def main(): image_paths = get_image_path_list() label_paths = get_label_path_list() classes = get_classes_matrix(label_paths) image_paths = get_data_subset(image_paths, classes) label_paths = get_data_subset(label_paths, classes) paths = {'color':image_paths, 'gray' :image_paths, 'label':label_paths} # prepare images image_dict = {} for t, p in paths.items(): with ex.optionset(t) as o: images= o.load_and_resize_images(p) images= o.pad_images_and_equalize_sizes(images) images=o.reshape_images(images) write_idx_file(o.options['filename'].format(**o.options), images) image_dict[t] = images # create samples class_samples = create_samples(image_dict['label']) # reduce number of background samples np.random.shuffle(class_samples[0]) class_samples[0] = class_samples[0][:500000] write_samples_to_idx(np.vstack(tuple(class_samples)), ex.options['all_samples_filename'].format(**ex.options))
def write_samples_to_idx(samples, filename): np.random.shuffle(samples) write_idx_file(filename, samples, byteswap=False)