def append_arrays(parsed_data):
existing_images, existing_labels = positive_images_arr(parsed_data)
existing_images, existing_labels = produce_more_data(existing_images, existing_labels)
app_imgs, app_lbls = negative_images_arr(parsed_data, len(existing_images))
for image in app_imgs:
existing_images.append(image)
for label in app_lbls:
existing_labels.append(label)
return shuffle(existing_images, existing_labels, len(existing_images))
def negative_bi_split(parsed_data):
image_array, label_array = length_and_non_negative_arrays(parsed_data)
image_array, label_array = produce_more_data(image_array, label_array)
negative_images, negative_labels = negative_image_array(
parsed_data, len(image_array))
negative_images, negative_labels = shuffle(negative_images,
negative_labels,
len(negative_images))
for image in negative_images:
image_array.append(image)
for label in negative_labels:
label_array.append(label)
return image_array, label_array
def benign_mass_split(parsed_data):
image_array, label_array = length_and_benign_arrays(parsed_data)
image_array, label_array = produce_more_data(image_array, label_array)
non_benign_imgs, non_benign_lbls = non_benign_images(
parsed_data, len(image_array))
non_benign_imgs, non_benign_lbls = shuffle(non_benign_imgs,
non_benign_lbls,
len(non_benign_imgs))
for image in non_benign_imgs:
image_array.append(image)
for label in non_benign_lbls:
label_array.append(label)
return image_array, label_array