List of usage examples for org.opencv.core Mat cols
public int cols()
From source file:org.sleuthkit.autopsy.coreutils.VideoUtils.java
License:Open Source License
@NbBundle.Messages({ "# {0} - file name",
"VideoUtils.genVideoThumb.progress.text=extracting temporary file {0}" })
static BufferedImage generateVideoThumbnail(AbstractFile file, int iconSize) {
java.io.File tempFile = getTempVideoFile(file);
if (tempFile.exists() == false || tempFile.length() < file.getSize()) {
ProgressHandle progress = ProgressHandle
.createHandle(Bundle.VideoUtils_genVideoThumb_progress_text(file.getName()));
progress.start(100);/*from w w w.j ava 2 s .co m*/
try {
Files.createParentDirs(tempFile);
ContentUtils.writeToFile(file, tempFile, progress, null, true);
} catch (IOException ex) {
LOGGER.log(Level.WARNING,
"Error extracting temporary file for " + ImageUtils.getContentPathSafe(file), ex); //NON-NLS
} finally {
progress.finish();
}
}
VideoCapture videoFile = new VideoCapture(); // will contain the video
if (!videoFile.open(tempFile.toString())) {
LOGGER.log(Level.WARNING, "Error opening {0} for preview generation.",
ImageUtils.getContentPathSafe(file)); //NON-NLS
return null;
}
double fps = videoFile.get(CV_CAP_PROP_FPS); // gets frame per second
double totalFrames = videoFile.get(CV_CAP_PROP_FRAME_COUNT); // gets total frames
if (fps <= 0 || totalFrames <= 0) {
LOGGER.log(Level.WARNING, "Error getting fps or total frames for {0}",
ImageUtils.getContentPathSafe(file)); //NON-NLS
return null;
}
double milliseconds = 1000 * (totalFrames / fps); //total milliseconds
double timestamp = Math.min(milliseconds, 500); //default time to check for is 500ms, unless the files is extremely small
int framkeskip = Double.valueOf(Math.floor((milliseconds - timestamp) / (THUMB_COLUMNS * THUMB_ROWS)))
.intValue();
Mat imageMatrix = new Mat();
BufferedImage bufferedImage = null;
for (int x = 0; x < THUMB_COLUMNS; x++) {
for (int y = 0; y < THUMB_ROWS; y++) {
if (!videoFile.set(CV_CAP_PROP_POS_MSEC,
timestamp + x * framkeskip + y * framkeskip * THUMB_COLUMNS)) {
LOGGER.log(Level.WARNING, "Error seeking to " + timestamp + "ms in {0}",
ImageUtils.getContentPathSafe(file)); //NON-NLS
break; // if we can't set the time, return black for that frame
}
//read the frame into the image/matrix
if (!videoFile.read(imageMatrix)) {
LOGGER.log(Level.WARNING, "Error reading frames at " + timestamp + "ms from {0}",
ImageUtils.getContentPathSafe(file)); //NON-NLS
break; //if the image for some reason is bad, return black for that frame
}
if (bufferedImage == null) {
bufferedImage = new BufferedImage(imageMatrix.cols() * THUMB_COLUMNS,
imageMatrix.rows() * THUMB_ROWS, BufferedImage.TYPE_3BYTE_BGR);
}
byte[] data = new byte[imageMatrix.rows() * imageMatrix.cols() * (int) (imageMatrix.elemSize())];
imageMatrix.get(0, 0, data); //copy the image to data
//todo: this looks like we are swapping the first and third channels. so we can use BufferedImage.TYPE_3BYTE_BGR
if (imageMatrix.channels() == 3) {
for (int k = 0; k < data.length; k += 3) {
byte temp = data[k];
data[k] = data[k + 2];
data[k + 2] = temp;
}
}
bufferedImage.getRaster().setDataElements(imageMatrix.cols() * x, imageMatrix.rows() * y,
imageMatrix.cols(), imageMatrix.rows(), data);
}
}
videoFile.release(); // close the file
return bufferedImage == null ? null : ScalrWrapper.resizeFast(bufferedImage, iconSize);
}
From source file:org.surmon.pattern.detector.houghcircle.HoughCircleDetector.java
