Lucas和Kanade的结构张量

Question

对于一项大学任务，我目前正在用Python在OpenCV中实现Lucas和Canade，但结果并不像预期的那样：

import numpy as np
import cv2 as cv

#begin func
def calculate_lc_displacement(img1,img2,imgsmooting,p):
    #smooth image

    if imgsmooting > 0:
        img1 = cv.GaussianBlur(img1, (0,0), imgsmooting)
        img2 = cv.GaussianBlur(img2, (0,0), imgsmooting)

    #calc gradients
    dx = cv.Sobel(img1, cv.CV_64F, 1, 0, ksize = 3, scale = 1.0/3.0)
    dy = cv.Sobel(img1, cv.CV_64F, 0, 1, ksize = 3, scale = 1.0/3.0)

    #calc temporal gradient
    dt = img2-img1

    #compute structure tensor components
    axx = cv.GaussianBlur(dx * dx, (0,0), p)
    ayy = cv.GaussianBlur(dy * dy, (0,0), p)
    axy = cv.GaussianBlur(dx * dy, (0,0), p)

    
    bx = cv.GaussianBlur(dx * dt, (0,0), p)
    by = cv.GaussianBlur(dy * dt, (0,0), p)

    #compute u and v according to lecture
    norm = (axx*ayy) - (axy*axy)
    u = ( (bx * ayy) - (axy * by)) / norm
    v = ( (by* axx) - (axy * bx) ) / norm
    return u,v
#endfuc

#get imgs
I1g = cv.imread(r"C:\Users\Tobias\Desktop\pyhonws\lucaskanada\yos1.pgm", cv.IMREAD_GRAYSCALE)
I2g = cv.imread(r"C:\Users\Tobias\Desktop\pyhonws\lucaskanada\yos2.pgm", cv.IMREAD_GRAYSCALE)
#calc displacement
u,v = calculate_lc_displacement(I1g,I2g,1.4,6.4)

# Use Hue, Saturation, Value colour model 
img3 = cv.imread(r"C:\Users\Tobias\Desktop\pyhonws\lucaskanada\yos1.pgm", cv.IMREAD_COLOR) #for 3 dimensions
hsv = np.zeros(img3.shape,dtype=np.uint8)
mag, ang = cv.cartToPolar(u,v)
hsv[..., 1] = 255
hsv[..., 0] = ang * 180 / np.pi / 2
hsv[..., 2] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)
cv.imshow("colored flow", bgr)
cv.waitKey(0)
cv.destroyAllWindows()

有人看到代码有什么问题吗？ 显示位移的函数实际上是正确的，它与Lucas和Canade的openCV实现配合得很好

优胜美地序列上给出的视差图与示例中给出的视差图完全不匹配

预期参数为：

但看起来是这样的：

下面是中间步骤的可视化：