List of usage examples for org.opencv.core Mat get
public int get(int row, int col, double[] data)
From source file:net.semanticmetadata.lire.imageanalysis.features.local.opencvfeatures.CvSiftExtractor.java
License:Open Source License
@Override public void extract(BufferedImage img) { MatOfKeyPoint keypoints = new MatOfKeyPoint(); Mat descriptors = new Mat(); List<KeyPoint> myKeys;/*from w ww .j a v a2 s . c o m*/ // Mat img_object = Highgui.imread(image, 0); //0 = CV_LOAD_IMAGE_GRAYSCALE // detector.detect(img_object, keypoints); byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData(); Mat matRGB = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3); matRGB.put(0, 0, data); Mat matGray = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC1); Imgproc.cvtColor(matRGB, matGray, Imgproc.COLOR_BGR2GRAY); //TODO: RGB or BGR? byte[] dataGray = new byte[matGray.rows() * matGray.cols() * (int) (matGray.elemSize())]; matGray.get(0, 0, dataGray); detector.detect(matGray, keypoints); extractor.compute(matGray, keypoints, descriptors); myKeys = keypoints.toList(); features = new LinkedList<CvSiftFeature>(); KeyPoint key; CvSiftFeature feat; double[] desc; int cols, rows = myKeys.size(); for (int i = 0; i < rows; i++) { cols = (descriptors.row(i)).cols(); desc = new double[cols]; key = myKeys.get(i); for (int j = 0; j < cols; j++) { desc[j] = descriptors.get(i, j)[0]; } feat = new CvSiftFeature(key.pt.x, key.pt.y, key.size, desc); features.add(feat); } }
From source file:net.semanticmetadata.lire.imageanalysis.features.local.opencvfeatures.CvSiftExtractor.java
License:Open Source License
public LinkedList<CvSiftFeature> computeSiftKeypoints(BufferedImage img) { MatOfKeyPoint keypoints = new MatOfKeyPoint(); List<KeyPoint> myKeys;/*from www . ja v a 2s .com*/ // Mat img_object = Highgui.imread(image, 0); //0 = CV_LOAD_IMAGE_GRAYSCALE // detector.detect(img_object, keypoints); byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData(); Mat matRGB = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3); matRGB.put(0, 0, data); Mat matGray = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC1); Imgproc.cvtColor(matRGB, matGray, Imgproc.COLOR_BGR2GRAY); //TODO: RGB or BGR? byte[] dataGray = new byte[matGray.rows() * matGray.cols() * (int) (matGray.elemSize())]; matGray.get(0, 0, dataGray); detector.detect(matGray, keypoints); myKeys = keypoints.toList(); LinkedList<CvSiftFeature> myKeypoints = new LinkedList<CvSiftFeature>(); KeyPoint key; CvSiftFeature feat; for (Iterator<KeyPoint> iterator = myKeys.iterator(); iterator.hasNext();) { key = iterator.next(); feat = new CvSiftFeature(key.pt.x, key.pt.y, key.size, null); myKeypoints.add(feat); } return myKeypoints; }
From source file:net.semanticmetadata.lire.imageanalysis.features.local.opencvfeatures.CvSurfExtractor.java
License:Open Source License
