【练习8.10】直接使用cvFindContour的结果图片和cvDrawContour的方式提取Hu矩，观察在图片缩放或旋转时的稳定性

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：

 旋转缩放图像

a、使用《学习OpenCV》书中提到的技巧，先使用cvDrawContour绘制找到的轮廓在提取矩

b、直接使用cvFindContour的结果图片提取矩

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-->--->-->--->-->--->-->--->//
//void imRotate(IplImage *src, IplImage *dst, double angle, CvPoint2D32f center, double scale);

IplImage * ImageZoomOrRotatopn(IplImage * image, double scale=1.0, double angle=0.0);
void CalcHuMoments(IplImage * image_source, string window_name);
void CalcHuMoments2(IplImage * image_source, string window_name);

//<--<--<--<--<--<--<--<--<--函数声明//

int _tmain(int argc, _TCHAR* argv[])
{
    string file_full_name = "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\字符A.jpg";
    IplImage * image_source = cvLoadImage(file_full_name.c_str(), CV_LOAD_IMAGE_GRAYSCALE);
    CV_Assert(image_source);
    
    //原始图像
    string window_name = "字符A原始图像";
    CalcHuMoments(image_source, window_name);


    //旋转图像
    IplImage *image_1 = ImageZoomOrRotatopn(image_source, 1.0, 73);
    window_name = "字符A旋转73度图像";
    CalcHuMoments(image_1, window_name);

    //缩放图像
    IplImage *image_2 = ImageZoomOrRotatopn(image_source, 1.5, 0.0);
    window_name = "字符A放大1.5倍图像";
    CalcHuMoments(image_2, window_name);


    file_full_name = "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第8章\字符F.jpg";
    IplImage * image_other = cvLoadImage(file_full_name.c_str(), CV_LOAD_IMAGE_GRAYSCALE);
    CV_Assert(image_other);

    //其它图像测试
    window_name = "其它图像";
    CalcHuMoments(image_other, window_name);

    //==============================================================================

    cout << endl << endl;

    //不使用cvDrawContour画的轮廓计算矩，直接使用cvFindContour的结果
    IplImage * image_temp = cvCloneImage(image_source);
    CalcHuMoments2(image_temp, window_name);

    CalcHuMoments2(image_1, window_name);

    CalcHuMoments2(image_2, window_name);

    CalcHuMoments2(image_other, window_name);

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

    //cvReleaseImage(&image_source);
    //cvReleaseImage(&image_source_2);
    cvDestroyAllWindows();

    return 0;
}

IplImage * ImageZoomOrRotatopn(IplImage * image, double scale, double angle)
{
    IplImage * affinedImage = cvCloneImage(image);
    CvMat *rot_mat = cvCreateMat(2, 3, CV_32FC1);
    CvPoint2D32f center = cvPoint2D32f(image->width / 2, image->height / 2);
    cv2DRotationMatrix(center, angle, scale, rot_mat);
    cvWarpAffine(image, affinedImage, rot_mat);
    return affinedImage;
}

void CalcHuMoments(IplImage * image_source,string window_name)
{
    CvMoments moments;
    CvHuMoments  Hu_moments;
    IplImage * image_contour = cvCloneImage(image_source);
    cvZero(image_contour);

    IplImage * image_binary = cvCloneImage(image_source);
    cvZero(image_binary);
    cvThreshold(image_source, image_binary, 100, 255, CV_THRESH_BINARY);//重要，调用从vFindContour前先二值化图像

    CvMemStorage *storage = cvCreateMemStorage();
    CvSeq * first_contour=NULL;
    int contour_num;
    contour_num = cvFindContours(image_binary, storage, &first_contour);// , sizeof(CvContour), CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
    cvDrawContours(image_contour, first_contour, cvScalar(255), cvScalar(255), 5);//重要,将轮廓绘制到图像，供下一步调用

    cvShowImage(window_name.c_str(), image_contour);

    cvMoments(image_contour, &moments, 1);
    cvGetHuMoments(&moments, &Hu_moments);
    cout << window_name<< "Hu矩结果：" << endl;
    cout << setiosflags(ios::scientific) << setprecision(4) << Hu_moments.hu1 << " ";
    cout << Hu_moments.hu2 << " ";
    cout << Hu_moments.hu3 << " ";
    cout << Hu_moments.hu4 << " ";
    cout << Hu_moments.hu5 << " ";
    cout << Hu_moments.hu6 << " ";
    cout << Hu_moments.hu7 << endl;

    //cout << "(" << setprecision(3) << dec <</* scalar <<*/ ")" << " ";
    //for (int i = 0; i<7; ++i){
    //    cout << setiosflags(ios::scientific) << setprecision(4) << ((double*)&Hu_moments)[i] << ' ';
    //}
    //cout << endl;

    cvReleaseImage(&image_contour);
    cvReleaseImage(&image_binary);
    cvClearSeq(first_contour);
    cvReleaseMemStorage(&storage);
}

void CalcHuMoments2(IplImage * image_source, string window_name)
{
    CvMemStorage *storage = cvCreateMemStorage();
    CvSeq * contour = NULL;

    cvThreshold(image_source, image_source, 100, 255, CV_THRESH_BINARY);

    cvFindContours(image_source, storage, &contour);
    //cvShowImage(window_name.c_str(), image_source);
    CvMoments moments;
    CvHuMoments huMoments;
    cvMoments(image_source, &moments, 1);
    cvGetHuMoments(&moments, &huMoments);

    cout << "CalcHuMoments2输出" << endl;
    ////cout << "(" << setprecision(3) << dec << scalar << ")" << " ";
    //for (int i = 0; i<7; ++i){
    //    cout << setiosflags(ios::scientific) << setprecision(4) << ((double*)&huMoments)[i] << ' ';
    //}
    //cout << endl;
    cout << window_name << "Hu矩结果：" << endl;
    cout << setiosflags(ios::scientific) << setprecision(4) << huMoments.hu1 << " ";
    cout << huMoments.hu2 << " ";
    cout << huMoments.hu3 << " ";
    cout << huMoments.hu4 << " ";
    cout << huMoments.hu5 << " ";
    cout << huMoments.hu6 << " ";
    cout << huMoments.hu7 << endl;

    cvClearSeq(contour);
    cvReleaseMemStorage(&storage);
}

//void imRotate(IplImage *src, IplImage *dst, double angle, CvPoint2D32f center, double scale)
//{
//    assert(src->width == dst->width && src->height == dst->height &&src->depth == dst->depth &&src->nChannels == dst->nChannels);
//    CvMat *mapMatrix = cvCreateMat(2, 3, CV_32FC1);
//    cv2DRotationMatrix(center, angle, scale, mapMatrix); //旋转缩放为仿射变换，此处求变换矩阵
//
//    cvWarpAffine(src, dst, mapMatrix);
//}

结果图片：

要言妙道：

 ①函数 cvFindContours 从二值图像寻找图像。此二值图像可以是从cvCanny函数得到的有边缘像素的图像，或者是从cvThreshold及cvAdaptiveThreshold得到的图像，这时的边缘是正合负区域之间的边界。如果在cvFindContours前没有二值化图像，会发现本练习第一个原始图像找轮廓后画出的轮廓没有结果，而且最后的hu矩完全无规律

② 一个有用的小技巧是：用从vDrawContour描绘一幅轮廓的图像后，调用一个矩的处理函数处理该图像

③再次强调 cvFindContour 的输入图像会直接被涂改

借鉴参考：