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我是一名初学者，使用该keras_flow_from_dataframe课程训练有关糖尿病视网膜病变的图像数据集。但我的模型一直欠拟合。所以我尝试了预处理，通过编写一个自定义预处理函数来传递到我的图像数据生成器类中，使用 OpenCV 的自适应阈值实现。当我在 Keras 之外使用它时，该函数工作得很好，但是当我将它添加到我的图像数据生成器类并适合我的模型时，它会bad argument type for built-in operation在我的第一个 epoch 开始之前返回一个类型错误。

这是预处理代码：

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

当我用我的数据帧中的图像测试它时，它在 Keras 之外运行良好，但是当我将它添加到我的图像数据生成器类时，它会引发错误。

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)

然后我从数据框中加载我的数据集：

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 拟合模型model.fit_generator，然后抛出类型错误：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                                 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

我还考虑过对图像进行预处理并将它们保存到一个文件夹中，然后我会将它们从该文件夹加载到一个数据帧中，但它的计算成本高且耗时。

4

2 回答 2

3

我遇到了像你这样的问题，我的老师通过将它指向 tf preprocess_function 的文档来帮助我，它说preprocess_function参数是一个图像，你可以阅读更多关于这个。

这就是为什么它会给你错误cv2.imread(image)。您应该删除该行，因为im它是生成器为您提供的图像。没有必要加载它，因为它已经加载了

我的一个很好，希望你的也很好。

于 2020-09-23T11:14:41.020 回答
0

我认为问题在于 Keras 解析 openCV 输出，因为当我使用另一个库执行名为 ImgAUg 的处理时，它运行良好。这是链接。https://pypi.org/project/imgaug/。

所以我只是用库编写了一个预处理函数，然后我将它传递给了 keras imageDataGenerator 类。它运行良好，没有向我抛出任何错误。

于 2019-08-17T09:19:00.037 回答