@Override public void extract(BufferedImage img) { MatOfKeyPoint keypoints = new MatOfKeyPoint(); Mat descriptors = new Mat(); List<KeyPoint> myKeys;// w w w . ja v a2 s . c o m // Mat img_object = Highgui.imread(image, 0); //0 = CV_LOAD_IMAGE_GRAYSCALE // detector.detect(img_object, keypoints); byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData(); Mat matRGB = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3); matRGB.put(0, 0, data); Mat matGray = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC1); Imgproc.cvtColor(matRGB, matGray, Imgproc.COLOR_BGR2GRAY); //TODO: RGB or BGR? byte[] dataGray = new byte[matGray.rows() * matGray.cols() * (int) (matGray.elemSize())]; matGray.get(0, 0, dataGray); detector.detect(matGray, keypoints); extractor.compute(matGray, keypoints, descriptors); myKeys = keypoints.toList(); features = new LinkedList<CvSurfFeature>(); KeyPoint key; CvSurfFeature feat; double[] desc; int cols, rows = myKeys.size(); for (int i = 0; i < rows; i++) { cols = (descriptors.row(i)).cols(); desc = new double[cols]; key = myKeys.get(i); for (int j = 0; j < cols; j++) { desc[j] = descriptors.get(i, j)[0]; } feat = new CvSurfFeature(key.pt.x, key.pt.y, key.size, desc); features.add(feat); } }
From source file:net.semanticmetadata.lire.imageanalysis.features.local.opencvfeatures.CvSurfExtractor.java
License:Open Source License
public LinkedList<CvSurfFeature> computeSurfKeypoints(BufferedImage img) { MatOfKeyPoint keypoints = new MatOfKeyPoint(); List<KeyPoint> myKeys;//from ww w . ja v a2 s . c o m // Mat img_object = Highgui.imread(image, 0); //0 = CV_LOAD_IMAGE_GRAYSCALE // detector.detect(img_object, keypoints); byte[] data = ((DataBufferByte) img.getRaster().getDataBuffer()).getData(); Mat matRGB = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC3); matRGB.put(0, 0, data); Mat matGray = new Mat(img.getHeight(), img.getWidth(), CvType.CV_8UC1); Imgproc.cvtColor(matRGB, matGray, Imgproc.COLOR_BGR2GRAY); //TODO: RGB or BGR? byte[] dataGray = new byte[matGray.rows() * matGray.cols() * (int) (matGray.elemSize())]; matGray.get(0, 0, dataGray); detector.detect(matGray, keypoints); myKeys = keypoints.toList(); LinkedList<CvSurfFeature> myKeypoints = new LinkedList<CvSurfFeature>(); KeyPoint key; CvSurfFeature feat; for (Iterator<KeyPoint> iterator = myKeys.iterator(); iterator.hasNext();) { key = iterator.next(); feat = new CvSurfFeature(key.pt.x, key.pt.y, key.size, null); myKeypoints.add(feat); } return myKeypoints; }
From source file:objectdetection.Mat2Image.java
BufferedImage getImage(Mat mat) { getSpace(mat); mat.get(0, 0, dat); img.getRaster().setDataElements(0, 0, mat.cols(), mat.rows(), dat); return img; }
From source file:opencv_java_template.TemplateTestWindow.java
private BufferedImage toBufferedImage(Mat m) { int type = BufferedImage.TYPE_BYTE_GRAY; if (m.channels() > 1) type = BufferedImage.TYPE_3BYTE_BGR; int bufferSize = m.channels() * m.cols() * m.rows(); byte[] b = new byte[bufferSize]; m.get(0, 0, b); // get all the pixels BufferedImage aux_image = new BufferedImage(m.cols(), m.rows(), type); final byte[] targetPixels = ((DataBufferByte) aux_image.getRaster().getDataBuffer()).getData(); System.arraycopy(b, 0, targetPixels, 0, b.length); return aux_image; }
From source file:org.akvo.caddisfly.sensor.colorimetry.strip.calibration.CalibrationCard.java
License:Open Source License
