我有一个项目，我试图跟踪一个人在整个房间里移动。我正在使用arduino，一些伺服电机和xbox kinect用于我的相机。
我的目标是让项目有一些训练时间，它可以扫描房间并为空房创建图像数据库。然后，当一个人进入房间时，程序可以做一个简单的差异图像，为该人创建一个白色的斑点。使用这个白色斑点，我将能够计算人的质心，并将其与图像框的中心进行比较，以便将命令传递给arduino，告诉它移动伺服电机的距离和方向。我正在使用eclipse，用java编写并使用opencv 2.4.6。
我被困在得到一个清晰的白色斑点。我已经编写了我的方法来计算从斑点的质心和框架的中心的距离，但没有明确定义的斑点，这是没用的。我一直试图让我的程序工作，拍摄我房间的背景，将图像更改为二进制，然后从我的房间的二进制图像中删除它。这没效果。我的训练系统的愿景是否有效，或者我应该采用不同的方式来检测物体？
我尝试过实现光流（）但它似乎不稳定而且不是非常准确。
有关该主题的任何信息都非常有用。我提前感谢你阅读我的问题。
-Trent
编辑：我已附上我的代码。有问题的区域是training（）和matdiff（）方法。
package testingV1;
//OpenCv + OpenNI + Java Libraries
import java.awt.FlowLayout;
import java.util.ArrayList;
import java.util.List;
import java.awt.image.BufferedImage;
import java.awt.image.DataBuffer;
import java.awt.image.DataBufferByte;
import java.io.*;
import java.nio.ByteBuffer;
import javax.imageio.ImageIO;
import javax.swing.*;
import org.opencv.core.*;
import org.opencv.imgproc.*;
import org.opencv.objdetect.CascadeClassifier;
import org.opencv.video.BackgroundSubtractorMOG;
import org.opencv.video.Video;
import org.opencv.highgui.*;
import org.opencv.*;
import org.OpenNI.*;
public class TestV1 {
static int imWidth = 640, imHeight = 480;
static ImageGenerator imageGen;
static Context context;
static int flag = CvType.CV_8UC3;
static int flag2 = CvType.CV_8UC1;
static Mat background;
public static void main(String[] args) throws GeneralException{
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
//We create a new "context" of the Kinect
context = new Context();
JFrame canvas = new JFrame("Optical Flow");
//need to create and add license to our "context"
License license = new License("PrimeSense", "0KOIk2JeIBYClPWVnMoRKn5cdY4=");
context.addLicense(license);
//defining the data we are taking from the kinect
MapOutputMode mapMode = null; //initialize it to null
mapMode = new MapOutputMode(imWidth, imHeight, 30); //create a 640x480 30fps feed definition
imageGen = ImageGenerator.create(context); //Rgb camera
imageGen.setMapOutputMode(mapMode); //change our feed to 640x480 30 fps
imageGen.setPixelFormat(PixelFormat.RGB24);///Pixel format, RGB 8-bit 3 channel
context.setGlobalMirror(true); //Mirrors our feed to make it more intuitive
BufferedImage rgbImage = new BufferedImage(imWidth, imHeight, BufferedImage.TYPE_INT_RGB);
BufferedImage prevImg = new BufferedImage(imWidth, imHeight, BufferedImage.TYPE_BYTE_GRAY);
BufferedImage currImg = new BufferedImage(imWidth, imHeight, BufferedImage.TYPE_BYTE_GRAY);
BufferedImage diffImg = new BufferedImage(imWidth, imHeight, BufferedImage.TYPE_BYTE_GRAY);
BufferedImage paintedImg = new BufferedImage(imWidth, imHeight, BufferedImage.TYPE_INT_RGB);
BufferedImage facesImg = new BufferedImage(imWidth, imHeight, BufferedImage.TYPE_INT_RGB);
Mat paintedMat = new Mat(imHeight, imWidth, flag);
Mat facesMat = new Mat(imHeight, imWidth, flag);
Mat currMat = new Mat(imHeight, imWidth, flag2);
Mat prevMat = new Mat(imHeight, imWidth, flag2);
Mat diffMat = new Mat(imHeight, imWidth, flag2);
Mat paintedMatg = new Mat(imHeight, imWidth, flag2);
ByteBuffer imageBB;
//First Frame
canvas.getContentPane().setLayout(new FlowLayout());
Icon video = new ImageIcon(rgbImage);
JLabel panel = new JLabel(video);
//Icon video2 = new ImageIcon(paintedImg);
//JLabel panel2 = new JLabel(video2);
//Icon video3 = new ImageIcon(facesImg);
//JLabel panel3 = new JLabel(video3);
Icon video4 = new ImageIcon(diffImg);
JLabel panel4 = new JLabel(video4);
canvas.getContentPane().add(panel);
//canvas.getContentPane().add(panel2);
//canvas.getContentPane().add(panel3);
canvas.getContentPane().add(panel4);
canvas.pack();
