poc.py 文件源码

def test(test_func):

    lena = imread('lena512.png')

    n = 100

    error_all = np.zeros([n])
    pbar = progressbar.ProgressBar(max_value=n)

    for i in range(n):

        pbar.update(i+1)

        x_true = np.random.random()*6-5
        y_true = np.random.random()*6-5

        # ex) left:5, up:30 => translation=(5, 30)
        t_form = tf.SimilarityTransform(translation=(x_true, y_true))
        lena_shift = tf.warp(lena, t_form)

        a1 = np.random.randint(10, 50)
        a2 = np.random.randint(10, 50)
        a3 = np.random.randint(10, 50)
        a4 = np.random.randint(10, 50)

        img1 = lena[a1:-a2, a3:-a4]
        img2 = lena_shift[a1:-a2, a3:-a4]

        x_est, y_est = test_func(img1, img2)

        # print("x: {0:.3f}, x: {0:.3f}".format(x_true, y_true))
        # print("x: {0:.3f}, y: {0:.3f}".format(x_est, y_est))

        value = math.sqrt((x_true - x_est)**2 + (y_true - y_est)**2)
        error_all[i] = value

    ave = np.average(error_all)
    std = np.std(error_all)

    print("\terror: {0:.3f} +- {1:.3f}".format(ave, std))

#------------------------------
# main
#------------------------------