generate_missing_data.py 文件源码

def corrupt_image(img, MAR_prob=0, min_rects=0, max_rects=0, min_width=0, max_width=0):
    new_img = img.copy()
    mask = np.zeros(img.shape[0:2], dtype=np.bool)
    if MAR_prob > 0:
        mask[(random_sample(mask.shape) < MAR_prob)] = True
    if max_rects > 0 and max_width > 0:
        h, w = mask.shape
        num_rects = random_integers(min_rects, max_rects)
        for i in range(num_rects):
            px1 = random_integers(0, w - min(max(min_width, 1), w))
            py1 = random_integers(0, h - min(max(min_width, 1), h))
            px2 = px1 + (min_width - 1) + random_integers(0, max(min(w - px1 - min_width, max_width - min_width), 0));
            py2 = py1 + (min_width - 1) + random_integers(0, max(min(h - py1 - min_width, max_width - min_width), 0));
            if px1 <= px2 and py1 <= py2:
                mask[py1:py2, px1:px2] = True
            else:
                # One of the sides has length 0, so we should remove any pixels4
                pass
    if len(new_img.shape) == 2:
        new_img[mask] = 0
    else:
        new_img[mask,:] = 0
    return (new_img, 1.0 * mask)

# Process command line inputs