opencv 获取手势的轮廓

Cite From :http://blog.csdn.net/yalexiaoqiang/article/details/5527871

 

获得手势识别的方法是根据肤色来进行的，内定了一个肤色的范围，通过肤色的二值化后再平滑处理，边缘接触，计算ROI区域，得到感兴趣区，最后将轮廓找出来。

//VERSION: HAND DETECTION 1.0
//AUTHOR: ANDOL LI@CW3/18, Live:lab
//PROJECT: HAND DETECTION PROTOTYPE
//LAST UPDATED: 03/2009

#include "cv.h"
#include "cxcore.h"
#include "highgui.h"
#include "math.h"
#include
#include
#include
#include
#include
#include
using namespace std;
/*
--------------------------------------------*/
int main()
{

int c = 0;
CvSeq* a = 0;
CvCapture* capture = cvCaptureFromCAM(0);//从对摄像头的初始化捕获
if(!cvQueryFrame(capture)){ cout<<"Video capture failed, please check the camera."< CvSize sz = cvGetSize(cvQueryFrame( capture));//得到摄像头图像大小
IplImage* src = cvCreateImage( sz, 8, 3 );//4通道，每个通道8位
IplImage* hsv_image = cvCreateImage( sz, 8, 3);//
IplImage* hsv_mask = cvCreateImage( sz, 8, 1);
IplImage* hsv_edge = cvCreateImage( sz, 8, 1);

CvScalar hsv_min = cvScalar(0, 30, 80, 0);//得到BGR，&，每个通道的和
CvScalar hsv_max = cvScalar(20, 150, 255, 0);
//
CvMemStorage* storage = cvCreateMemStorage(0);//分配大小为0的内存空间
CvMemStorage* areastorage = cvCreateMemStorage(0);
CvMemStorage* minStorage = cvCreateMemStorage(0);
CvMemStorage* dftStorage = cvCreateMemStorage(0);
CvSeq* contours = NULL;
//
cvNamedWindow( "src",1);
//在屏幕上创建一个窗口，第一个参数为窗口标题，第二个参数为窗口属性，
//设置为0（默认值），或者CV_WINDOW_AUTOSIZE,设置为0，则窗口不会因图像的大小而改变
//图像只能在窗口中根据窗口的大小进行拉伸或缩放；设置为CV_WINDOW_AUTOSIZE时，窗口会根据图像
//的实际大小进行自动拉伸或缩放。
//cvNamedWindow( "hsv-msk",1);
//cvNamedWindow( "contour",1);
//
while( c != 27)//27为ASCII键值（ESC），
{

IplImage* bg = cvCreateImage( sz, 8, 3);//
cvRectangle( bg, cvPoint(0,0), cvPoint(bg->width,bg->height), CV_RGB( 255, 255, 255), -1, 8, 0 );//画矩形
bg->origin = 1;
for(int b = 0; b< int(bg->width/10); b++)
{

cvLine( bg, cvPoint(b*20, 0), cvPoint(b*20, bg->height), CV_RGB( 200, 200, 200), 1, 8, 0 );//画线
cvLine( bg, cvPoint(0, b*20), cvPoint(bg->width, b*20), CV_RGB( 200, 200, 200), 1, 8, 0 );//画线

}

src = cvQueryFrame( capture);//得到一帧图像
cvCvtColor(src, hsv_image, CV_BGR2HSV);//色彩空间转换，HSV

cvInRangeS (hsv_image, hsv_min, hsv_max, hsv_mask);//检查数组元素是否在两个数量之间，输出新图像hsv_mask
//cvSmooth( hsv_mask, hsv_mask, CV_MEDIAN, 27, 0, 0, 0 );

cvSmooth( hsv_mask, hsv_mask, CV_MEDIAN, 27, 0, 0);//图像平滑，
//CV_MEDIAN (median blur) - 对图像进行核大小为param1×param1 的中值滤波 (i.e. 邻域是方的).
cvCanny(hsv_mask, hsv_edge, 1, 3, 5);//采用 Canny 算法做边缘检测

cvFindContours( hsv_mask, storage, &contours, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );
//对单通道图像检索轮廓，返回第一个轮廓的指针
CvSeq* contours2 = NULL;
double result = 0, result2 = 0;
while(contours)
{

result = fabs( cvContourArea( contours, CV_WHOLE_SEQ ) );//计算感兴趣区域
if ( result > result2) {result2 = result; contours2 = contours;};
contours = contours->h_next;

}
if ( contours2 )