@NonNull private static Mat doIlluminationCorrection(@NonNull Mat imgLab, @NonNull CalibrationData calData) { // create HLS image for homogeneous illumination calibration int pHeight = imgLab.rows(); int pWidth = imgLab.cols(); RealMatrix points = createWhitePointMatrix(imgLab, calData); // create coefficient matrix for all three variables L,A,B // the model for all three is y = ax + bx^2 + cy + dy^2 + exy + f // 6th row is the constant 1 RealMatrix coefficient = new Array2DRowRealMatrix(points.getRowDimension(), 6); coefficient.setColumnMatrix(0, points.getColumnMatrix(0)); coefficient.setColumnMatrix(2, points.getColumnMatrix(1)); //create constant, x^2, y^2 and xy terms for (int i = 0; i < points.getRowDimension(); i++) { coefficient.setEntry(i, 1, Math.pow(coefficient.getEntry(i, 0), 2)); // x^2 coefficient.setEntry(i, 3, Math.pow(coefficient.getEntry(i, 2), 2)); // y^2 coefficient.setEntry(i, 4, coefficient.getEntry(i, 0) * coefficient.getEntry(i, 2)); // xy coefficient.setEntry(i, 5, 1d); // constant = 1 }// w w w.j a v a 2 s . co m // create vectors RealVector L = points.getColumnVector(2); RealVector A = points.getColumnVector(3); RealVector B = points.getColumnVector(4); // solve the least squares problem for all three variables DecompositionSolver solver = new SingularValueDecomposition(coefficient).getSolver(); RealVector solutionL = solver.solve(L); RealVector solutionA = solver.solve(A); RealVector solutionB = solver.solve(B); // get individual coefficients float La = (float) solutionL.getEntry(0); float Lb = (float) solutionL.getEntry(1); float Lc = (float) solutionL.getEntry(2); float Ld = (float) solutionL.getEntry(3); float Le = (float) solutionL.getEntry(4); float Lf = (float) solutionL.getEntry(5); float Aa = (float) solutionA.getEntry(0); float Ab = (float) solutionA.getEntry(1); float Ac = (float) solutionA.getEntry(2); float Ad = (float) solutionA.getEntry(3); float Ae = (float) solutionA.getEntry(4); float Af = (float) solutionA.getEntry(5); float Ba = (float) solutionB.getEntry(0); float Bb = (float) solutionB.getEntry(1); float Bc = (float) solutionB.getEntry(2); float Bd = (float) solutionB.getEntry(3); float Be = (float) solutionB.getEntry(4); float Bf = (float) solutionB.getEntry(5); // compute mean (the luminosity value of the plane in the middle of the image) float L_mean = (float) (0.5 * La * pWidth + 0.5 * Lc * pHeight + Lb * pWidth * pWidth / 3.0 + Ld * pHeight * pHeight / 3.0 + Le * 0.25 * pHeight * pWidth + Lf); float A_mean = (float) (0.5 * Aa * pWidth + 0.5 * Ac * pHeight + Ab * pWidth * pWidth / 3.0 + Ad * pHeight * pHeight / 3.0 + Ae * 0.25 * pHeight * pWidth + Af); float B_mean = (float) (0.5 * Ba * pWidth + 0.5 * Bc * pHeight + Bb * pWidth * pWidth / 3.0 + Bd * pHeight * pHeight / 3.0 + Be * 0.25 * pHeight * pWidth + Bf); // Correct image // we do this per row. We tried to do it in one block, but there is no speed difference. byte[] temp = new byte[imgLab.cols() * imgLab.channels()]; int valL, valA, valB; int ii, ii3; float iiSq, iSq; int imgCols = imgLab.cols(); int imgRows = imgLab.rows(); // use lookup tables to speed up computation // create lookup tables float[] L_aii = new float[imgCols]; float[] L_biiSq = new float[imgCols]; float[] A_aii = new float[imgCols]; float[] A_biiSq = new float[imgCols]; float[] B_aii = new float[imgCols]; float[] B_biiSq = new float[imgCols]; float[] Lci = new float[imgRows]; float[] LdiSq = new float[imgRows]; float[] Aci = new float[imgRows]; float[] AdiSq = new float[imgRows]; float[] Bci = new float[imgRows]; float[] BdiSq = new float[imgRows]; for (ii = 0; ii < imgCols; ii++) { iiSq = ii * ii; L_aii[ii] = La * ii; L_biiSq[ii] = Lb * iiSq; A_aii[ii] = Aa * ii; A_biiSq[ii] = Ab * iiSq; B_aii[ii] = Ba * ii; B_biiSq[ii] = Bb * iiSq; } for (int i = 0; i < imgRows; i++) { iSq = i * i; Lci[i] = Lc * i; LdiSq[i] = Ld * iSq; Aci[i] = Ac * i; AdiSq[i] = Ad * iSq; Bci[i] = Bc * i; BdiSq[i] = Bd * iSq; } // We can also improve the performance of the i,ii term, if we want, but it won't make much difference. for (int i = 0; i < imgRows; i++) { // y imgLab.get(i, 0, temp); ii3 = 0; for (ii = 0; ii < imgCols; ii++) { //x valL = capValue( Math.round((temp[ii3] & 0xFF) - (L_aii[ii] + L_biiSq[ii] + Lci[i] + LdiSq[i] + Le * i * ii + Lf) + L_mean), 0, 255); valA = capValue( Math.round((temp[ii3 + 1] & 0xFF) - (A_aii[ii] + A_biiSq[ii] + Aci[i] + AdiSq[i] + Ae * i * ii + Af) + A_mean), 0, 255); valB = capValue( Math.round((temp[ii3 + 2] & 0xFF) - (B_aii[ii] + B_biiSq[ii] + Bci[i] + BdiSq[i] + Be * i * ii + Bf) + B_mean), 0, 255); temp[ii3] = (byte) valL; temp[ii3 + 1] = (byte) valA; temp[ii3 + 2] = (byte) valB; ii3 += 3; } imgLab.put(i, 0, temp); } return imgLab; }
From source file:org.akvo.caddisfly.sensor.colorimetry.strip.calibration.CalibrationCard.java
License:Open Source License
@NonNull private static float[] measurePatch(@NonNull Mat imgMat, double x, double y, @NonNull CalibrationData calData) { float[] LAB_result = new float[3]; float totL = 0; float totA = 0; float totB = 0; int totNum = 0; calData.hSizePixel = imgMat.cols();/* w w w .j av a2 s .c o m*/ double hPixels = calData.hSizePixel / calData.hSize; // pixel per mm calData.vSizePixel = imgMat.rows(); double vPixels = calData.vSizePixel / calData.vSize; // pixel per mm int xp = (int) Math.round(x * hPixels); int yp = (int) Math.round(y * vPixels); int dp = (int) Math.round(calData.getPatchSize() * hPixels * 0.25); byte[] temp = new byte[(2 * dp + 1) * imgMat.channels()]; int ii3; for (int i = -dp; i <= dp; i++) { imgMat.get(yp - i, xp - dp, temp); ii3 = 0; for (int ii = 0; ii <= 2 * dp; ii++) { totL += temp[ii3] & 0xFF; //imgMat.get(yp + i, xp + ii)[0]; totA += temp[ii3 + 1] & 0xFF; //imgMat.get(yp + i, xp + ii)[1]; totB += temp[ii3 + 2] & 0xFF; //imgMat.get(yp + i, xp + ii)[2]; totNum++; ii3 += 3; } } LAB_result[0] = totL / totNum; LAB_result[1] = totA / totNum; LAB_result[2] = totB / totNum; return LAB_result; }
From source file:org.akvo.caddisfly.sensor.colorimetry.strip.calibration.CalibrationCard.java
License:Open Source License
@NonNull private static Mat do1D_3DCorrection(@NonNull Mat imgMat, @Nullable CalibrationData calData) throws CalibrationException { if (calData == null) { throw new CalibrationException("no calibration data."); }/* w ww. j a v a 2 s. c om*/ final WeightedObservedPoints obsL = new WeightedObservedPoints(); final WeightedObservedPoints obsA = new WeightedObservedPoints(); final WeightedObservedPoints obsB = new WeightedObservedPoints(); Map<String, double[]> calResultIllumination = new HashMap<>(); // iterate over all patches try { for (String label : calData.getCalValues().keySet()) { CalibrationData.CalValue cal = calData.getCalValues().get(label); CalibrationData.Location loc = calData.getLocations().get(label); float[] LAB_color = measurePatch(imgMat, loc.x, loc.y, calData); // measure patch color obsL.add(LAB_color[0], cal.getL()); obsA.add(LAB_color[1], cal.getA()); obsB.add(LAB_color[2], cal.getB()); calResultIllumination.put(label, new double[] { LAB_color[0], LAB_color[1], LAB_color[2] }); } } catch (Exception e) { throw new CalibrationException("1D calibration: error iterating over all patches.", e); } // Instantiate a second-degree polynomial fitter. final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(2); // Retrieve fitted parameters (coefficients of the polynomial function). // order of coefficients is (c + bx + ax^2), so [c,b,a] try { final double[] coefficientL = fitter.fit(obsL.toList()); final double[] coefficientA = fitter.fit(obsA.toList()); final double[] coefficientB = fitter.fit(obsB.toList()); double[] valIllumination; double L_orig, A_orig, B_orig, L_new, A_new, B_new; // transform patch values using the 1d calibration results Map<String, double[]> calResult1D = new HashMap<>(); for (String label : calData.getCalValues().keySet()) { valIllumination = calResultIllumination.get(label); L_orig = valIllumination[0]; A_orig = valIllumination[1]; B_orig = valIllumination[2]; L_new = coefficientL[2] * L_orig * L_orig + coefficientL[1] * L_orig + coefficientL[0]; A_new = coefficientA[2] * A_orig * A_orig + coefficientA[1] * A_orig + coefficientA[0]; B_new = coefficientB[2] * B_orig * B_orig + coefficientB[1] * B_orig + coefficientB[0]; calResult1D.put(label, new double[] { L_new, A_new, B_new }); } // use the 1D calibration result for the second calibration step // Following http://docs.scipy.org/doc/scipy/reference/tutorial/linalg.html#solving-linear-least-squares-problems-and-pseudo-inverses // we will solve P = M x int total = calData.getLocations().keySet().size(); RealMatrix coefficient = new Array2DRowRealMatrix(total, 3); RealMatrix cal = new Array2DRowRealMatrix(total, 3); int index = 0; // create coefficient and calibration vectors for (String label : calData.getCalValues().keySet()) { CalibrationData.CalValue calv = calData.getCalValues().get(label); double[] cal1dResult = calResult1D.get(label); coefficient.setEntry(index, 0, cal1dResult[0]); coefficient.setEntry(index, 1, cal1dResult[1]); coefficient.setEntry(index, 2, cal1dResult[2]); cal.setEntry(index, 0, calv.getL()); cal.setEntry(index, 1, calv.getA()); cal.setEntry(index, 2, calv.getB()); index++; } DecompositionSolver solver = new SingularValueDecomposition(coefficient).getSolver(); RealMatrix sol = solver.solve(cal); float a_L, b_L, c_L, a_A, b_A, c_A, a_B, b_B, c_B; a_L = (float) sol.getEntry(0, 0); b_L = (float) sol.getEntry(1, 0); c_L = (float) sol.getEntry(2, 0); a_A = (float) sol.getEntry(0, 1); b_A = (float) sol.getEntry(1, 1); c_A = (float) sol.getEntry(2, 1); a_B = (float) sol.getEntry(0, 2); b_B = (float) sol.getEntry(1, 2); c_B = (float) sol.getEntry(2, 2); //use the solution to correct the image double L_temp, A_temp, B_temp, L_mid, A_mid, B_mid; int L_fin, A_fin, B_fin; int ii3; byte[] temp = new byte[imgMat.cols() * imgMat.channels()]; for (int i = 0; i < imgMat.rows(); i++) { // y