本文参考了网上对于opencv矩形识别的程序，并对其适当修改，使之可以在自己电脑上运行为自己想要的结果。主要做的修改是读取图像的方式，调整识别图中矩形的大小。转载原文的链接和修改后的程序如下。
参考原文链接：https://blog.csdn.net/Liuqz2009/article/details/47623191?locationNum=5&fps=1
参考原文源程序和修改后的程序分别为：
#include "cv.h"
#include "highgui.h"
#include <stdio.h>
#include <math.h>
#include <string.h>
#include <iostream>
int thresh = 50;
IplImage* img =NULL;
IplImage* img0 = NULL;
CvMemStorage* storage =NULL;
const char * wndname = "正方形检测 demo";
//angle函数用来返回（两个向量之间找到角度的余弦值）
double angle( CvPoint* pt1, CvPoint* pt2, CvPoint* pt0 )
{
double dx1 = pt1->x - pt0->x;
double dy1 = pt1->y - pt0->y;
double dx2 = pt2->x - pt0->x;
double dy2 = pt2->y - pt0->y;
return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
}
// 返回图像中找到的所有轮廓序列，并且序列存储在内存存储器中
CvSeq* findSquares4( IplImage* img, CvMemStorage* storage )
{
CvSeq* contours;
int i, c, l, N = 11;
CvSize sz = cvSize( img->width & -2, img->height & -2 );
IplImage* timg = cvCloneImage( img );
IplImage* gray = cvCreateImage( sz, 8, 1 );
IplImage* pyr = cvCreateImage( cvSize(sz.width/2, sz.height/2), 8, 3 );
IplImage* tgray;
CvSeq* result;
double s, t;
// 创建一个空序列用于存储轮廓角点
CvSeq* squares = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvPoint), storage );
cvSetImageROI( timg, cvRect( 0, 0, sz.width, sz.height ));
// 过滤噪音
cvPyrDown( timg, pyr, 7 );
cvPyrUp( pyr, timg, 7 );
tgray = cvCreateImage( sz, 8, 1 );
// 红绿蓝3色分别尝试提取
for( c = 0; c < 3; c++ )
{
// 提取 the c-th color plane
cvSetImageCOI( timg, c+1 );
cvCopy( timg, tgray, 0 );
// 尝试各种阈值提取得到的（N=11）
for( l = 0; l < N; l++ )
{
// apply Canny. Take the upper threshold from slider
// Canny helps to catch squares with gradient shading
if( l == 0 )
{
cvCanny( tgray, gray, 0, thresh, 5 );
//使用任意结构元素膨胀图像
cvDilate( gray, gray, 0, 1 );
}
else
{
// apply threshold if l!=0:
cvThreshold( tgray, gray, (l+1)*255/N, 255, CV_THRESH_BINARY );
}
// 找到所有轮廓并且存储在序列中
cvFindContours( gray, storage, &contours, sizeof(CvContour),
CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );
// 遍历找到的每个轮廓contours
while( contours )
{
//用指定精度逼近多边形曲线
result = cvApproxPoly( contours, sizeof(CvContour), storage,
CV_POLY_APPROX_DP, cvContourPerimeter(contours)*0.02, 0 );
if( result->total == 4 &&
fabs(cvContourArea(result,CV_WHOLE_SEQ)) > 500 &&
fabs(cvContourArea(result,CV_WHOLE_SEQ)) < 100000 &&
cvCheckContourConvexity(result) )
{
s = 0;
for( i = 0; i < 5; i++ )
{
// find minimum angle between joint edges (maximum of cosine)
if( i >= 2 )
{
t = fabs(angle(
(CvPoint*)cvGetSeqElem( result, i ),
(CvPoint*)cvGetSeqElem( result, i-2 ),
(CvPoint*)cvGetSeqElem( result, i-1 )));
s = s > t ? s : t;
}
}
// if 余弦值 足够小，可以认定角度为90度直角
//cos0.1=83度，能较好的趋近直角
if( s < 0.1 )
for( i = 0; i < 4; i++ )
cvSeqPush( squares,
