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 | 1 | initial version |
I tried this CPP code and adopt it to Java side as below. I changed some of the lines because no need of them. So, the last state of it with Java goes here. I used *.mp4 file but i resize the frame before processing because more than a 640x480 resolution, processing is too slow. I prefer to dynamic resize the frame to 320x240 or max. 640x480 before processing :
// Fields...
Mat fg;
Mat blurred;
Mat thresholded;
Mat gray;
Mat blob;
Mat bgmodel;
BackgroundSubtractorMOG2 bgs;
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
CascadeClassifier body;
// Initializer part (you may put it to a private method and call it from constructor)...
bgs = new BackgroundSubtractorMOG2();
bgs.setInt("nmixtures", 3);
bgs.setInt("history", 1000);
bgs.setInt("varThresholdGen", 15);
//bgs.setBool("bShadowDetection", true); // This feautee does not exist on OpenCV-2.4.11
bgs.setInt("nShadowDetection",0);
bgs.setDouble("fTau", 0.5);
body=new CascadeClassifier("mapbase/hogcascade_pedestrians.xml"); // Standart sample pedestrian trained data file
if(body.empty())
{
System.out.println("--(!)Error loading hogcascade file\n");
return;
}
else
{
System.out.println("Pedestrian classifier loaded up");
}
// Frame reader part...
VideoCapture cam = new VideoCapture("D:\\Sample_Videos\\pedestrian-much.mp4"); // int 0 : Webcam, String : rtsp://IP:PORT/PATH for ip camera etc.
if(cam.isOpened())
{
Thread.sleep(500); /// This one-time delay allows the Webcam to initialize itself
while( true )
{
cam.read(cam_image);
if(!cam_image.empty())
{
// Thread.sleep(50); // If too much computation, you may give a time to CPU
Imgproc.resize(cam_image, resizeImage, new Size(320, 240));
//Apply the classifier to the captured image
renderImage=faceDetector.detect(resizeImage);
//Display the image
BufferedImage img = facePanel.matToBufferedImage(renderImage);
facePanel.repaint();
renderImage.release();
}
else
{
System.out.println(" --(!) No captured frame from video source!");
break;
}
}
}
cam.release(); //release the webcam }
// Detect method which is called in while read frame loop... public Mat detect(Mat inputframe){
Imgproc.resize(webcam_image, resizeImage, new Size(320, 240));
Mat mRgba=new Mat();
inputframe.copyTo(mRgba); // The frame read from video source (camera, *.mp4, *.avi file etc)
blurred = new Mat();
fg = blurred.clone();
thresholded = fg.clone();
blob = thresholded.clone();
gray = blob.clone();
Imgproc.cvtColor(mRgba,gray,Imgproc.COLOR_BGR2GRAY); // Before processing we convert to gray
Imgproc.GaussianBlur(gray,blurred,new Size(3,3),0,0,Imgproc.BORDER_DEFAULT); // Avoid from unnecessary pixels
bgs.apply(blurred,fg); // Extract background from scene
Imgproc.threshold(fg,thresholded,70.0f,255,Imgproc.THRESH_BINARY); // Apply max and min threshold
Mat elementCLOSE = new Mat(5,5,CvType.CV_8U,new Scalar(255,255,255));
Imgproc.morphologyEx(thresholded,thresholded,Imgproc.MORPH_CLOSE,elementCLOSE); // Do a body figure analysis (for object scene change optimization)
Mat v = new Mat();
Imgproc.findContours(thresholded,contours,v, Imgproc.RETR_EXTERNAL,Imgproc.CHAIN_APPROX_SIMPLE); // Find the changes of scene (the moving parts)
Imgproc.equalizeHist(gray, gray); // Balance the contrast to gain proper result
int cmin = 500;
int cmax = 80000;
MatOfRect rects = new MatOfRect();
if (contours != null && !contours.isEmpty()) {
//if (contours.size() >= 50) { // You may use this control and a threshold value if too much contour you have
//Imgproc.drawContours(mRgba,contours,-1,new Scalar(0, 0, 255),2); // If you want you may draw the changed pixels in red color
for (int r = 0; r < contours.size(); r++) {
Mat contour = contours.get(r);
double contourarea = Imgproc.contourArea(contour);
if (contourarea > cmin && contourarea < cmax) { // If the changed area in ranges
body.detectMultiScale(gray, rects); // If the change is compatible with the predefined vector data
Rect[] rcts = rects.toArray();
for (int i=0;i<rcts.length;i++) {
Core.rectangle(mRgba, new Point(rcts[i].x, rcts[i].y),
new Point(rcts[i].x+rcts[i].width, rcts[i].y+rcts[i].height),
new Scalar(0, 255, 0), 4); // Draw the detected area (human body part) in green color
}
}
}
//}
}
inputframe.release();
v.release();
releaseAll(); // Very important if you don't want to get a OutofMemory error
return mRgba;
}
private void releaseAll() {
blurred.release();
fg.release();
thresholded.release();
blob.release();
gray.release();
contours.clear();
}
| | 2 | No.2 Revision |
I tried this CPP code and adopt it to Java side as below. I changed some of the lines because no need of them. So, the last state of it with Java goes here. I used *.mp4 file but i resize the frame before processing because more than a 640x480 resolution, processing is too slow. I prefer to dynamic resize the frame to 320x240 or max. 640x480 before processing :
// Fields...
