds))
    120         except Exception as e:
    121             if wrap_exception and func is not _helper_reraises_exception:

~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get_index(uid, i)
    399         The value at index `i`.
    400     """
--> 401     return _SHARED_SEQUENCES[uid][i]
    402 
    403 

~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in __getitem__(self, idx)
     63         index_array = self.index_array[self.batch_size * idx:
     64                                        self.batch_size * (idx + 1)]
---> 65         return self._get_batches_of_transformed_samples(index_array)
     66 
     67     def __len__(self):

~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in _get_batches_of_transformed_samples(self, index_array)
    233                 params = self.image_data_generator.get_random_transform(x.shape)
    234                 x = self.image_data_generator.apply_transform(x, params)
--> 235                 x = self.image_data_generator.standardize(x)
    236             batch_x[i] = x
    237         # optionally save augmented images to disk for debugging purposes

~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py in standardize(self, x)
    695         """
    696         if self.preprocessing_function:
--> 697             x = self.preprocessing_function(x)
    698         if self.rescale:
    699             x *= self.rescale

<ipython-input-112-7bddefa5e731> in preprocess(im)
      1 def preprocess(im):
----> 2     im = cv2.imread(im, 1)
      3     im= cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
      4     im=cv2.resize(im, (300,300))
      5     im.resize(300, 300, 1)

TypeError: bad argument type for built-in operation


TypeError                                 Traceback (most recent call last)
<ipython-input-126-30ceb84a2574> in <module>()
      2 
      3 history = model.fit_generator(traingen, validation_data = validgen, epochs=100, steps_per_epoch=10,
----> 4                                   validation_steps=10, verbose=1, callbacks=[lr_reduction])
      5 
      6 

~/var/python/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~/var/python/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1416             use_multiprocessing=use_multiprocessing,
   1417             shuffle=shuffle,
-> 1418             initial_epoch=initial_epoch)
   1419 
   1420     @interfaces.legacy_generator_methods_support

~/var/python/lib/python3.6/site-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    179             batch_index = 0
    180             while steps_done < steps_per_epoch:
--> 181                 generator_output = next(output_generator)
    182 
    183                 if not hasattr(generator_output, '__len__'):

~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
    599         except Exception as e:
    600             self.stop()
--> 601             six.reraise(*sys.exc_info())
    602 
    603 

~/var/python/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
    691             if value.__traceback__ is not tb:
    692                 raise value.with_traceback(tb)
--> 693             raise value
    694         finally:
    695             value = None

~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
    593         try:
    594             while self.is_running():
--> 595                 inputs = self.queue.get(block=True).get()
    596                 self.queue.task_done()
    597                 if inputs is not None:

~/var/python/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
    642             return self._value
    643         else:
--> 644             raise self._value
    645 
    646     def _set(self, i, obj):

~/var/python/lib/python3.6/multiprocessing/pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
    117         job, i, func, args, kwds = task
    118         try:
--> 119             result = (True, func(*args, **kwds))
    120         except Exception as e:
    121             if wrap_exception and func is not _helper_reraises_exception:

~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get_index(uid, i)
    399         The value at index `i`.
    400     """
--> 401     return _SHARED_SEQUENCES[uid][i]
    402 
    403 

~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in __getitem__(self, idx)
     63         index_array = self.index_array[self.batch_size * idx:
     64                                        self.batch_size * (idx + 1)]
---> 65         return self._get_batches_of_transformed_samples(index_array)
     66 
     67     def __len__(self):

~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in _get_batches_of_transformed_samples(self, index_array)
    233                 params = self.image_data_generator.get_random_transform(x.shape)
    234                 x = self.image_data_generator.apply_transform(x, params)
--> 235                 x = self.image_data_generator.standardize(x)
    236             batch_x[i] = x
    237         # optionally save augmented images to disk for debugging purposes

~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py in standardize(self, x)
    695         """
    696         if self.preprocessing_function:
--> 697             x = self.preprocessing_function(x)
    698         if self.rescale:
    699             x *= self.rescale

