opencv HoughCircle and trackbar

opencv HoughCircle and trackbar

本文关键字：trackbar and HoughCircle opencv      更新时间：2023-10-16

我正在研究霍夫圆函数。基本上有4个参数，我可以玩得到我想要的正确的圆。

所以我想到我想创建一个跟踪条来监控正在处理的图像的状态。

所以我修改了我的代码，像这样

#include <sstream>
#include <string>
#include <iostream>
#include <vector>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <opencvcv.h>
#include <opencvhighgui.h>
#include <stdlib.h>
#include <stdio.h>
using namespace std;
using namespace cv;

int main(int argc, char** argv) {
//Create a window for trackbars
namedWindow("Trackbar Window", CV_WINDOW_AUTOSIZE);
//Create trackbar to change brightness
int iSliderValue1 = 50;
createTrackbar("Brightness", "Trackbar Window", &iSliderValue1, 100);
//Create trackbar to change contrast
int iSliderValue2 = 50;
createTrackbar("Contrast", "Trackbar Window", &iSliderValue2, 100);
int param1 = 10;
createTrackbar("param1", "Trackbar Window", &param1, 300);
int param2 = 10;
createTrackbar("param2", "Trackbar Window", &param2, 300);

Mat src;
VideoCapture capture;
capture.open("movingBall.wmv");
capture.read(src);
capture.set(CV_CAP_PROP_FRAME_HEIGHT, 480);
capture.set(CV_CAP_PROP_FRAME_WIDTH, 640);

if (!src.data) {
    std::cout << "ERROR:topening image" << std::endl;
    return -1;
}
cv::namedWindow("image1", CV_WINDOW_AUTOSIZE);
cv::namedWindow("image2", CV_WINDOW_AUTOSIZE);
while (true){
    capture.read(src);
    Mat dst;
    int iBrightness = iSliderValue1 - 50;
    double dContrast = iSliderValue2 / 50.0;
    src.convertTo(src, -1, dContrast, iBrightness);
cv::imshow("image1", src);
Mat src_gray2;
cvtColor(src, src_gray2, CV_BGR2GRAY);
GaussianBlur(src_gray2, src_gray2, cv::Size(9, 9), 2, 2);
vector<Vec3f> circles;
HoughCircles(src_gray2, circles, CV_HOUGH_GRADIENT,
    2,   // accumulator resolution (size of the image / 2)
    5,  // minimum distance between two circles
    param1, // Canny high threshold
    param2, // minimum number of votes
    0, 0); // min and max radius
std::cout << circles.size() << std::endl;
std::cout << "end of test" << std::endl;

for (size_t i = 0; i < circles.size(); i++)
{
    Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
    int radius = cvRound(circles[i][2]);
    circle(src, center, 3, Scalar(0, 255, 0), -1, 8, 0);
    // circle outline
    circle(src, center, radius, Scalar(0, 0, 255), 3, 8, 0);
}
/*std::vector<cv::Vec3f>::
    const_iterator itc = circles.begin();
while (itc != circles.end()) {
    cv::circle(src_gray2,
        cv::Point((*itc)[0], (*itc)[1]), // circle centre
        (*itc)[2],       // circle radius
        cv::Scalar(0,0,0), // color
        2);              // thickness
    ++itc;
}*/
cv::imshow("image2", src_gray2);
cvWaitKey(33);
}
return 0;
}

如霍夫圆函数所示，我使用int param1;作为我想要改变的值。但是，代码没有语法错误，但无法编译。我想知道我的轨迹条设置是否有问题。谢谢你

这里我已经使用Python尝试过了，你可以尝试从它移植…

import cv2
import numpy as np
img = cv2.imread('C:/Python34/images/2.jpg',0)
cv2.namedWindow('image')
def nothing(x):
    pass
cv2.createTrackbar('Param 1','image',0,100,nothing)
cv2.createTrackbar('Param 2','image',0,100,nothing)
switch = '0 : OFF n1 : ON'
cv2.createTrackbar(switch, 'image',0,1,nothing)
while(1):
    cv2.imshow('image',img)
    k = cv2.waitKey(1) & 0xFF
    if k == 27:
        break
    #To Get Parameter values from Trackbar Values
    para1 = cv2.getTrackbarPos('Param 1','image')
    para2 = cv2.getTrackbarPos('Param 2','image')
    s = cv2.getTrackbarPos(switch,'image')
    if s == 0:
        cv2.imshow('image', img)
    else:
    #For finding Hough Circles according to trackbar parameters
        circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,para1,para2,minRadius=0,maxRadius=0)
        circles = np.uint16(np.around(circles))
        #For drawing Hough Circles
        for i in circles[0,:]:
           cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2)
           cv2.circle(img,(i[0],i[1]),2,(0,0,255),3)
           cv2.imshow('image', img)
        cv2.waitKey(0)
        img = cv2.imread('C:/Python34/images/2.jpg',0)

cv2.destroyAllWindows()

你可以使用上面的代码作为参考，首先它为开关创建了一个窗口和跟踪条，为霍夫圆创建了两个参数。然后在while循环中，para1和para2将把轨迹条的位置存储为canny参数的值。然后在cv2中使用。HoughCircles函数并绘制圆。再次加载图像，以便每次更改参数时输出都是新的图像，以避免混淆。

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