List of usage examples for org.opencv.core Mat channels
public int channels()
From source file:opencv.fark.ResimSecMainFrame.java
private void jButtonFarkBulActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButtonFarkBulActionPerformed farkli_pixel_say = 0;/*from w w w . j a va 2 s . c om*/ Thread t = new Thread() { @Override public void run() { Mat sonuc = new Mat(resim1.rows(), resim1.cols(), CvType.CV_8UC1); Mat gray1 = new Mat(resim1.rows(), resim1.cols(), CvType.CV_8UC1); Mat gray2 = new Mat(resim1.rows(), resim1.cols(), CvType.CV_8UC1); System.out.println(gray1.channels()); Imgproc.cvtColor(resim1, gray1, Imgproc.COLOR_BGR2GRAY); Imgproc.cvtColor(resim2, gray2, Imgproc.COLOR_BGR2GRAY); for (int i = 0; i < gray1.rows(); i++) { for (int j = 0; j < gray1.cols(); j++) { double[] kresim1 = gray1.get(i, j); double[] kresim2 = gray2.get(i, j); //System.out.println(resim1[0]+" "+resim2[0]); if (kresim1[0] != kresim2[0]) { // System.out.println(kresim1[0]); g1.setFont(new Font("TimesRoman", Font.PLAIN, 2)); g1.drawString("*", j, i); //gray2.put(i, j, kresim2[0]*255); jLabelPixelCount.setText(String.valueOf(++farkli_pixel_say)); } } jProgressBar1.setValue(i); } //forend // Mat a = new Mat(); // Core.compare(resim1, resim2, a, Core.CMP_NE); // Imgproc.blur(gray2, gray2, new Size(10, 10)); // MatToBufImg matToBufImage = new MatToBufImg(); // matToBufImage.setMatrix(gray2, ".jpg"); // g1.drawImage(matToBufImage.getBufferedImage(), 0, 0, null); } }; t.start(); }
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.helper.ImageHelper.java
License:Open Source License
/** * Gets the center of the backdrop in the test chamber * * @param bitmap the photo to analyse//from w w w. jav a 2 s.c o m * @return the center point of the found circle */ public static Point getCenter(@NonNull Bitmap bitmap) { // convert bitmap to mat Mat mat = new Mat(bitmap.getWidth(), bitmap.getHeight(), CvType.CV_8UC1); Mat grayMat = new Mat(bitmap.getWidth(), bitmap.getHeight(), CvType.CV_8UC1); Utils.bitmapToMat(bitmap, mat); // convert to grayScale int colorChannels = (mat.channels() == 3) ? Imgproc.COLOR_BGR2GRAY : ((mat.channels() == 4) ? Imgproc.COLOR_BGRA2GRAY : 1); Imgproc.cvtColor(mat, grayMat, colorChannels); // reduce the noise so we avoid false circle detection //Imgproc.GaussianBlur(grayMat, grayMat, new Size(9, 9), 2, 2); // param1 = gradient value used to handle edge detection // param2 = Accumulator threshold value for the // cv2.CV_HOUGH_GRADIENT method. // The smaller the threshold is, the more circles will be // detected (including false circles). // The larger the threshold is, the more circles will // potentially be returned. double param1 = 10, param2 = 100; // create a Mat object to store the circles detected Mat circles = new Mat(bitmap.getWidth(), bitmap.getHeight(), CvType.CV_8UC1); // find the circle in the image Imgproc.HoughCircles(grayMat, circles, Imgproc.CV_HOUGH_GRADIENT, RESOLUTION_INVERSE_RATIO, (double) MIN_CIRCLE_CENTER_DISTANCE, param1, param2, MIN_RADIUS, MAX_RADIUS); int numberOfCircles = (circles.rows() == 0) ? 0 : circles.cols(); // draw the circles found on the image if (numberOfCircles > 0) { double[] circleCoordinates = circles.get(0, 0); int x = (int) circleCoordinates[0], y = (int) circleCoordinates[1]; org.opencv.core.Point center = new org.opencv.core.Point(x, y); int foundRadius = (int) circleCoordinates[2]; // circle outline Imgproc.circle(mat, center, foundRadius, COLOR_GREEN, 4); Utils.matToBitmap(mat, bitmap); return new Point((int) center.x, (int) center.y); } return null; }
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 }/*from w w w . j av a 2s .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();/*from ww w . j a va 2 s . com*/ 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."); }//from ww w . java 2 s . co m 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
private Mat makeLab(byte[] data) { if (format == ImageFormat.NV21) { //convert preview data to Mat object in CIELab format Mat rgb = new Mat(height, width, CvType.CV_8UC3); Mat labImg = new Mat(height, width, CvType.CV_8UC3); Mat previewMat = new Mat(height + height / 2, width, CvType.CV_8UC1); previewMat.put(0, 0, data);//from w w w. j a va2s. co m Imgproc.cvtColor(previewMat, rgb, Imgproc.COLOR_YUV2RGB_NV21, rgb.channels()); Imgproc.cvtColor(rgb, labImg, Imgproc.COLOR_RGB2Lab, rgb.channels()); return labImg; } return null; }
From source file:org.openpnp.vision.FluentCv.java
License:Open Source License
public static double calculatePsnr(Mat I1, Mat I2) { Mat s1 = new Mat(); Core.absdiff(I1, I2, s1); // |I1 - I2| s1.convertTo(s1, CvType.CV_32F); // cannot make a square on 8 bits s1 = s1.mul(s1); // |I1 - I2|^2 Scalar s = Core.sumElems(s1); // sum elements per channel double sse = s.val[0] + s.val[1] + s.val[2]; // sum channels if (sse <= 1e-10) // for small values return zero return 0; else {/* w w w . ja v a 2 s . c om*/ double mse = sse / (double) (I1.channels() * I1.total()); double psnr = 10.0 * Math.log10((255 * 255) / mse); return psnr; } }
From source file:org.openpnp.vision.FluentCv.java
License:Open Source License
/** * From FireSight: https://github.com/firepick1/FireSight/wiki/op-Sharpness * /*w w w . j a va2 s. c om*/ * @param image * @return */ public static double calculateSharpnessGRAS(Mat image) { int sum = 0; Mat matGray = new Mat(); if (image.channels() == 1) { matGray = image; } else { Imgproc.cvtColor(image, matGray, Imgproc.COLOR_BGR2GRAY); } byte[] b1 = new byte[1]; byte[] b2 = new byte[1]; for (int r = 0; r < matGray.rows(); r++) { for (int c = 0; c < matGray.cols() - 1; c++) { matGray.get(r, c, b1); matGray.get(r, c + 1, b2); int df = (int) b1[0] - (int) b2[0]; sum += df * df; } } return ((double) sum / matGray.rows() / (matGray.cols() - 1)); }
From source file:org.sikuli.script.Finder.java
License:MIT License
private static void printMatI(Mat mat) { int[] data = new int[mat.channels()]; for (int r = 0; r < mat.rows(); r++) { for (int c = 0; c < mat.cols(); c++) { mat.get(r, c, data);// w ww .j a va2s . co m log(lvl, "(%d, %d) %s", r, c, Arrays.toString(data)); } } }