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如何用python在OpenCv中找到轮廓的颜色

  •  1
  • Seenu69  · 技术社区  · 8 年前

    我正在处理一个要求,我需要找到内部轮廓区域的颜色。我们在Python中使用OpenCv,下面是我的Python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()
    

    enter image description here

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

    1 回复  |  直到 8 年前
        1
  •  1
  •   Fred Guth    8 年前

    我简化了你的代码,不用矩就能得到质心的颜色。

    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]