canvas.setVisible(true);
canvas.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
CascadeClassifier faceDetectorAlg = new CascadeClassifier("C:/Users/Trent/Desktop/Capstone"
+ "/ComputerVisionCode/November16/testingV1/src/testingV1/haarcascade_frontalface_alt.xml");
boolean firstTime = true;
imageGen.startGenerating();
while(true){
context.waitOneUpdateAll(imageGen);
imageBB = imageGen.getImageMap().createByteBuffer(); //get KinectData
rgbImage = bufToImage(imageBB); //take data from kinect and put in BufferedImage
prevMat = currMat;
currMat = img2Mat(rgb2Gray(rgbImage));
if(firstTime){
training(rgbImage);
firstTime = false;
}
else{
diffMat = findDiff(currMat);
diffImg = mat2Img(diffMat);
}
//optical flow - inaccurate
//paintedMatg = opticalFlow(img2Mat(prevImg), img2Mat(currImg), 300, 0.01, 10);
//Imgproc.cvtColor(paintedMatg, paintedMat, Imgproc.COLOR_GRAY2RGB); //change from gray to color
//paintedImg = mat2Img(paintedMat);
//face detection - extremely resource intensive
//facesMat = faceDetector(img2Mat(rgbImage), faceDetectorAlg);
//facesImg = mat2Img(facesMat);
panel.setIcon(new ImageIcon(rgbImage));
//panel2.setIcon(new ImageIcon(paintedImg));
//panel3.setIcon(new ImageIcon(facesImg));
panel4.setIcon(new ImageIcon(diffImg));
canvas.repaint();
canvas.revalidate();
}
}
//establishes a background for better diff images
private static void training(BufferedImage in){
background = new Mat(imHeight, imWidth, flag2);
background = img2Mat(rgb2Gray(in));
System.out.println("Training Complete");
}
private static Mat findDiff(Mat in){
Mat output = new Mat(imHeight, imWidth, flag2);
Core.absdiff(background, in, output);
Imgproc.threshold(output, output, 20, 255, Imgproc.THRESH_BINARY);
return output;
}
//Face Detection
private static Mat faceDetector(Mat in, CascadeClassifier Alg){
Mat output = in;
MatOfRect faceDetections = new MatOfRect();
if(Alg.empty()){
System.out.println("didnt load");
return output;
}
Alg.detectMultiScale(in, faceDetections);
for(Rect rect : faceDetections.toArray()){
Core.rectangle(output, new Point(rect.x, rect.y),
new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0), 2);
}
return output;
}
//Returns an image with vectors painted to show movement.
private static Mat opticalFlow(Mat curr, Mat prev, int maxDetectionCount, double qualityLevel, double minDistance){
List<MatOfPoint2f> trackedPoints = new ArrayList<MatOfPoint2f>();
MatOfPoint initial = new MatOfPoint();
MatOfFloat err = new MatOfFloat();
MatOfByte status = new MatOfByte();
MatOfPoint2f initial2f = new MatOfPoint2f();
MatOfPoint2f next2f = new MatOfPoint2f();
double[] temp;
Point p1 = new Point();
Point p2 = new Point();
Mat output = new Mat(imHeight, imWidth, flag);
Scalar red = new Scalar(255, 0, 0);
//Finds Tracking points
if(trackedPoints.size() < 1){
Imgproc.goodFeaturesToTrack(curr, initial, maxDetectionCount, qualityLevel, minDistance);
initial.convertTo(initial2f, CvType.CV_32FC2);
trackedPoints.add(initial2f);
}
//catches first time frame
if(prev.empty())
curr.copyTo(prev);
//find points in current image
if(trackedPoints.get(0).total() > 0){
Video.calcOpticalFlowPyrLK(prev, curr, trackedPoints.get(0), next2f, status, err);
trackedPoints.add(next2f);
}
output = curr;
//draw red lines
for(int i = 0; i < trackedPoints.get(0).cols(); i++){
for(int j = 0; j < trackedPoints.get(0).rows(); j++){
temp = trackedPoints.get(0).get(j, i);
p1.set(temp);
temp = trackedPoints.get(1).get(j, i);
p2.set(temp);
Core.line(output, p1, p2, red);
}
}
return output;
}
//Returns a vector to indicate how the magnitude of movement.
