Python+OpenCV实现寻找到圆点标定板的角点

图像大小按原图计算

dis_mm是标定板上的实际距离，要根据真实情况计算。

示例代码

# coding:utf-8
import math
import cv2
import numpy as np
import xml.etree.ElementTree as ET

import matplotlib.pyplot as plt

global DPI
DPI =  0.00245

def mainFigure(img):
    w = 20
    h = 5
    params = cv2.SimpleBlobDetector_Params()
    # Setup SimpleBlobDetector parameters.
    # print('params')
    # print(params)
    # print(type(params))

    # Filter by Area.
    params.filterByArea = True
    params.minArea = 10e1
    params.maxArea = 10e4
    # 图大要修改  100
    params.minDistBetweenBlobs = 100
    # params.filterByColor = True
    params.filterByConvexity = False
    # tweak these as you see fit
    # Filter by Circularity
    # params.filterByCircularity = False
    # params.minCircularity = 0.2
    # params.blobColor = 0
    # # # Filter by Convexity
    # params.filterByConvexity = True
    # params.minConvexity = 0.87
    # Filter by Inertia
    # params.filterByInertia = True
    # params.filterByInertia = False
    # params.minInertiaRatio = 0.01

    gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    # Detect blobs.
    # image = cv2.resize(gray_img, (int(img.shape[1]/4),int(img.shape[0]/4)), 1, 1, cv2.INTER_LINEAR)
    # image = cv2.resize(gray_img, dsize=None, fx=0.25, fy=0.25, interpolation=cv2.INTER_LINEAR)
    minThreshValue = 60
    _, gray = cv2.threshold(gray, minThreshValue, 255, cv2.THRESH_BINARY)
    # gray = cv2.resize(gray, dsize=None, fx=1, fy=1, interpolation=cv2.INTER_LINEAR)
    # gray = cv2.resize(gray, dsize=None, fx=2, fy=2, interpolation=cv2.INTER_LINEAR)

    # plt.imshow(gray)
    # cv2.imshow("gray",gray)

    # 找到距离原点（0，0）最近和最远的点
    h, w = img.shape[:2]

    detector = cv2.SimpleBlobDetector_create(params)
    keypoints = detector.detect(gray)
    print("检测点为", len(keypoints))
    # opencv
    im_with_keypoints = cv2.drawKeypoints(gray, keypoints, np.array([]), (0, 255, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    # plt
    # fig = plt.figure()
    # im_with_keypoints = cv2.drawKeypoints(gray, keypoints, np.array([]), (0, 0, 255),  cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    color_img = cv2.cvtColor(im_with_keypoints, cv2.COLOR_BGR2RGB)

    DPIall = []

    if keypoints is not None:
        # 找到距离（0，0）最近和最远的点
        kpUpLeft = []
        disUpLeft = []
        for i in range(len(keypoints)):
            dis = math.sqrt(math.pow(keypoints[i].pt[0],2) + math.pow(keypoints[i].pt[1],2))
            disUpLeft.append(dis)
            kpUpLeft.append(keypoints[i].pt)
            # cv2.circle(img, (int(keypoints[i].pt[0]), int(keypoints[i].pt[1])), 10, (0, 255, 0), 2)

        # 找到距离（640*2，0）最近和最远的点
        kpUpRight = []
        disUpRight=[]
        for i in range(len(keypoints)):
            # 最大距离坐标
            dis2 = math.sqrt(math.pow(abs(keypoints[i].pt[0]-w),2) + math.pow(abs(keypoints[i].pt[1]),2))
            disUpRight.append(dis2)
            kpUpRight.append(keypoints[i].pt)

        if disUpRight and disUpLeft:
            disDownLeftIndex = disUpRight.index(max(disUpRight))
            pointDL = kpUpRight[disDownLeftIndex]

            disUpRightIndex = disUpRight.index(min(disUpRight))
            pointUR = kpUpLeft[disUpRightIndex]

            disDownRightIndex = disUpLeft.index(max(disUpLeft))
            pointDR = kpUpLeft[disDownRightIndex]

            disUpLeftIndex = disUpLeft.index(min(disUpLeft))
            pointUL = kpUpLeft[disUpLeftIndex]

