List of usage examples for org.opencv.core MatOfKeyPoint toArray
public KeyPoint[] toArray()
From source file:cn.xiongyihui.webcam.JpegFactory.java
License:Open Source License
public void onPreviewFrame(byte[] data, Camera camera) { YuvImage yuvImage = new YuvImage(data, ImageFormat.NV21, mWidth, mHeight, null); mJpegOutputStream.reset();/* w w w .jav a2 s . co m*/ try { //Log.e(TAG, "Beginning to read values!"); double distanceTemplateFeatures = this.globalClass.getDistanceTemplateFeatures(); double xTemplateCentroid = this.globalClass.getXtemplateCentroid(); double yTemplateCentroid = this.globalClass.getYtemplateCentroid(); int x0template = this.globalClass.getX0display(); int y0template = this.globalClass.getY0display(); int x1template = this.globalClass.getX1display(); int y1template = this.globalClass.getY1display(); Mat templateDescriptor = this.globalClass.getTemplateDescriptor(); MatOfKeyPoint templateKeyPoints = this.globalClass.getKeyPoints(); KeyPoint[] templateKeyPointsArray = templateKeyPoints.toArray(); int numberOfTemplateFeatures = this.globalClass.getNumberOfTemplateFeatures(); int numberOfPositiveTemplateFeatures = this.globalClass.getNumberOfPositiveTemplateFeatures(); KeyPoint[] normalisedTemplateKeyPoints = this.globalClass.getNormalisedTemplateKeyPoints(); double normalisedXcentroid = this.globalClass.getNormalisedXcentroid(); double normalisedYcentroid = this.globalClass.getNormalisedYcentroid(); int templateCapturedBitmapWidth = this.globalClass.getTemplateCapturedBitmapWidth(); int templateCapturedBitmapHeight = this.globalClass.getTemplateCapturedBitmapHeight(); //Log.e(TAG, "Ended reading values!"); globalClass.setJpegFactoryDimensions(mWidth, mHeight); double scalingRatio, scalingRatioHeight, scalingRatioWidth; scalingRatioHeight = (double) mHeight / (double) templateCapturedBitmapHeight; scalingRatioWidth = (double) mWidth / (double) templateCapturedBitmapWidth; scalingRatio = (scalingRatioHeight + scalingRatioWidth) / 2; //Just to account for any minor variations. //Log.e(TAG, "Scaling ratio:" + String.valueOf(scalingRatio)); //Log.e("Test", "Captured Bitmap's dimensions: (" + templateCapturedBitmapHeight + "," + templateCapturedBitmapWidth + ")"); //Scale the actual features of the image int flag = this.globalClass.getFlag(); if (flag == 0) { int iterate = 0; int iterationMax = numberOfTemplateFeatures; for (iterate = 0; iterate < (iterationMax); iterate++) { Log.e(TAG, "Point detected " + iterate + ":(" + templateKeyPointsArray[iterate].pt.x + "," + templateKeyPointsArray[iterate].pt.y + ")"); if (flag == 0) { templateKeyPointsArray[iterate].pt.x = scalingRatio * (templateKeyPointsArray[iterate].pt.x + (double) x0template); templateKeyPointsArray[iterate].pt.y = scalingRatio * (templateKeyPointsArray[iterate].pt.y + (double) y0template); } Log.e(TAG, "Scaled points:(" + templateKeyPointsArray[iterate].pt.x + "," + templateKeyPointsArray[iterate].pt.y + ")"); } this.globalClass.setFlag(1); } templateKeyPoints.fromArray(templateKeyPointsArray); //Log.e(TAG, "Template-features have been scaled successfully!"); long timeBegin = (int) System.currentTimeMillis(); Mat mYuv = new Mat(mHeight + mHeight / 2, mWidth, CvType.CV_8UC1); mYuv.put(0, 0, data); Mat mRgb = new Mat(); Imgproc.cvtColor(mYuv, mRgb, Imgproc.COLOR_YUV420sp2RGB); Mat result = new Mat(); Imgproc.cvtColor(mRgb, result, Imgproc.COLOR_RGB2GRAY); int detectorType = FeatureDetector.ORB; FeatureDetector featureDetector = FeatureDetector.create(detectorType); MatOfKeyPoint keypointsImage = new MatOfKeyPoint(); featureDetector.detect(result, keypointsImage); KeyPoint[] imageKeypoints = keypointsImage.toArray(); Scalar color = new Scalar(0, 0, 0); DescriptorExtractor descriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.ORB); Mat imageDescriptor = new Mat(); descriptorExtractor.compute(result, keypointsImage, imageDescriptor); //BRUTEFORCE_HAMMING apparently finds even the suspicious feature-points! So, inliers and outliers can turn out to be a problem DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING); MatOfDMatch matches = new MatOfDMatch(); matcher.match(imageDescriptor, templateDescriptor, matches); //Log.e("Prasad", String.valueOf(mWidth) + "," + String.valueOf(mHeight)); DMatch[] matchesArray = matches.toArray(); double minimumMatchDistance = globalClass.getHammingDistance(); int iDescriptorMax = matchesArray.length; int iterateDescriptor; double xMatchedPoint, yMatchedPoint; int flagDraw = Features2d.NOT_DRAW_SINGLE_POINTS; Point point; double rHigh = this.globalClass.getRhigh(); double rLow = this.globalClass.getRlow(); double gHigh = this.globalClass.getGhigh(); double gLow = this.globalClass.getGlow(); double bHigh = this.globalClass.getBhigh(); double bLow = this.globalClass.getBlow(); double[] colorValue; double red, green, blue; int[] featureCount; double xKernelSize = 9, yKernelSize = 9; globalClass.setKernelSize(xKernelSize, yKernelSize); double xImageKernelScaling, yImageKernelScaling; xImageKernelScaling = xKernelSize / mWidth; yImageKernelScaling = yKernelSize / mHeight; int[][] kernel = new int[(int) xKernelSize][(int) yKernelSize]; double[][] kernelCounter = new double[(int) xKernelSize][(int) yKernelSize]; int numberKernelMax = 10; globalClass.setNumberKernelMax(numberKernelMax); int[][][] kernelArray = new int[(int) xKernelSize][(int) yKernelSize][numberKernelMax]; double featureImageResponse; double xImageCentroid, yImageCentroid; double xSum = 0, ySum = 0; double totalImageResponse = 0; for (iterateDescriptor = 0; iterateDescriptor < iDescriptorMax; iterateDescriptor++) { if (matchesArray[iterateDescriptor].distance < minimumMatchDistance) { //MatchedPoint: Awesome match without color feedback xMatchedPoint = imageKeypoints[matchesArray[iterateDescriptor].queryIdx].pt.x; yMatchedPoint = imageKeypoints[matchesArray[iterateDescriptor].queryIdx].pt.y; colorValue = mRgb.get((int) yMatchedPoint, (int) xMatchedPoint); red = colorValue[0]; green = colorValue[1]; blue = colorValue[2]; int xKernelFeature, yKernelFeature; //Color feedback if ((rLow < red) & (red < rHigh) & (gLow < green) & (green < gHigh) & (bLow < blue) & (blue < bHigh)) { try { featureImageResponse = imageKeypoints[matchesArray[iterateDescriptor].queryIdx].response; if (featureImageResponse > 0) { xSum = xSum + featureImageResponse * xMatchedPoint; ySum = ySum + featureImageResponse * yMatchedPoint; totalImageResponse = totalImageResponse + featureImageResponse; point = imageKeypoints[matchesArray[iterateDescriptor].queryIdx].pt; xKernelFeature = (int) (xMatchedPoint * xImageKernelScaling); yKernelFeature = (int) (yMatchedPoint * yImageKernelScaling); kernelCounter[xKernelFeature][yKernelFeature]++; //Core.circle(result, point, 3, color); } } catch (Exception e) { } } //Log.e(TAG, iterateDescriptor + ": (" + xMatchedPoint + "," + yMatchedPoint + ")"); } } int iKernel = 0, jKernel = 0; for (iKernel = 0; iKernel < xKernelSize; iKernel++) { for (jKernel = 0; jKernel < yKernelSize; jKernel++) { if (kernelCounter[iKernel][jKernel] > 0) { kernel[iKernel][jKernel] = 1; } else { kernel[iKernel][jKernel] = 0; } } } xImageCentroid = xSum / totalImageResponse; yImageCentroid = ySum / totalImageResponse; if ((Double.isNaN(xImageCentroid)) | (Double.isNaN(yImageCentroid))) { //Log.e(TAG, "Centroid