def METHOD_NAME(image, prediction, dst_size, include_bound=True):
    bboxes = np.c_[prediction.x_mins, prediction.y_mins, prediction.x_maxs, prediction.y_maxs, prediction.scores]
    img = image.data
    bboxes = rerec(bboxes)
    bboxes[:, 0:4] = np.fix(bboxes[:, 0:4])
    dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph, mask = pad(bboxes, *img.shape[:2])
    bboxes = bboxes[mask]
    numbox = bboxes.shape[0]
    tempimg = np.zeros((numbox, dst_size, dst_size, 3))
    for k in range(numbox):
        tmp_k_h, tmp_k_w = int(tmph[k]) + int(include_bound), int(tmpw[k]) + int(include_bound)
        tmp = np.zeros((tmp_k_h, tmp_k_w, 3))
        tmp_ys, tmp_xs = slice(int(dy[k]), int(edy[k]) + 1), slice(int(dx[k]), int(edx[k]) + 1)
        img_ys, img_xs = slice(int(y[k]), int(ey[k]) + 1), slice(int(x[k]), int(ex[k]) + 1)
        tmp[tmp_ys, tmp_xs] = img[img_ys, img_xs]
        tempimg[k, :, :, :] = cv2.resize(tmp, (dst_size, dst_size))
    image.data = tempimg
    return image