Mat fg;
Mat blurred;
Mat thresholded;
Mat gray;
Mat blob;
Mat bgmodel;
BackgroundSubtractorMOG2 bgs;
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
CascadeClassifier body;
// Initializer part (you may put it to a private method and call it from constructor)...
bgs = new BackgroundSubtractorMOG2();
bgs.setInt("nmixtures", 3);
bgs.setInt("history", 1000);
bgs.setInt("varThresholdGen", 15);
//bgs.setBool("bShadowDetection", true); // This feautee does not exist on OpenCV-2.4.11
bgs.setInt("nShadowDetection",0);
bgs.setDouble("fTau", 0.5);
body=new CascadeClassifier("mapbase/hogcascade_pedestrians.xml"); // Standart sample pedestrian trained data file
if(body.empty())
{
System.out.println("--(!)Error loading hogcascade file\n");
return;
}
else
{
System.out.println("Pedestrian classifier loaded up");
}
// Frame reader part...
VideoCapture cam = new VideoCapture("D:\\Sample_Videos\\pedestrian-much.mp4"); // int 0 : Webcam, String : rtsp://IP:PORT/PATH for ip camera etc.
if(cam.isOpened())
{
Thread.sleep(500); /// This one-time delay allows the Webcam to initialize itself
while( true )
{
cam.read(cam_image);
if(!cam_image.empty())
{
// Thread.sleep(50); // If too much computation, you may give a time to CPU
Imgproc.resize(cam_image, resizeImage, new Size(320, 240));
//Apply the classifier to the captured image
renderImage=faceDetector.detect(resizeImage);
//Display the image
BufferedImage img = facePanel.matToBufferedImage(renderImage);
facePanel.repaint();
renderImage.release();
}
else
{
System.out.println(" --(!) No captured frame from video source!");
break;
}
}
}
cam.release(); //release the webcam }
// Detect method which is called in while read frame loop... public Mat detect(Mat inputframe){
Imgproc.resize(webcam_image, resizeImage, new Size(320, 240));
Mat mRgba=new Mat();
inputframe.copyTo(mRgba); // The frame read from video source (camera, *.mp4, *.avi file etc)
blurred = new Mat();
fg = blurred.clone();
thresholded = fg.clone();
blob = thresholded.clone();
gray = blob.clone();
Imgproc.cvtColor(mRgba,gray,Imgproc.COLOR_BGR2GRAY); // Before processing we convert to gray
Imgproc.GaussianBlur(gray,blurred,new Size(3,3),0,0,Imgproc.BORDER_DEFAULT); // Avoid from unnecessary pixels
bgs.apply(blurred,fg); // Extract background from scene
Imgproc.threshold(fg,thresholded,70.0f,255,Imgproc.THRESH_BINARY); // Apply max and min threshold
Mat elementCLOSE = new Mat(5,5,CvType.CV_8U,new Scalar(255,255,255));
Imgproc.morphologyEx(thresholded,thresholded,Imgproc.MORPH_CLOSE,elementCLOSE); // Do a body figure analysis (for object scene change optimization)
Mat v = new Mat();
Imgproc.findContours(thresholded,contours,v, Imgproc.RETR_EXTERNAL,Imgproc.CHAIN_APPROX_SIMPLE); // Find the changes of scene (the moving parts)
Imgproc.equalizeHist(gray, gray); // Balance the contrast to gain proper result
int cmin = 500;
int cmax = 80000;
MatOfRect rects = new MatOfRect();
if (contours != null && !contours.isEmpty()) {
//if (contours.size() >= 50) { // You may use this control and a threshold value if too much contour you have
//Imgproc.drawContours(mRgba,contours,-1,new Scalar(0, 0, 255),2); // If you want you may draw the changed pixels in red color
for (int r = 0; r < contours.size(); r++) {
Mat contour = contours.get(r);
double contourarea = Imgproc.contourArea(contour);
if (contourarea > cmin && contourarea < cmax) { // If the changed area in ranges
body.detectMultiScale(gray, rects); // If the change is compatible with the predefined vector data
Rect[] rcts = rects.toArray();
for (int i=0;i<rcts.length;i++) {
Core.rectangle(mRgba, new Point(rcts[i].x, rcts[i].y),
new Point(rcts[i].x+rcts[i].width, rcts[i].y+rcts[i].height),
new Scalar(0, 255, 0), 4); // Draw the detected area (human body part) in green color
}
}
}
//}
}
inputframe.release();
v.release();
releaseAll(); // Very important if you don't want to get a OutofMemory error
return mRgba;
}
private void releaseAll() {
blurred.release();
fg.release();
thresholded.release();
blob.release();
gray.release();
contours.clear();
}
Here are 2 sample pictures. This is a basic work so don't forget that if you want proper results you must develop the algorithm and generate a HOG file with INRA or MIT pedestrian database.
