def random_sized_crop()

in pycls/datasets/transforms.py [0:0]

• 32 lines of code
• 8 McCabe index (conditional complexity)

def random_sized_crop(image, size, area_frac=0.08):
    for _ in range(0, 10):
        height = image.shape[0]
        width = image.shape[1]
        area = height * width
        target_area = np.random.uniform(area_frac, 1.0) * area
        aspect_ratio = np.random.uniform(3.0 / 4.0, 4.0 / 3.0)
        w = int(round(math.sqrt(float(target_area) * aspect_ratio)))
        h = int(round(math.sqrt(float(target_area) / aspect_ratio)))
        if np.random.uniform() < 0.5:
            w, h = h, w
        if h <= height and w <= width:
            if height == h:
                y_offset = 0
            else:
                y_offset = np.random.randint(0, height - h)
            if width == w:
                x_offset = 0
            else:
                x_offset = np.random.randint(0, width - w)
            y_offset = int(y_offset)
            x_offset = int(x_offset)
            cropped = image[y_offset:y_offset + h, x_offset:x_offset + w, :]
            assert cropped.shape[0] == h and cropped.shape[1] == w, \
                "Wrong crop size"
            cropped = cv2.resize(
                cropped,
                (size, size),
                interpolation=cv2.INTER_LINEAR
            )
            return cropped.astype(np.float32)
    return center_crop(size, scale(size, image))