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)