<ipython-input-112-7bddefa5e731> in preprocess(im)
      1 def preprocess(im):
----> 2     im = cv2.imread(im, 1)
      3     im= cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
      4     im=cv2.resize(im, (300,300))
      5     im.resize(300, 300, 1)

TypeError: bad argument type for built-in operation


TypeError                                 Traceback (most recent call last)
<ipython-input-126-30ceb84a2574> in <module>()
      2 
      3 history = model.fit_generator(traingen, validation_data = validgen, epochs=100, steps_per_epoch=10,
----> 4                                   validation_steps=10, verbose=1, callbacks=[lr_reduction])
      5 
      6 

~/var/python/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~/var/python/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1416             use_multiprocessing=use_multiprocessing,
   1417             shuffle=shuffle,
-> 1418             initial_epoch=initial_epoch)
   1419 
   1420     @interfaces.legacy_generator_methods_support

~/var/python/lib/python3.6/site-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    179             batch_index = 0
    180             while steps_done < steps_per_epoch:
--> 181                 generator_output = next(output_generator)
    182 
    183                 if not hasattr(generator_output, '__len__'):

~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
    599         except Exception as e:
    600             self.stop()
--> 601             six.reraise(*sys.exc_info())
    602 
    603 

~/var/python/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
    691             if value.__traceback__ is not tb:
    692                 raise value.with_traceback(tb)
--> 693             raise value
    694         finally:
    695             value = None

~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
    593         try:
    594             while self.is_running():
--> 595                 inputs = self.queue.get(block=True).get()
    596                 self.queue.task_done()
    597                 if inputs is not None:

~/var/python/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
    642             return self._value
    643         else:
--> 644             raise self._value
    645 
    646     def _set(self, i, obj):

~/var/python/lib/python3.6/multiprocessing/pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
    117         job, i, func, args, kwds = task
    118         try:
--> 119             result = (True, func(*args, **kwds))
    120         except Exception as e:
    121             if wrap_exception and func is not _helper_reraises_exception:

~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get_index(uid, i)
    399         The value at index `i`.
    400     """
--> 401     return _SHARED_SEQUENCES[uid][i]
    402 
    403 

~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in __getitem__(self, idx)
     63         index_array = self.index_array[self.batch_size * idx:
     64                                        self.batch_size * (idx + 1)]
---> 65         return self._get_batches_of_transformed_samples(index_array)
     66 
     67     def __len__(self):

~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in _get_batches_of_transformed_samples(self, index_array)
    233                 params = self.image_data_generator.get_random_transform(x.shape)
    234                 x = self.image_data_generator.apply_transform(x, params)
--> 235                 x = self.image_data_generator.standardize(x)
    236             batch_x[i] = x
    237         # optionally save augmented images to disk for debugging purposes

~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py in standardize(self, x)
    695         """
    696         if self.preprocessing_function:
--> 697             x = self.preprocessing_function(x)
    698         if self.rescale:
    699             x *= self.rescale

<ipython-input-112-7bddefa5e731> in preprocess(im)
      1 def preprocess(im):
----> 2     im = cv2.imread(im, 1)
      3     im= cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
      4     im=cv2.resize(im, (300,300))
      5     im.resize(300, 300, 1)

