List of usage examples for org.opencv.core MatOfFloat MatOfFloat
public MatOfFloat()
From source file:com.android.cts.verifier.sensors.RVCVXCheckAnalyzer.java
License:Apache License
/** * 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 */// ww w.j a v a 2 s . c om private int analyzeVideo(ArrayList<AttitudeRec> recs) { VideoMetaInfo meta = new VideoMetaInfo(new File(mPath, "videometa.json")); int decimation = 1; boolean use_timestamp = true; // roughly determine if decimation is necessary 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); 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(); long[] ts = new long[1]; while ((frame = videoDecoder.getFrame(ts)) != null) { if (LOCAL_LOGV) { Log.v(TAG, "got a frame " + i); } if (use_timestamp && ts[0] == -1) { use_timestamp = false; } // 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.findCirclesGrid(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. use ratio to frame height to avoid // discriminating higher definition videos if (error < REPROJECTION_THREASHOLD_RATIO * frameSize.height) { double[] rv = new double[3]; double timestamp; rvec.get(0, 0, rv); if (use_timestamp) { timestamp = (double) ts[0] / 1e6; } else { timestamp = (double) i / meta.fps; } if (LOCAL_LOGV) Log.v(TAG, String.format("Added frame %d ts = %f", i, timestamp)); recs.add(new AttitudeRec(timestamp, rodr2rpy(rv))); } if (OUTPUT_DEBUG_IMAGE) { Calib3d.drawChessboardCorners(frame, patternSize, reprojCenters, true); Imgcodecs.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; }
From source file:com.trandi.opentld.tld.LKTracker.java
License:Apache License
/** * @return Pair of new, FILTERED, last and current POINTS, or null if it hasn't managed to track anything. *///from ww w .j a v a 2 s . c o m Pair<Point[], Point[]> track(final Mat lastImg, final Mat currentImg, Point[] lastPoints) { final int size = lastPoints.length; final MatOfPoint2f currentPointsMat = new MatOfPoint2f(); final MatOfPoint2f pointsFBMat = new MatOfPoint2f(); final MatOfByte statusMat = new MatOfByte(); final MatOfFloat errSimilarityMat = new MatOfFloat(); final MatOfByte statusFBMat = new MatOfByte(); final MatOfFloat errSimilarityFBMat = new MatOfFloat(); //Forward-Backward tracking Video.calcOpticalFlowPyrLK(lastImg, currentImg, new MatOfPoint2f(lastPoints), currentPointsMat, statusMat, errSimilarityMat, WINDOW_SIZE, MAX_LEVEL, termCriteria, 0, LAMBDA); Video.calcOpticalFlowPyrLK(currentImg, lastImg, currentPointsMat, pointsFBMat, statusFBMat, errSimilarityFBMat, WINDOW_SIZE, MAX_LEVEL, termCriteria, 0, LAMBDA); final byte[] status = statusMat.toArray(); float[] errSimilarity = new float[lastPoints.length]; //final byte[] statusFB = statusFBMat.toArray(); final float[] errSimilarityFB = errSimilarityFBMat.toArray(); // compute the real FB error (relative to LAST points not the current ones... final Point[] pointsFB = pointsFBMat.toArray(); for (int i = 0; i < size; i++) { errSimilarityFB[i] = Util.norm(pointsFB[i], lastPoints[i]); } final Point[] currPoints = currentPointsMat.toArray(); // compute real similarity error errSimilarity = normCrossCorrelation(lastImg, currentImg, lastPoints, currPoints, status); //TODO errSimilarityFB has problem != from C++ // filter out points with fwd-back error > the median AND points with similarity error > median return filterPts(lastPoints, currPoints, errSimilarity, errSimilarityFB, status); }
From source file:se.hb.jcp.bindings.opencv.DenseDoubleMatrix1D.java
License:Open Source License
/** * Constructs a matrix with a given number of columns. * All entries are initially <tt>0</tt>. * @param columns the number of columns the matrix shall have. * @throws IllegalArgumentException if//w ww .ja v a 2 s .c o m <tt>columns<0 || columns > Integer.MAX_VALUE</tt>. */ public DenseDoubleMatrix1D(int columns) { _mat = new MatOfFloat(); Mat.zeros(1, columns, CvType.CV_32F).assignTo(_mat); setUp(columns); }
