data planning

data.py

+def get_train_test_generator(dir_path, train_size, stego_percentage):
+    """
+    Return two generators. One for the train cover images, the second for the test cover images and a third for the
+    test stego images
+    :param dir_path: images directory path
+    :param train_size: percentage of the training set size
+    :param stego_percentage: percentage of the stego images among the test images
+    :return: three generators.
+    """
+    pass
+
+
+def embed_images(img_generator, payload):
+    """
+    Embed random messages into images with J-UNIWARD
+    :param img_generator: image generator
+    :param payload: payload of the message in bpnzac
+    :return: a generator of stego images
+    """
+    pass
+
+
+def variance_filter(img_generator, variance_threshold, block_per_threshold):
+    """
+    Filter images based on the variance of each block. If a block does not have enough variance, it is discarded. The
+    whole image is discarded if too many blocks are discarded
+    :param img_generator: image generator
+    :param variance_threshold: block with a variance below this threshold are discarded
+    :param block_per_threshold: image with a percentage of accepted blocks below this threshold are discarded
+    :return: a generator of blocks
+    """
+    pass
+
+
+def feature_extractor(block_generator):
+    """
+    Extract the spatial rounding error from views
+    :param block_generator: a generator of list of blocks. One list for one image
+    :return: a generator of views
+    """
+    pass

main.py

+from data import get_train_test_generator, embed_images, variance_filter, feature_extractor
+
+dir_path = ""
+train_size = 0.0
+payload = 0.0
+stego_percentage = 0.0
+variance_threshold = 0.0
+block_per_threshold = 0.0
+
+if __name__ == "__main__":
+    train_gen, test_cover_gen, test_stego_gen = get_train_test_generator(dir_path, train_size, stego_percentage)
+
+    train_features = feature_extractor(variance_filter(train_gen,
+                                                       variance_threshold,
+                                                       block_per_threshold))
+    test_cover_features = feature_extractor(variance_filter(test_cover_gen,
+                                                            variance_threshold,
+                                                            block_per_threshold))
+    test_stego_features = feature_extractor(variance_filter(embed_images(test_stego_gen,
+                                                                         payload),
+                                                            variance_threshold,
+                                                            block_per_threshold))