is not getting detected! Increasing hamming distance (error-tolerance)!"); globalClass.setHammingDistance((int) (minimumMatchDistance + 2)); } else { //Log.e(TAG, "Centroid is getting detected! Decreasing and optimising hamming (error-tolerance)!"); globalClass.setHammingDistance((int) (minimumMatchDistance - 1)); int jpegCount = globalClass.getJpegFactoryCallCount(); jpegCount++; globalClass.setJpegFactoryCallCount(jpegCount); int initialisationFlag = globalClass.getInitialisationFlag(); int numberOfDistances = 10; globalClass.setNumberOfDistances(numberOfDistances); if ((jpegCount > globalClass.getNumberKernelMax()) & (jpegCount > numberOfDistances)) { globalClass.setInitialisationFlag(1); } int[][] kernelSum = new int[(int) xKernelSize][(int) yKernelSize], mask = new int[(int) xKernelSize][(int) yKernelSize]; int iJpeg, jJpeg; kernelSum = globalClass.computeKernelSum(kernel); Log.e(TAG, Arrays.deepToString(kernelSum)); for (iJpeg = 0; iJpeg < xKernelSize; iJpeg++) { for (jJpeg = 0; jJpeg < yKernelSize; jJpeg++) { if (kernelSum[iJpeg][jJpeg] > (numberKernelMax / 4)) {//Meant for normalised kernel mask[iJpeg][jJpeg]++; } } } Log.e(TAG, Arrays.deepToString(mask)); int maskedFeatureCount = 1, xMaskFeatureSum = 0, yMaskFeatureSum = 0; for (iJpeg = 0; iJpeg < xKernelSize; iJpeg++) { for (jJpeg = 0; jJpeg < yKernelSize; jJpeg++) { if (mask[iJpeg][jJpeg] == 1) { xMaskFeatureSum = xMaskFeatureSum + iJpeg; yMaskFeatureSum = yMaskFeatureSum + jJpeg; maskedFeatureCount++; } } } double xMaskMean = xMaskFeatureSum / maskedFeatureCount; double yMaskMean = yMaskFeatureSum / maskedFeatureCount; double xSquaredSum = 0, ySquaredSum = 0; for (iJpeg = 0; iJpeg < xKernelSize; iJpeg++) { for (jJpeg = 0; jJpeg < yKernelSize; jJpeg++) { if (mask[iJpeg][jJpeg] == 1) { xSquaredSum = xSquaredSum + (iJpeg - xMaskMean) * (iJpeg - xMaskMean); ySquaredSum = ySquaredSum + (jJpeg - yMaskMean) * (jJpeg - yMaskMean); } } } double xRMSscaled = Math.sqrt(xSquaredSum); double yRMSscaled = Math.sqrt(ySquaredSum); double RMSimage = ((xRMSscaled / xImageKernelScaling) + (yRMSscaled / yImageKernelScaling)) / 2; Log.e(TAG, "RMS radius of the image: " + RMSimage); /*//Command the quadcopter and send PWM values to Arduino double throttlePWM = 1500, yawPWM = 1500, pitchPWM = 1500; double deltaThrottle = 1, deltaYaw = 1, deltaPitch = 1; throttlePWM = globalClass.getThrottlePWM(); pitchPWM = globalClass.getPitchPWM(); yawPWM = globalClass.getYawPWM(); deltaThrottle = globalClass.getThrottleDelta(); deltaPitch = globalClass.getPitchDelta(); deltaYaw = globalClass.getYawDelta(); if(yImageCentroid>yTemplateCentroid) { throttlePWM = throttlePWM + deltaThrottle; }else{ throttlePWM = throttlePWM - deltaThrottle; } if(RMSimage>distanceTemplateFeatures) { pitchPWM = pitchPWM + deltaPitch; }else{ pitchPWM = pitchPWM - deltaPitch; } if(xImageCentroid>xTemplateCentroid) { yawPWM = yawPWM + deltaYaw; }else{ yawPWM = yawPWM - deltaYaw; } if(1000>throttlePWM){ throttlePWM = 1000; } if(2000<throttlePWM){ throttlePWM = 2000; } if(1000>pitchPWM){ pitchPWM = 1000; } if(2000<pitchPWM){ pitchPWM = 2000; } if(1000>yawPWM){ yawPWM = 1000; } if(2000<yawPWM){ yawPWM = 2000; } globalClass.setPitchPWM(pitchPWM); globalClass.setYawPWM(yawPWM); globalClass.setThrottlePWM(throttlePWM);*/ //Display bounding circle int originalWidthBox = x1template - x0template; int originalHeightBox = y1template - y0template; double scaledBoundingWidth = (originalWidthBox * RMSimage / distanceTemplateFeatures); double scaledBoundingHeight = (originalHeightBox * RMSimage / distanceTemplateFeatures); double displayRadius = (scaledBoundingWidth + scaledBoundingHeight) / 2; displayRadius = displayRadius * 1.4826; displayRadius = displayRadius / numberKernelMax; double distanceAverage = 0; if (Double.isNaN(displayRadius)) { //Log.e(TAG, "displayRadius is NaN!"); } else { distanceAverage = globalClass.imageDistanceAverage(displayRadius); //Log.e(TAG, "Average distance: " + distanceAverage); } if ((Double.isNaN(xImageCentroid)) | Double.isNaN(yImageCentroid)) { //Log.e(TAG, "Centroid is NaN!"); } else { globalClass.centroidAverage(xImageCentroid, yImageCentroid); } if (initialisationFlag == 1) { //int displayRadius = 50; Point pointDisplay = new Point(); //pointDisplay.x = xImageCentroid; //pointDisplay.y = yImageCentroid; pointDisplay.x = globalClass.getXcentroidAverageGlobal(); pointDisplay.y = globalClass.getYcentroidAverageGlobal(); globalClass.centroidAverage(xImageCentroid, yImageCentroid); int distanceAverageInt = (int) distanceAverage; Core.circle(result, pointDisplay, distanceAverageInt, color); } } Log.e(TAG, "Centroid in the streamed image: (" + xImageCentroid + "," + yImageCentroid + ")"); /*try { //Features2d.drawKeypoints(result, keypointsImage, result, color, flagDraw); Features2d.drawKeypoints(result, templateKeyPoints, result, color, flagDraw); }catch(Exception e){}*/ //Log.e(TAG, "High (R,G,B): (" + rHigh + "," + gHigh + "," + bHigh + ")"); //Log.e(TAG, "Low (R,G,B): (" + rLow + "," + gLow + "," + bLow + ")"); //Log.e(TAG, Arrays.toString(matchesArray)); try { Bitmap bmp = Bitmap.createBitmap(result.cols(), result.rows(), Bitmap.Config.ARGB_8888); Utils.matToBitmap(result, bmp); //Utils.matToBitmap(mRgb, bmp); bmp.compress(Bitmap.CompressFormat.JPEG, mQuality, mJpegOutputStream); } catch (Exception e) { Log.e(TAG, "JPEG not working!"); } long timeEnd = (int) System.currentTimeMillis(); Log.e(TAG, "Time consumed is " + String.valueOf(timeEnd - timeBegin) + "milli-seconds!"); mJpegData = mJpegOutputStream.toByteArray(); synchronized (mJpegOutputStream) { mJpegOutputStream.notifyAll(); } } catch (Exception e) { Log.e(TAG, "JPEG-factory is not working!"); } }
From source file:cn.xiongyihui.webcam.setup.java
License:Open Source License
@Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_setup); this.setRequestedOrientation(ActivityInfo.SCREEN_ORIENTATION_LANDSCAPE); final Button cameraButton = (Button) findViewById(R.id.cameraButton); final Button selectButton = (Button) findViewById(R.id.selectButton); final Button templateButton = (Button) findViewById(R.id.templateButton); final Button instructionButton = (Button) findViewById(R.id.instructionButton); final ImageView imageView = (ImageView) findViewById(R.id.imageView); try {//from www .j a va2 s . co m int NUMBER_OF_CORES = Runtime.getRuntime().availableProcessors(); Toast.makeText(this, NUMBER_OF_CORES, Toast.LENGTH_SHORT).show(); } catch (Exception e) { Log.e(TAG, "Processor-cores are not getting detected!"); } try { final Toast toast = Toast.makeText(this, "Please capture image; \n" + "select image; \n" + "Drag-and-drop, swipe on the desired region and confirm template!", Toast.LENGTH_LONG); final TextView v = (TextView) toast.getView().findViewById(android.R.id.message); instructionButton.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View arg0) { if (v != null) v.setGravity(Gravity.CENTER); toast.show(); } }); } catch (Exception e) { Log.e(TAG, "Instructions are not getting displayed!"); } try { cameraButton.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View arg0) { Intent intent = new Intent("android.media.action.IMAGE_CAPTURE"); startActivityForResult(intent, requestCode); } }); } catch (Exception e) { Log.e(TAG, "Camera is not working!"); } try { selectButton.