public static void bhadis() {
Mat a =
new Mat(6, 4, CvType.CV_32F) {
{
put(0, 0, 0, 1, 1, 5);
put(1, 0, 1, 0, 2, 4);
put(2, 0, 2, 2, 3, 6);
put(3, 0, 0, 4, 6, 1);
put(4, 0, 5, 4, 6, 1);
put(5, 0, 4, 2, 8, 1);
}
};
Mat b =
new Mat(6, 4, CvType.CV_32F) {
{
put(0, 0, 4, 9, 2, 1);
put(1, 0, 9, 6, 0, 3);
put(2, 0, 2, 0, 8, 6);
put(3, 0, 1, 3, 6, 9);
put(4, 0, 5, 7, 6, 1);
put(5, 0, 4, 2, 8, 1);
}
};
System.out.println(a.dump());
System.out.println(b.dump());
double d = Distance.Bhattacharyya(b, b);
System.out.println("Bhattacharyya distance = " + d);
}
public static void normlise() {
Mat data =
new Mat(3, 4, CvType.CV_32F) {
{
put(0, 0, 1, 2, 2, 4);
put(1, 0, 2, 4, 4, 8);
put(2, 0, 3, 6, 6, 12);
}
};
System.out.println(data.dump());
Mat norm = WormGene.normaliseMeanVariance(data);
System.out.println(norm.dump());
}
public static Mat getCCH(Mat image) {
ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
Imgproc.findContours(
image, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_NONE);
Mat chainHistogram = Mat.zeros(1, 8, CvType.CV_32F);
int n = 0;
MatOfPoint2f approxCurve = new MatOfPoint2f();
for (MatOfPoint contour : contours) {
// get the freeman chain code from the contours
int rows = contour.rows();
// System.out.println("\nrows"+rows+"\n"+contour.dump());
int direction = 7;
Mat prevPoint = contours.get(0).row(0);
n += rows - 1;
for (int i = 1; i < rows; i++) {
// get the current point
double x1 = contour.get(i - 1, 0)[1];
double y1 = contour.get(i - 1, 0)[0];
// get the second point
double x2 = contour.get(i, 0)[1];
double y2 = contour.get(i, 0)[0];
if (x2 == x1 && y2 == y1 + 1) direction = 0;
else if (x2 == x1 - 1 && y2 == y1 + 1) direction = 1;
else if (x2 == x1 - 1 && y2 == y1) direction = 2;
else if (x2 == x1 - 1 && y2 == y1 - 1) direction = 3;
else if (x2 == x1 && y2 == y1 - 1) direction = 4;
else if (x2 == x1 + 1 && y2 == y1 - 1) direction = 5;
else if (x2 == x1 + 1 && y2 == y1) direction = 6;
else if (x2 == x1 + 1 && y2 == y1 + 1) direction = 7;
else System.out.print("err");
double counter = chainHistogram.get(0, direction)[0];
chainHistogram.put(0, direction, ++counter);
System.out.print(direction);
}
}
System.out.println("\n" + chainHistogram.dump());
Scalar alpha = new Scalar(n); // the factor
Core.divide(chainHistogram, alpha, chainHistogram);
System.out.println("\nrows=" + n + " " + chainHistogram.dump());
return chainHistogram;
}
public static void V2M() {
Vector<double[]> v = new Vector<double[]>();
double[] temp = {1.0, 2.0, 3.0, 4.0};
v.add(temp);
v.add(temp);
v.add(temp);
Mat m = DataConverter.jvector2Mat(v);
System.out.println(m.dump());
}
public static void main(String[] args) {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
// Mat mat = Mat.eye( 3, 3, CvType.CV_8UC1 );
// System.out.println( "mat = " + mat.dump() );
Sample n = new Sample();
// n.templateMatching();
// put text in image
// Mat data= Highgui.imread("images/erosion.jpg");
// Core.putText(data, "Sample", new Point(50,80), Core.FONT_HERSHEY_SIMPLEX, 1, new
// Scalar(0,0,0),2);
//
// Highgui.imwrite("images/erosion2.jpg", data);
// getting dct of an image
String path = "images/croppedfeature/go (20).jpg";
path = "images/wordseg/img1.png";
Mat image = Highgui.imread(path, Highgui.IMREAD_GRAYSCALE);
ArrayList<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.threshold(image, image, 0, 255, Imgproc.THRESH_OTSU);
Imgproc.threshold(image, image, 220, 128, Imgproc.THRESH_BINARY_INV);
Mat newImg = new Mat(45, 100, image.type());
newImg.setTo(new Scalar(0));
n.copyMat(image, newImg);
int vgap = 25;
int hgap = 45 / 3;
Moments m = Imgproc.moments(image, false);
Mat hu = new Mat();
Imgproc.HuMoments(m, hu);
System.out.println(hu.dump());
// //divide the mat into 12 parts then get the features of each part
// int count=1;
// for(int j=0; j<45; j+=hgap){
// for(int i=0;i<100;i+=vgap){
// Mat result = newImg.submat(j, j+hgap, i, i+vgap);
//
//
// Moments m= Imgproc.moments(result, false);
// double m01= m.get_m01();
// double m00= m.get_m00();
// double m10 = m.get_m10();
// int x= m00!=0? (int)(m10/m00):0;
// int y= m00!=0? (int)(m01/m00):0;
// Mat hu= new Mat();
// Imgproc.HuMoments(m, hu);
// System.out.println(hu.dump());
