BicycleGAN-Tensorflow/data_loader.py

import os
from glob import glob

#from scipy.misc import imread, imresize
import imageio
from imageio import imread
import numpy
from PIL import Image
import numpy as np
from skimage.transform import resize
from tqdm import tqdm
import h5py

datasets = ['maps', 'cityscapes', 'facades', 'edges2handbags', 'edges2shoes']

def read_image(path):
    image = imread(path)
    if len(image.shape) != 3 or image.shape[2] != 3:
        print('Wrong image {} with shape {}'.format(path, image.shape))
        return None

    # split image
    h, w, c = image.shape
    assert w in [256, 512, 1200], 'Image size mismatch ({}, {})'.format(h, w)
    assert h in [128, 256, 600], 'Image size mismatch ({}, {})'.format(h, w)
    if 'maps' in path:
        image_a = image[:, int(w/2):, :].astype(np.float32) / 255.0
        image_b = image[:, :int(w/2), :].astype(np.float32) / 255.0
    else:
        image_a = image[:, :int(w/2), :].astype(np.float32) / 255.0
        image_b = image[:, int(w/2):, :].astype(np.float32) / 255.0

    # range of pixel values = [-1.0, 1.0]
    image_a = image_a * 2.0 - 1.0
    image_b = image_b * 2.0 - 1.0
    return image_a, image_b

def read_images(base_dir):
    ret = []
    for dir_name in ['train', 'val']:
        data_dir = os.path.join(base_dir, dir_name)
        paths = glob(os.path.join(data_dir, '*.jpg'))
        print('# images in {}: {}'.format(data_dir, len(paths)))

        images_A = []
        images_B = []
        for path in tqdm(paths):
            image_A, image_B = read_image(path)
            if image_A is not None:
                images_A.append(image_A)
                images_B.append(image_B)
        ret.append((dir_name + 'A', images_A))
        ret.append((dir_name + 'B', images_B))
    return ret

def store_h5py(base_dir, dir_name, images, image_size):
    f = h5py.File(os.path.join(base_dir, '{}_{}.hy'.format(dir_name, image_size)), 'w')
    for i in range(len(images)):
        grp = f.create_group(str(i))
        if images[i].shape[0] != image_size:
            #image = imresize(images[i], (image_size, image_size, 3))
            #print(i)
            #image = numpy.array(Image.fromarray(images[i]).resize((image_size, image_size, 3)))
            image = resize(images[i], (image_size, image_size, 3))
            # range of pixel values = [-1.0, 1.0]
            image = image.astype(np.float32) / 255.0
            image = image * 2.0 - 1.0
            grp['image'] = image
        else:
            grp['image'] = images[i]
    f.close()

def convert_h5py(task_name):
    print('Generating h5py file')
    base_dir = os.path.join('datasets', task_name)
    data = read_images(base_dir)
    for dir_name, images in data:
        if images[0].shape[0] == 256:
            store_h5py(base_dir, dir_name, images, 256)
        store_h5py(base_dir, dir_name, images, 128)

def read_h5py(task_name, image_size):
    base_dir = 'datasets/' + task_name
    paths = glob(os.path.join(base_dir, '*_{}.hy'.format(image_size)))
    if len(paths) != 4:
        convert_h5py(task_name)
    ret = []
    for dir_name in ['trainA', 'trainB', 'valA', 'valB']:
        try:
            dataset = h5py.File(os.path.join(base_dir, '{}_{}.hy'.format(dir_name, image_size)), 'r')
        except:
            raise IOError('Dataset is not available. Please try it again')

        images = []
        for id in dataset:
            images.append(dataset[id]['image'].value.astype(np.float32))
        ret.append(images)
    return ret

def download_dataset(task_name):
    print('Download data %s' % task_name)
    cmd = './download_pix2pix_dataset.sh ' +  task_name
    os.system(cmd)

def get_data(task_name, image_size):
    assert task_name in datasets, 'Dataset {}_{} is not available'.format(
        task_name, image_size)

    if not os.path.exists('datasets'):
        os.makedirs('datasets')

    base_dir = os.path.join('datasets', task_name)
    print('Check data %s' % base_dir)
    if not os.path.exists(base_dir):
        print('Dataset not found. Start downloading...')
        download_dataset(task_name)
        convert_h5py(task_name)

    print('Load data %s' % task_name)
    train_A, train_B, test_A, test_B = \
        read_h5py(task_name, image_size)
    return train_A, train_B, test_A, test_B