自学教程：C++ CV_RGB函数代码示例

static void show(){    if(!exist_image || !exist_scan){        return;    }    IplImage* image_view = cvCreateImage(cvGetSize(&image), image.depth, image.nChannels);    cvCopy(&image, image_view);	float min_d, max_d;	min_d = max_d = scan_image.distance.at(0);	for(int i = 1; i < IMAGE_WIDTH * IMAGE_HEIGHT; i++){		float di = scan_image.distance.at(i);		max_d = di > max_d ? di : max_d;		min_d = di < min_d ? di : min_d;	}	float wid_d = max_d - min_d;    /*     * Plot depth points on an image     */    CvPoint pt;    int height, width;    for(int i = 0; i < (int)scan_image.distance.size(); i++) {        height = (int)(i % IMAGE_HEIGHT);        width = (int)(i / IMAGE_HEIGHT);        if(scan_image.distance.at(i) != 0.0) {            pt.x = width;            pt.y = height;			int colorid= wid_d ? ( (scan_image.distance.at(i) - min_d) * 255 / wid_d ) : 128;			cv::Vec3b color=colormap.at<cv::Vec3b>(colorid);			int g = color[1];			int b = color[2];			int r = color[0];            cvCircle(image_view, pt, 2, CV_RGB (r, g, b), CV_FILLED, 8, 0);        }    }  drawRects(image_view,            car_fused_objects.obj,            cvScalar(255.0, 255.0, 0,0),            (image_view->height)*.3);  drawRects(image_view,            pedestrian_fused_objects.obj,            cvScalar(0.0, 255.0, 0,0),            (image_view->height)*.3);  /* PUT DISTANCE text on image */  putDistance(image_view,              car_fused_objects.obj,              (image_view->height)*.3,              car_fused_objects.type.c_str());  putDistance(image_view,              pedestrian_fused_objects.obj,              (image_view->height)*.3,              pedestrian_fused_objects.type.c_str());    /*     * Show image     */    cvShowImage(window_name, image_view);    cvWaitKey(2);    cvReleaseImage(&image_view);}

void imageCallback(const sensor_msgs::ImageConstPtr& msg){	//bridge that will transform the message (image) from ROS code back to "image" code  sensor_msgs::CvBridge bridge;  fprintf(stderr, "/n call Back funtion /n");  //publish data (obstacle waypoints) back to the boat  //ros::NodeHandle n;  //std_msgs::Float32 xWaypoint_msg;         // X coordinate obstacle message  //std_msgs::Float32 yWaypoint_msg;         // Y coordinate obstacle message  //publish the waypoint data               //ros::Publisher waypoint_info_pub = n.advertise<std_msgs::Float32>("waypoint_info", 1000);  //ros::Publisher Ywaypoint_info_pub = n.advertise<std_msgs::Float32>("waypoint_info", 1000);  //std::stringstream ss;    /***********************************************************************/    //live image coming streamed straight from the boat's camera    IplImage* boatFront = bridge.imgMsgToCv(msg, "bgr8");    //The boat takes flipped images, so you need to flip them back to normal    cvFlip(boatFront, boatFront, 0);    IplImage* backUpImage = cvCloneImage(boatFront);    boatFront->origin = IPL_ORIGIN_TL;   //sets image origin to top left corner    int X = boatFront->height;    int Y = boatFront->width;    //cout << "height " << X << endl;    //cout << "width " << Y << endl;            /*********************Image Filtering variables****************************/    //these images are used for segmenting objects from the overall background     //create a one channel image to convert from RGB to GRAY    IplImage* grayImage = cvCreateImage(cvGetSize(boatFront),IPL_DEPTH_8U,1);    //convert grayImage to binary (final step after converting from GRAY)    IplImage* bwImage = cvCreateImage(cvGetSize(grayImage),IPL_DEPTH_8U,1);    //variables used for the flood fill segmentation    CvPoint seed_point = cvPoint(boatFront->height/1.45,0);       //not sure how this variable works    CvScalar color = CV_RGB(250,0,0);    CvMemStorage* grayStorage = NULL;     //memory storage for contour sequence    CvSeq* contours = 0;    // get blobs and filter them using their area    //IplConvKernel* morphKernel = cvCreateStructuringElementEx(5, 5, 1, 1, CV_SHAPE_RECT, NULL);    //IplImage* original, *originalThr;    //IplImage* segmentated = cvCreateImage(cvGetSize(boatFront), 8, 1);    //unsigned int blobNumber = 0;    //IplImage* labelImg = cvCreateImage(cvGetSize(boatFront), IPL_DEPTH_LABEL, 1);    CvMoments moment;        /***********************************************************************/    //boat's edge distance from the camera. This is used for visual calibration    //to know the distance from the boat to the nearest obstacles.    //With respect to the mounted camera, distance is 21 inches (0.5334 m) side to side    //and 15 inches (0.381 m).    //float boatFrontDistance = 0.381;    //distance in meters    //float boatSideDistance = 0.5334;    //distance in meters    // These variables tell the distance from the center bottom of the image    // (the camera) to the square surrounding a the obstacle    float xObstacleDistance = 0.0;    float yObstacleDistance = 0.0;    float obstacleDistance = 0.0;    float obstacleHeading = 0.0;        int pixelsNumber = 50;  //number of pixels for an n x n matrix and # of neighbors    const int arraySize = pixelsNumber;    const int threeArraySize = pixelsNumber;    //if n gets changed, then the algorithm might have to be    //recalibrated. Try to keep it constant    //these variables are used for the k nearest neighbors    //int accuracy;    //reponses for each of the classifications    float responseWaterH, responseWaterS, responseWaterV;     float responseGroundH, responseGroundS, responseGroundV;    float responseSkyH, responseSkyS, responseSkyV;    float averageHue = 0.0;    float averageSat = 0.0;    float averageVal = 0.0;    CvMat* trainClasses = cvCreateMat( pixelsNumber, 1, CV_32FC1 );    CvMat* trainClasses2 = cvCreateMat( pixelsNumber, 1, CV_32FC1 );    //CvMat sample = cvMat( 1, 2, CV_32FC1, _sample );    //used with the classifier     CvMat* trainClassesH = cvCreateMat( pixelsNumber, 1, CV_32FC1 );    CvMat* trainClassesS = cvCreateMat( pixelsNumber, 1, CV_32FC1 );    CvMat* trainClassesV = cvCreateMat( pixelsNumber, 1, CV_32FC1 );    //CvMat* trainClasses2 = cvCreateMat( pixelsNumber, 1, CV_32FC1 );    //CvMat sample = cvMat( 1, 2, CV_32FC1, _sample );    //used with the classifier     /*CvMat* nearestWaterH = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* nearestWaterS = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* nearestWaterV = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* nearestGroundH = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* nearestGroundS = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* nearestGroundV = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* nearestSkyH = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* nearestSkyS = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* nearestSkyV = cvCreateMat(1, pixelsNumber, CV_32FC1);    //Distance    CvMat* distanceWaterH = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* distanceWaterS = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* distanceWaterV = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* distanceGroundH = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* distanceGroundS = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* distanceGroundV = cvCreateMat(1, pixelsNumber, CV_32FC1);    CvMat* distanceSkyH = cvCreateMat(1, pixelsNumber, CV_32FC1);//.........这里部分代码省略.........