/** * {@inheritDoc}/*w w w . ja v a 2s . co m*/ * * Runs detection of circles in image. Hough detector parameters are taken * from declared fields. This method detects circle particles in given image. */ @Override public List<CircleParticle> detectIn(PatternImage image) { List<CircleParticle> circles = new ArrayList<>(); Mat mat = image.getPixels(); Mat rawCircles = new Mat(); Imgproc.GaussianBlur(mat, mat, new Size(ksize, ksize), sigma); Imgproc.HoughCircles(mat, rawCircles, Imgproc.CV_HOUGH_GRADIENT, dp, minDist, param1, param2, minRadius, maxRadius); // creates particle and assignes to image for (int i = 0; i < rawCircles.cols(); i++) { double[] var = rawCircles.get(0, i); CircleParticle circle = new CircleParticle(i, var[0], var[1], var[2]); circles.add(circle); } return circles; }
From source file:overwatchteampicker.OverwatchTeamPicker.java
public static ReturnValues findImage(String template, String source, int flag) { File lib = null;// w w w. j a v a2 s .co m BufferedImage image = null; try { image = ImageIO.read(new File(source)); } catch (Exception e) { e.printStackTrace(); } String os = System.getProperty("os.name"); String bitness = System.getProperty("sun.arch.data.model"); if (os.toUpperCase().contains("WINDOWS")) { if (bitness.endsWith("64")) { lib = new File("C:\\Users\\POWERUSER\\Downloads\\opencv\\build\\java\\x64\\" + System.mapLibraryName("opencv_java2413")); } else { lib = new File("libs//x86//" + System.mapLibraryName("opencv_java2413")); } } System.load(lib.getAbsolutePath()); String tempObject = "images\\hero_templates\\" + template + ".png"; String source_pic = source; Mat objectImage = Highgui.imread(tempObject, Highgui.CV_LOAD_IMAGE_GRAYSCALE); Mat sceneImage = Highgui.imread(source_pic, Highgui.CV_LOAD_IMAGE_GRAYSCALE); MatOfKeyPoint objectKeyPoints = new MatOfKeyPoint(); FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.SURF); featureDetector.detect(objectImage, objectKeyPoints); KeyPoint[] keypoints = objectKeyPoints.toArray(); MatOfKeyPoint objectDescriptors = new MatOfKeyPoint(); DescriptorExtractor descriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.SURF); descriptorExtractor.compute(objectImage, objectKeyPoints, objectDescriptors); // Create the matrix for output image. Mat outputImage = new Mat(objectImage.rows(), objectImage.cols(), Highgui.CV_LOAD_IMAGE_COLOR); Scalar newKeypointColor = new Scalar(255, 0, 0); Features2d.drawKeypoints(objectImage, objectKeyPoints, outputImage, newKeypointColor, 0); // Match object image with the scene image MatOfKeyPoint sceneKeyPoints = new MatOfKeyPoint(); MatOfKeyPoint sceneDescriptors = new MatOfKeyPoint(); featureDetector.detect(sceneImage, sceneKeyPoints); descriptorExtractor.compute(sceneImage, sceneKeyPoints, sceneDescriptors); Mat matchoutput = new Mat(sceneImage.rows() * 2, sceneImage.cols() * 2, Highgui.CV_LOAD_IMAGE_COLOR); Scalar matchestColor = new Scalar(0, 255, 25); List<MatOfDMatch> matches = new LinkedList<MatOfDMatch>(); DescriptorMatcher descriptorMatcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED); descriptorMatcher.knnMatch(objectDescriptors, sceneDescriptors, matches, 2); LinkedList<DMatch> goodMatchesList = new LinkedList<DMatch>(); float nndrRatio = .78f; for (int i = 0; i < matches.size(); i++) { MatOfDMatch matofDMatch = matches.get(i); DMatch[] dmatcharray = matofDMatch.toArray(); DMatch m1 = dmatcharray[0]; DMatch m2 = dmatcharray[1]; if (m1.distance <= m2.distance * nndrRatio) { goodMatchesList.addLast(m1); } } if (goodMatchesList.size() >= 