(CvPoint*)cvGetSeqElem( result, i ));
}
// 继续查找下一个轮廓
contours = contours->h_next;
}
}
}
cvReleaseImage( &gray );
cvReleaseImage( &pyr );
cvReleaseImage( &tgray );
cvReleaseImage( &timg );
return squares;
}
//drawSquares函数用来画出在图像中找到的所有正方形轮廓
void drawSquares( IplImage* img, CvSeq* squares )
{
CvSeqReader reader;
IplImage* cpy = cvCloneImage( img );
int i;
cvStartReadSeq( squares, &reader, 0 );
// read 4 sequence elements at a time (all vertices of a square)
for( i = 0; i < squares->total; i += 4 )
{
CvPoint pt[4], *rect = pt;
int count = 4;
// read 4 vertices
CV_READ_SEQ_ELEM( pt[0], reader );
CV_READ_SEQ_ELEM( pt[1], reader );
CV_READ_SEQ_ELEM( pt[2], reader );
CV_READ_SEQ_ELEM( pt[3], reader );
// draw the square as a closed polyline
cvPolyLine( cpy, &rect, &count, 1, 1, CV_RGB(0,255,0), 2, CV_AA, 0 );
}
cvShowImage( wndname, cpy );
cvReleaseImage( &cpy );
}
char* names[] = { "pic1.png", "pic2.png", "pic3.png",
"pic4.png", "pic5.png", "pic6.png","pic7.png","pic8.png",
"pic9.png","pic10.png","pic11.png","pic12.png", 0 };
int main(int argc, char** argv)
{
int i, c;
storage = cvCreateMemStorage(0);
for( i = 0; names[i] != 0; i++ )
{
img0 = cvLoadImage( names[i], 1 );
if( !img0 )
{
cout<<"不能载入"<<names[i]<<"继续下一张图片"<<endl;
continue;
}
img = cvCloneImage( img0 );
cvNamedWindow( wndname, 1 );
// find and draw the squares
drawSquares( img, findSquares4( img, storage ) );
c = cvWaitKey(0);
cvReleaseImage( &img );
cvReleaseImage( &img0 );
cvClearMemStorage( storage );
if( (char)c == 27 )
break;
}
cvDestroyWindow( wndname );
return 0;
}
#include "opencv/cv.h"
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc.hpp"
#include <stdio.h>
#include <math.h>
#include <string.h>
#include <iostream>
#include<opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int thresh = 100;
IplImage* img = NULL;
CvMemStorage* storage = NULL;
const char * wndname = "正方形检测 demo";
//angle函数用来返回（两个向量之间找到角度的余弦值）
double angle(CvPoint* pt1, CvPoint* pt2, CvPoint* pt0)
{
double dx1 = pt1->x - pt0->x;
double dy1 = pt1->y - pt0->y;
double dx2 = pt2->x - pt0->x;
double dy2 = pt2->y - pt0->y;
return (dx1*dx2 + dy1 * dy2) / sqrt((dx1*dx1 + dy1 * dy1)*(dx2*dx2 + dy2 * dy2) + 1e-10);
}
// 返回图像中找到的所有轮廓序列，并且序列存储在内存存储器中
CvSeq* findSquares4(IplImage* img, CvMemStorage* storage)
{
CvSeq* contours;
int i, c, l, N = 11;
CvSize sz = cvSize(img->width & -2, img->height & -2);
IplImage* timg = cvCloneImage(img);
IplImage* gray = cvCreateImage(sz, 8, 1);
IplImage* pyr = cvCreateImage(cvSize(sz.width / 2, sz.height / 2), 8, 3);
IplImage* tgray;
CvSeq* result;
double s, t;
// 创建一个空序列用于存储轮廓角点
CvSeq* squares = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvPoint), storage);
cvSetImageROI(timg, cvRect(0, 0, sz.width, sz.height));
