opencv计算相机到物体的距离

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本文介绍了一个使用OpenCV库进行网络摄像头实时图像处理的C++程序实例。程序实现了从网络摄像头捕获图像，转换为灰度，应用阈值、腐蚀和膨胀操作，寻找轮廓，并计算目标距离和置信度。该程序展示了如何利用OpenCV函数进行图像预处理，目标检测和尺寸估算。

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使用网络摄像头

#include "stdafx.h"
#include "cv.h"
#include "highgui.h"
#include "opencv2\imgproc\imgproc_c.h"
#include<fstream>
#include "math.h"

int _tmain(int argc, _TCHAR* argv[])
{
using namespace std;
using namespace cv;

   Mat img, img_gray, channel[3];
   VideoCapture cam(1);
   double distance = 0;

   //FILE *data;
   //data = fopen("data320.csv","a");

   cam.set(CV_CAP_PROP_FRAME_WIDTH, 1280);
   cam.set(CV_CAP_PROP_FRAME_HEIGHT, 720);
   cam.set(CV_CAP_PROP_CONVERT_RGB, 1);
   namedWindow("Frame", WINDOW_AUTOSIZE);
   while(waitKey(10) != 'a')
   {
       cam >> img;
       cvtColor(img, img_gray, COLOR_RGB2GRAY);
       split(img, channel);
       subtract(channel[2], img_gray, img_gray);
       //convertScaleAbs(img, img);
       threshold(img_gray, img_gray, 90, 255, THRESH_BINARY);

erode(img_gray, img_gray, Mat(), Point(-1,-1), 4);
dilate(img_gray, img_gray, Mat(), Point(-1,-1), 4);

       vector<vector<Size>> contors;
       vector<Vec4i> heirarcy;
       findContours(img_gray, contors, heirarcy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0,0));

       vector<Rect> boundRect(contors.size());
       vector<vector<Point>> contor_poly(contors.size());

       for(int i =0; i< contors.size(); i++)
       {
           approxPolyDP(Mat(contors[i]), contor_poly[i], 3, true);
           boundRect[i] = boundingRect(Mat(contor_poly[i]));
       }
       int max_index = 0,max_area = 0;
       for(int i =0; i< boundRect.size(); i++)
       {
           int a = boundRect[i].area();
           rectangle(img, boundRect[i].tl(), boundRect[i].br(), Scalar(255,255,0), 2, 8, 0);
           if( a > max_area)
           {
               max_area = a;
               max_index = i;
           }
       }
       int confidence = 0;
       for(int i=0; i< boundRect.size(); i++)
       {
           if((boundRect[i].x < boundRect[max_index].x + boundRect[max_index].width && boundRect[i].x > boundRect[max_index].x - int(0.1*boundRect[max_index].width)) && (boundRect[i].y > boundRect[max_index].y) )
               confidence += 45;

       }
       if(boundRect.size() > 0)
       {
           if(confidence > 99)
               confidence = 0;
           //try{
           //Mat sub_image = Mat(img, Rect(max(boundRect[max_index].x-30, 0), max(boundRect[max_index].y-30, 0), min(int(boundRect[max_index].width*1.75), img.cols - boundRect[max_index].x+30), min(boundRect[max_index].height*3, img.rows - boundRect[max_index].y+30)));
           //imshow("Frame", sub_image);
           //}catch(int e){
           //   cout<<"Error occured"<<endl;
           //}
           rectangle(img, boundRect[max_index].tl(), boundRect[max_index].br(), Scalar(0,255,0), 2, 8, 0);

           //fprintf(data,"%d , %d , %d\n", boundRect[max_index].width, boundRect[max_index].height, boundRect[max_index].area());
           distance = 8414.7*pow(boundRect[max_index].area(), -0.468);
           cout << distance << " cm." <<" Confidence: "<<confidence<<endl;
           imshow("Frame", img);

       }

       else
           imshow("Frame", img);
   }