4) { List<KeyPoint> objKeypointlist = objectKeyPoints.toList(); List<KeyPoint> scnKeypointlist = sceneKeyPoints.toList(); LinkedList<Point> objectPoints = new LinkedList<>(); LinkedList<Point> scenePoints = new LinkedList<>(); for (int i = 0; i < goodMatchesList.size(); i++) { objectPoints.addLast(objKeypointlist.get(goodMatchesList.get(i).queryIdx).pt); scenePoints.addLast(scnKeypointlist.get(goodMatchesList.get(i).trainIdx).pt); } MatOfPoint2f objMatOfPoint2f = new MatOfPoint2f(); objMatOfPoint2f.fromList(objectPoints); MatOfPoint2f scnMatOfPoint2f = new MatOfPoint2f(); scnMatOfPoint2f.fromList(scenePoints); Mat homography = Calib3d.findHomography(objMatOfPoint2f, scnMatOfPoint2f, Calib3d.RANSAC, 3); Mat obj_corners = new Mat(4, 1, CvType.CV_32FC2); Mat scene_corners = new Mat(4, 1, CvType.CV_32FC2); obj_corners.put(0, 0, new double[] { 0, 0 }); obj_corners.put(1, 0, new double[] { objectImage.cols(), 0 }); obj_corners.put(2, 0, new double[] { objectImage.cols(), objectImage.rows() }); obj_corners.put(3, 0, new double[] { 0, objectImage.rows() }); Core.perspectiveTransform(obj_corners, scene_corners, homography); Mat img = Highgui.imread(source_pic, Highgui.CV_LOAD_IMAGE_COLOR); Core.line(img, new Point(scene_corners.get(0, 0)), new Point(scene_corners.get(1, 0)), new Scalar(0, 255, 255), 4); Core.line(img, new Point(scene_corners.get(1, 0)), new Point(scene_corners.get(2, 0)), new Scalar(255, 255, 0), 4); Core.line(img, new Point(scene_corners.get(2, 0)), new Point(scene_corners.get(3, 0)), new Scalar(0, 255, 0), 4); Core.line(img, new Point(scene_corners.get(3, 0)), new Point(scene_corners.get(0, 0)), new Scalar(0, 255, 0), 4); MatOfDMatch goodMatches = new MatOfDMatch(); goodMatches.fromList(goodMatchesList); Features2d.drawMatches(objectImage, objectKeyPoints, sceneImage, sceneKeyPoints, goodMatches, matchoutput, matchestColor, newKeypointColor, new MatOfByte(), 2); if (new Point(scene_corners.get(0, 0)).x < new Point(scene_corners.get(1, 0)).x && new Point(scene_corners.get(0, 0)).y < new Point(scene_corners.get(2, 0)).y) { System.out.println("found " + template); Highgui.imwrite("points.jpg", outputImage); Highgui.imwrite("matches.jpg", matchoutput); Highgui.imwrite("final.jpg", img); if (flag == 0) { ReturnValues retVal = null; int y = (int) new Point(scene_corners.get(3, 0)).y; int yHeight = (int) new Point(scene_corners.get(3, 0)).y - (int) new Point(scene_corners.get(2, 0)).y; if (y < image.getHeight() * .6) { //if found hero is in upper half of image then return point 3,0 retVal = new ReturnValues(y + (int) (image.getHeight() * .01), yHeight); } else { //if found hero is in lower half of image then return point 2,0 y = (int) new Point(scene_corners.get(2, 0)).y; retVal = new ReturnValues(y + (int) (image.getHeight() * .3), yHeight); } return retVal; } else if (flag == 1) { int[] xPoints = new int[4]; int[] yPoints = new int[4]; xPoints[0] = (int) (new Point(scene_corners.get(0, 0)).x); xPoints[1] = (int) (new Point(scene_corners.get(1, 0)).x); xPoints[2] = (int) (new Point(scene_corners.get(2, 0)).x); xPoints[3] = (int) (new Point(scene_corners.get(3, 0)).x); yPoints[0] = (int) (new Point(scene_corners.get(0, 0)).y); yPoints[1] = (int) (new Point(scene_corners.get(1, 0)).y); yPoints[2] = (int) (new Point(scene_corners.get(2, 0)).y); yPoints[3] = (int) (new Point(scene_corners.get(3, 0)).y); ReturnValues retVal = new ReturnValues(xPoints, yPoints); return retVal; } } } return null; }
From source file:pfe.Segmentation.java