imgMat.get(i, 0, temp); ii3 = 0; for (int ii = 0; ii < imgMat.cols(); ii++) { //x L_temp = temp[ii3] & 0xFF; A_temp = temp[ii3 + 1] & 0xFF; B_temp = temp[ii3 + 2] & 0xFF; L_mid = coefficientL[2] * L_temp * L_temp + coefficientL[1] * L_temp + coefficientL[0]; A_mid = coefficientA[2] * A_temp * A_temp + coefficientA[1] * A_temp + coefficientA[0]; B_mid = coefficientB[2] * B_temp * B_temp + coefficientB[1] * B_temp + coefficientB[0]; L_fin = (int) Math.round(a_L * L_mid + b_L * A_mid + c_L * B_mid); A_fin = (int) Math.round(a_A * L_mid + b_A * A_mid + c_A * B_mid); B_fin = (int) Math.round(a_B * L_mid + b_B * A_mid + c_B * B_mid); // cap values L_fin = capValue(L_fin, 0, 255); A_fin = capValue(A_fin, 0, 255); B_fin = capValue(B_fin, 0, 255); temp[ii3] = (byte) L_fin; temp[ii3 + 1] = (byte) A_fin; temp[ii3 + 2] = (byte) B_fin; ii3 += 3; } imgMat.put(i, 0, temp); } return imgMat; } catch (Exception e) { throw new CalibrationException("error while performing calibration: ", e); } }
From source file:org.akvo.caddisfly.sensor.colorimetry.strip.detect.DetectStripTask.java
License:Open Source License
@Nullable @Override//w ww. j a v a 2s .com protected Void doInBackground(Intent... params) { Intent intent = params[0]; if (intent == null) { return null; } String uuid = intent.getStringExtra(Constant.UUID); StripTest stripTest = new StripTest(); int numPatches = stripTest.getPatchCount(uuid); format = intent.getIntExtra(Constant.FORMAT, ImageFormat.NV21); width = intent.getIntExtra(Constant.WIDTH, 0); height = intent.getIntExtra(Constant.HEIGHT, 0); if (width == 0 || height == 0) { return null; } JSONArray imagePatchArray = null; int imageCount = -1; Mat labImg; // Mat for image from NV21 data Mat labStrip; // Mat for detected strip try { String json = FileUtil.readFromInternalStorage(context, Constant.IMAGE_PATCH); imagePatchArray = new JSONArray(json); } catch (Exception e) { Timber.e(e); } for (int i = 0; i < numPatches; i++) { try { if (imagePatchArray != null) { // sub-array for each patch JSONArray array = imagePatchArray.getJSONArray(i); // get the image number from the json array int imageNo = array.getInt(0); if (imageNo > imageCount) { // Set imageCount to current number imageCount = imageNo; byte[] data = FileUtil.readByteArray(context, Constant.DATA + imageNo); if (data == null) { throw new IOException(); } //make a L,A,B Mat object from data try { labImg = makeLab(data); } catch (Exception e) { if (context != null) { Timber.e(e); } continue; } //perspectiveTransform try { if (labImg != null) { warp(labImg, imageNo); } } catch (Exception e) { if (context != null) { Timber.e(e); } continue; } //divide into calibration and strip areas try { if (context != null) { divideIntoCalibrationAndStripArea(); } } catch (Exception e) { Timber.e(e); continue; } //save warped image to external storage // if (DEVELOP_MODE) { // Mat rgb = new Mat(); // Imgproc.cvtColor(warpMat, rgb, Imgproc.COLOR_Lab2RGB); // Bitmap bitmap = Bitmap.createBitmap(rgb.width(), rgb.height(), Bitmap.Config.ARGB_8888); // Utils.matToBitmap(rgb, bitmap); // // //if (FileUtil.isExternalStorageWritable()) { // FileUtil.writeBitmapToExternalStorage(bitmap, "/warp", UUID.randomUUID().toString() + ".png"); //} // //Bitmap.createScaledBitmap(bitmap, BITMAP_SCALED_WIDTH, BITMAP_SCALED_HEIGHT, false); // } //calibrate