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@crearo
Created June 14, 2019 16:09
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A bug in Keras where it resizes first and then applies the preprocessing function
'''
Keras's ImageDataGenerator is buggy. It first resizes based on `target_size`,
and then applies the preprocessing function you specified. This sucks.
Who wants that to happen anyway.
'''
def crop_image(img):
print(img.shape)
return img
def load_data():
train_datagen = ImageDataGenerator(rotation_range=90,
rescale=1. / 255,
preprocessing_function=crop_image)
test_datagen = ImageDataGenerator(rescale=1. / 255,
preprocessing_function=crop_image)
train_gen = train_datagen.flow_from_directory(
'%s/train' % base,
target_size=(256, 256),
color_mode='grayscale',
batch_size=batch_size,
class_mode='categorical')
test_gen = test_datagen.flow_from_directory(
'%s/test' % base,
target_size=(256, 256),
color_mode='grayscale',
batch_size=batch_size,
class_mode='categorical')
return train_gen, test_gen
def plot_data_gen(train_gen, test_gen):
for X, y in train_gen:
plt.figure(figsize=(16, 16))
for i in range(25):
plt.subplot(5, 5, i + 1)
plt.axis('off')
plt.title('Label: %d' % np.argmax(y[i]))
img = np.uint8(255 * X[i, :, :, 0])
plt.imshow(img, cmap='gray')
break
plt.show()
if __name__ == '__main__':
model = load_model()
train_gen, test_gen = load_data()
plot_data_gen(train_gen, test_gen)
@mohapatras
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Isn't it the correct way ? resize first and then divide by 255.

@crearo
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crearo commented Jun 25, 2019

I'm not talking about the divide by 255.
I'd like Keras to apply the preprocessing_function before it transforms the image into target_size.
Right now it does it the other way around.

@joshinkumar
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joshinkumar commented Mar 23, 2022

Is this bug corrected yet? Reading the current documentation of preprocessing_function seems like it is not. (https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator)

Is there a workaround to this bug? I need to apply the preprocessing function first to make my images square after filling them with empty spaces on the edges and then resize it.

Thank you,

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