python opencv 找到圆点标定板所有点后通过距离找四个角点3

# 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 = 10e3
    params.minDistBetweenBlobs = 25
    # 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 = 120
    _, gray = cv2.threshold(gray, minThreshValue, 255, cv2.THRESH_BINARY)
    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

    detector = cv2.SimpleBlobDetector_create(params)
    keypoints = detector.detect(gray)
    # opencv
    im_with_keypoints = cv2.drawKeypoints(gray, keypoints, np.array([]), (255, 0, 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)

        # 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]-640*2),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
                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
                DPIall.append(dis_UR_DL)
                # print(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
                DPI = ( math.sqrt(2)) / sum(DPIall)


                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.imshow('findCorners', img)
    cv2.waitKey()



if __name__ == "__main__":

    # # # 单张图片测试
    # img = cv2.imread("circles/Snap_7.jpg",1)
    # mainFigure(img)

    # 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)