@Override public void dilate_erose_ocr() { // pour le ocr Mat rgbImage = Imgcodecs.imread(ocrReadFrom); Mat destination2 = new Mat(rgbImage.rows(), rgbImage.cols(), rgbImage.type()); // l objectif et de corriger les erreur de la transformation en noire et blan int dilation_size1 = 2; // la matrice de la dilatation on cherche a dilater en forme de rectange ( Imgproc.MORPH_RECT ) Mat element11 = Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, new Size(dilation_size1 + 1, dilation_size1 + 1)); // on dilate l image Imgproc.dilate(rgbImage, destination2, element11); Imgcodecs.imwrite(ocrReadFrom, destination2); }
From source file:pfe.Segmentation.java
/** * la dilatation et l erosion nous permette de reparer les forme * triangulaire afin de facilit leur detection ainsi elle permet de bien * remplir les texts pour qu il sois facilement detectable par les Ocrs *//*from ww w.ja v a2s . c o m*/ @Override public void dilate_erose(JLabel jLabelErosion, JLabel jLabelDilated) { // lecture de l image a traiter Pour la segmentation Mat source = Imgcodecs.imread(blackAndWhite, Imgcodecs.CV_LOAD_IMAGE_COLOR); // preparation de l image resultante Mat destination = new Mat(source.rows(), source.cols(), source.type()); // taille de l erosion a 1 si petit juste pour corriger les contour int erosion_size = 1; // taille de dilatation inferieur a la moyenne pour evit la destruction des details et des formes // l objectif et de corriger les erreur de la transformation en noire et blan int dilation_size = 3; destination = source; // la matrice de la dilatation on cherche a dilater en forme de rectange ( Imgproc.MORPH_RECT ) Mat element1 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(dilation_size + 1, dilation_size + 1)); // on dilate l image Imgproc.dilate(source, destination, element1); // on sauvegarde Imgcodecs.imwrite(dilation, destination); //on va maintenant eroser l image dilat ( repar la finition ) source = Imgcodecs.imread(dilation, Imgcodecs.CV_LOAD_IMAGE_COLOR); destination = source; // on repare en ellipse pour repar les coins de l ecriture Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, new Size(erosion_size + 1, erosion_size + 1)); Imgproc.erode(source, destination, element); Imgcodecs.imwrite(erosion, destination); jLabelErosion.setIcon(new ImageIcon(erosion)); jLabelDilated.setIcon(new ImageIcon(dilation)); }
From source file:pfe.Segmentation.java
@Override public void toB_A_W(JLabel jLabel) { Mat rgbImage = Imgcodecs.imread(original); Mat destination = new Mat(rgbImage.rows(), rgbImage.cols(), rgbImage.type()); // l objectif et de corriger les erreur de la transformation en noire et blan int dilation_size = 2; // la matrice de la dilatation on cherche a dilater en forme de rectange ( Imgproc.MORPH_RECT ) puis a eroser Mat element1 = Imgproc.getStructuringElement(Imgproc.MORPH_ERODE, new Size(dilation_size + 1, dilation_size + 1)); // on dilate l image puis elle erose Imgproc.dilate(rgbImage, destination, element1); // on prepare une nouvelle matrice pour l image noire et blan Mat labImage = new Mat(); // ici on va convertir les couleur vers bgr2gray ( noire et blan ) et mettre le resultat dans labimage cvtColor(destination, labImage, Imgproc.COLOR_BGR2GRAY); Imgcodecs.imwrite(ocrReadFrom, labImage); jLabel.setIcon(new ImageIcon(ocrReadFrom)); // JOptionPane.showConfirmDialog(null, ""); }
From source file:pipeline.TextRegion.java