void face_detector::face_marker_lbp(Mat *frame){    static vector<Rect> faces;    static Mat frame_gray;    cvtColor(*frame, frame_gray, COLOR_BGR2GRAY);    equalizeHist(frame_gray, frame_gray);    static String face_cascade_name = "lbpcascade_frontalface.xml";    static String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";    static CascadeClassifier face_cascade;    static CascadeClassifier eyes_cascade;    static bool loaded = false;    if (!loaded) {        if(!face_cascade.load(face_cascade_name)) {            fprintf(stderr, "Could not load face classifier cascade!");            exit(1);        }        if(!eyes_cascade.load( eyes_cascade_name)) {            fprintf(stderr, "Could not load eye classifier cascade!");            exit(1);        }    }    loaded = true;    static Cgt_Rect _area;    static Rect largestFace;    static Mat smallImgROI;    static Rect eyeArea;    static Cgt_Eye _iris_point;    static Rect leftEyeRect(0,0,0,0), rightEyeRect(0,0,0,0);    static vector<Rect> eyes;    static Point left_top, right_bottom;    face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0, Size(40, 40));    if(faces.empty() == false) {        //取最大的脸        largestFace.width=0;        largestFace.height=0;        for (vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++) {            if ((r->width * r->height) > (largestFace.width * largestFace.height))                largestFace = *r;        }        // 将最大人脸区域赋给area        _area.left  = largestFace.x;        _area.right  = largestFace.x + largestFace.width;        _area.top  = largestFace.y;        _area.bottom = largestFace.y + largestFace.height;        ///////////针对最大的脸检测人眼////////////////////////////////        eyeArea = largestFace;        eyeArea.height = eyeArea.height/1.2; //仅对人脸的上半部分检测人眼，以减少错误率 //调整一下参数，只对上半部分有时检测不出来        smallImgROI = (*frame)(eyeArea);        eyes_cascade.detectMultiScale(smallImgROI, eyes, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );        if(eyes.size()>=2) { //必须至少有两只眼被检出            vector<Rect>::const_iterator nr = eyes.begin();            leftEyeRect = *nr;            nr++;            rightEyeRect = *nr;        } else {            //fprintf(stderr, "必须至少有两只眼被检出!/n");            return;        }        _iris_point.xleft = cvRound(largestFace.x + leftEyeRect.x + leftEyeRect.width*0.5);  //左眼中心的x坐标        _iris_point.yleft = cvRound(largestFace.y + leftEyeRect.y + leftEyeRect.height*0.5);  //左眼中心的y坐标        _iris_point.xright = cvRound(largestFace.x + rightEyeRect.x + rightEyeRect.width*0.5);  //右眼中心的x坐标        _iris_point.yright = cvRound(largestFace.y + rightEyeRect.y + rightEyeRect.height*0.5); //右眼中心的y坐标        //对眼睛的后期验证：        //不允许左眼在右眼右边//        if(iris_point.xleft >= iris_point.xright )//            fprintf(stderr, "11111111111"), nRetCode = false;        //不允许眼睛在边界（由于，初始化的值为0，这也意味着如果少于两个眼检测出来，则认为检测失败）        if ((_iris_point.xleft==0) || (_iris_point.yleft==0) ||(_iris_point.xright==0) || (_iris_point.yright==0) )            return;        //不允许两只眼上下倾斜过多（也防止一些误检）        if (abs(_iris_point.yright-_iris_point.yleft) > (largestFace.width/3) )            return;        //不允许两只眼左右间距小于1/4人脸宽度（也防止一些误检）        if (abs(_iris_point.xright-_iris_point.xleft) < (largestFace.width/4) )            return;        //画出框到的人脸，验证调试用        left_top.x = _area.left;        left_top.y = _area.top;        right_bottom.x = _area.right;        right_bottom.y = _area.bottom;        rectangle(*frame, left_top, right_bottom, CV_RGB(0,255,0), 2, 8, 0);        left_top.x = _iris_point.xleft;        left_top.y = _iris_point.yleft;        right_bottom.x = left_top.x;        right_bottom.y = left_top.y;        rectangle(*frame, left_top, right_bottom, CV_RGB(90, 255, 0), 2, 8, 0);        left_top.x = _iris_point.xright;        left_top.y = _iris_point.yright;        right_bottom.x = left_top.x;        right_bottom.y = left_top.y;        rectangle(*frame, left_top, right_bottom, CV_RGB(0, 255, 0), 2, 8, 0);    }}

Mat faceDetection(Mat &videoFrame, double scaleFactor, int scaledWidth, bool eyesDetect, bool mouthDetect, CascadeClassifier &mFaceDetector, CascadeClassifier &mMouthDetector, CascadeClassifier &mEyeDetector, QString personName ) {    Mat defaultFace;    Mat grayscaleFace;    Mat equalizedFace;    Mat resizedFace;    Mat savedFace;const double CHANGE_IMAGE = 0.5; //0.3;      // Change in face from last timeconst double CHANGE_TIME = 0.5;//1.0;       // Time change old - new/*grayscale image*/    videoFrame.copyTo( defaultFace );     if (defaultFace.channels() == 3) {         cvtColor(defaultFace, grayscaleFace, CV_BGR2GRAY);     }     else if (defaultFace.channels() == 4) {         cvtColor(defaultFace, grayscaleFace, CV_BGRA2GRAY);     }     else {         grayscaleFace = defaultFace;     }/*shrinked image*/     float scale = defaultFace.cols / (float)scaledWidth;     if (defaultFace.cols > scaledWidth) {         int scaledHeight = cvRound(defaultFace.rows / scale);         resize(grayscaleFace, resizedFace, Size(scaledWidth, scaledHeight));     }     else {         resizedFace = grayscaleFace;     }/**/     vector< cv::Rect > faceVec;     mFaceDetector.detectMultiScale( resizedFace, faceVec, scaleFactor );     for( size_t i=0; i<faceVec.size(); i++ ) {          cv::rectangle( defaultFace, faceVec[i], CV_RGB(255,200,0), 2 );          cv::Mat face = resizedFace( faceVec[i] );          // EyesDetection          if( eyesDetect == 1 ) {                vector< cv::Rect > eyeVec;                mEyeDetector.detectMultiScale(face, eyeVec);                for( size_t j=0; j<eyeVec.size(); j++ ) {                    cv::Rect rect = eyeVec[j];                    rect.x += faceVec[i].x;                    rect.y += faceVec[i].y;                    //saveFace(face);                    cv::rectangle(resizedFace, rect, CV_RGB(0,255,0), 2 );                }            }         // MouthDetection         /*if mouth was detected on second part of face, face is equalized, normalized by size and saved, if it is not as the face saved last time.*/         if( mouthDetect == 1 ) {                vector< cv::Rect > mouthVec;                Rect halfRect = faceVec[i];                halfRect.height /= 2;                halfRect.y += halfRect.height;                Mat halfFace = videoFrame( halfRect );                mMouthDetector.detectMultiScale( halfFace, mouthVec, 3 );                for( size_t j=0; j<mouthVec.size(); j++ ) {                    cv::Rect rect = mouthVec[j];                    rect.x += halfRect.x;                    rect.y += halfRect.y;                    Mat equalizedImg;                    equalizeHist(face, equalizedImg);                    Mat face_resized;                    resize(equalizedImg, face_resized, Size(faceSize, faceSize), 1.0, 1.0, INTER_CUBIC);                    double imageDiff = 10000000000.0;                    if (oldFace.data) {                        imageDiff = norm(face_resized, oldFace, NORM_L2)/(double)(face_resized.rows * face_resized.cols);                    }                    double current_time = (double)getTickCount();                    double timeDiff_seconds = (current_time - oldTime)/getTickFrequency();                    cout << "imageDiff " << imageDiff << "  and timeDiff " << timeDiff_seconds <<  "  and oldtime " << oldTime << "  and currentTime " <<current_time << "/n";                    if ((imageDiff > CHANGE_IMAGE) && (timeDiff_seconds > CHANGE_TIME)) {                            saveFace(face_resized, personName);                            qDebug() << "Photo was saved";                            cv::rectangle( defaultFace, rect, CV_RGB(255,255,255), 2 );                            Mat displayedFaceRegion = defaultFace( faceVec[i]);                            displayedFaceRegion += CV_RGB(90,90,90);                            oldFace = face_resized.clone();                            oldTime = current_time;                }            }         }//.........这里部分代码省略.........

