def preprocess_images()

in data/preprocess.py [0:0]

• 30 lines of code
• 10 McCabe index (conditional complexity)

def preprocess_images():

    if os.path.isfile(IMG_PATH):
        print("%s exists, nothing to do." % IMG_PATH)
        return

    print("Reading images from img_align_celeba/ ...")
    raw_images = []
    for i in range(1, N_IMAGES + 1):
        if i % 10000 == 0:
            print(i)
        raw_images.append(mpimg.imread('img_align_celeba/%06i.jpg' % i)[20:-20])

    if len(raw_images) != N_IMAGES:
        raise Exception("Found %i images. Expected %i" % (len(raw_images), N_IMAGES))

    print("Resizing images ...")
    all_images = []
    for i, image in enumerate(raw_images):
        if i % 10000 == 0:
            print(i)
        assert image.shape == (178, 178, 3)
        if IMG_SIZE < 178:
            image = cv2.resize(image, (IMG_SIZE, IMG_SIZE), interpolation=cv2.INTER_AREA)
        elif IMG_SIZE > 178:
            image = cv2.resize(image, (IMG_SIZE, IMG_SIZE), interpolation=cv2.INTER_LANCZOS4)
        assert image.shape == (IMG_SIZE, IMG_SIZE, 3)
        all_images.append(image)

    data = np.concatenate([img.transpose((2, 0, 1))[None] for img in all_images], 0)
    data = torch.from_numpy(data)
    assert data.size() == (N_IMAGES, 3, IMG_SIZE, IMG_SIZE)

    print("Saving images to %s ..." % IMG_PATH)
    torch.save(data[:20000].clone(), 'images_%i_%i_20000.pth' % (IMG_SIZE, IMG_SIZE))
    torch.save(data, IMG_PATH)