public static String SplitFiles(File fileIn) { String result = ""; try {//from w ww.ja va 2 s . com String nomeFile = fileIn.getName(); //System.out.println("il nome del file "+nomeFile); FileInputStream in = new FileInputStream("src/pipeline/receivedImg/" + nomeFile); JPEGImageDecoder decoder = JPEGCodec.createJPEGDecoder(in); BufferedImage image = decoder.decodeAsBufferedImage(); in.close(); TextRecognition myget = new TextRecognition(image); LinkedList boxes = myget.getTextBoxes(); String nomeFileOut = "src/pipeline/outputImg/" + Global.getJPGNameFile() + " out.jpg"; FileOutputStream out = new FileOutputStream(nomeFileOut); JPEGImageEncoder encoder = JPEGCodec.createJPEGEncoder(out); encoder.encode(myget.isolateText(boxes)); out.close(); //parte con opencv System.loadLibrary(Core.NATIVE_LIBRARY_NAME); File f = new File("src/pipeline/receivedImg/" + nomeFile); BufferedImage imageFile = ImageIO.read(f); byte[] data = ((DataBufferByte) imageFile.getRaster().getDataBuffer()).getData(); Mat mat = new Mat(imageFile.getHeight(), imageFile.getWidth(), CvType.CV_8UC3); mat.put(0, 0, data); int tolleranza = 15; for (int i = 0; i < boxes.size(); i++) { TextRegion app = (TextRegion) boxes.get(i); // System.out.println("RIGA: "+i+" -> "+app.x1 +" "+app.x2 +" "+app.y1 +" "+app.y2 +" "); Rect roi1 = new Rect(app.x1 - tolleranza, app.y1 - tolleranza, app.x2 - app.x1 + tolleranza, app.y2 - app.y1 + 2 * tolleranza); Mat mat1 = new Mat(mat, roi1); byte[] data1 = new byte[mat1.rows() * mat1.cols() * (int) (mat1.elemSize())]; mat1.get(0, 0, data1); BufferedImage image1 = new BufferedImage(mat1.cols(), mat1.rows(), BufferedImage.TYPE_3BYTE_BGR); image1.getRaster().setDataElements(0, 0, mat1.cols(), mat1.rows(), data1); String nomeFileUscrita = "src/pipeline/outputImg/" + i + Global.getJPGNameFile() + " uscita.jpg"; File tmp = new File(nomeFileUscrita); File output = new File(nomeFileUscrita); ImageIO.write(image1, "jpg", output); result += (i + 1) + ")" + OCR_Processing.performOCR_String2Text(output); tmp.delete(); } f.delete(); File foo = new File(nomeFileOut); foo.delete(); } catch (Exception e) { System.out.println("Exception: " + e); } return result; }
From source file:processdata.ExperimentalDataProcessingUI.java
public static BufferedImage Mat2BufferedImage(Mat m) { // source: http://answers.opencv.org/question/10344/opencv-java-load-image-to-gui/ // Fastest code // The output can be assigned either to a BufferedImage or to an Image int type = BufferedImage.TYPE_BYTE_GRAY; if (m.channels() > 1) { type = BufferedImage.TYPE_3BYTE_BGR; }/*from ww w . j a va2 s. c o m*/ int bufferSize = m.channels() * m.cols() * m.rows(); byte[] b = new byte[bufferSize]; m.get(0, 0, b); // get all the pixels BufferedImage image = new BufferedImage(m.cols(), m.rows(), type); final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData(); System.arraycopy(b, 0, targetPixels, 0, b.length); return image; }
From source file:processdata.ExperimentalDataProcessingUI.java
private void jButtonProcessImageActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButtonProcessImageActionPerformed try {/*from w w w .j a v a2s. c om*/ // TODO add your handling code here: //load library System.loadLibrary(Core.NATIVE_LIBRARY_NAME); // TODO add your handling code here: folderName = textField2.getText(); int currentFrameIndex = Integer.parseInt(initialFrameIndexBox.getText()) - 1; datasetIndex = Integer.parseInt(textField1.getText()); String videoImageFileName = "./videoFrames//" + folderName + "//" + "frame_outVideo_" + currentFrameIndex + ".jpg"; String