private static double[] opticalFlowAnalysis(Mat curr, Mat prev, int maxDetectionCount, double qualityLevel, double minDistance){
List<MatOfPoint2f> trackedPoints = new ArrayList<MatOfPoint2f>();
MatOfPoint initial = new MatOfPoint();
MatOfFloat err = new MatOfFloat();
MatOfByte status = new MatOfByte();
MatOfPoint2f initial2f = new MatOfPoint2f();
MatOfPoint2f next2f = new MatOfPoint2f();
double[] total = new double[2];
total[0] = 0;
total[1] = 0;
double[] point1;
double[] point2;
double[] output = new double[2];
//Finds Tracking points
if(trackedPoints.size() < 1){
Imgproc.goodFeaturesToTrack(curr, initial, maxDetectionCount, qualityLevel, minDistance);
initial.convertTo(initial2f, CvType.CV_32FC2);
trackedPoints.add(initial2f);
}
//catches first time frame
if(prev.empty())
curr.copyTo(prev);
//find points in current image
if(trackedPoints.get(0).total() > 0){
Video.calcOpticalFlowPyrLK(prev, curr, trackedPoints.get(0), next2f, status, err);
trackedPoints.add(next2f);
}
//average the distance moved
// (-) signifies distance moved right and down
// (+) signifies distance moved left and up
for(int i = 0; i < trackedPoints.get(0).cols(); i++){
for(int j = 0; j < trackedPoints.get(0).rows(); j++){
point1 = trackedPoints.get(0).get(j, i);
point2 = trackedPoints.get(1).get(j, i);
total[0] += point1[0] - point2[0];
total[1] += point1[1] - point2[0];
}
}
output[0] = total[0] / trackedPoints.get(0).cols();
output[1] = total[1] / trackedPoints.get(0).rows();
return output;
}
private static Mat img2Mat(BufferedImage in){
Mat out;
byte[] data;
int r, g, b;
if(in.getType() == BufferedImage.TYPE_INT_RGB){
out = new Mat(imHeight, imWidth, flag);
data = new byte[imWidth * imHeight * (int)out.elemSize()];
int[] dataBuff = in.getRGB(0, 0, imWidth, imHeight, null, 0, imWidth);
for(int i = 0; i < dataBuff.length; i++){
data[i*3] = (byte) ((dataBuff[i] >> 16) & 0xFF);
data[i*3 + 1] = (byte) ((dataBuff[i] >> 8) & 0xFF);
data[i*3 + 2] = (byte) ((dataBuff[i] >> 0) & 0xFF);
}
}
else{
out = new Mat(imHeight, imWidth, flag2);
data = new byte[imWidth * imHeight * (int)out.elemSize()];
int[] dataBuff = in.getRGB(0, 0, imWidth, imHeight, null, 0, imWidth);
for(int i = 0; i < dataBuff.length; i++){
r = (byte) ((dataBuff[i] >> 16) & 0xFF);
g = (byte) ((dataBuff[i] >> 8) & 0xFF);
b = (byte) ((dataBuff[i] >> 0) & 0xFF);
data[i] = (byte)((0.21 * r) + (0.71 * g) + (0.07 * b)); //luminosity
}
}
out.put(0, 0, data);
return out;
}
private static BufferedImage mat2Img(Mat in){
BufferedImage out;
byte[] data = new byte[imWidth * imHeight * (int)in.elemSize()];
int type;
in.get(0, 0, data);
if(in.channels() == 1)
type = BufferedImage.TYPE_BYTE_GRAY;
else
type = BufferedImage.TYPE_3BYTE_BGR;
out = new BufferedImage(imWidth, imHeight, type);
out.getRaster().setDataElements(0, 0, imWidth, imHeight, data);
return out;
}
private static BufferedImage rgb2Gray(BufferedImage in){
BufferedImage out = new BufferedImage(imWidth, imHeight, BufferedImage.TYPE_BYTE_GRAY);
Mat color = new Mat(imHeight, imWidth, flag);
Mat gray = new Mat(imHeight, imWidth, flag);
color = img2Mat(in); //converting bufferedImage to Mat
Imgproc.cvtColor(color, gray, Imgproc.COLOR_RGB2GRAY); //change from color to grayscale
out = mat2Img(gray); //converting Mat to bufferedImage
return out;
}
//Converts bytebuffer to buffered image
private static BufferedImage bufToImage(ByteBuffer pixelsRGB){
int[] pixelInts = new int[imWidth * imHeight];
int rowStart = 0;
int bbIdx; //index to ByteBuffer
int i = 0; //index to pixels
int rowLen = imWidth * 3;
for (int row = 0; row < imHeight; row++){
bbIdx = rowStart;
for(int col = 0; col < imWidth; col++){
int pixR = pixelsRGB.get(bbIdx++);
int pixG = pixelsRGB.get(bbIdx++);
int pixB = pixelsRGB.get(bbIdx++);
pixelInts[i++] = 0xFF000000 | ((pixR & 0xFF) << 16) | ((pixG & 0xFF) << 8) | (pixB & 0xFF);
}
rowStart += rowLen; //Move to next row
}
BufferedImage im = new BufferedImage(imWidth, imHeight, BufferedImage.TYPE_INT_RGB);
im.setRGB(0, 0, imWidth, imHeight, pixelInts, 0, imWidth);
return im;
}
}