【练习8.11】等级匹配cvMatchContourTrees、凸缺陷计算cvConvexityDefects

页内索引

题目要求

程序代码

结果图片

要言妙道

借鉴参考

  

题目要求：

 缩放旋转字符A，然后保存

用cvMatchContourTrees和cvConvexityDefects处理这些图像，查看匹配效果

程序代码：

// OpenCVExerciseTesting.cpp : 定义控制台应用程序的入口点。
//
//    string file_full_name = "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\r20.jpg";


#include "stdafx.h"
#include<string>
#include <cv.h>
#include <highgui.h>
#include <iostream>
#include<math.h>

#include <opencv2/legacy/legacy.hpp>
//#pragma comment(lib, "opencv_legacy2411.lib")

using namespace cv;
using namespace std;

//函数声明-->--->-->--->-->--->-->--->//

CvSeq * ApproxImage(IplImage * image_source);
double UseContourTreesToMatch(IplImage * image_1, IplImage * image_2);
void CalcConvexityDefects(IplImage * image);

//<--<--<--<--<--<--<--<--<--函数声明//

int _tmain(int argc, _TCHAR* argv[])
{
    string file_full_name[9] = { "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\字符A.jpg"
        , "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\字符A_-30度.jpg"
        , "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\字符A_50%.jpg"
        , "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\字符A_120度.jpg"
        , "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\字符A_150%_-60度.jpg"
        , "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\字符A_200%.jpg"
        , "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\字符F.jpg"
        , "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\字符A.jpg"
        , "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\Other.jpg" };

    IplImage * image_source[9];

    image_source[0] = cvLoadImage(file_full_name[0].c_str(), CV_LOAD_IMAGE_GRAYSCALE);
    CV_Assert(image_source[0]);
    cvShowImage("原始图像", image_source[0]);

    string window_name = "";
    for (int i = 1; i < 9; ++i)
    {    
        image_source[i] = cvLoadImage(file_full_name[i].c_str(), CV_LOAD_IMAGE_GRAYSCALE);
        CV_Assert(image_source[i]);

        window_name = window_name + "。";
        cvShowImage(window_name.c_str(), image_source[i]);

        double cTree = UseContourTreesToMatch(image_source[0], image_source[i]);

        cout << cTree << endl;
    }

    //输出凸缺陷的信息
    cout << endl << endl<<"凸缺陷计算："<<endl;
    CalcConvexityDefects(image_source[5]);

    cvWaitKey(0);
    //system("pause");

    cvDestroyAllWindows();

    return 0;
}

double UseContourTreesToMatch(IplImage * image_1, IplImage * image_2)
{
    CvSeq * approx_seq1 = ApproxImage(image_1);

    CvMemStorage *storage1 = cvCreateMemStorage();
    CvContourTree * contoruTree1=NULL;
    contoruTree1 = cvCreateContourTree(approx_seq1, storage1, 0);

    CvSeq * approx_seq2 = ApproxImage(image_2);
    CvMemStorage *storage2 = cvCreateMemStorage();
    CvContourTree * contoruTree2 = NULL;
    contoruTree2 = cvCreateContourTree(approx_seq2, storage2, 0);

    double result = cvMatchContourTrees(contoruTree1, contoruTree2, CV_CONTOUR_TREES_MATCH_I1, 100);
    return result;
}

CvSeq * ApproxImage(IplImage * image_source)
{
    CV_Assert(image_source);

    IplImage * image_binary = cvCloneImage(image_source);
    cvZero(image_binary);

    cvThreshold(image_source, image_binary, 128, 255, CV_THRESH_BINARY);

    CvMemStorage *storage = cvCreateMemStorage();
    CvSeq* first_contour = NULL;
    int contour_num;
    contour_num = cvFindContours(image_binary, storage, &first_contour, sizeof(CvContour), CV_RETR_TREE);//注意：cvMatchContourTrees匹配的时候，cvFindContours如果用CV_RETR_LIST得不到结果 

    double contour_length;
    contour_length = cvContourPerimeter(first_contour);

    double perimeter = contour_length / 66;
    CvMemStorage* storage_approx = cvCreateMemStorage();

    CvSeq *seq_approx = NULL;

    seq_approx = cvApproxPoly(first_contour, sizeof(CvContour), storage_approx, CV_POLY_APPROX_DP, perimeter, 0);

    cvReleaseImage(&image_binary);

    return seq_approx;
}
 
void CalcConvexityDefects(IplImage * image)
{
    //查找轮廓
    IplImage * image_temp = cvCloneImage(image);
    cvZero(image_temp);
    cvThreshold(image, image_temp, 128, 255, CV_THRESH_BINARY);

    CvMemStorage * storage = cvCreateMemStorage();
    CvSeq * first_contour;
    int contour_num = cvFindContours(image_temp, storage, &first_contour, sizeof(CvContour), CV_RETR_EXTERNAL);

    //IplImage *image_contour = cvCloneImage(image);
    //cvZero(image_contour);
    //cvDrawContours(image_contour, first_contour, cvScalar(255), cvScalar(255), 0);
    //cvShowImage("轮廓", image_contour);


    //检查是否是凸轮廓
    int isContourConvexity = cvCheckContourConvexity(first_contour);

    //计算凸包
    CvMemStorage * storage_hull = cvCreateMemStorage();
    CvSeq * seq_convex = cvConvexHull2(first_contour, storage_hull);

    //计算凸缺陷
    //cvFixPointOrder(polyOutline, hull);    
    CvMemStorage * storage_defects = cvCreateMemStorage();
    CvSeq * seq_defects = cvConvexityDefects(first_contour, seq_convex, storage_defects);

    for (int i = 0; i < seq_defects->total; ++i)
    {
        CvConvexityDefect * defect = (CvConvexityDefect *)cvGetSeqElem(seq_defects, i);

        cout << defect->start->x << " " << defect->start->y << " "
            << defect->end->x << " " << defect->end->y << " "
            << defect->depth_point->x << " " << defect->depth_point->y << " "
            << defect->depth << endl;
    }

    cvReleaseMemStorage(&storage);
    cvReleaseMemStorage(&storage_hull);
    cvReleaseMemStorage(&storage_defects);
    cvReleaseImage(&image_temp);
}

结果图片：

要言妙道：

①建立轮廓树前，为了得到较好的描述，需要首先使用函数cvApproxPoly，之后将轮廓排列（运用循环移动）成最初的三角形不怎么受到旋转影响的状态。

②注意，如果查找轮廓时 cvFindContours 的mode参数选择 CV_RETR_LIST或 CV_RETR_FLOODFILL ，则最后使用 cvMatchContourTrees 匹配的时候得不到有效的值

③再次强调， cvFindContours 查找轮廓前记得使用 cvCanny 或 cvThreshold 或 cvAdaptiveThreshold 处理图像 

④ cvConvexityDefects 的参数是 cvFindContours 和 cvConvexHull2 的结果

借鉴参考：