如何使用python在OpenCv中查找轮廓颜色

Question

我正在处理一个需要查找轮廓内部区域颜色的需求。我们正在使用OpenCv和Python，这是我在Python中的代码：

import imutils
import cv2
import numpy as np

path = "multiple_grains_1.jpeg"
img = cv2.imread(path)
resized = imutils.resize(img, width=900)
ratio = img.shape[0] / float(resized.shape[0])
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)

(ret, thresh) = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
edge = cv2.Canny(thresh, 100, 200)
( _,cnts, _) = cv2.findContours(edge.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

for c in cnts:
    rect = cv2.minAreaRect(c)
    box = cv2.boxPoints(rect)
    box = np.int0(box)
    area = cv2.contourArea(c)
    if area > 1:
        cv2.drawContours(resized,[box],0,(0,0,255),2)
        cv2.drawContours(resized, [c], -1, (0, 255, 0), 2)
        #print("area : "+str(area))
        #print('\nContours: ' + str(c[0]))
        #img[c[0]]
        pixelpoints = np.transpose(np.nonzero(c))
        #print('\pixelpoints: ' + str(pixelpoints))

        #  accessed the center of the contour using the followi
        M = cv2.moments(c)
        if M["m00"] != 0:
            cX = int((M["m10"] / M["m00"]) * ratio)
            cY = int((M["m01"] / M["m00"]) * ratio)
            #print (cX,cY)

            cord = img[int(cX)+3,int(cY)+3]
            print(cord)


cv2.imshow("Output", resized)
cv2.waitKey(0)
exit()

当我检查轮廓的质心颜色时，我无法获取正确的颜色。有谁知道如何使用OpenCv和python获取轮廓内的颜色吗？

Answer 1

我简化了您的代码，无需使用矩便可以获得质心的颜色。

import imutils
import cv2
import numpy as np
import matplotlib.pyplot as plt


img = cv2.imread("multiplegrains.png")
resized = imutils.resize(img, width=900)
ratio = img.shape[0] / float(resized.shape[0])
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)

ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
_, cnts, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# if you want cv2.contourArea >1, you can just comment line bellow
cnts = np.array(cnts)[[cv2.contourArea(c)>10 for c in cnts]]
grains = [np.int0(cv2.boxPoints(cv2.minAreaRect(c))) for c in cnts]
centroids =[(grain[2][1]-(grain[2][1]-grain[0][1])//2, grain[2][0]-(grain[2][0]-grain[0][0])//2) for grain in grains]

colors = [resized[centroid] for centroid in centroids]