{

//cout << "contours2: " << contours2->total << endl;
CvRect rect = cvBoundingRect( contours2, 0 );
cvRectangle( bg, cvPoint(rect.x, rect.y + rect.height), cvPoint(rect.x + rect.width, rect.y), CV_RGB(200, 0, 200), 1, 8, 0 );
//cout << "Ratio: " << rect.width << ", " << rect.height << ", " << (float)rect.width / rect.height << endl;
int checkcxt = cvCheckContourConvexity( contours2 );
//cout << checkcxt < CvSeq* hull = cvConvexHull2( contours2, 0, CV_CLOCKWISE, 0 );
CvSeq* defect = cvConvexityDefects( contours2, hull, dftStorage );
if( defect->total >=40 ) {cout << " Closed Palm " << endl;}
else if( defect->total >=30 && defect->total <40 ) {cout << " Open Palm " << endl;}
else{ cout << " Fist " << endl;}
cout << "defet: " << defect->total << endl;

CvBox2D box = cvMinAreaRect2( contours2, minStorage );
//cout << "box angle: " << (int)box.angle << endl;
cvCircle( bg, cvPoint(box.center.x, box.center.y), 3, CV_RGB(200, 0, 200), 2, 8, 0 );
cvEllipse( bg, cvPoint(box.center.x, box.center.y), cvSize(box.size.height/2, box.size.width/2), box.angle, 0, 360, CV_RGB(220, 0, 220), 1, 8, 0 );
//cout << "Ratio: " << (float)box.size.width/box.size.height <

}
//cvShowImage( "hsv-msk", hsv_mask); hsv_mask->origin = 1;
//IplImage* contour = cvCreateImage( sz, 8, 3 );

cvDrawContours( bg, contours2, CV_RGB( 0, 200, 0), CV_RGB( 0, 100, 0), 1, 1, 8, cvPoint(0,0));
cvShowImage( "src", src);
//contour->origin = 1; cvShowImage( "contour", contour);
//cvReleaseImage( &contour);

cvNamedWindow("bg",0);
cvShowImage("bg",bg);
cvReleaseImage( &bg);

c = cvWaitKey( 10);

}
//
cvReleaseCapture( &capture);
cvDestroyAllWindows();

}

下面的程序是我后来修改过的，上面说的两个错误已修改，添加了注释，但我为了做一些测试改了几个语句，也发上来吧，

--------------------------------------------*/
int main()
{

int c = 0;
CvSeq* a = 0;
CvCapture* capture = cvCaptureFromCAM(0);//从对摄像头的初始化捕获
if(!cvQueryFrame(capture)) cout<<"Video capture failed, please check the camera."< else cout<<"Video camera capture status: OK"< CvSize sz = cvGetSize(cvQueryFrame( capture));//得到摄像头图像大小
IplImage* src = cvCreateImage( sz, 8, 3 );//3通道，每个通道8位
IplImage* hsv_image = cvCreateImage( sz, 8, 3);//
IplImage* hsv_mask = cvCreateImage( sz, 8, 1);
IplImage* hsv_mask2 = cvCreateImage( sz, 8, 1);
IplImage* hsv_edge = cvCreateImage( sz, 8, 1);

CvScalar hsv_min = cvScalar(0, 30, 80, 0);//最小像素的RGB值
CvScalar hsv_max = cvScalar(20, 150, 255, 0);//最大像素的RGB值
//CvScalar hsv_min = cvScalar(30, 30, 30, 0);//最小像素的RGB值
//CvScalar hsv_max = cvScalar(200, 200, 200, 0);//最大像素的RGB值
//
CvMemStorage* storage = cvCreateMemStorage(0);//分配大小为0的内存空间
CvMemStorage* areastorage = cvCreateMemStorage(0);
CvMemStorage* minStorage = cvCreateMemStorage(0);
CvMemStorage* dftStorage = cvCreateMemStorage(0);
CvSeq* contours = NULL;
//
cvNamedWindow( "src",1);
//在屏幕上创建一个窗口，第一个参数为窗口标题，第二个参数为窗口属性，
//设置为0（默认值），或者CV_WINDOW_AUTOSIZE,设置为0，则窗口不会因图像的大小而改变
//图像只能在窗口中根据窗口的大小进行拉伸或缩放；设置为CV_WINDOW_AUTOSIZE时，窗口会根据图像
//的实际大小进行自动拉伸或缩放。
//cvNamedWindow( "hsv-msk",1);
//cvNamedWindow( "contour",1);
//
IplImage * background=cvLoadImage("002.jpg");
CvRect rect=cvRect(0,0,background->width,background->height);
cvSetImageROI(background,rect);
IplImage * src3=cvCreateImage(cvGetSize(background),background->depth,background->nChannels);

while( c != 27)//27为ASCII键值（ESC），
{

IplImage* bg = cvCreateImage( sz, 8, 3);//
cvRectangle( bg, cvPoint(0,0), cvPoint(bg->width,bg->height), CV_RGB( 255, 255, 255), -1, 8, 0 );//画矩形，参数：Image，两个顶点坐标，线的颜色，线的厚度
bg->origin = 1;
for(int b = 0; b< int(bg->width/10); b++)//画网格
{

cvLine( bg, cvPoint(b*20, 0), cvPoint(b*20, bg->height), CV_RGB( 200, 200, 200), 1, 8, 0 );//画竖线
cvLine( bg, cvPoint(0, b*20), cvPoint(bg->width, b*20), CV_RGB( 200, 200, 200), 1, 8, 0 );//画横线