// 过滤噪音
cvPyrDown(timg, pyr, 7);//gaussian金字塔分解对输入图像下采样。（输入图像，输出图像，滤波器类型）
cvPyrUp(pyr, timg, 7);
tgray = cvCreateImage(sz, 8, 1);
// 红绿蓝3色分别尝试提取
for (c = 0; c < 3; c++)
{
// 提取 the c-th color plane
cvSetImageCOI(timg, c + 1);
cvCopy(timg, tgray, 0);//（输入，输出）
// 尝试各种阈值提取得到的（N=11）
for (l = 0; l < N; l++)
{
// apply Canny. Take the upper threshold from slider
// Canny helps to catch squares with gradient shading
if (l == 0)
{
cvCanny(tgray, gray, 0, thresh, 5);
//使用任意结构元素膨胀图像
cvDilate(gray, gray, 0, 1);
}
else
{
// apply threshold if l!=0:
cvThreshold(tgray, gray, (l + 1) * 255 / N, 255, CV_THRESH_BINARY);
}
// 找到所有轮廓并且存储在序列中
cvFindContours(gray, storage, &contours, sizeof(CvContour),
CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0, 0));
// 遍历找到的每个轮廓contours
while (contours)
{
//用指定精度逼近多边形曲线
result = cvApproxPoly(contours, sizeof(CvContour), storage,
CV_POLY_APPROX_DP, cvContourPerimeter(contours)*0.02, 0);
if (result->total == 4 &&
fabs(cvContourArea(result, CV_WHOLE_SEQ)) > 500 &&
fabs(cvContourArea(result, CV_WHOLE_SEQ)) < 100000 &&
cvCheckContourConvexity(result))
{
s = 0;
for (i = 0; i < 5; i++)
{
// find minimum angle between joint edges (maximum of cosine)
if (i >= 2)
{
t = fabs(angle(
(CvPoint*)cvGetSeqElem(result, i),
(CvPoint*)cvGetSeqElem(result, i - 2),
(CvPoint*)cvGetSeqElem(result, i - 1)));
s = s > t ? s : t;
}
}
// if 余弦值 足够小，可以认定角度为90度直角
//cos0.1=83度，能较好的趋近直角
if (s < 0.1)
for (i = 0; i < 4; i++)
cvSeqPush(squares,
(CvPoint*)cvGetSeqElem(result, i));
}
// 继续查找下一个轮廓
contours = contours->h_next;
}
}
}
cvReleaseImage(&gray);
cvReleaseImage(&pyr);
cvReleaseImage(&tgray);
cvReleaseImage(&timg);
return squares;
}
//drawSquares函数用来画出在图像中找到的所有正方形轮廓
void drawSquares(IplImage* img, CvSeq* squares)
{
CvSeqReader reader;
IplImage* cpy = cvCloneImage(img);
int i;
cvStartReadSeq(squares, &reader, 0);
// read 4 sequence elements at a time (all vertices of a square)
for (i = 0; i < squares->total; i += 4)
{
CvPoint pt[4], *rect = pt;
int count = 4;
// read 4 vertices
CV_READ_SEQ_ELEM(pt[0], reader);
CV_READ_SEQ_ELEM(pt[1], reader);
CV_READ_SEQ_ELEM(pt[2], reader);
CV_READ_SEQ_ELEM(pt[3], reader);
// draw the square as a closed polyline
cvPolyLine(cpy, &rect, &count, 1, 1, CV_RGB(0, 255, 0), 2, CV_AA, 0);
}
cvShowImage(wndname, cpy);
cvReleaseImage(&cpy);
}
int main(int argc, char** argv)
{
int i, c;
storage = cvCreateMemStorage(0);
img = cvLoadImage("E:\\work\\1原始大图.jpg", 1);
cvNamedWindow(wndname, 1);//设置显示图片的窗口，窗口名字为wndname，标志位为1即窗口可以根据图像大小自动调整
// find and draw the squares
drawSquares(img, findSquares4(img, storage));
c = cvWaitKey(0);
cvReleaseImage(&img);
cvClearMemStorage(storage);
cvDestroyWindow(wndname);
return 0;
}