I am a beginner training an image dataset on diabetic retinopathy, using the keras_flow_from_dataframe class. But my model has been underfitting. So I tried preprocessing, by writing a custom preprocessing function to be passed in my image data generator class, using OpenCV's adaptive thresholding implementation. The function works very well when I use it outside of Keras, but when I add it to my image data generator class and fit my model, it returns a type error saying bad argument type for built-in operation before my first epoch starts.
Here's the preprocessing code:
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
It runs well outside of Keras when I test it with the images in my dataframe, but when I add it to my image data generator class, it throws an error.
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)
Then I load in my dataset from dataframe:
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)
Then I fit the model using model.fit_generator, which then throws me the type error: 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
I have also thought about preprocessing the images and saving them to a folder then I'll load them from that folder to a dataframe, but it's computationally expensive and time consuming.
答案 0 :(得分：3)
我遇到了一个像您一样的问题，我的老师通过将其指向tf preprocess_function的文档来帮助我，它说preprocess_function参数是一个图像，您可以在this上阅读更多内容。
这就是为什么它会向您显示cv2.imread(image)处的错误。您应该删除该行，因为im是生成器为您提供的图像。无需加载，因为它已经加载
我的一个很好，希望你的情况也很好。
答案 1 :(得分：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/。
因此，我只是使用该库编写了预处理函数的脚本，然后将其传递给keras imageDataGenerator类。运行正常，没有向我抛出任何错误。