我试图检测彩色球像ps3移动控制器球从2米距离。我有10个相机在同一个房间悬挂在天花板上。房间很暗，球已经进入里面。我有4-5个球。（红色，蓝色，绿色，黄色，粉红色）。我想用opencv跟踪他们的位置。在opencv中这样做的权利是什么？你可以给出链接，这个例子吗？
我使用此代码，但我有延迟问题。当我评论//我的trackFilteredObject行没有滞后。但是当使用此代码时我有很多延迟。我无法理解为什么发生因为我的正常CPU使用〜％15 ram使用6.3GB / 15GB（％40）运行此代码cpu使用率~20-23 ram使用率6.4GB。我认为它不是关于cpu-ram性能。我做错了什么？
视频：https://www.youtube.com/watch?v=_BKtJpPrkO4（您可以在前10秒看到延迟。在10 sen之后我会评论跟踪代码。）
注意：Kamerasayisi意味着cameracount我的追踪功能：
void trackFilteredObject(Object theObject,Mat threshold,Mat HSV, Mat &cameraFeed){
//max number of objects to be detected in frame
const int FRAME_WIDTH = 5120;
const int FRAME_HEIGHT = 480;
const int MAX_NUM_OBJECTS=50;
//minimum and maximum object area
const int MIN_OBJECT_AREA = 10*10;
const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5;
vector <Object> objects;
Mat temp;
threshold.copyTo(temp);
//these two vectors needed for output of findContours
vector< vector<Point> > contours;
vector<Vec4i> hierarchy;
//find contours of filtered image using openCV findContours function
findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE );
//use moments method to find our filtered object
double refArea = 0;
bool objectFound = false;
if (hierarchy.size() > 0) {
int numObjects = hierarchy.size();
//if number of objects greater than MAX_NUM_OBJECTS we have a noisy filter
if(numObjects<MAX_NUM_OBJECTS){
for (int index = 0; index >= 0; index = hierarchy[index][0]) {
Moments moment = moments((cv::Mat)contours[index]);
double area = moment.m00;
//if the area is less than 20 px by 20px then it is probably just noise
//if the area is the same as the 3/2 of the image size, probably just a bad filter
//we only want the object with the largest area so we safe a reference area each
//iteration and compare it to the area in the next iteration.
if(area>MIN_OBJECT_AREA){
Object object;
object.setXPos(moment.m10/area);
object.setYPos(moment.m01/area);
object.setType(theObject.getType());
object.setColor(theObject.getColor());
objects.push_back(object);
objectFound = true;
}else objectFound = false;
}
//let user know you found an object
if(objectFound ==true){
//draw object location on screen
drawObject(objects,cameraFeed,temp,contours,hierarchy);}
}else putText(cameraFeed,"TOO MUCH NOISE! ADJUST FILTER",Point(0,50),1,2,Scalar(0,0,255),2);
}
}
};
主要代码：
void Run()
{
int w, h;
_fps = 30;
IplImage *pCapImage[kameraSayisi];
IplImage *pDisplayImage;
PBYTE pCapBuffer = NULL;
// Create camera instance
for(int i = 0; i < kameraSayisi; i++)
{
_cam[i] = CLEyeCreateCamera(_cameraGUID[i], _mode, _resolution, _fps);
if(_cam[i] == NULL) return;
// Get camera frame dimensions
CLEyeCameraGetFrameDimensions(_cam[i], w, h);
// Create the OpenCV images
pCapImage[i] = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, 1);
// Set some camera parameters
CLEyeSetCameraParameter(_cam[i], CLEYE_GAIN, 0);
CLEyeSetCameraParameter(_cam[i], CLEYE_EXPOSURE, 511);
// Start capturing
CLEyeCameraStart(_cam[i]);
}
pDisplayImage = cvCreateImage(cvSize(w*kameraSayisi / 2, h * kameraSayisi/4 ), IPL_DEPTH_8U ,1);
if(_cam == NULL) return;
int iLastX = -1;
int iLastY = -1;
//Capture a temporary image from the camera
//program
bool trackObjects = true;
bool useMorphOps = true;
Mat HSV;
//Create a black image with the size as the camera output
Mat imgLines;
// imgLines = Mat::zeros( cvarrToMat(image).size(), CV_8UC3 );;
Mat threshold;
//x and y values for the location of the object
int x=0, y=0;
bool calibrationMode = false;
if(calibrationMode){
//create slider bars for HSV filtering
createTrackbars();
}
// image capturing loop
while(_running)
{
PBYTE pCapBuffer;
// Capture camera images
for(int i = 0; i < kameraSayisi; i++)
{
cvGetImageRawData(pCapImage[i], &pCapBuffer);
CLEyeCameraGetFrame(_cam[i], pCapBuffer, (i==0)?2000:0);
}
// Display stereo image
for(int i = 0; i < kameraSayisi; i++)
{
cvSetImageROI(pDisplayImage, cvRect(w * (i%4) ,i/4 * h, w, h));
cvCopy(pCapImage[i], pDisplayImage);
}
cvResetImageROI(pDisplayImage);
Mat imgOriginal;
Mat imgConverted = cvarrToMat(pDisplayImage);
if(calibrationMode==true)
{
//need to find the appropriate color range values
// calibrationMode must be false
//if in calibration mode, we track objects based on the HSV slider values.