static GstFlowReturngst_motiondetect_transform_ip (GstBaseTransform * trans, GstBuffer * buf){  GstMessage *m = NULL;  StbtMotionDetect *filter = GST_MOTIONDETECT (trans);  if ((!filter) || (!buf)) {    return GST_FLOW_OK;  }  GST_OBJECT_LOCK(filter);  if (filter->enabled && filter->state != MOTION_DETECT_STATE_INITIALISING) {    IplImage *referenceImageGrayTmp = NULL;    static int frameNo = 1;    filter->cvCurrentImage->imageData = (char *) GST_BUFFER_DATA (buf);    cvCvtColor( filter->cvCurrentImage,        filter->cvCurrentImageGray, CV_BGR2GRAY );    if (filter->debugDirectory) {      gst_motiondetect_log_image (filter->cvCurrentImageGray,           filter->debugDirectory, frameNo, "source.png");    }    if (filter->state == MOTION_DETECT_STATE_REFERENCE_IMAGE_ACQUIRED) {      gboolean result;            result = gst_motiondetect_apply(          filter->cvReferenceImageGray, filter->cvCurrentImageGray,          filter->cvMaskImage, filter->noiseThreshold);      if (filter->debugDirectory) {        if (result) {          gst_motiondetect_log_image (filter->cvReferenceImageGray,               filter->debugDirectory, frameNo,              "absdiff_not_masked_motion.png");        } else {          gst_motiondetect_log_image (filter->cvReferenceImageGray,               filter->debugDirectory, frameNo,              "absdiff_not_masked_no_motion.png");        }        gst_motiondetect_log_image (filter->cvMaskImage,              filter->debugDirectory, frameNo, "mask.png");      }      GstStructure *s = gst_structure_new ("motiondetect",          "has_motion", G_TYPE_BOOLEAN, result,          "masked", G_TYPE_BOOLEAN, (filter->mask != NULL),          "mask_path", G_TYPE_STRING, filter->mask, NULL);      m = gst_message_new_element (GST_OBJECT (filter), s);      if (filter->display) {        buf = gst_buffer_make_writable (buf);        cvSubS (filter->cvCurrentImage, CV_RGB(100, 100, 100),            filter->cvCurrentImage, filter->cvInvertedMaskImage);        if (result) {          cvAddS (filter->cvCurrentImage, CV_RGB(50, 0, 0),              filter->cvCurrentImage, filter->cvMaskImage);        }      }    }    referenceImageGrayTmp = filter->cvReferenceImageGray;    filter->cvReferenceImageGray = filter->cvCurrentImageGray;    filter->cvCurrentImageGray = referenceImageGrayTmp;    filter->state = MOTION_DETECT_STATE_REFERENCE_IMAGE_ACQUIRED;    ++frameNo;  }  GST_OBJECT_UNLOCK(filter);  if (m) {    gst_element_post_message (GST_ELEMENT (filter), m);  }  return GST_FLOW_OK;}

/* * -d 0,1,2,3,4 =>/dev/video0,video1,... * -f static file *  */int main (int argc, char * argv[]) {	//Two boolean variables	char quit = 0; //Exit main program loop?	char grab_frame = 1; //Do we grab a new frame from the camera?		int thresh1=DEFAULT_TRACKBAR_VAL, thresh2=DEFAULT_TRACKBAR_VAL; //These two variables will hold trackbar positions.	if(argc==1){		camID=0;		printf("set default camera 0/n");	}else if(option(argc,argv) < 0 ){		printf( "Wrong args!!!/n");		exit(EXIT_FAILURE);	}	//These are pointers to IPL images, which will hold the result of our calculations	IplImage *small_image = NULL; /*cvCreateImage(cvSize(IMG_WIDTH,IMG_HEIGHT),IPL_DEPTH_8U,3)*/; //size, depth, channels (RGB = 3)	IplImage *small_grey_image = NULL;/*cvCreateImage(cvGetSize(small_image), IPL_DEPTH_8U, 1)*/; //1 channel for greyscale	IplImage *edge_image = NULL; /*cvCreateImage(cvGetSize(small_image), IPL_DEPTH_8U, 1)*/; //We use cvGetSize to make sure the images are the same size. 		//CvMemStorage and CvSeq are structures used for dynamic data collection. CvMemStorage contains pointers to the actual	//allocated memory, but CvSeq is used to access this data. Here, it will hold the list of image contours.	CvMemStorage *storage = cvCreateMemStorage(0);	CvSeq *contours = 0;		CvCapture *camera=NULL;	if(camID>=0){		camera = cvCreateCameraCapture(camID); //This function tries to connect to the first (0th) camera it finds and creates a structure to refer to it.		if(!camera){ //cvCreateCameraCapture failed, most likely there is no camera connected.			printf("Could not find a camera to capture from.../n"); //Notify the user...			return -1; //And quit with an error.		}	}	cvNamedWindow(MAIN_WINNAME, CV_WINDOW_AUTOSIZE); //Here we create a window and give it a name. The second argument tells the window to not automatically adjust its size.		//We add two trackbars (sliders) to the window. These will be used to set the parameters for the Canny edge detection.	cvCreateTrackbar("Thresh1", MAIN_WINNAME, &thresh1, 256, 0);	cvCreateTrackbar("Thresh2", MAIN_WINNAME, &thresh2, 256, 0);		//Set the trackbar position to the default value. 	cvSetTrackbarPos("Thresh1", MAIN_WINNAME, DEFAULT_TRACKBAR_VAL); //Trackbar name, window name, position	cvSetTrackbarPos("Thresh2", MAIN_WINNAME, DEFAULT_TRACKBAR_VAL);		//Now set the mouse callback function. We need to pass the location of the contours so that it will be able to access this information.	cvSetMouseCallback(MAIN_WINNAME, onMouse, &contours); //Window name, function pointer, user-defined parameters.		IplImage *frame=NULL; //This will point to the IPL image we will retrieve from the camera.	IplImage *frame_org=NULL;	if(camID < 0){		frame_org = cvLoadImage(orgFile, CV_LOAD_IMAGE_UNCHANGED);		frame = cvLoadImage(inFile, CV_LOAD_IMAGE_UNCHANGED);	}	//This is the main program loop. We exit the loop when the user sets quit to true by pressing the "esc" key	while(!quit){		int c =0;		if(camID >= 0){			c=cvWaitKey(30); //Wait 30 ms for the user to press a key.						//Respond to key pressed.			switch(c){				case 32: //Space					grab_frame = !grab_frame; //Reverse the value of grab_frame. That way, the user can toggle by pressing the space bar.					break;				case 27: //Esc: quit application when user presses the 'esc' key.					quit = 1; //Get out of loop					break;			};			//If we don't have to grab a frame, we're done for this pass.			if(!grab_frame)continue;				//Grab a frame from the camera.			frame = cvQueryFrame(camera);						if(!frame) continue; //Couldn't get an image, try again next time.		}				//In computer vision, it's always better to work with the smallest images possible, for faster performance.		//cvResize will use inter-linear interpolation to fit frame into small_image.		if(small_image==NULL){			small_image = cvCreateImage(cvSize(frame->width,frame->height),IPL_DEPTH_8U,3); //size, depth, channels (RGB = 3)			printf("w=%d, h=%d/n", frame->width,frame->height);		}		if(small_grey_image==NULL) small_grey_image=cvCreateImage(cvGetSize(small_image), IPL_DEPTH_8U, 1);		if(edge_image==NULL) edge_image = cvCreateImage(cvGetSize(small_image), IPL_DEPTH_8U, 1);		cvResize(frame, small_image, CV_INTER_LINEAR);				//Many computer vision algorithms do not use colour information. Here, we convert from RGB to greyscale before processing further.		cvCvtColor(small_image, small_grey_image, CV_RGB2GRAY);		//We then detect edges in the image using the Canny algorithm. This will return a binary image, one where the pixel values will be 255 for 		//pixels that are edges and 0 otherwise. This is unlike other edge detection algorithms like Sobel, which compute greyscale levels.		cvCanny(small_grey_image, edge_image, (double)thresh1, (double)thresh2, 3); //We use the threshold values from the trackbars and set the window size to 3		//.........这里部分代码省略.........

int main( int argc, char** argv ){    const char *pstrWindowsSrcTitle = "Original picture";    const char *pstrWindowsOutLineTitle = "Outline picture";    const int IMAGE_WIDTH = 400;    const int IMAGE_HEIGHT = 200;    // 创建图像    IplImage *pSrcImage = cvCreateImage(cvSize(IMAGE_WIDTH, IMAGE_HEIGHT), IPL_DEPTH_8U, 3);    // 填充成白色    cvRectangle(pSrcImage, cvPoint(0, 0), cvPoint(pSrcImage->width, pSrcImage->height), CV_RGB(255, 255, 255), CV_FILLED);    // 画圆    CvPoint ptCircleCenter = cvPoint(IMAGE_WIDTH / 4, IMAGE_HEIGHT / 2);    int nRadius = 80;    cvCircle(pSrcImage, ptCircleCenter, nRadius, CV_RGB(255, 255, 0), CV_FILLED);    ptCircleCenter = cvPoint(IMAGE_WIDTH / 4, IMAGE_HEIGHT / 2);    nRadius = 30;    cvCircle(pSrcImage, ptCircleCenter, nRadius, CV_RGB(255, 255, 255), CV_FILLED);    // 画矩形    CvPoint ptLeftTop = cvPoint(IMAGE_WIDTH / 2 + 20, 20);    CvPoint ptRightBottom = cvPoint(IMAGE_WIDTH - 20, IMAGE_HEIGHT - 20);    cvRectangle(pSrcImage, ptLeftTop, ptRightBottom, CV_RGB(0, 255, 255), CV_FILLED);    ptLeftTop = cvPoint(IMAGE_WIDTH / 2 + 60, 40);    ptRightBottom = cvPoint(IMAGE_WIDTH - 60, IMAGE_HEIGHT - 40);    cvRectangle(pSrcImage, ptLeftTop, ptRightBottom, CV_RGB(255, 255, 255), CV_FILLED);    // 显示原图    cvNamedWindow(pstrWindowsSrcTitle, CV_WINDOW_AUTOSIZE);    cvShowImage(pstrWindowsSrcTitle, pSrcImage);    // 转为灰度图    IplImage *pGrayImage =  cvCreateImage(cvGetSize(pSrcImage), IPL_DEPTH_8U, 1);    cvCvtColor(pSrcImage, pGrayImage, CV_BGR2GRAY);    // 转为二值图    IplImage *pBinaryImage = cvCreateImage(cvGetSize(pGrayImage), IPL_DEPTH_8U, 1);    cvThreshold(pGrayImage, pBinaryImage, 250, 255, CV_THRESH_BINARY);    // 检索轮廓并返回检测到的轮廓的个数    CvMemStorage *pcvMStorage = cvCreateMemStorage();    CvSeq *pcvSeq = NULL;    cvFindContours(pBinaryImage, pcvMStorage, &pcvSeq, sizeof(CvContour), CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cvPoint(0, 0));    // 画轮廓图    IplImage *pOutlineImage = cvCreateImage(cvGetSize(pSrcImage), IPL_DEPTH_8U, 3);    int nLevels = 5;    // 填充成白色    cvRectangle(pOutlineImage, cvPoint(0, 0), cvPoint(pOutlineImage->width, pOutlineImage->height), CV_RGB(255, 255, 255), CV_FILLED);    cvDrawContours(pOutlineImage, pcvSeq, CV_RGB(255,0,0), CV_RGB(0,255,0), nLevels, 2);    // 显示轮廓图    cvNamedWindow(pstrWindowsOutLineTitle, CV_WINDOW_AUTOSIZE);    cvShowImage(pstrWindowsOutLineTitle, pOutlineImage);    cvWaitKey(0);    cvReleaseMemStorage(&pcvMStorage);    cvDestroyWindow(pstrWindowsSrcTitle);    cvDestroyWindow(pstrWindowsOutLineTitle);    cvReleaseImage(&pSrcImage);    cvReleaseImage(&pGrayImage);    cvReleaseImage(&pBinaryImage);    cvReleaseImage(&pOutlineImage);    return 0;}

