手动Homography对齐RGB与热成像图像失败，求问题排查

我正在开发一款采用手动校准方式对齐两张图像的应用，因自动校准在该场景下效果不佳，故选择手动选取像素点实现近乎像素级的对齐。但最终结果未达预期——按计算出的点应该能将图像精准重叠，却未实现。

单应矩阵结果 #

[[ 7.43200521e-01 -1.79170744e-02 -1.76782990e+02]
 [ 1.00046389e-02  7.84106136e-01 -3.22549155e+01]
 [ 5.10695284e-05 -8.48641135e-05  1.00000000e+00]]

手动选取的点 #

RGB图像点 #

[[ 277  708]
 [1108  654]
 [ 632  545]
 [ 922  439]
 [ 874  403]
 [ 398  376]
 [ 409  645]
 [ 445  593]
 [ 693  342]
 [ 739  244]
 [ 505  234]
 [ 408  275]
 [ 915  162]
 [1094  126]
 [ 483  115]
 [ 951  366]
 [ 517  355]]

热成像图像点 #

[[  8 549]
 [634 491]
 [282 397]
 [496 318]
 [461 289]
 [113 269]
 [122 479]
 [148 438]
 [325 236]
 [360 162]
 [194 156]
 [121 188]
 [484 106]
 [621  67]
 [178  62]
 [515 261]
 [203 253]]

实现代码 #

def manual_calibration(self, rgb: cv2.UMat, thermal: cv2.UMat) -> Tuple[cv2.UMat, Tuple[int, int, int, int]]:
           
    rgb_gray = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY) 
    thermal_gray = cv2.cvtColor(thermal, cv2.COLOR_BGR2GRAY) 
    
    h_rgb, w_rgb = rgb_gray.shape
    h_th, w_th = thermal_gray.shape
    
    thermal_gray = cv2.copyMakeBorder(thermal_gray, 0, h_rgb - h_th, 0, 0, cv2.BORDER_CONSTANT, value=[0, 0, 0])
    
    merged = cv2.hconcat((rgb_gray, thermal_gray))
    
    self.merged = cv2.cvtColor(merged, cv2.COLOR_GRAY2RGB)
    
    def point_validation(ix, iy):
        if ix > w_rgb:
            ix -= w_rgb
        return ix, iy

    self.points_left = np.array([])
    self.points_right = np.array([])
    self.label = True

    def select_point(event, x, y, flags, param):
        if event == cv2.EVENT_LBUTTONDOWN: # captures left button double-click
            ix, iy = x, y
            cv2.circle(img=self.merged, center=(x,y), radius=5, color=(0,255,0),thickness=-1)
            
            ix, iy = point_validation(ix, iy)
            
            pt = np.array([ix, iy])
            
            if self.label:
                # self.points_left = np.vstack((self.points_left, pt))
                self.points_left = np.vstack((self.points_left, pt)) if self.points_left.size else pt
                self.label = False
            else:
                # self.points_right = np.vstack((self.points_right, pt))
                self.points_right = np.vstack((self.points_right, pt)) if self.points_right.size else pt
                self.label = True
                
                
            print(ix, iy)
        
    
    cv2.namedWindow('calibration')
    cv2.setMouseCallback('calibration', select_point)
    
    while True:
        cv2.imshow("calibration", self.merged)
        if cv2.waitKey(20) & 0xFF == 27:
            break
    cv2.destroyAllWindows()
    
    print(self.points_left)
    print(self.points_right)
    
    ### EDIT NEW POINT VALIDATION
    rgb_gray_check = rgb_gray
    thermal_gray_check = thermal_gray
    
    for point in self.points_left:
        cv2.circle(img=rgb_gray_check, center=point, radius=5, color=(0,255,0),thickness=-1)
        
    for point in self.points_right:
        cv2.circle(img=thermal_gray_check, center=point, radius=5, color=(0,255,0),thickness=-1)
    
    cv2.imshow('rgb', rgb_gray_check)
    cv2.imshow('thermal', thermal_gray_check)
    cv2.waitKey(0)
   
    ### EDIT NEW POINT VALIDATION



    # Compute homography

    # 0 - a regular method using all the points
    # CV_RANSAC - RANSAC-based robust method
    # CV_LMEDS - Least-Median robust method
    matrix, mask = cv2.findHomography(self.points_left, self.points_right, 0)
    
    print(matrix)
               
    # matrix[0][3] += (w_th/2)
    # matrix[1][3] += (h_th/2)
    
    warp_src = cv2.warpPerspective(thermal, matrix, (rgb.shape[1], rgb.shape[0]))
            
    alpha = 0.5
    beta = (1.0 - alpha)
    dst_warp_blended = cv2.addWeighted(rgb, alpha, warp_src, beta, 0.0)
    
    cv2.imshow('Blended destination and warped image', dst_warp_blended)
    cv2.waitKey(0)

原始图像 #

• RGB图像：
• 热成像图像：

当前结果 #

请问我哪里出错了？

内容的提问来源于stack exchange，提问作者Blank