//cvtColor(imgOriginal,imgOriginal,CV_BayerRG2RGB);
cvtColor(imgConverted,imgOriginal,CV_BayerGB2BGR);
cvtColor(imgOriginal,HSV,CV_BGR2HSV);
inRange(HSV,Scalar(H_MIN,S_MIN,V_MIN),Scalar(H_MAX,S_MAX,V_MAX),threshold);
morphOps(threshold);
imshow(_windowName + 'T',threshold);
//the folowing for canny edge detec
/// Create a matrix of the same type and size as src (for dst)
dst.create( imgOriginal.size(), src.type() );
/// Convert the image to grayscale
cvtColor( imgOriginal, src_gray, CV_BGR2GRAY );
/// Create a window
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
/// Create a Trackbar for user to enter threshold
// createTrackbar( "Min Threshold:", window_name, &lowThreshold, max_lowThreshold, CannyThreshold );
/// Show the image
Object a = Object(H_MIN,S_MIN,V_MIN,H_MAX,S_MAX,V_MAX);
trackFilteredObject(a,threshold,HSV,imgOriginal);
}
else{
//we can use their member functions/information
Object blue("blue"), yellow("yellow"), red("red"), orange("orange"),white("white");
cvtColor(imgConverted,imgOriginal,CV_BayerGB2BGR);
//first find blue objects
cvtColor(imgOriginal,HSV,CV_RGB2HSV);
inRange(HSV,blue.getHSVmin(),blue.getHSVmax(),threshold);
morphOps(threshold);
//then yellows
inRange(HSV,yellow.getHSVmin(),yellow.getHSVmax(),threshold);
//then reds
inRange(HSV,red.getHSVmin(),red.getHSVmax(),threshold);
//then white
inRange(HSV,white.getHSVmin(),white.getHSVmax(),threshold);
//then orange
inRange(HSV,orange.getHSVmin(),orange.getHSVmax(),threshold);
trackFilteredObject(yellow,threshold,HSV,imgOriginal);
trackFilteredObject(white,threshold,HSV,imgOriginal);
trackFilteredObject(red,threshold,HSV,imgOriginal);
trackFilteredObject(blue,threshold,HSV,imgOriginal);
trackFilteredObject(orange,threshold,HSV,imgOriginal);
}
//delay 10ms so that screen can refresh.
//image will not appear without this waitKey() command
if (cvWaitKey(30) == 27) //wait for 'esc' key press for 30ms. If 'esc' key is pressed, break loop
{
cout << "esc key is pressed by user" << endl;
break;
}
// cvShowImage(_windowName, image);
imshow(_windowName,imgOriginal);
}
for(int i = 0; i < kameraSayisi; i++)
{
// Stop camera capture
CLEyeCameraStop(_cam[i]);
// Destroy camera object
CLEyeDestroyCamera(_cam[i]);
// Destroy the allocated OpenCV image
cvReleaseImage(&pCapImage[i]);
_cam[i] = NULL;
}
}