//.........这里部分代码省略.........            return;            for(i=0;i<points2.size();i++)            {                bmp.push_back( points2[i]);                tempBd.push_back(bdescriptors.row(queryIdxs[i]));                tempbk.push_back(keypoints2[trainIdxs[i]]);                tempBd.push_back(descriptors2.row(trainIdxs[i]));                tempbk.push_back(keypoints2[trainIdxs[i]]);            }        }        else        {            H12 = findHomography( Mat(points1), Mat(points2), CV_RANSAC, RANSAC_THREHOLD );            if( !H12.empty() )            {                Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);                for( size_t i1 = 0; i1 < points1.size(); i1++ )                {                    double diff = norm(points2[i1] - points1t.at<Point2f>((int)i1,0));                    if(diff  <= 20)                    {                        matchesMask[i1]=1;                        bmp.push_back( points2[i1]);                        tempBd.push_back(bdescriptors.row(queryIdxs[i1]));                        tempbk.push_back(keypoints2[trainIdxs[i1]]);                        tempBd.push_back(descriptors2.row(trainIdxs[i1]));                        tempbk.push_back(keypoints2[trainIdxs[i1]]);                    }                }                drawMatches( img1ROI, bkeypoints, img2ROI, keypoints2, filteredMatches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask             #if DRAW_RICH_KEYPOINTS_MODE                             , DrawMatchesFlags::DRAW_RICH_KEYPOINTS             #endif                             );            }        }        imshow("bm",drawImg);        //============edit part ====        shiftPoints(bmp, box);        vector<int> bflag(bmp.size(),0);        for(i=0;i<bmp.size();i++)            bflag[i]=0;        vector<int> ft(matchedPoints2.size(),0);        for(i=0;i<matchedPoints2.size();i++)        {            ft[i]=0;            for(j=0; j< bmp.size(); j++)            {                double diff = norm (matchedPoints2[i] - bmp[j]);                // cout << diff << endl;                if(diff < 0.5)                {                    bflag[j]=1;                    ft[i]=1;                    break;                }            }

//.........这里部分代码省略.........        mDesc1.push_back(descriptors1.row(queryIdxs[i]));        mDesc2.push_back(descriptors2.row(trainIdxs[i]));    }    vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);    vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);    vector<char> matchesMask( filteredMatches.size(), 0 );//,  matchesMask2( filteredMatches.size(), 1 );;    Mat drawImg;// drawImg2;    cout << "points2.size /t" << points2.size() << endl;    cout <<"HELLO /t" << endl;    if( RANSAC_THREHOLD >= 0 )    {        if (points2.size() < 4 )        {            cout << "matchedPoints1 less than 4, hence prev ROI is retained" << endl;            for(size_t i1=0;i1<points2.size();i1++)            {                matchesMask[i1] = 1;                matchedPoints1.push_back( points1[i1]);                matchedPoints2.push_back( points2[i1]);                matchedDesc1.push_back(descriptors1.row(queryIdxs[i1]));                matchedDesc2.push_back(descriptors2.row(trainIdxs[i1]));                tempkey.push_back(keypoints2[trainIdxs[i1]]);                MP1.push_back(points2[i1]);            }        }        else        {            H12 = findHomography( Mat(points1), Mat(points2), CV_RANSAC, RANSAC_THREHOLD );            if( !H12.empty() )            {                Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);                vector<Point2f> points2Shift(points2.size());                points2Shift = points2;                shiftPoints(points2Shift, box);                Point2f boxCenter;                boxCenter.x = box.x + box.width/2;                boxCenter.y = box.y + box.height/2;                for( size_t i1 = 0; i1 < points1.size(); i1++ )                {                    double descDiff = pow(norm(mDesc1.row(i1) - mDesc2.row(i1)) , 2);                    //  if(descDiff < 0.08)                    {                        double diff = norm(points2[i1] - points1t.at<Point2f>((int)i1,0));                        if(diff  <= 30)                        {                          //  cout << diff << endl;                            matchesMask[i1] = 1;                            matchedPoints1.push_back( points1[i1]);                            matchedPoints2.push_back( points2[i1]);                            matchedDesc1.push_back(descriptors1.row(queryIdxs[i1]));                            matchedDesc2.push_back(descriptors2.row(trainIdxs[i1]));                            tempkey.push_back(keypoints2[trainIdxs[i1]]);                            MP1.push_back(points2[i1]);                        }                    }                }            }            //              drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg2, CV_RGB(255, 255, 0), CV_RGB(255,255, 255), matchesMask2            //              #if DRAW_RICH_KEYPOINTS_MODE            //                                   , DrawMatchesFlags::DRAW_RICH_KEYPOINTS            //              #endif            //                                 );            drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask             #if DRAW_RICH_KEYPOINTS_MODE                         , DrawMatchesFlags::DRAW_RICH_KEYPOINTS             #endif                         );            cout << endl;            imshow( "doiter", drawImg );//            Mat newimg = img1.clone();//             KeyPoint::convert(keypoints1, points1);//            for(size_t i=0;i<points1.size();i++)//                 circle(newimg, points1[i], 2, Scalar(255,0,255),2);//             imshow( "doimg", newimg );//            points1.clear();// waitKey(0);        }    }    // waitKey(0);}

void cvRenderTracks(CvTracks const tracks, IplImage *imgSource, IplImage *imgDest, unsigned short mode, CvFont *font){  CV_FUNCNAME("cvRenderTracks");  __CV_BEGIN__;  CV_ASSERT(imgDest&&(imgDest->depth==IPL_DEPTH_8U)&&(imgDest->nChannels==3));  if ((mode&CV_TRACK_RENDER_ID)&&(!font))  {    if (!defaultFont)    {  font = defaultFont = new CvFont;  cvInitFont(font, CV_FONT_HERSHEY_DUPLEX, 0.5, 0.5, 0, 1);  // Other fonts:  //   CV_FONT_HERSHEY_SIMPLEX, CV_FONT_HERSHEY_PLAIN,  //   CV_FONT_HERSHEY_DUPLEX, CV_FONT_HERSHEY_COMPLEX,  //   CV_FONT_HERSHEY_TRIPLEX, CV_FONT_HERSHEY_COMPLEX_SMALL,  //   CV_FONT_HERSHEY_SCRIPT_SIMPLEX, CV_FONT_HERSHEY_SCRIPT_COMPLEX    }    else  font = defaultFont;  }  if (mode)  {    for (CvTracks::const_iterator it=tracks.begin(); it!=tracks.end(); ++it)    {  if (mode&CV_TRACK_RENDER_ID)    if (!it->second->inactive)    {      stringstream buffer;      buffer << it->first;      cvPutText(imgDest, buffer.str().c_str(), cvPoint((int)it->second->centroid.x, (int)it->second->centroid.y), font, CV_RGB(255.,255.,0.));    }  if (mode&CV_TRACK_RENDER_BOUNDING_BOX)    if (it->second->inactive)      cvRectangle(imgDest, cvPoint(it->second->minx, it->second->miny), cvPoint(it->second->maxx-1, it->second->maxy-1), CV_RGB(0., 0., 50.));    else      cvRectangle(imgDest, cvPoint(it->second->minx, it->second->miny), cvPoint(it->second->maxx-1, it->second->maxy-1), CV_RGB(0., 255., 0.));  if (mode&CV_TRACK_RENDER_TO_LOG)  {    clog << "Track " << it->second->id << endl;    if (it->second->inactive)      clog << " - Inactive for " << it->second->inactive << " frames" << endl;    else      clog << " - Associated with blob " << it->second->label << endl;    clog << " - Lifetime " << it->second->lifetime << endl;    clog << " - Active " << it->second->active << endl;    clog << " - Bounding box: (" << it->second->minx << ", " << it->second->miny << ") - (" << it->second->maxx << ", " << it->second->maxy << ")" << endl;    clog << " - Centroid: (" << it->second->centroid.x << ", " << it->second->centroid.y << ")" << endl;    clog << endl;  }  if (mode&CV_TRACK_RENDER_TO_STD)  {    cout << "Track " << it->second->id << endl;    if (it->second->inactive)      cout << " - Inactive for " << it->second->inactive << " frames" << endl;    else      cout << " - Associated with blobs " << it->second->label << endl;    cout << " - Lifetime " << it->second->lifetime << endl;    cout << " - Active " << it->second->active << endl;    cout << " - Bounding box: (" << it->second->minx << ", " << it->second->miny << ") - (" << it->second->maxx << ", " << it->second->maxy << ")" << endl;    cout << " - Centroid: (" << it->second->centroid.x << ", " << it->second->centroid.y << ")" << endl;    cout << endl;  }    }  }  __CV_END__;}

