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• keras-image-segmentation-master
  • segmentation_dh
    • img
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  • callbacks.py
  • model
    • fcn.py
    • pspnet.py
    • unet.py
    • __init__.py
  • test.py

from __future__ import print_function

import h5py
import os
import numpy as np
import cv2
import argparse

# Current python dir path
dir_path = os.path.dirname(os.path.realpath('__file__'))

parser = argparse.ArgumentParser()
parser.add_argument("--path", required=True, help="path of leftImg8bit folder.")
parser.add_argument("--gtpath", required=True, help="path of gtFine folder.")

args = parser.parse_args()
img_folder_path = args.path
gt_folder_path = args.gtpath

# Use only 3 classes.
# labels = ['background', 'person', 'car', 'road']


# Reads paths from cityscape data.
def get_data(mode):
    if mode == 'train' or mode == 'val' or mode == 'test':
        x_paths = []
        y_paths = []
        tmp_img_folder_path = os.path.join(img_folder_path, mode)
        tmp_gt_folder_path = os.path.join(gt_folder_path, mode)

        # os.walk helps to find all files in directory.
        for (path, dirname, files) in os.walk(tmp_img_folder_path):
            for filename in files:
                x_paths.append(os.path.join(path, filename))

        # Find ground_truth file paths with x_paths.
        idx = len(tmp_img_folder_path)
        for x_path in x_paths:
            y_paths.append(tmp_gt_folder_path + x_path[idx:-15] + 'gtFine_labelIds.png')

        return x_paths, y_paths
    else:
        print("Please call get_data function with arg 'train', 'val', 'test'.")


# Make h5 group and write data
def write_data(h5py_file, mode, x_paths, y_paths):
    num_data = len(x_paths)

    # h5py special data type for image.
    uint8_dt = h5py.special_dtype(vlen=np.dtype('uint8'))

    # Make group and data set.
    group = h5py_file.create_group(mode)
    x_dset = group.create_dataset('x', shape=(num_data, ), dtype=uint8_dt)
    y_dset = group.create_dataset('y', shape=(num_data, ), dtype=uint8_dt)

    for i in range(num_data):
        # Read image and resize
        x_img = cv2.imread(x_paths[i])
        x_img = cv2.resize(x_img, None, fx=0.25, fy=0.25, interpolation=cv2.INTER_CUBIC)
        x_img = cv2.cvtColor(x_img, cv2.COLOR_BGR2RGB)

        y_img = cv2.imread(y_paths[i])
        y_img = cv2.resize(y_img, None, fx=0.25, fy=0.25, interpolation=cv2.INTER_NEAREST)
        y_img = y_img[:, :, 0]

        x_dset[i] = x_img.flatten()
        y_dset[i] = y_img.flatten()


# Make h5 file.
def make_h5py():
    x_train_paths, y_train_paths = get_data('train')
    x_val_paths, y_val_paths = get_data('val')
    x_test_paths, y_test_paths = get_data('test')

    # Make h5py file with write option.
    h5py_file = h5py.File(os.path.join(dir_path, 'data.h5'), 'w')

    # Write data
    print('Parsing train datas...')
    write_data(h5py_file, 'train', x_train_paths, y_train_paths)
    print('Finish.')

    print('Parsing val datas...')
    write_data(h5py_file, 'val', x_val_paths, y_val_paths)
    print('Finish.')

    print('Parsing test datas...')
    write_data(h5py_file, 'test', x_test_paths, y_test_paths)
    print('Finish.')


make_h5py()