}

src = cvQueryFrame( capture);//得到一帧图像

cvCvtColor(src, hsv_image, CV_BGR2HSV);//色彩空间转换，HSV

cvInRangeS (hsv_image, hsv_min, hsv_max, hsv_mask);//检查数组元素是否在两个数量之间，输出新图像hsv_mask，从3通道到1通道
//hsv_mask的数据要么为0，要么为1，在min和max范围内为1，得到ROI区域，找到在像素RGB范围内的数据
//cvSmooth( hsv_mask, hsv_mask, CV_MEDIAN, 27, 0, 0, 0 );

cvSmooth( hsv_mask, hsv_mask2, CV_MEDIAN, 27, 0, 0);//图像平滑，
//CV_MEDIAN (median blur) - 对图像进行核大小为param1×param1 的中值滤波 (i.e. 邻域是方的).
cvCanny(hsv_mask2, hsv_edge, 1, 3, 5);
//cvCanny(hsv_mask, hsv_edge, 1, 3, 5);//采用 Canny 算法做边缘检测

cvFindContours( hsv_mask, storage, &contours, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );
//对单通道图像检索轮廓，返回第一个轮廓的指针
CvSeq* contours2 = NULL;
double result = 0, result2 = 0;
while(contours)//得到最大的两个感兴趣区
{

result = fabs( cvContourArea( contours, CV_WHOLE_SEQ ) );//计算感兴趣区域的像素点数
if ( result > result2) {result2 = result; contours2 = contours;};
contours = contours->h_next;

}
if ( contours2 )//最大的感兴趣区，ROI
{

//cout << "contours2: " << contours2->total << endl;
CvRect rect = cvBoundingRect( contours2, 0 );//返回一个2d矩形的点集合
cvRectangle( bg, cvPoint(rect.x, rect.y + rect.height), cvPoint(rect.x + rect.width, rect.y), CV_RGB(200, 0, 200), 1, 8, 0 );
//在bg的矩形区域中画rect的图形
//cout << "Ratio: " << rect.width << ", " << rect.height << ", " << (float)rect.width / rect.height << endl;
int checkcxt = cvCheckContourConvexity( contours2 );//检测输入的轮廓是否是凸的
//cout << checkcxt < CvSeq* hull = cvConvexHull2( contours2, 0, CV_CLOCKWISE, 0 );//二维凸包
CvSeq* defect = cvConvexityDefects( contours2, hull, dftStorage );//凸包中的缺陷
if( defect->total >=40 ) {cout << " Closed Palm " << endl;}
else if( defect->total >=30 && defect->total <40 ) {cout << " Open Palm " << endl;}
else{ cout << " Fist " << endl;}
cout << "defet: " << defect->total << endl;

CvBox2D box = cvMinAreaRect2( contours2, minStorage );//包围所有点的轮廓的最小矩形
//cout << "box angle: " << (int)box.angle << endl;
cvCircle( bg, cvPoint(box.center.x, box.center.y), 3, CV_RGB(200, 0, 200), 2, 8, 0 );//画圆
cvEllipse( bg, cvPoint(box.center.x, box.center.y), cvSize(box.size.height/2, box.size.width/2), box.angle, 0, 360, CV_RGB(220, 0, 220), 1, 8, 0 );//椭圆
//cout << "Ratio: " << (float)box.size.width/box.size.height <

}
//cvShowImage( "hsv-msk", hsv_mask); hsv_mask->origin = 1;
//IplImage* contour = cvCreateImage( sz, 8, 3 );

cvDrawContours( bg, contours2, CV_RGB( 0, 200, 0), CV_RGB( 0, 100, 0), 1, 1, 8, cvPoint(0,0));//绘制轮廓的图像到bg图像中

cvShowImage( "src", src);
//cvShowImage("src",src3);
//contour->origin = 1; cvShowImage( "contour", contour);
//cvReleaseImage( &contour);

cvNamedWindow("bg",0);
cvShowImage("bg",bg);
cvNamedWindow( "ROI",0);
cvShowImage("ROI",hsv_mask2);
//cvShowImage("ROI",hsv_mask2);
cvReleaseImage( &bg);

c = cvWaitKey( 10);

}
//
cvReleaseCapture( &capture);
cvDestroyAllWindows();

 

}