void ImageViewer::displayMatches(QPainter& painter)const{	QPoint pt1, pt2;	if (siftObj1.keypoints==NULL){		printf("ERROR : Keypoints NULL/n");		exit(-1);	}			if (dispMatch && lastComparison.tab_match!=NULL && !siftObj1.IsEmpty() ){		// Display matches		for (int i=0;i<lastComparison.nb_match;i++) {			pt1.setX(ROUND(lastComparison.tab_match[i].x1));			pt1.setY(ROUND(lastComparison.tab_match[i].y1));			pt2.setX(ROUND(lastComparison.tab_match[i].x2));			pt2.setY(ROUND(lastComparison.tab_match[i].y2 + siftObj1.im->height));						painter.setBrush(Qt::white);			if (lastComparison.tab_match[i].id==0)				painter.setPen(Qt::red); //red for discarded matches			else painter.setPen(Qt::green); //green						painter.drawLine(pt1, pt2);			painter.drawEllipse(pt1, 3, 3);			painter.drawEllipse(pt2, 3, 3);		}	}		#ifdef AAA			//IplImage * im,* imcol;	QSize s;	//QPoint pt1, pt2;	//CvScalar color;	int i,j,im2null=0;	Keypoint k1*=siftObj1->keypoints;	Keypoint k2*=siftObj2->keypoints;		/*Affine transform of the image border*/				if (param.size_m()>0) {			Matrice p1(2,1), p2(2,1), p3(2,1), p4(2,1), transl(2,1);			transl.set_val(0,0,0);			transl.set_val(1,0,im1->height);			p1.set_val(0,0,0);			p1.set_val(1,0,0);			p2.set_val(0,0,im1->width);			p2.set_val(1,0,0);			p3.set_val(0,0,im1->width);			p3.set_val(1,0,im1->height);			p4.set_val(0,0,0);			p4.set_val(1,0,im1->height);						p1=Transform(p1,param)+transl;			p2=Transform(p2,param)+transl;			p3=Transform(p3,param)+transl;			p4=Transform(p4,param)+transl;						color=CV_RGB(0,128,255); //light blue			pt1.x=ROUND(p1.get_val(0,0));			pt1.y=ROUND(p1.get_val(1,0));			pt2.x=ROUND(p2.get_val(0,0));			pt2.y=ROUND(p2.get_val(1,0));			cvLine(imcol, pt1, pt2, color, 1);			pt1.x=ROUND(p2.get_val(0,0));			pt1.y=ROUND(p2.get_val(1,0));			pt2.x=ROUND(p3.get_val(0,0));			pt2.y=ROUND(p3.get_val(1,0));			cvLine(imcol, pt1, pt2, color, 1);			pt1.x=ROUND(p3.get_val(0,0));			pt1.y=ROUND(p3.get_val(1,0));			pt2.x=ROUND(p4.get_val(0,0));			pt2.y=ROUND(p4.get_val(1,0));			cvLine(imcol, pt1, pt2, color, 1);			pt1.x=ROUND(p4.get_val(0,0));			pt1.y=ROUND(p4.get_val(1,0));			pt2.x=ROUND(p1.get_val(0,0));			pt2.y=ROUND(p1.get_val(1,0));			cvLine(imcol, pt1, pt2, color, 1);						/* Draw the border of the object */			CvMemStorage *storage= cvCreateMemStorage (0); /* Memory used by openCV */			int header_size = sizeof( CvContour );			CvSeq *contours;						IplImage* imthres = cvCreateImage(cvSize(im1->width,im1->height),IPL_DEPTH_8U, 1 );			cvCopy( im1, imthres, 0 );						/* First find the contour of a thresholded image*/						cvThreshold(imthres, imthres, border_threshold, 255, CV_THRESH_BINARY );			cvFindContours ( imthres, storage, &contours, header_size, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);						/* For each contour found*/						while ( contours != NULL) {				double area=fabs(cvContourArea(contours,CV_WHOLE_SEQ)); // compute area				if ( area > 20) {					for (int i=0;i<contours->total;i++) {												/* Compute transform of contour*///.........这里部分代码省略.........

static void computeVectors( IplImage* img, IplImage* dst, short wROI, short hROI){	if(DEBUG){		std::cout << "-- VECTOR COMPUTING" << std::endl;			}	double timestamp = (double)clock()/CLOCKS_PER_SEC; // get current time in seconds	CvSize size = cvSize(img->width,img->height); // get current frame size 640x480	int i, idx1 = last, idx2;	CvSeq* seq;	CvRect comp_rect;	CvRect roi;	double count;	double angle;	CvPoint center;	double magnitude;	CvScalar color;	//--SURF CORNERS--	if(DEBUG){		std::cout << "--- SURF CORNERS" << std::endl;	}        color = CV_RGB(0,255,0);        CvMemStorage* storage2 = cvCreateMemStorage(0);        CvSURFParams params = cvSURFParams(SURF_THRESHOLD, 1);        CvSeq *imageKeypoints = 0, *imageDescriptors = 0;        cvExtractSURF( dst, 0, &imageKeypoints, &imageDescriptors, storage2, params );        if(DEBUG){			printf("Image Descriptors: %d/n", imageDescriptors->total);		}        for( int j = 0; j < imageKeypoints->total; j++ ){            CvSURFPoint* r = (CvSURFPoint*)cvGetSeqElem( imageKeypoints, j );			center.x = cvRound(r->pt.x);            center.y = cvRound(r->pt.y);			if(DEBUG){				printf("j: %d /t", j);               				printf("total: %d /t", imageKeypoints->total);               				printf("valor hessiano: %f /t", r->hessian);				printf("x: %d /t", center.x);				printf("y: %d /n", center.y);			}			// Agrego el Punto en donde es la region que nos interesa			cvCircle( dst, center, cvRound(r->hessian*0.02), color, 3, CV_AA, 0 );			// Lleno la matriz con los vectores			relevancePointToVector(center.x, center.y, wROI, hROI, 5);		}	//--SURF CORNERS	// calculate motion gradient orientation and valid orientation mask	cvCalcMotionGradient( mhi, mask, orient, MAX_TIME_DELTA, MIN_TIME_DELTA, 3 );		// Compute Motion on 4x4 Cuadrants	if(DEBUG){		std::cout << "--- MOTION CUADRANTS" << std::endl;	}	i	 = 25;	color = CV_RGB(255,0,0);	magnitude = 30;	for (int r = 0; r < size.height; r += hROI){		for (int c = 0; c < size.width; c += wROI){			comp_rect.x = c;			comp_rect.y = r;			comp_rect.width = (c + wROI > size.width) ? (size.width - c) : wROI;			comp_rect.height = (r + hROI > size.height) ? (size.height - r) : hROI;			cvSetImageROI( mhi, comp_rect );			cvSetImageROI( orient, comp_rect );			cvSetImageROI( mask, comp_rect );			cvSetImageROI( silh, comp_rect );			cvSetImageROI( img, comp_rect );			// Process Motion			angle = cvCalcGlobalOrientation( orient, mask, mhi, timestamp, MHI_DURATION);			angle = 360.0 - angle;  // adjust for images with top-left origin			count = cvNorm( silh, 0, CV_L1, 0 ); // calculate number of points within silhouette ROI			roi = cvGetImageROI(mhi);			center = cvPoint( (comp_rect.x + comp_rect.width/2),					  (comp_rect.y + comp_rect.height/2) );			cvCircle( dst, center, cvRound(magnitude*1.2), color, 3, CV_AA, 0 );			cvLine( dst, center, cvPoint( cvRound( center.x + magnitude*cos(angle*CV_PI/180)),			cvRound( center.y - magnitude*sin(angle*CV_PI/180))), color, 3, CV_AA, 0 );							if(DEBUG){				std::cout << "Motion " << i << " -> x: " << roi.x << " y: " << roi.y << " count: " << count << " angle: " << angle << std::endl; // print the roi			}			cvResetImageROI( mhi );			cvResetImageROI( orient );			cvResetImageROI( mask );			cvResetImageROI( silh );			cvResetImageROI(img);			relevanceDirectionToVector(i, angle);			++i;		}	}	// Compute Global Motion	if(DEBUG){		std::cout << "--- MOTION GLOBAL" << std::endl;	}	comp_rect = cvRect( 0, 0, size.width, size.height );	color = CV_RGB(255,255,255);//.........这里部分代码省略.........

void ObjectTester::RunVideoDemo(){	GenericObjectDetector detector;	visualsearch::io::OpenCVCameraIO cam;	if( !cam.InitCamera() )		return;	//KinectDataMan kinectDM;	//if( !kinectDM.InitKinect() )		//return;	bool doRank = true;	// start fetching stream data	while(1)	{		Mat cimg, dmap;		//kinectDM.GetColorDepth(cimg, dmap);		cam.QueryNextFrame(visualsearch::io::STREAM_COLOR, cimg);		// resize image		Size newsz;		ToolFactory::compute_downsample_ratio(Size(cimg.cols, cimg.rows), 300, newsz);		resize(cimg, cimg, newsz);		//resize(dmap, dmap, newsz);		//normalize(dmap, dmap, 0, 255, NORM_MINMAX);		// get objects		vector<ImgWin> objwins, salwins;		if( !detector.ProposeObjects(cimg, dmap, objwins, salwins, doRank) )			continue;		//////////////////////////////////////////////////////////////////////////		// draw best k windows		int topK = MIN(6, objwins.size());		int objimgsz = newsz.height / topK;		int canvas_h = newsz.height;		int canvas_w = newsz.width + 10 + objimgsz*2;		Mat canvas(canvas_h, canvas_w, CV_8UC3);		canvas.setTo(Vec3b(0,0,0));		// get top windows		vector<Mat> detimgs(topK);		vector<Mat> salimgs(topK);		for (int i=0; i<topK; i++)		{			cimg(objwins[i]).copyTo(detimgs[i]);			resize(detimgs[i], detimgs[i], Size(objimgsz, objimgsz));			cimg(salwins[i]).copyTo(salimgs[i]);			resize(salimgs[i], salimgs[i], Size(objimgsz, objimgsz));		}		// draw boxes on input		Scalar objcolor(0, 255, 0);		Scalar salcolor(0, 0, 255);		for(int i=0; i<topK; i++)		{			//rectangle(cimg, objwins[i], objcolor);			rectangle(cimg, salwins[i], salcolor);		}		circle(cimg, Point(cimg.cols/2, cimg.rows/2), 2, CV_RGB(255,0,0));		// copy input image		cimg.copyTo(canvas(Rect(0, 0, cimg.cols, cimg.rows)));		// copy subimg		for (int i=0; i<detimgs.size(); i++)		{			Rect objbox(cimg.cols+10, i*objimgsz, objimgsz, objimgsz);			detimgs[i].copyTo(canvas(objbox));			Rect salbox(cimg.cols+10+objimgsz, i*objimgsz, objimgsz, objimgsz);			salimgs[i].copyTo(canvas(salbox));		}		resize(canvas, canvas, Size(canvas.cols*2, canvas.rows*2));		//if(doRank)			//putText(canvas, "Use Ranking", Point(50, 50), CV_FONT_HERSHEY_COMPLEX, 1, CV_RGB(255, 0, 0));		imshow("object proposals", canvas);		if( waitKey(10) == 'q' )			break;	}}

static void putDistance(IplImage *Image,                        std::vector<cv_tracker::image_rect_ranged> objects,                        int threshold_height,                        const char* objectLabel){  char distance_string[32];  CvFont dfont;  float hscale	    = 0.7f;  float vscale	    = 0.7f;  float italicscale = 0.0f;  int	thickness   = 1;  CvFont      dfont_label;  float       hscale_label = 0.5f;  float       vscale_label = 0.5f;  CvSize      text_size;  int         baseline     = 0;  cvInitFont(&dfont_label, CV_FONT_HERSHEY_COMPLEX, hscale_label, vscale_label, italicscale, thickness, CV_AA);  cvGetTextSize(objectLabel,                &dfont_label,                &text_size,                &baseline);  for (unsigned int i=0; i<objects.size(); i++)    {      if (objects.at(i).rect.y > threshold_height) // temporal way to avoid drawing detections in the sky        {          if (!isNearlyNODATA(objects.at(i).range))            {              /* put label */              CvPoint labelOrg = cvPoint(objects.at(i).rect.x - OBJ_RECT_THICKNESS,                                         objects.at(i).rect.y - baseline - OBJ_RECT_THICKNESS);              cvRectangle(Image,                          cvPoint(labelOrg.x + 0, labelOrg.y + baseline),                          cvPoint(labelOrg.x + text_size.width, labelOrg.y - text_size.height),                          CV_RGB(0, 0, 0), // label background is black                          -1, 8, 0                          );              cvPutText(Image,                        objectLabel,                        labelOrg,                        &dfont_label,                        CV_RGB(255, 255, 255) // label text color is white                        );              /* put distance data */              cvRectangle(Image,                          cv::Point(objects.at(i).rect.x + (objects.at(i).rect.width/2) - (((int)log10(objects.at(i).range/100)+1) * 5 + 45),                                    objects.at(i).rect.y + objects.at(i).rect.height + 5),                          cv::Point(objects.at(i).rect.x + (objects.at(i).rect.width/2) + (((int)log10(objects.at(i).range/100)+1) * 8 + 38),                                    objects.at(i).rect.y + objects.at(i).rect.height + 30),                          cv::Scalar(255,255,255),                          -1);              cvInitFont (&dfont,                          CV_FONT_HERSHEY_COMPLEX,                          hscale,                          vscale,                          italicscale,                          thickness,                          CV_AA);              sprintf(distance_string, "%.2f m", objects.at(i).range / 100); //unit of length is meter              cvPutText(Image,                        distance_string,                        cvPoint(objects.at(i).rect.x + (objects.at(i).rect.width/2) - (((int)log10(objects.at(i).range/100)+1) * 5 + 40),                                objects.at(i).rect.y + objects.at(i).rect.height + 25),                        &dfont,                        CV_RGB(255, 0, 0));            }        }    }}

