手掌手指分割算法（源码）
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开发环境
开发环境
- 64 bits Windows OS (Win8.1)
- VS2013
- OpenCV 2.4.9
功能原理
算法要求
完成将Camera拍摄的手掌图片中分割出每个手指用于指纹识别
算法流程
核心代码
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double duration = static_cast<double>(cv::getTickCount());//time
cout << "filename=" << filename <<endl;
Mat src = imread(filename, CV_LOAD_IMAGE_COLOR);
if (src.empty())
{
cout << "imread error!!!";
getchar();
return -1;
}
memset(out_filename, 0, sizeof(out_filename) / sizeof(char));
sprintf(out_filename, "%s-%s.jpg",out_name,"0-0src");
imwrite(out_filename, src);
int scaleSize = 4;
resize(src, src, Size(src.cols / scaleSize, src.rows / scaleSize), 0, 0, CV_INTER_AREA);
memset(out_filename, 0, sizeof(out_filename) / sizeof(char));
sprintf(out_filename, "%s-%s.jpg", out_name, "0-0src");
imwrite(out_filename, src);
cout << "cut..." << endl;
int width = src.cols;
int height = src.rows;
float scale = 0.8;
cout << "width=" << width << ",height=" << height << endl;
Rect rect(0, 0, width, height*scale);
Mat imgCut;
imgCut = src(rect).clone();
//Mat imgCut = src;
cout << "filter..." << endl;
// filter2D(imgCut, imgCut, -1, kernel);
GaussianBlur(imgCut, imgCut, Size(5, 5), 0, 0);
// blur(imgCut, imgCut, Size(5, 5));
cout << "EqualizeHist..." << endl;
Mat matOutEqualizeHist = Mat(imgCut.size(), CV_8UC3);
//IplImage* pImgOutEqualizeHist = cvCreateImage(cvSize(cameraFrame.cols, cameraFrame.rows), IPL_DEPTH_8U, 3);
IplImage pImgInEqualizeHist = (IplImage)(imgCut); // Mat-> IplImage
IplImage* pImgOutEqualizeHist = EqualizeHistColorImage(&pImgInEqualizeHist);
matOutEqualizeHist = pImgOutEqualizeHist; //IplImage -> Mat
// out
Mat imgSrc = Mat(imgCut.size(), CV_8UC1);
imgCut.copyTo(imgSrc);
Mat imgContour = Mat(imgSrc.size(), CV_8UC1);
cout << "Nigth,Threshold..." << endl;
Mat imgTmp;// = Mat(imgCut.size(), CV_8UC1);
cvtColor(imgSrc, imgTmp, CV_RGB2GRAY);
cvThresholdOtsu(&((IplImage)imgTmp), &((IplImage)imgTmp));
imgTmp.copyTo(imgContour);
cout << "Day,Skin..." << endl;
Mat imgSkin2 = Mat(imgSrc.size(), CV_8UC1);
IplImage* pImgSkin2 = cvCreateImage(cvSize(imgSrc.cols, imgSrc.rows), IPL_DEPTH_8U, 1);
IplImage pImg2 = (IplImage)(imgSrc); // Mat-> IplImage
cvSkinOtsu(&pImg2, pImgSkin2);
imgSkin2 = pImgSkin2; //IplImage -> Mat
//Mat imgSkin = Mat(imgSrc.size(), CV_8UC1);
imgSkin2.copyTo(imgContour);
/////////////////////// Contours
cout << "Find Contours..." << endl;
vector<vector<cv::Point> > contours;
vector<Vec4i> hierarchy;
findContours(imgContour, contours, hierarchy,
CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0));
sort(contours.begin(), contours.end(), compareContourAreas);
int contours_num = contours.size();
cout << "contours_num=" << contours_num << endl;
vector<vector<Point>>::const_iterator itContours = contours.begin();
//for (int i = 0; i < contours.size(); i++)
for (; itContours != contours.end(); ++itContours)
{
cout << "Size: " << itContours->size() << endl;//每个轮廓包含的点数
}
// Eliminate too short or too long contours
int cmin = 100; // minimum contour length
//int cmax= 1000; // maximum contour length
vector<vector<Point>>::const_iterator itc = contours.begin();
while (itc != contours.end())
{
//if (itc->size() < cmin || itc->size() > cmax)
if (itc->size() < cmin) {
itc = contours.erase(itc);
}
else
++itc;
}
contours_num = contours.size();
cout << endl << "contours_num after Eliminate=" << contours_num << endl;
// extract the contour img
cout << "Extract Contours..." << endl;
if (contours_num >= 4)
{
Mat img1, img2, img3, img4;
std::vector<cv::Point> biggest1Contour = contours[contours_num - 1];
std::vector<cv::Point> biggest2Contour = contours[contours_num - 2];
std::vector<cv::Point> biggest3Contour = contours[contours_num - 3];
std::vector<cv::Point> biggest4Contour = contours[contours_num - 4];
std::vector<cv::Point> smallestContour = contours[0];
extractFingerImg2(contours, imgSrc, img1, contours_num, 1);
extractFingerImg2(contours, imgSrc, img2, contours_num, 2);
extractFingerImg2(contours, imgSrc, img3, contours_num, 3);
extractFingerImg2(contours, imgSrc, img4, contours_num, 4);
}
else if (contours_num == 3)
{
Mat img1, img2, img3;
std::vector<cv::Point> biggest1Contour = contours[contours_num - 1];
std::vector<cv::Point> biggest2Contour = contours[contours_num - 2];
std::vector<cv::Point> biggest3Contour = contours[contours_num - 3];
std::vector<cv::Point> smallestContour = contours[0];
extractFingerImg2(contours, imgSrc, img1, contours_num, 1);
extractFingerImg2(contours, imgSrc, img2, contours_num, 2);
extractFingerImg2(contours, imgSrc, img3, contours_num, 3);
}
else if (contours_num == 2)
{
Mat img1, img2;
std::vector<cv::Point> biggest1Contour = contours[contours_num - 1];
std::vector<cv::Point> biggest2Contour = contours[contours_num - 2];
std::vector<cv::Point> smallestContour = contours[0];
extractFingerImg2(contours, imgSrc, img1, contours_num, 1);
extractFingerImg2(contours, imgSrc, img2, contours_num, 2);
}
else if (contours_num == 1)
{
Mat img1;
std::vector<cv::Point> biggest1Contour = contours[contours_num - 1];
std::vector<cv::Point> smallestContour = contours[0];
extractFingerImg2(contours, imgSrc, img1, contours_num, 1);
}
else
{
cout << "error" << endl;
}
duration = static_cast<double>(cv::getTickCount()) - duration;
duration /= cv::getTickFrequency(); // the elapsed time in ms
cout << "time cost=" << duration << "s"<<endl;
memset(out_filename, 0, sizeof(out_filename) / sizeof(char));
sprintf(out_filename, "%s-%s.jpg", out_name, "4-imgContoursInSrc");
imwrite(out_filename, imgSrc);
//imwrite("4-imgContoursInSrc.jpg", imgSrc);
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算法效果
白天复杂场景
晚上场景
批量测试场景
转至：http://skyseraph.com/2014/07/24/CV/%E6%89%8B%E6%8E%8C%E6%89%8B%E6%8C%87%E5%88%86%E5%89%B2%E7%AE%97%E6%B3%95/
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