Question

我有兴趣计算两组轮廓元素之间的平均最小距离。

以下是示例图片：

到目前为止，这是我的代码：

    import cv2
    import numpy as np

def contours(layer):
    gray = cv2.cvtColor(layer, cv2.COLOR_BGR2GRAY)
    ret,binary = cv2.threshold(gray, 1,255,cv2.THRESH_BINARY) 
    image, contours, hierarchy =         cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
    drawn = cv2.drawContours(image,contours,-1,(150,150,150),3)
    return contours, drawn

def minDistance(contour, contourOther):
    distanceMin = 99999999
    for xA, yA in contour[0]:
        for xB, yB in contourOther[0]:
            distance = ((xB-xA)**2+(yB-yA)**2)**(1/2) # distance formula
            if (distance < distanceMin):
                distanceMin = distance
    return distanceMin

def cntDistanceCompare(contoursA, contoursB):
    cumMinDistList = []
    for contourA in contoursA:
        indMinDistList = []
        for contourB in contoursB:
            minDist = minDistance(contourA,contourB)
            indMinDistList.append(minDist)
        cumMinDistList.append(indMinDistList)
    l = cumMinDistList  
    return sum(l)/len(l) #returns mean distance

def maskBuilder(bgr,hl,hh,sl,sh,vl,vh):
    hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)
    lower_bound = np.array([hl,sl,vl],dtype=np.uint8)
    upper_bound = np.array([hh,sh,vh],dtype=np.uint8)
    return cv2.inRange(hsv, lower_bound,upper_bound)

img = cv2.imread("sample.jpg")
maskA=maskBuilder(img, 150,185, 40,220, 65,240) 
maskB=maskBuilder(img, 3,20, 50,180, 20,250)
layerA = cv2.bitwise_and(img, img, mask = maskA)
layerB = cv2.bitwise_and(img, img, mask = maskB)
contoursA = contours(layerA)[0]
contoursB = contours(layerA)[1]

print cntDistanceCompare(contoursA, contoursB)

从这些图像中可以看出，遮罩和遮挡工作（显示第一组轮廓）：

cntDistanceCompare（）函数循环遍历集合A和B的每个轮廓，输出轮廓之间的平均最小距离。在此函数中，minDistance（）从每组轮廓A和B上的（x，y）点计算最小毕达哥拉斯距离（使用距离公式）。

抛出以下错误： Traceback（最近一次调用最后一次）：文件＆＃34; mindistance.py＆＃34;，第46行，in cntDistanceCompare（contoursA，contoursB）在cntDistanceCompare中输入＆＃34; mindistance.py＆＃34;，第26行 minDist = minDistance（contourA，contourB）文件＆＃34; mindistance.py：，第15行，在minDistance中 for xO，yB in contourOther [0]： TypeError：＆＃39; numpy.uint8＆＃39;对象不可迭代

我怀疑这个问题是由于我缺乏如何在cv2.findContours（）给出的数据结构中引用每个轮廓顶点的x，y坐标的知识。

Answer 1

我使用的是旧版本的openCV，其中findContours只返回两个值，但希望此代码的重要部分有意义。我没有测试你的功能，但我确实展示了如何获得轮廓中心。你必须在＆＃34;时刻做一些事情。＆＃34;

import cv2
import numpy as np

def contours(layer):
    gray = cv2.cvtColor(layer, cv2.COLOR_BGR2GRAY)
    ret,binary = cv2.threshold(gray, 1,255,cv2.THRESH_BINARY) 
    contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
    #drawn = cv2.drawContours(image,contours,-1,(150,150,150),3)
    return contours #, drawn

def minDistance(contour, contourOther):
    distanceMin = 99999999
    for xA, yA in contour[0]:
        for xB, yB in contourOther[0]:
            distance = ((xB-xA)**2+(yB-yA)**2)**(1/2) # distance formula
            if (distance < distanceMin):
                distanceMin = distance
    return distanceMin

def cntDistanceCompare(contoursA, contoursB):
    cumMinDistList = []
    for contourA in contoursA:
        indMinDistList = []
        for contourB in contoursB:
            minDist = minDistance(contourA,contourB)
            indMinDistList.append(minDist)
        cumMinDistList.append(indMinDistList)
    l = cumMinDistList  
    return sum(l)/len(l) #returns mean distance

def maskBuilder(bgr,hl,hh,sl,sh,vl,vh):
    hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)
    lower_bound = np.array([hl,sl,vl],dtype=np.uint8)
    upper_bound = np.array([hh,sh,vh],dtype=np.uint8)
    return cv2.inRange(hsv, lower_bound,upper_bound)

def getContourCenters(contourData):
    contourCoordinates = []
    for contour in contourData:
        moments = cv2.moments(contour)
        contourX = int(moments['m10'] / float(moments['m00']))
        contourY = int(moments['m01'] / float(moments['m00']))
        contourCoordinates += [[contourX, contourY]]
    return contourCoordinates

img = cv2.imread("sample.jpg")
maskA=maskBuilder(img, 150,185, 40,220, 65,240) 
maskB=maskBuilder(img, 3,20, 50,180, 20,250)
layerA = cv2.bitwise_and(img, img, mask = maskA)
layerB = cv2.bitwise_and(img, img, mask = maskB)
contoursA = contours(layerA)
contoursB = contours(layerB)

print getContourCenters(contoursA)
print getContourCenters(contoursB)

#print cntDistanceCompare(contoursA, contoursB)

编辑：我现在正在玩你的功能，我担心我误解了这个问题。让我知道，我将删除我的答案。

OpenCV：任意大型轮廓组之间的最小距离（Python）

1 个答案: