c++ android 手势识别,基本例程（4-1）手势识别C++ 和简单形状匹配
opencv3.3+扩展库
/************************************************************************/
/*
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);
VideoCapture captRefrnc(0);
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
{
convexHull(Mat(filterContours[j]), hull, true);
int hullcount = (int)hull.size();
for (int i = 0; i
{
line(dst, hull[i + 1], hull[i], Scalar(255, 0, 0), 2, CV_AA);
}
line(dst, hull[hullcount - 1], hull[0], Scalar(255, 0, 0), 2, CV_AA);
}
imshow(WIN_RESULT, dst);
dst.release();
// End
c = cvWaitKey(delay);
if (c == 27) break;
}
}