TypeError: bad argument type for built-in operation

TypeError回溯（最近一次调用）
在（）
2.
3历史记录=模型拟合发生器（列车发电机，验证数据=有效数据，历元数=100，每历元数=10步，
---->4个验证步骤=10，详细=1，回调=[lr\U缩减]）
5.
6.
包装器中的~/var/python/lib/python3.6/site-packages/keras/legacy/interfaces.py（*args，**kwargs）
89 warnings.warn（'Update your`'+object\u name+'`调用+
90'Keras 2 API:'+签名，堆栈级别=2）
--->91返回函数（*args，**kwargs）
92包装器._原始函数=func
93返回包装器
~/var/python/lib/python3.6/site-packages/keras/engine/training.py in-fit\u生成器（self、生成器、每个历元的步骤、历元、冗余、回调、验证数据、验证步骤、类权重、最大队列大小、工作者、使用多处理、无序、初始历元）
1416使用多处理=使用多处理，
1417洗牌=洗牌，
->1418初始_历元=初始_历元）
1419
1420@interfaces.legacy\u生成器\u方法\u支持
~/var/python/lib/python3.6/site-packages/keras/engine/training\u generator.py in-fit\u generator（模型、生成器、每个历元的步骤、历元、详细信息、回调、验证数据、验证步骤、类权重、最大队列大小、工作者、使用多处理、无序、初始历元）
179批次指数=0
180当步骤完成时<每个步骤：
-->181发电机输出=下一个（发电机输出）
182
183如果不是HASTATR（发电机输出，'''.'透镜''.''：
get（self）中的~/var/python/lib/python3.6/site-packages/keras/utils/data\u utils.py
599例外情况除外，如e：
600自动停止（）
-->601六.重新播放（*sys.exc_info（））
602
603
reraise中的~/var/python/lib/python3.6/site-packages/six.py（tp，value，tb）
691如果值.\uuuu回溯\uuuuu不是tb：
692通过回溯（tb）提升值
-->693提高价值
694最后：
695值=无
get（self）中的~/var/python/lib/python3.6/site-packages/keras/utils/data\u utils.py
593尝试：
594当self.u正在运行（）时：
-->595 inputs=self.queue.get（block=True）.get（）
596 self.queue.task_done（）
597如果输入不是无：
get中的~/var/python/lib/python3.6/multiprocessing/pool.py（self，timeout）
642返回自身值
643其他：
-->644提高自我价值
645
646 def_设置（自、i、obj）：
工人中的~/var/python/lib/python3.6/multiprocessing/pool.py（inqueue、outqueue、initializer、initargs、maxtasks、wrap_异常）
117作业，i，func，args，kwds=任务
118尝试：
-->119结果=（真，函数（*args，**kwds））
120例外情况除外，如e：
121如果换行异常和func不是(帮助程序)重新出现(异常):
get\u索引中的~/var/python/lib/python3.6/site-packages/keras/utils/data\u utils.py（uid，i）
399索引'i'处的值。
400     """
-->401返回共享序列[uid][i]
402
403
~/var/python/lib/python3.6/site-packages/keras\u preprocessing/image/iterator.py in\uuuuuu getitem\uuuuuu（self，idx）
63索引数组=self.index数组[self.batch\u size*idx:
64自我批处理大小*（idx+1）]
--->65返回自身。获取转换样本的批次（索引数组）
66
67 def____________（自）：
~/var/python/lib/python3.6/site-packages/keras\u preprocessing/image/iterator.py in\u get\u batches\u of\u transformed\u样本（self，index\u数组）
233 params=self.image\u data\u generator.get\u random\u transform（x.shape）
234 x=自身。图像数据发生器。应用变换（x，参数）
-->235 x=自映像数据发生器标准化（x）
236批次_x[i]=x
237#可选择将增强映像保存到磁盘以进行调试
标准化（self，x）中的~/var/python/lib/python3.6/site-packages/keras\u preprocessing/image/image\u data\u generator.py
695         """
696如果self.preprocessing_函数：
-->697 x=自预处理函数（x）
698如果自重缩放：
699 x*=自重缩放
在预处理中（im）
1 def预处理（im）：
---->2 im=cv2.imread（im，1）
3 im=cv2.CVT颜色（im，cv2.COLOR\u BGR2GRAY）
4 im=cv2。调整大小（im，（300300））
5 im.调整大小（300、300、1）
TypeError:内置操作的参数类型错误
TypeError回溯（最近一次调用上次）
在（）
2.
3历史记录=模型拟合发生器（列车发电机，验证数据=有效数据，历元数=100，每历元数=10步，
----> 4