![]() ![]() Train_generator = zip(image_generator, path in flow from directory, i gave the directory as path which contains two subdirectories one named images contains images and other named masks contain all masks. Seed=seed) combine generators into one which yields image and masks Mask_generator = mask_datagen.flow_from_directory( Image_generator = image_datagen.flow_from_directory( Mask_datagen.fit(masks, augment=True, seed=seed) Image_datagen.fit(images, augment=True, seed=seed) Mask_datagen = ImageDataGenerator(**data_gen_args) Provide the same seed and keyword arguments to the fit and flow methods Image_datagen = ImageDataGenerator(**data_gen_args) we create two instances with the same argumentsÄata_gen_args = dict(featurewise_center=True, ![]() If anyone else gets here from search, the new answer is that you can do this with the imagedatageneratorÄ®xample of transforming images and masks together.
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