手势动态识别（基于opencv的简单实现）
http://blog.csdn.net/anqing715/article/details/17653709
基于OpenCV的实时手势识别算法研究_许艳旭
http://wenku.baidu.com/view/c7a2c97e998fcc22bdd10d11.html
基于OpenCV动态手势识别的实现
http://wenku.baidu.com/view/e0c708efad51f01dc281f1dd.html
/************************************************************************/
/*
Description: 手势检测
先滤波去噪
-->转换到HSV空间
-->根据皮肤在HSV空间的分布做出阈值判断，这里用到了inRange函数，
然后进行一下形态学的操作，去除噪声干扰，是手的边界更加清晰平滑
-->得到的2值图像后用findContours找出手的轮廓，去除伪轮廓后，再用convexHull函数得到凸包络
Author: Yang Xian
History:
*/
/************************************************************************/
#include // for standard I/O
#include // for strings
#include // for controlling float print precision
#include // string to number conversion
#include // Gaussian Blur
#include // Basic OpenCV structures (cv::Mat, Scalar)
#include // OpenCV window I/O
using namespace cv;
using namespace std;
int main(int argc, char *argv[])
{
const std::string sourceReference = "test3.avi";
int delay = 1;
char c;
int frameNum = -1; // Frame counter
VideoCapture captRefrnc(sourceReference);
if ( !captRefrnc.isOpened())
{
// cout << "Could not open reference " << sourceReference << endl;
return -1;
}
Size refS = Size( (int) captRefrnc.get(CV_CAP_PROP_FRAME_WIDTH),
(int) captRefrnc.get(CV_CAP_PROP_FRAME_HEIGHT) );
bool bHandFlag = false;
const char* WIN_SRC = "Source";
const char* WIN_RESULT = "Result";
// Windows
namedWindow(WIN_SRC, CV_WINDOW_AUTOSIZE );
namedWindow(WIN_RESULT, CV_WINDOW_AUTOSIZE);
Mat frame; // 输入视频帧序列
Mat frameHSV; // hsv空间
Mat mask(frame.rows, frame.cols, CV_8UC1); // 2值掩膜
Mat dst(frame); // 输出图像
// Mat frameSplit[4];
vector< vector > contours; // 轮廓
vector< vector > filterContours; // 筛选后的轮廓
vector< Vec4i > hierarchy; // 轮廓的结构信息
vector< Point > hull; // 凸包络的点集
while(true) //Show the image captured in the window and repeat
{
captRefrnc >> frame;
if( frame.empty() )
{
cout << " < < < Game over! > > > ";
break;
}
imshow( WIN_SRC, frame);
// Begin
// 中值滤波，去除椒盐噪声
medianBlur(frame, frame, 5);
// GaussianBlur( frame, frameHSV, Size(9, 9), 2, 2 );
// imshow("blur2", frameHSV);
// pyrMeanShiftFiltering(frame, frameHSV, 10, 10);
// imshow(WIN_BLUR, frameHSV);
// 转换到HSV颜色空间，更容易处理
cvtColor( frame, frameHSV, CV_BGR2HSV );
// split(frameHSV, frameSplit);
// imshow(WIN_H, frameSplit[0]);
// imshow(WIN_S, frameSplit[1]);
// imshow(WIN_V, frameSplit[2]);
Mat dstTemp1(frame.rows, frame.cols, CV_8UC1);
Mat dstTemp2(frame.rows, frame.cols, CV_8UC1);
// 对HSV空间进行量化，得到2值图像，亮的部分为手的形状
inRange(frameHSV, Scalar(0,30,30), Scalar(40,170,256), dstTemp1);
inRange(frameHSV, Scalar(156,30,30), Scalar(180,170,256), dstTemp2);
bitwise_or(dstTemp1, dstTemp2, mask);
// inRange(frameHSV, Scalar(0,30,30), Scalar(180,170,256), dst);
// 形态学操作，去除噪声，并使手的边界更加清晰
Mat element = getStructuringElement(MORPH_RECT, Size(3,3));
erode(mask, mask, element);
morphologyEx(mask, mask, MORPH_OPEN, element);
dilate(mask, mask, element);
morphologyEx(mask, mask, MORPH_CLOSE, element);
frame.copyTo(dst, mask);
contours.clear();
hierarchy.clear();
filterContours.clear();
// 得到手的轮廓
findContours(mask, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// 去除伪轮廓
for (size_t i = 0; i < contours.size(); i++)
{
// approxPolyDP(Mat(contours[i]), Mat(approxContours[i]), arcLength(Mat(contours[i]), true)*0.02, true);
if (fabs(contourArea(Mat(contours[i]))) > 30000) //判断手进入区域的阈值
{
filterContours.push_back(contours[i]);
}
}
// 画轮廓
drawContours(dst, filterContours, -1, Scalar(0,0,255), 3/*, 8, hierarchy*/);
// 得到轮廓的凸包络
for (size_t j=0; j