depthFrameFileName = initialImagePath + datasetIndex + "//" + folderName + "//" + "depthData//" + "outDepthByte_" + currentFrameIndex; rgbFrame = Highgui.imread(videoImageFileName, Highgui.CV_LOAD_IMAGE_GRAYSCALE); depthFrame = depthDataProcessingUtilities.processDepthDataFile(depthFrameFileName, jSlider2.getValue(), jSlider1.getValue()); Mat[] backgroundFrames = readBackground(); rgbBackgroundFrame = backgroundFrames[0]; depthBackgroundFrame = backgroundFrames[1]; //subtract depth background Mat depthFrameBackgroundSubtracted = new Mat(); Core.subtract(depthBackgroundFrame, depthFrame, depthFrameBackgroundSubtracted); Imgproc.threshold(depthFrameBackgroundSubtracted, depthFrameBackgroundSubtracted, 0, 255, Imgproc.THRESH_BINARY); displayImage(Mat2BufferedImage( videoProcessingUtilities.resizeImage(depthFrameBackgroundSubtracted, new Size(448, 234))), depthBckgSubtractedFrames); //remove the red-colored elements from depth image and leave only blue ones Mat depthImageCleaned = new Mat(); Core.inRange(depthFrameBackgroundSubtracted, new Scalar(253, 0, 0), new Scalar(255, 0, 0), depthImageCleaned); //apply morphologic opening to remove noise Imgproc.morphologyEx(depthImageCleaned, depthImageCleaned, 2, Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(3, 3))); displayImage( Mat2BufferedImage(videoProcessingUtilities.resizeImage(depthImageCleaned, new Size(448, 234))), depthCleanedFramesPanel); //apply the homographic transform to cleaned depth image Mat hDepthImageCleaned = videoProcessingUtilities.performHomographyTransformation(depthImageCleaned, new Size(1920, 1080)); //extract all contours //sort all extracted contours and choose top 2 //overlay top 2 contours on the image and fill them with white color //mask the rgb frame // do all necessary rotation operations //offer user to save the image //extract all suitable contours between MIN and MAX areas: MatOfPoint[] contours = videoProcessingUtilities.extractLargestContours(hDepthImageCleaned, 100000, 160000); System.out.println("Number of contorus extracted " + contours.length); //draw contours List<MatOfPoint> tempContours = new ArrayList<MatOfPoint>(); Mat hDepthImageCleanedContours = hDepthImageCleaned.clone(); for (MatOfPoint cnt : contours) { System.out.println("Extracted Contour Area is " + Imgproc.contourArea(cnt)); tempContours.add(cnt); } Imgproc.cvtColor(hDepthImageCleanedContours, hDepthImageCleanedContours, Imgproc.COLOR_GRAY2BGR); Imgproc.drawContours(hDepthImageCleanedContours, tempContours, -1, new Scalar(0, 0, 255), 5); displayImage( Mat2BufferedImage( videoProcessingUtilities.resizeImage(hDepthImageCleanedContours, new Size(448, 234))), extractedContoursPanel); //prepare final mask Mat hDepthImageFilledContours = new Mat(hDepthImageCleaned.rows(), hDepthImageCleaned.cols(), hDepthImageCleaned.type()); Imgproc.drawContours(hDepthImageFilledContours, tempContours, -1, new Scalar(255, 255, 255), -1); displayImage( Mat2BufferedImage( videoProcessingUtilities.resizeImage(hDepthImageFilledContours, new Size(448, 234))), maskedContoursPanel); //subtract video background // Mat rgbFrameBackgroundSubtracted = new Mat(); // Core.subtract(rgbBackgroundFrame,rgbFrame, rgbFrameBackgroundSubtracted, hDepthImageCleaned); // displayImage(Mat2BufferedImage(videoProcessingUtilities.resizeImage(rgbFrameBackgroundSubtracted, new Size(448,234))),videoBckgSubtractedFrames); // //mask Mat preMaskedRGBFrame = new