Kinect+OpenNI学习笔记之9(不需要骨骼跟踪的人体手部分割)
前言
手势识别非常重要的一个特点是要体验要好,即需要以用户为核心。而手势的定位一般在手势识别过程的前面,在上一篇博文Kinect+OpenNI学习笔记之8(Robert Walter手部提取代码的分析) 中已经介绍过怎样获取手势区域,且取得了不错的效果,但是那个手势部位的提取有一个大的缺点,即需要人站立起来,当站立起来后才能够分隔出手。而手势在人之间的交流时,并不一定要处于站立状态,所以这不是一个好的HCI。因此本文介绍的手势部位的提取并不需要人处于站立状态,同样取得了不错的效果。
实验说明
其实,本实验实现的过程非常简单。首先通过手部的跟踪来获取手所在的坐标,手部跟踪可以参考本人前面的博文:Kinect+OpenNI学习笔记之7(OpenNI自带的类实现手部跟踪)。当定位到手所在的坐标后,因为该坐标是3D的,因此在该坐标领域的3维空间领域内提取出手的部位即可,整个过程的大概流程图如下:
OpenCV知识点总结:
调用Mat::copyTo()函数时,如果需要有mask操作,则不管源图像是多少通道的,其mask矩阵都要定义为单通道,另外可以对一个mask矩阵画一个填充的矩形来达到使mask矩阵中对应ROI的位置的值为设定值,这样就不需要去一一扫描赋值了。
在使用OpenCV的Mat矩阵且需要对该矩阵进行扫描时,一定要注意其取值顺序,比如说列和行的顺序,如果弄反了,则经常会报内存错误。
实验结果
本实验并不要求人的手一定要放在人体的前面,且也不需要人一定是处在比较简单的背景环境中,本实验结果允许人处在复杂的背景环境下,且手可以到处随便移动。当然了,环境差时有时候效果就不太好。
下面是3张实验结果的截图,手势分隔图1:
手势分隔图2:
手势分隔图3:
实验主要部分代码即注释(附录有工程code下载链接):
main.cpp:
#include <iostream> #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include <opencv2/core/core.hpp> #include "copenni.cpp" #include <iostream> #define DEPTH_SCALE_FACTOR 255./4096. #define ROI_HAND_WIDTH 140 #define ROI_HAND_HEIGHT 140 #define MEDIAN_BLUR_K 5 int XRES = 640; int YRES = 480; #define DEPTH_SEGMENT_THRESH 5 using namespace cv; using namespace xn; using namespace std; int main (int argc, char **argv) { COpenNI openni; int hand_depth; Rect roi; roi.x = XRES/2; roi.y = YRES/2; roi.width = ROI_HAND_WIDTH; roi.height = ROI_HAND_HEIGHT; if(!openni.Initial()) return 1; namedWindow("color image", CV_WINDOW_AUTOSIZE); namedWindow("depth image", CV_WINDOW_AUTOSIZE); namedWindow("hand_segment", CV_WINDOW_AUTOSIZE);//显示分割出来的手的区域 if(!openni.Start()) return 1; while(1) { if(!openni.UpdateData()) { return 1; } /*获取并显示色彩图像*/ Mat color_image_src(openni.image_metadata.YRes(), openni.image_metadata.XRes(), CV_8UC3, (char *)openni.image_metadata.Data()); Mat color_image; cvtColor(color_image_src, color_image, CV_RGB2BGR); circle(color_image, Point(hand_point.X, hand_point.Y), 5, Scalar(255, 0, 0), 3, 8); imshow("color image", color_image); /*获取并显示深度图像*/ Mat depth_image_src(openni.depth_metadata.YRes(), openni.depth_metadata.XRes(), CV_16UC1, (char *)openni.depth_metadata.Data());//因为kinect获取到的深度图像实际上是无符号的16位数据 Mat depth_image; depth_image_src.convertTo(depth_image, CV_8U, DEPTH_SCALE_FACTOR); imshow("depth image", depth_image); /*下面的代码是提取手的轮廓部分*/ hand_depth = hand_point.Z * DEPTH_SCALE_FACTOR; roi.x = hand_point.X - ROI_HAND_WIDTH/2; roi.y = hand_point.Y - ROI_HAND_HEIGHT/2; if(roi.x <= 0) roi.x = 0; if(roi.x >= XRES) roi.x = XRES; if(roi.y <=0 ) roi.y = 0; if(roi.y >= YRES) roi.y = YRES; //取出手的mask部分 //不管原图像时多少通道的,mask矩阵声明为单通道就ok Mat hand_segment_mask(depth_image.size(), CV_8UC1, Scalar::all(0)); for(int i = roi.x; i < std::min(roi.x+roi.width, XRES); i++) for(int j = roi.y; j < std::min(roi.y+roi.height, YRES); j++) { hand_segment_mask.at<unsigned char>(j, i) = ((hand_depth-DEPTH_SEGMENT_THRESH) < depth_image.at<unsigned char>(j, i)) & ((hand_depth+DEPTH_SEGMENT_THRESH) > depth_image.at<unsigned char>(j,i)); } medianBlur(hand_segment_mask, hand_segment_mask, MEDIAN_BLUR_K); Mat hand_segment(color_image.size(), CV_8UC3); color_image.copyTo(hand_segment, hand_segment_mask); imshow("hand_segment", hand_segment); waitKey(20); } }
copenni,cpp:
#ifndef COPENNI_CLASS #define COPENNI_CLASS #include <XnCppWrapper.h> #include <iostream> #include <map> using namespace xn; using namespace std; static DepthGenerator depth_generator; static HandsGenerator hands_generator; static XnPoint3D hand_point; static std::map<XnUserID, vector<XnPoint3D>> hands_track_points; class COpenNI { public: ~COpenNI() { context.Release();//释放空间 } bool Initial() { //初始化 status = context.Init(); if(CheckError("Context initial failed!")) { return false; } context.SetGlobalMirror(true);//设置镜像 xmode.nXRes = 640; xmode.nYRes = 480; xmode.nFPS = 30; //产生颜色node status = image_generator.Create(context); if(CheckError("Create image generator error!")) { return false; } //设置颜色图片输出模式 status = image_generator.SetMapOutputMode(xmode); if(CheckError("SetMapOutputMdoe error!")) { return false; } //产生深度node status = depth_generator.Create(context); if(CheckError("Create depth generator error!")) { return false; } //设置深度图片输出模式 status = depth_generator.SetMapOutputMode(xmode); if(CheckError("SetMapOutputMdoe error!")) { return false; } //产生手势node status = gesture_generator.Create(context); if(CheckError("Create gesture generator error!")) { return false; } /*添加手势识别的种类*/ gesture_generator.AddGesture("Wave", NULL); gesture_generator.AddGesture("click", NULL); gesture_generator.AddGesture("RaiseHand", NULL); gesture_generator.AddGesture("MovingHand", NULL); //产生手部的node status = hands_generator.Create(context); if(CheckError("Create hand generaotr error!")) { return false; } //产生人体node status = user_generator.Create(context); if(CheckError("Create gesturen generator error!")) { return false; } //视角校正 status = depth_generator.GetAlternativeViewPointCap().SetViewPoint(image_generator); if(CheckError("Can't set the alternative view point on depth generator!")) { return false; } //设置与手势有关的回调函数 XnCallbackHandle gesture_cb; gesture_generator.RegisterGestureCallbacks(CBGestureRecognized, CBGestureProgress, NULL, gesture_cb); //设置于手部有关的回调函数 XnCallbackHandle hands_cb; hands_generator.RegisterHandCallbacks(HandCreate, HandUpdate, HandDestroy, NULL, hands_cb); //设置有人进入视野的回调函数 XnCallbackHandle new_user_handle; user_generator.RegisterUserCallbacks(CBNewUser, NULL, NULL, new_user_handle); user_generator.GetSkeletonCap().SetSkeletonProfile(XN_SKEL_PROFILE_ALL);//设定使用所有关节(共15个) //设置骨骼校正完成的回调函数 XnCallbackHandle calibration_complete; user_generator.GetSkeletonCap().RegisterToCalibrationComplete(CBCalibrationComplete, NULL, calibration_complete); return true; } bool Start() { status = context.StartGeneratingAll(); if(CheckError("Start generating error!")) { return false; } return true; } bool UpdateData() { status = context.WaitNoneUpdateAll(); if(CheckError("Update date error!")) { return false; } //获取数据 image_generator.GetMetaData(image_metadata); depth_generator.GetMetaData(depth_metadata); return true; } //得到色彩图像的node ImageGenerator& getImageGenerator() { return image_generator; } //得到深度图像的node DepthGenerator& getDepthGenerator() { return depth_generator; } //得到人体的node UserGenerator& getUserGenerator() { return user_generator; } //得到手势姿势node GestureGenerator& getGestureGenerator() { return gesture_generator; } public: DepthMetaData depth_metadata; ImageMetaData image_metadata; // static std::map<XnUserID, vector<XnPoint3D>> hands_track_points; private: //该函数返回真代表出现了错误,返回假代表正确 bool CheckError(const char* error) { if(status != XN_STATUS_OK ) { //QMessageBox::critical(NULL, error, xnGetStatusString(status)); cerr << error << ": " << xnGetStatusString( status ) << endl; return true; } return false; } //手势某个动作已经完成检测的回调函数 static void XN_CALLBACK_TYPE CBGestureRecognized(xn::GestureGenerator &generator, const XnChar *strGesture, const XnPoint3D *pIDPosition, const XnPoint3D *pEndPosition, void *pCookie) { // COpenNI *openni = (COpenNI*)pCookie; // openni->hands_generator.StartTracking(*pIDPosition); hands_generator.StartTracking(*pIDPosition); } //手势开始检测的回调函数 static void XN_CALLBACK_TYPE CBGestureProgress(xn::GestureGenerator &generator, const XnChar *strGesture, const XnPoint3D *pPosition, XnFloat fProgress, void *pCookie) { // COpenNI *openni = (COpenNI*)pCookie; // openni->hands_generator.StartTracking(*pPosition); hands_generator.StartTracking(*pPosition); } //手部开始建立的回调函数 static void XN_CALLBACK_TYPE HandCreate(HandsGenerator& rHands, XnUserID xUID, const XnPoint3D* pPosition, XnFloat fTime, void* pCookie) { // COpenNI *openni = (COpenNI*)pCookie; XnPoint3D project_pos; depth_generator.ConvertRealWorldToProjective(1, pPosition, &project_pos); // openni->hand_point = project_pos; //返回手部所在点的位置 hand_point = project_pos; pair<XnUserID, vector<XnPoint3D>> hand_track_point(xUID, vector<XnPoint3D>()); hand_track_point.second.push_back(project_pos); hands_track_points.insert(hand_track_point); } //手部开始更新的回调函数 static void XN_CALLBACK_TYPE HandUpdate(HandsGenerator& rHands, XnUserID xUID, const XnPoint3D* pPosition, XnFloat fTime, void* pCookie) { // COpenNI *openni = (COpenNI*)pCookie; XnPoint3D project_pos; depth_generator.ConvertRealWorldToProjective(1, pPosition, &project_pos); // openni->hand_point = project_pos; //返回手部所在点的位置 hand_point = project_pos; hands_track_points.find(xUID)->second.push_back(project_pos); } //销毁手部的回调函数 static void XN_CALLBACK_TYPE HandDestroy(HandsGenerator& rHands, XnUserID xUID, XnFloat fTime, void* pCookie) { // COpenNI *openni = (COpenNI*)pCookie; //openni->hand_point.clear(); //返回手部所在点的位置 hands_track_points.erase(hands_track_points.find(xUID)); } //有人进入视野时的回调函数 static void XN_CALLBACK_TYPE CBNewUser(UserGenerator &generator, XnUserID user, void *p_cookie) { //得到skeleton的capability,并调用RequestCalibration函数设置对新检测到的人进行骨骼校正 generator.GetSkeletonCap().RequestCalibration(user, true); } //完成骨骼校正的回调函数 static void XN_CALLBACK_TYPE CBCalibrationComplete(SkeletonCapability &skeleton, XnUserID user, XnCalibrationStatus calibration_error, void *p_cookie) { if(calibration_error == XN_CALIBRATION_STATUS_OK) { skeleton.StartTracking(user);//骨骼校正完成后就开始进行人体跟踪了 } else { UserGenerator *p_user = (UserGenerator*)p_cookie; skeleton.RequestCalibration(user, true);//骨骼校正失败时重新设置对人体骨骼继续进行校正 } } private: XnStatus status; Context context; ImageGenerator image_generator; // DepthGenerator depth_generator; UserGenerator user_generator; GestureGenerator gesture_generator; // HandsGenerator hands_generator; // map<XnUserID, vector<XnPoint3D>> hands_track_points; XnMapOutputMode xmode; public: // static XnPoint3D hand_point; }; #endif
实验总结:
本次实验简单的利用OpenNI的手部跟踪功能提实时分隔出了人体手所在的部位。但是该分隔效果并不是特别好,以后可以改进手利用色彩信息来分隔出手的区域,或者计算出自适应手部位的区域。另外,本程序只是暂时分隔出一个手,以后可以扩展到分隔出多个手的部位.
参考资料:
Kinect+OpenNI学习笔记之8(Robert Walter手部提取代码的分析)
http://dl.dropbox.com/u/5505209/FingertipTuio3d.zip
附录:实验工程code下载。