/*!  Initialize the display size, position and title.  /param width, height : Width and height of the window.  /param x, y : The window is set at position x,y (column index, row index).  /param title : Window title.*/voidvpDisplayOpenCV::init(unsigned int width, unsigned int height,                      int x, int y,                      const char *title){  this->width  = width;  this->height = height;  this->windowXPosition = x;  this->windowYPosition = y;  int flags = CV_WINDOW_AUTOSIZE;  if (title != NULL)  {    if (this->title != NULL)    {      delete [] this->title ;      this->title = NULL ;    }    this->title = new char[strlen(title) + 1] ;    strcpy(this->title, title) ;  }  else{    if (this->title != NULL)    {      delete [] this->title ;      this->title = NULL ;    }    this->title = new char[50] ;    sprintf(this->title,"Unnamed ViSP display <%02d>",count) ;  }  count++;  /* Create the window*/  window = cvNamedWindow( this->title, flags );  cvMoveWindow( this->title, x, y );  move = false;  lbuttondown = false;  mbuttondown = false;  rbuttondown = false;  lbuttonup = false;  mbuttonup = false;  rbuttonup = false;  cvSetMouseCallback( this->title, on_mouse, this );  /* Create background pixmap *///   background = cvCreateImage(cvSize((int)width,(int)height),IPL_DEPTH_8U,3);////   cvShowImage( this->title,background);  col = new CvScalar[vpColor::id_unknown] ;  /* Create color */  vpColor pcolor; // Predefined colors  pcolor = vpColor::lightBlue;  col[vpColor::id_lightBlue]   = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::blue;  col[vpColor::id_blue]   = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::darkBlue;  col[vpColor::id_darkBlue]   = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::lightRed;  col[vpColor::id_lightRed]   = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::red;  col[vpColor::id_red]    = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::darkRed;  col[vpColor::id_darkRed]   = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::lightGreen;  col[vpColor::id_lightGreen]   = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::green;  col[vpColor::id_green]  = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::darkGreen;  col[vpColor::id_darkGreen]   = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::yellow;  col[vpColor::id_yellow] = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::cyan;  col[vpColor::id_cyan]   = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::orange;  col[vpColor::id_orange] = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::purple;  col[vpColor::id_purple] = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::white;  col[vpColor::id_white]  = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::black;  col[vpColor::id_black]  = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::lightGray;  col[vpColor::id_lightGray]   = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::gray;  col[vpColor::id_gray]  = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  pcolor = vpColor::darkGray;  col[vpColor::id_darkGray]   = CV_RGB(pcolor.R, pcolor.G, pcolor.B) ;  font = new CvFont;  cvInitFont( font, CV_FONT_HERSHEY_PLAIN, 0.70f,0.70f);  CvSize fontSize;  int baseline;//.........这里部分代码省略.........

//.........这里部分代码省略.........               {                    perspectiveTransform(Mat(points1), points1t, H12);                    double maxInlierDist = 10;//ransacReprojThreshold < 0 ? 3 : ransacReprojThreshold;                    for(i = 0; i < points1.size(); i++ )                    {                        if( norm(points2[i] - points1t.at<Point2f>((int)i,0)) <= 5)// maxInlierDist ) // inlier                        {                            matchesMask[i] = 1;                            npoints2.push_back(points2[i]);                            npoints1.push_back(points1[i]);                        }                    }                    for(i=0; i<npoints2.size();i++)                    {                        for(j=0;j<MP1.size();j++)                        {                            double dist = norm(npoints2[i]-MP1[j]);                            // cout <<"dist /t" <<dist << endl;                            //  waitKey(0);                            if(dist < 0.1)                            {                                Mpoints2.push_back(npoints2[i]);                                Mpoints1.push_back(npoints1[i]);                                break;                            }                        }                    }                }                Mat drawImg;                drawMatches( img1ROI, keypoints1, img2ROI, keypoints2, filteredMatches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask             #if DRAW_RICH_KEYPOINTS_MODE                             , DrawMatchesFlags::DRAW_RICH_KEYPOINTS             #endif                             );                imshow( "correspondance", drawImg );                cout << "npoints1.size /t" << Mpoints1.size() << "/t" << Mpoints2.size() << endl;                if(Mpoints1.size() > 8)                    weightScalingAspect(Mpoints1,Mpoints2,&scale);            }        }        img1=img2;        img1ROI = img2ROI;        boxOrg =box;        keypoints1 = keypoints2;        descriptors1 =descriptors2;        box.x += box.width/2;        box.y += box.height/2;        box.height = round(boxOrg.height *scale);        box.width = round(( float(boxOrg.width)/float(boxOrg.height) ) * box.height);        box.x -= box.width/2;        box.y -= box.height/2;        boundaryCheckRect(box);        cout <<"SCALE /t" << scale << endl;        gettimeofday(&t2, NULL);       double diff = (float)((t2.tv_sec * 1000000 + t2.tv_usec) - (t1.tv_sec * 1000000 + t1.tv_usec));       diff = diff/1000;        cout <<"Time taken in mili sec /t" <<  diff<< endl;       // cout << tpdescriptors.rows << endl;        //cout <<"BD /t" << bdescriptors.rows << endl;        f1 <<  l << "/t" << FG_mp << "/t"   << BG_mp  << "/t"   << FG   << "/t"<< msI << "/n";        cout << "l /t" << l << "/t" <<" msI /t"<< msI << endl;        imshow("img2",temp1);        writer << temp1;         waitKey(0);       // boxOrg = eBox;        char c = (char)waitKey(10);        if( c == '/x1b' ) // esc        {            cout << "Exiting ..." << endl;            break;        }    }    trajectory.close();    return 0;}

//マップを描画するvoid Map::showMap(){    IplImage *img = 0;//
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