Mat(); rgbFrame.copyTo(preMaskedRGBFrame, hDepthImageCleaned); displayImage( Mat2BufferedImage(videoProcessingUtilities.resizeImage(preMaskedRGBFrame, new Size(448, 234))), videoBckgSubtractedFrames); //postmask Mat betterMaskedRGBFrame = new Mat(); rgbFrame.copyTo(betterMaskedRGBFrame, hDepthImageFilledContours); displayImage( Mat2BufferedImage( videoProcessingUtilities.resizeImage(betterMaskedRGBFrame, new Size(448, 234))), videoMaskedPanel); //clear ArrayList containig all processed images finalImages.clear(); javax.swing.JLabel[] jLabArray = { extractedShoePanel1, extractedShoePanel2 }; //segment all images int panelIdx = 0; for (MatOfPoint contour : tempContours) { MatOfPoint2f newMatOfPoint2fContour = new MatOfPoint2f(contour.toArray()); RotatedRect finalROI = Imgproc.minAreaRect(newMatOfPoint2fContour); Mat newMask = videoProcessingUtilities.getContourMasked(hDepthImageFilledContours.clone(), contour); Mat imageROIRegistred = new Mat(); betterMaskedRGBFrame.copyTo(imageROIRegistred, newMask); Mat maskedRGBFrameROI = videoProcessingUtilities.rotateExtractedShoeprint(imageROIRegistred, finalROI, new Size(500, 750), 2); finalImages.add(maskedRGBFrameROI); displayImage( Mat2BufferedImage( videoProcessingUtilities.resizeImage(maskedRGBFrameROI, new Size(203, 250))), jLabArray[panelIdx]); panelIdx++; } //MatOfInt parameters = new MatOfInt(); //parameters.fromArray(Highgui.CV_IMWRITE_JPEG_QUALITY, 100); //Highgui.imwrite(".//backgrounds//"+"test.jpg", depthFrameBackgroundSubtracted, parameters); } catch (FileNotFoundException ex) { Logger.getLogger(ExperimentalDataProcessingUI.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:Recognizer.Recognizer.java
public Image TemplateMatching(Image imQuery, Image imDB, int match_method) { System.out.println("Running Template Matching ..."); //Mat img = Highgui.imread(inFile); // Image in which area has to be searched //Mat template_img = Highgui.imread(templateFile); // Search Image Mat matQuery = imQuery.Image3CtoMat_CV(); Mat matDB = imDB.Image3CtoMat_CV(); Mat hsvQ = new Mat(), hsvDB = new Mat(); Imgproc.cvtColor(matQuery, hsvQ, COLOR_RGB2HSV); Imgproc.cvtColor(matDB, hsvDB, COLOR_RGB2HSV); // Create result image matrix int resultImg_cols = matDB.cols() - matQuery.cols() + 1; int resultImg_rows = matDB.rows() - matQuery.rows() + 1; Mat matRes = new Mat(resultImg_rows, resultImg_cols, CvType.CV_32FC1); // Template Matching with Normalization Imgproc.matchTemplate(hsvDB, hsvQ, matRes, match_method); Core.normalize(matRes, matRes, 0, 1, Core.NORM_MINMAX, -1, new Mat()); // / Localizing the best match with minMaxLoc Core.MinMaxLocResult Location_Result = Core.minMaxLoc(matRes); Point matchLocation;//from w w w.j a v a 2 s . c o m if (match_method == Imgproc.TM_SQDIFF || match_method == Imgproc.TM_SQDIFF_NORMED) { matchLocation = Location_Result.minLoc; } else { matchLocation = Location_Result.maxLoc; } // Display Area by Rectangle Core.rectangle(matDB, matchLocation, new Point(matchLocation.x + matQuery.cols(), matchLocation.y + matQuery.rows()), new Scalar(0, 255, 0)); Image imOut = new Image(matDB.width(), matDB.height()); //Image imOut = new Image(matQuery.cols(), matQuery.rows()); //Mat m = new Mat(matDB); //m =//matDB.submat((int)matchLocation.y, (int)matchLocation.y + matQuery.rows(),(int)matchLocation.x, (int)matchLocation.x + matQuery.cols()); imOut.Mat_CVtoImage3C(matDB); System.out.println("Location: " + Location_Result.minLoc.x + " " + Location_Result.minLoc.y + " " + Location_Result.maxLoc.x + " " + Location_Result.maxLoc.y); return imOut; }