// System.out.println(count+" :"+x+" and "+y);
// Imgproc.threshold(result, result, 0,254, Imgproc.THRESH_BINARY_INV);
// Highgui.imwrite("images/submat/"+count+".jpg", result);
// count++;
//
// }
// }
//
// for(int i=vgap;i<100;i+=vgap){
// Point pt1= new Point(i, 0);
// Point pt2= new Point(i, 99);
// Core.line(newImg, pt1, pt2, new Scalar(0,0,0));
// }
// for(int i=hgap;i<45;i+=hgap){
// Point pt1= new Point(0, i);
// Point pt2= new Point(99, i);
// Core.line(newImg, pt1, pt2, new Scalar(0,0,0));
// }
// Highgui.imwrite("images/submat/copyto.jpg", newImg);
}
/**
* Analyze video frames using computer vision approach and generate a ArrayList<AttitudeRec>
*
* @param recs output ArrayList of AttitudeRec
* @return total number of frame of the video
*/
private int analyzeVideo(ArrayList<AttitudeRec> recs) {
VideoMetaInfo meta = new VideoMetaInfo(new File(mPath, "videometa.json"));
int decimation = 1;
if (meta.fps > DECIMATION_FPS_TARGET) {
decimation = (int) (meta.fps / DECIMATION_FPS_TARGET);
meta.fps /= decimation;
}
VideoDecoderForOpenCV videoDecoder =
new VideoDecoderForOpenCV(
new File(mPath, "video.mp4"), decimation); // every 3 frame process 1 frame
Mat frame;
Mat gray = new Mat();
int i = -1;
Size frameSize = videoDecoder.getSize();
if (frameSize.width != meta.frameWidth || frameSize.height != meta.frameHeight) {
// this is very unlikely
return -1;
}
if (TRACE_VIDEO_ANALYSIS) {
Debug.startMethodTracing("cvprocess");
}
Size patternSize = new Size(4, 11);
float fc = (float) (meta.frameWidth / 2.0 / Math.tan(meta.fovWidth / 2.0));
Mat camMat = cameraMatrix(fc, new Size(frameSize.width / 2, frameSize.height / 2));
MatOfDouble coeff = new MatOfDouble(); // dummy
MatOfPoint2f centers = new MatOfPoint2f();
MatOfPoint3f grid = asymmetricalCircleGrid(patternSize);
Mat rvec = new MatOfFloat();
Mat tvec = new MatOfFloat();
MatOfPoint2f reprojCenters = new MatOfPoint2f();
if (LOCAL_LOGV) {
Log.v(TAG, "Camera Mat = \n" + camMat.dump());
}
long startTime = System.nanoTime();
while ((frame = videoDecoder.getFrame()) != null) {
if (LOCAL_LOGV) {
Log.v(TAG, "got a frame " + i);
}
// has to be in front, as there are cases where execution
// will skip the later part of this while
i++;
// convert to gray manually as by default findCirclesGridDefault uses COLOR_BGR2GRAY
Imgproc.cvtColor(frame, gray, Imgproc.COLOR_RGB2GRAY);
boolean foundPattern =
Calib3d.findCirclesGridDefault(
gray, patternSize, centers, Calib3d.CALIB_CB_ASYMMETRIC_GRID);
if (!foundPattern) {
// skip to next frame
continue;
}
if (OUTPUT_DEBUG_IMAGE) {
Calib3d.drawChessboardCorners(frame, patternSize, centers, true);
}
// figure out the extrinsic parameters using real ground truth 3D points and the pixel
// position of blobs found in findCircleGrid, an estimated camera matrix and
// no-distortion are assumed.
boolean foundSolution =
Calib3d.solvePnP(grid, centers, camMat, coeff, rvec, tvec, false, Calib3d.CV_ITERATIVE);
if (!foundSolution) {
// skip to next frame
if (LOCAL_LOGV) {
Log.v(TAG, "cannot find pnp solution in frame " + i + ", skipped.");
}
continue;
}
// reproject points to for evaluation of result accuracy of solvePnP
Calib3d.projectPoints(grid, rvec, tvec, camMat, coeff, reprojCenters);
// error is evaluated in norm2, which is real error in pixel distance / sqrt(2)
double error = Core.norm(centers, reprojCenters, Core.NORM_L2);
if (LOCAL_LOGV) {
Log.v(TAG, "Found attitude, re-projection error = " + error);
}
// if error is reasonable, add it into the results
if (error < REPROJECTION_THREASHOLD) {
double[] rv = new double[3];
rvec.get(0, 0, rv);
recs.add(new AttitudeRec((double) i / meta.fps, rodr2rpy(rv)));
}
if (OUTPUT_DEBUG_IMAGE) {
Calib3d.drawChessboardCorners(frame, patternSize, reprojCenters, true);
Highgui.imwrite(
Environment.getExternalStorageDirectory().getPath()
+ "/RVCVRecData/DebugCV/img"
+ i
+ ".png",
frame);
}
}
if (LOCAL_LOGV) {
Log.v(TAG, "Finished decoding");
}
if (TRACE_VIDEO_ANALYSIS) {
Debug.stopMethodTracing();
}
if (LOCAL_LOGV) {
// time analysis
double totalTime = (System.nanoTime() - startTime) / 1e9;
Log.i(TAG, "Total time: " + totalTime + "s, Per frame time: " + totalTime / i);
}
return i;
}
