vision.py 文件源码

def profile_score(contour, binary):
    """
    Calculate a score based on the "profile" of the target, basically how closely its geometry matches with the expected
    geometry of the goal
    :param contour:
    :param binary:
    :return:
    """
    bounding = cv2.boundingRect(contour)
    pixels = np.zeros((binary.shape[0], binary.shape[1]))
    cv2.drawContours(pixels, [contour], -1, 255, -1)
    col_averages = np.mean(pixels, axis=0)[bounding[0]:bounding[0] + bounding[2]]
    row_averages = np.mean(pixels, axis=1)[bounding[1]:bounding[1] + bounding[3]]
    # normalize to between 0 and 1
    col_averages *= 1.0 / col_averages.max()
    row_averages *= 1.0 / row_averages.max()

    col_diff = np.subtract(col_averages, col_profile(col_averages.shape[0], bounding[2]))
    row_diff = np.subtract(row_averages, row_profile(row_averages.shape[0], bounding[3]))

    # average difference should be close to 0
    avg_diff = np.mean([np.mean(col_diff), np.mean(row_diff)])
    return 100 - (avg_diff * 50)