使用opencv计算质心

Question

我有一个多个圆圈的图像，圆圈内有热点区域，具有高强度（高像素值）和冷点区域（低像素值）。我想用Python中的OpenCV计算每个圆的加权质心。我正在使用这段代码：

im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
     # calculate moments for each contour
     M = cv2.moments(c)

     # calculate x,y coordinate of center
     if M["m00"] != 0:
         cX = int(M["m10"] / M["m00"])
         cY = int(M["m01"] / M["m00"])
      else:
         cX, cY = 0, 0

好的，这段代码只需获取二值图像，提取所有圆，并找到每个圆的轮廓。

问题是，我需要找到RGB/灰度图像（考虑像素强度）的加权质心，而不是二值图像。我该怎么做？

非常感谢。

Answer 1

@yapws87答案的一些修正：

import numpy as np
import cv2

# create a meshgrid for coordinate calculation
r,c = np.shape(ori_img)
r_ = np.linspace(0,r,r+1)
c_ = np.linspace(0,c,c+1)
x_m, y_m = np.meshgrid(c_, r_, sparse=False, indexing='xy')


im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)


for c in contours:
     # Get the boundingbox
     x,y,w,h = cv2.boundingRect(c)

     # calculate x,y coordinate of center
     # Get the corresponding roi for calculation
     weights = ori_img[y:y+h,x:x+w]
     roi_grid_x = x_m[y:y+h,x:x+w]
     roi_grid_y = y_m[y:y+h,x:x+w]
     
     # get the weighted sum
     weighted_x = weights * roi_grid_x
     weighted_y = weights * roi_grid_y
     
     cx = np.sum(weighted_x) / np.sum(weights)
     cy = np.sum(weighted_y) / np.sum(weights)  

Answer 2

下面是一种python伪代码解决方案。该代码用于计算质心的加权中心。在计算中，图像的强度级别用作权重。因此，强度越高，重量越高。

为了简化计算过程，我们需要将x坐标和y坐标的网格划分为原始图像的大小。x和y坐标的加权平均值将给出加权质心

import numpy as np
import cv2

# create a meshgrid for coordinate calculation
r,c = np.shape(ori_img)
r_ = np.linspace(0,r,r+1)
c_ = np.linspace(0,c,c+1)
x_m, y_m = np.meshgrid(c_, r_, sparse=False, indexing='ij')


im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)


for c in contours:
     # Get the boundingbox
     x,y,w,h = cv2.boundingRect(c)

     # calculate x,y coordinate of center
     # Get the corresponding roi for calculation
     weights = ori_img[y:y+h,x:x+w]
     roi_grid_x = x_m[y:y+h,x:x+w]
     roi_grid_y = y_m[y:y+h,x:x+w]

     # get the weighted sum
     weighted_x = weights * roi_grid_x
     weighted_y = weights * roi_grid_y

     cx = np.sum(weighted_x) / np.sum(weights)
     cy = np.sum(roi_grid_y) / np.sum(weights)