Mat calibrationMat; try { CalibrationResultData calResult = getCalibratedImage(warpMat); if (calResult == null) { return null; } else { calibrationMat = calResult.getCalibratedImage(); } // Log.d(this.getClass().getSimpleName(), "E94 error mean: " + String.format(Locale.US, "%.2f", calResult.meanE94) // + ", max: " + String.format(Locale.US, "%.2f", calResult.maxE94) // + ", total: " + String.format(Locale.US, "%.2f", calResult.totalE94)); // if (AppPreferences.isDiagnosticMode()) { // listener.showError("E94 mean: " + String.format(Locale.US, "%.2f", calResult.meanE94) // + ", max: " + String.format(Locale.US, "%.2f", calResult.maxE94) // + ", total: " + String.format(Locale.US, "%.2f", calResult.totalE94)); // } } catch (Exception e) { Timber.e(e); return null; } //show calibrated image // if (DEVELOP_MODE) { // Mat rgb = new Mat(); // Imgproc.cvtColor(calibrationMat, rgb, Imgproc.COLOR_Lab2RGB); // Bitmap bitmap = Bitmap.createBitmap(rgb.width(), rgb.height(), Bitmap.Config.ARGB_8888); // Utils.matToBitmap(rgb, bitmap); // if (FileUtil.isExternalStorageWritable()) { // FileUtil.writeBitmapToExternalStorage(bitmap, "/warp", UUID.randomUUID().toString() + "_cal.png"); // } // //Bitmap.createScaledBitmap(bitmap, BITMAP_SCALED_WIDTH, BITMAP_SCALED_HEIGHT, false); // } // cut out black area that contains the strip Mat stripArea = null; if (roiStripArea != null) { stripArea = calibrationMat.submat(roiStripArea); } if (stripArea != null) { Mat strip = null; try { StripTest.Brand brand = stripTest.getBrand(uuid); strip = OpenCVUtil.detectStrip(stripArea, brand, ratioW, ratioH); } catch (Exception e) { Timber.e(e); } String error = ""; if (strip != null) { labStrip = strip.clone(); } else { if (context != null) { Timber.e(context.getString(R.string.error_calibrating)); } labStrip = stripArea.clone(); error = Constant.ERROR; //draw a red cross over the image Scalar red = RED_LAB_COLOR; // Lab color Imgproc.line(labStrip, new Point(0, 0), new Point(labStrip.cols(), labStrip.rows()), red, 2); Imgproc.line(labStrip, new Point(0, labStrip.rows()), new Point(labStrip.cols(), 0), red, 2); } try { // create byte[] from Mat and store it in internal storage // In order to restore the byte array, we also need the rows and columns dimensions // these are stored in the last 8 bytes int dataSize = labStrip.cols() * labStrip.rows() * 3; byte[] payload = new byte[dataSize + 8]; byte[] matByteArray = new byte[dataSize]; labStrip.get(0, 0, matByteArray); // pack cols and rows into byte arrays byte[] rows = FileUtil.leIntToByteArray(labStrip.rows()); byte[] cols = FileUtil.leIntToByteArray(labStrip.cols()); // append them to the end of the array, in order rows, cols System.arraycopy(matByteArray, 0, payload, 0, dataSize); System.arraycopy(rows, 0, payload, dataSize, 4); System.arraycopy(cols, 0, payload, dataSize + 4, 4); FileUtil.writeByteArray(context, payload, Constant.STRIP + imageNo + error); } catch (Exception e) { Timber.e(e); } } } } } catch (@NonNull JSONException | IOException e) { if (context != null) { Timber.e(context.getString(R.string.error_cut_out_strip)); } } } return null; }