            if (pointDR is not None) and (pointUL is not None) and (pointDL is not None) and (pointUR is not None):
                # cv2.circle(color_img, (int(pointDR[0]),int(pointDR[1])), 30, (0, 255, 0),2)
                # cv2.circle(color_img, (int(pointUL[0]),int(pointUL[1])), 30, (0, 255, 0),2)
                # cv2.line(color_img,(int(pointDR[0]),int(pointDR[1])), (int(pointDL[0]),int(pointDL[1])),(0, 0, 255),2)
                #
                # cv2.circle(color_img, (int(pointDL[0]),int(pointDL[1])), 30, (0, 255, 0),2)
                # cv2.circle(color_img, (int(pointUR[0]),int(pointUR[1])), 30, (0, 255, 0),2)
                # cv2.line(color_img, (int(pointDL[0]),int(pointDL[1])), (int(pointUR[0]),int(pointUR[1])), (0, 0, 255), 2)
                # cv2.line(color_img, (int(pointUL[0]),int(pointUL[1])), (int(pointUR[0]),int(pointUR[1])), (0, 0, 255), 2)

                # 显示在原图上 原图减半因为之前放大了
                # cv2.circle(img, (int(pointDR[0]/2), int(pointDR[1]/2)), 10, (0, 255, 0), 2)
                # cv2.circle(img, (int(pointUL[0]/2), int(pointUL[1]/2)), 10, (0, 255, 0), 2)
                # cv2.line(img,(int(pointDR[0]/2),int(pointDR[1]/2)), (int(pointUL[0]/2),int(pointUL[1]/2)),(0, 0, 255),2)
                # dis_UR_DL = math.sqrt(math.pow(pointUR[0]-pointDL[0], 2) + math.pow(pointUR[1]-pointDL[1], 2))/2

                cv2.circle(img, (int(pointDR[0] ), int(pointDR[1] )), 10, (0, 255, 0), 2)
                cv2.circle(img, (int(pointUL[0] ), int(pointUL[1] )), 10, (0, 255, 0), 2)
                cv2.line(img, (int(pointDR[0] ), int(pointDR[1] )), (int(pointUL[0] ), int(pointUL[1] )),
                         (0, 0, 255), 2)
                dis_UR_DL = math.sqrt(math.pow(pointUR[0] - pointDL[0], 2) + math.pow(pointUR[1] - pointDL[1], 2))

                DPIall.append(dis_UR_DL)

                global DPI
                # 只计算斜对角线，约束条件简单一些，增加适用性
                # 单边长a = 0.05*19 对角线
                # DPI = (math.sqrt(1.3435)) / sum(DPIall)

                dis_mm = math.sqrt(math.pow(15, 2) + math.pow(15, 2))
                print("两点的像素距离为", dis_UR_DL, "实际距离为", dis_mm)
                DPI = dis_mm / dis_UR_DL
                print("DPI", DPI)

                # configFile_xml = "wellConfig.xml"
                # tree = ET.parse(configFile_xml)
                # root = tree.getroot()
                # secondRoot = root.find("DPI")
                # print(secondRoot.text)
                #
                # secondRoot.text = str(DPI)
                # tree.write("wellConfig.xml")
                # print("DPI", DPI)
            else:
                pass
            print(DPI)

    # plt.imshow(color_img,interpolation='bicubic')
    # fname = "key points"
    # titlestr = '%s found %d keypoints' % (fname, len(keypoints))
    # plt.title(titlestr)
    # # fig.canvas.set_window_title(titlestr)
    # plt.show()

    # cv2.imshow('findCorners', color_img)
    cv2.namedWindow('findCorners',2)
    cv2.imshow('findCorners', img)
    cv2.waitKey()

if __name__ == "__main__":

    # # # 单张图片测试
    # DPI hole
    # 0.01221465904139037
    #
    # DPI needle
    # 0.012229753249515942
    # img = cv2.imread("TwoBiaoDing/ROI_needle.jpg",1)
    img = cv2.imread("TwoBiaoDing/ROI_holes.jpg",1)

    img_roi = img.copy()
    # img_roi = img[640:2000, 1530:2800]
    # cv2.namedWindow("img_roi",2)
    # cv2.imshow("img_roi", img_roi)
    # cv2.waitKey()
    # img = cv2.imread("circles/Snap_0.jpg",1)

    mainFigure(img_roi)

    # # 所有图片测试
    # for i in range(15):
    #     fileName = "Snap_" + str(i) + ".jpg"
    # # img = cv2.imread("circles/Snap_007.jpg",1)
    #     img = cv2.imread("circles/" + fileName,1)
    #     print(fileName)
    #     mainFigure(img)

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