使用Opencv和Python的实时测量工具

Question

我尝试建立一个实时测量算法。我的问题是，你可以看到一幅画enter image description here

我需要一个对角线测量，但我刚刚找到了这两条边和一条对角线。当我开始寻找其他对角线时，我犯了一个错误。因为计算只是从这个角开始，我不能再开始另一个角。当我试图开始另一个弯角时，我犯了一个错误。这个角是0

我不明白为什么我不能从另一个角落开始

测量代码

import cv2
import utlis

###################################
webcam = True
path = '1.jpg'
cap = cv2.VideoCapture(0)
cap.set(10, 160)
cap.set(3, 1920)
cap.set(4, 1080)
scale = 3
wP = 210 * scale
hP = 297 * scale
###################################

while True:
    if webcam:
        success, img = cap.read()
    else:
        img = cv2.imread(path)

    imgContours, conts = utlis.getContours(img, minArea=50000, filter=4)
    if len(conts) !=0:
        biggest = conts[0][2]

        print(biggest)
        imgWarp = utlis.warpImg(img, biggest, wP, hP)
        imgContours2, conts2 = utlis.getContours(imgWarp,
                                                 minArea=2000, filter=4,
                                                 cThr=[50, 50], draw=False)
        if len(conts) != 0:
            for obj in conts2:
                cv2.polylines(imgContours2, [obj[2]], True, (0, 255, 0), 2)
                nPoints = utlis.reorder(obj[2])
                nW = round((utlis.findDis(nPoints[0][0] // scale, nPoints[1][0] // scale) / 10), 1)
                nH = round((utlis.findDis(nPoints[0][0] // scale, nPoints[2][0] // scale) / 10), 1)
                nQ = round((utlis.findDis(nPoints[0][0] // scale, nPoints[3][0] // scale) / 10), 1)
                nW2 = round((utlis.findDis(nPoints[2][2] // scale, nPoints[1][2] // scale) / 10), 1)
                #nH2 = round((utlis.findDis(nPoints[3][3] // scale, nPoints[2][0] // scale) / 10), 1)
                #nZ = round((utlis.findDis(nPoints[3][3] // scale, nPoints[3][0] // scale) / 10), 1)
                cv2.arrowedLine(imgContours2, (nPoints[0][0][0], nPoints[0][0][1]),
                                (nPoints[1][0][0], nPoints[1][0][1]),
                                (255, 0, 255), 3, 8, 0, 0.05)
                cv2.arrowedLine(imgContours2, (nPoints[0][0][0], nPoints[0][0][1]),
                                (nPoints[2][0][0], nPoints[2][0][1]),
                                (255, 0, 255), 3, 8, 0, 0.05)
                cv2.arrowedLine(imgContours2, (nPoints[0][0][0], nPoints[0][0][1]),
                                (nPoints[3][0][0], nPoints[3][0][1]),
                                (255, 0, 255), 3, 8, 0, 0.05)
                #cv2.arrowedLine(imgContours2, (nPoints[3][3][3], nPoints[3][3][1]),
                                #(nPoints[1][0][0], nPoints[1][0][1]),
                                #(255, 0, 255), 3, 8, 0, 0.05)
                x, y, w, h = obj[3]

                cv2.putText(imgContours2, '{}cm'.format(nW), (x + 30, y - 10), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
                            (255, 0, 255), 2)
                cv2.putText(imgContours2, '{}cm'.format(nH), (x - 70, y + h // 2), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
                            (255, 0, 255), 2)
                cv2.putText(imgContours2, '{}cm'.format(nQ), (x + 200 , y + 200 ), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
                            (255, 0, 255), 2)
        cv2.imshow('A4', imgContours2)

    img = cv2.resize(img, (0, 0), None, 0.5, 0.5)
    cv2.imshow('Original', img)
    cv2.waitKey(1)

乌特里斯

import cv2
import numpy as np


def getContours(img, cThr=[100, 100], showCanny=False, minArea=1000, filter=0, draw=False):
    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1)
    imgCanny = cv2.Canny(imgBlur, cThr[0], cThr[1])
    kernel = np.ones((5, 5))
    imgDial = cv2.dilate(imgCanny, kernel, iterations=3)
    imgThre = cv2.erode(imgDial, kernel, iterations=2)
    if showCanny: cv2.imshow('Canny', imgThre)
    contours, hiearchy = cv2.findContours(imgThre, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    finalCountours = []
    for i in contours:
        area = cv2.contourArea(i)
        if area > minArea:
            peri = cv2.arcLength(i, True)
            approx = cv2.approxPolyDP(i, 0.02 * peri, True)
            bbox = cv2.boundingRect(approx)
            if filter > 0:
                if len(approx) == filter:
                    finalCountours.append([len(approx), area, approx, bbox, i])
            else:
                finalCountours.append([len(approx), area, approx, bbox, i])
    finalCountours = sorted(finalCountours, key=lambda x: x[1], reverse=True)
    if draw:
        for con in finalCountours:
            cv2.drawContours(img, con[4], -1, (0, 0, 255), 3)
    return img, finalCountours


def reorder(myPoints):
    # print(myPoints.shape)
    myPointsNew = np.zeros_like(myPoints)
    myPoints = myPoints.reshape((4, 2))
    add = myPoints.sum(1)
    myPointsNew[0] = myPoints[np.argmin(add)]
    myPointsNew[3] = myPoints[np.argmax(add)]
    diff = np.diff(myPoints, axis=1)
    myPointsNew[1] = myPoints[np.argmin(diff)]
    myPointsNew[2] = myPoints[np.argmax(diff)]
    return myPointsNew


def warpImg(img, points, w, h, pad=20):
    # print(points)
    points = reorder(points)
    pts1 = np.float32(points)
    pts2 = np.float32([[0, 0], [w, 0], [0, h], [w, h]])
    matrix = cv2.getPerspectiveTransform(pts1, pts2)
    imgWarp = cv2.warpPerspective(img, matrix, (w, h))
    imgWarp = imgWarp[pad:imgWarp.shape[0] - pad, pad:imgWarp.shape[1] - pad]
    return imgWarp


def findDis(pts1, pts2):
    return ((pts2[0] - pts1[0]) ** 2 + (pts2[1] - pts1[1]) ** 2) ** 0.5