From source file:se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D.java
License:Open Source License
/** * Constructs a matrix with a given number of rows and columns. * All entries are initially <tt>0</tt>. * @param rows the number of rows the matrix shall have. * @param columns the number of columns the matrix shall have. * @throws IllegalArgumentException if//from ww w .ja va 2 s .c o m <tt>rows<0 || columns<0 || (double)columns*rows > Integer.MAX_VALUE</tt>. */ public DenseDoubleMatrix2D(int rows, int columns) { _mat = new MatOfFloat(); Mat.zeros(rows, columns, CvType.CV_32F).assignTo(_mat); setUp(rows, columns); }
From source file:se.hb.jcp.bindings.opencv.DenseDoubleMatrix2D.java
License:Open Source License
/** Constructs and returns a new <i>slice view</i> representing the columns of the given row.// w w w .jav a 2 s . c om The returned view is backed by this matrix, so changes in the returned view are reflected in this matrix, and vice-versa. To obtain a slice view on subranges, construct a sub-ranging view (<tt>viewPart( ...)</tt>), then apply this method to the sub-range view. <p> <b>Example:</b> <table border="0"> <tr nowrap> <td valign="top">2 x 3 matrix: <br> 1, 2, 3<br> 4, 5, 6</td> <td>viewRow(0) ==></td> <td valign="top">Matrix1D of size 3:<br> 1, 2, 3</td> </tr> </table> @param row the row to fix. @return a new slice view. @throws IndexOutOfBoundsException if <tt>row < 0 || row >= rows()</tt>. @see #viewColumn(int) */ public DoubleMatrix1D viewRow(int row) { checkRow(row); MatOfFloat rowMat = new MatOfFloat(); _mat.row(row).assignTo(rowMat); return new DenseDoubleMatrix1D(columns, rowMat); }
From source file:syncleus.dann.data.video.LKTracker.java
License:Apache License
/** * @return Pair of new, FILTERED, last and current POINTS, or null if it hasn't managed to track anything. *///from w w w.j a v a 2s.c o m public Pair<Point[], Point[]> track(final Mat lastImg, final Mat currentImg, Point[] lastPoints) { final int size = lastPoints.length; final MatOfPoint2f currentPointsMat = new MatOfPoint2f(); final MatOfPoint2f pointsFBMat = new MatOfPoint2f(); final MatOfByte statusMat = new MatOfByte(); final MatOfFloat errSimilarityMat = new MatOfFloat(); final MatOfByte statusFBMat = new MatOfByte(); final MatOfFloat errSimilarityFBMat = new MatOfFloat(); //Forward-Backward tracking Video.calcOpticalFlowPyrLK(lastImg, currentImg, new MatOfPoint2f(lastPoints), currentPointsMat, statusMat, errSimilarityMat, WINDOW_SIZE, MAX_LEVEL, termCriteria, 0, LAMBDA); Video.calcOpticalFlowPyrLK(currentImg, lastImg, currentPointsMat, pointsFBMat, statusFBMat, errSimilarityFBMat, WINDOW_SIZE, MAX_LEVEL, termCriteria, 0, LAMBDA); final byte[] status = statusMat.toArray(); float[] errSimilarity = new float[lastPoints.length]; //final byte[] statusFB = statusFBMat.toArray(); final float[] errSimilarityFB = errSimilarityFBMat.toArray(); // compute the real FB error (relative to LAST points not the current ones... final Point[] pointsFB = pointsFBMat.toArray(); for (int i = 0; i < size; i++) { errSimilarityFB[i] = TLDUtil.norm(pointsFB[i], lastPoints[i]); } final Point[] currPoints = currentPointsMat.toArray(); // compute real similarity error errSimilarity = normCrossCorrelation(lastImg, currentImg, lastPoints, currPoints, status); //TODO errSimilarityFB has problem != from C++ // filter out points with fwd-back error > the median AND points with similarity error > median return filterPts(lastPoints, currPoints, errSimilarity, errSimilarityFB, status); }