python - 在 Keras 中编写用于图像预处理的自定义函数

Question

我是一名初学者，正在使用 keras_flow_from_dataframe 类训练有关糖尿病视网膜病变的图像数据集。但是我的模型一直欠拟合。所以我尝试预处理，通过使用 OpenCV 的自适应阈值实现编写一个自定义预处理函数传递到我的图像数据生成器类中。当我在 Keras 之外使用它时，该函数运行良好，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，它返回一个类型错误，提示 bad argument type for built-in operation在我的第一个纪元开始之前。

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

当我使用我的数据框中的图像对其进行测试时，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，它会抛出错误。

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 model.fit_generator 拟合模型，然后抛出类型错误:内置操作的错误参数类型。

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

我也考虑过预处理图像并将它们保存到一个文件夹中，然后我将它们从该文件夹加载到一个数据框中，但这在计算上非常昂贵且耗时。

Answer 1

我遇到了像你这样的问题，我的老师通过指向 tf preprocess_function 的文档帮助我，它说 preprocess_function 参数是一个图像，你可以在 this 上阅读更多信息.

这就是它在 cv2.imread(image) 处给您错误的原因。您应该删除该行，因为 im 是生成器提供给您的图像。不需要加载它，因为它已经加载了

我的一个很好，希望你的也很好。