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View arg0) { Intent i = new Intent(Intent.ACTION_PICK, android.provider.MediaStore.Images.Media.EXTERNAL_CONTENT_URI); startActivityForResult(i, requestCode); bitmap = BitmapFactory.decodeFile(filePath); imageView.setImageBitmap(bitmap); } }); } catch (Exception e) { Log.e(TAG, "Selection is not working!"); } try { templateButton.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View arg0) { if (imageView.getDrawable() == null) { Log.e(TAG, "Null ImageView!"); } Log.e(TAG, "Button is working."); try { bitmap = BitmapFactory.decodeFile(filePath); bitmap = Bitmap.createScaledBitmap(bitmap, bitmapWidth, bitmapHeight, true); Mat frame = new Mat(bitmap.getWidth(), bitmap.getHeight(), CvType.CV_8UC4); Utils.bitmapToMat(bitmap, frame); GlobalClass globalVariable = (GlobalClass) getApplicationContext(); globalVariable.setTemplateCapturedBitmapHeight(bitmapHeight); globalVariable.setTemplateCapturedBitmapWidth(bitmapWidth); Log.e(TAG, "Bitmap has been set successfully; Template is being generated!"); rect = new Rect(x0final, y0final, x1final - x0final, y1final - y0final); Utils.matToBitmap(frame, bitmap); if (x0final < x1final) { x0display = x0final; x1display = x1final; } if (x0final > x1final) { x1display = x0final; x0display = x1final; } if (y0final < y1final) { y0display = y0final; y1display = y1final; } if (y0final > y1final) { y1display = y0final; y0display = y1final; } long timeBegin = (int) System.currentTimeMillis(); bitmap = Bitmap.createBitmap(bitmap, x0display, y0display, x1display - x0display, y1display - y0display); /*String path = Environment.getExternalStorageDirectory().toString(); Log.e(TAG, "File is about to be written!"); //File file = new File(path, "TraQuad"); //bitmap.compress(Bitmap.CompressFormat.PNG, 100, fOutputStream); //Log.e(TAG, "Stored image successfully!"); //fOutputStream.flush(); //fOutputStream.close(); //MediaStore.Images.Media.insertImage(getContentResolver(), file.getAbsolutePath(), file.getName(), file.getName());*/ /*Prominent colors code; This is not working in Android; OpenCV assertion error Log.e(TAG, "Retrieved image successfully!"); Imgproc.medianBlur(frame, frame, 3); Log.e(TAG, "Filtered image successfully!"); try { Mat mask = new Mat(bitmap.getWidth(), bitmap.getHeight(), CvType.CV_8UC1); MatOfFloat range = new MatOfFloat(0f, 255f); Mat hist = new Mat(); MatOfInt mHistSize = new MatOfInt(256); List<Mat> lHsv = new ArrayList<Mat>(3); Mat hsv = new Mat(); Imgproc.cvtColor(frame, hsv, Imgproc.COLOR_RGB2HSV); Core.split(frame, lHsv); Mat mH = lHsv.get(0); Mat mS = lHsv.get(1); Mat mV = lHsv.get(2); ArrayList<Mat> ListMat = new ArrayList<Mat>(); ListMat.add(mH); Log.e(TAG, String.valueOf(ListMat)); MatOfInt channels = new MatOfInt(0, 1); Imgproc.calcHist(Arrays.asList(mH), channels, mask, hist, mHistSize, range); ListMat.clear(); }catch (Exception e){ Log.e(TAG, "Prominent colors are not getting detected!"); }*/ Mat colorFrame = frame; colorFrame = frame.clone(); Utils.bitmapToMat(bitmap, frame); Imgproc.cvtColor(frame, frame, Imgproc.COLOR_RGB2GRAY); Log.e(TAG, "Converted color successfully!"); int detectorType = FeatureDetector.ORB; //int detectorType = FeatureDetector.SIFT; //SIFT and SURF are not working! //int detectorType = FeatureDetector.SURF; FeatureDetector featureDetector = FeatureDetector.create(detectorType); Log.e(TAG, "Feature detection has begun!"); MatOfKeyPoint keypoints = new MatOfKeyPoint(); featureDetector.detect(frame, keypoints); Log.e(TAG, "Feature detection has ended successfully!"); /*if (!featureDetector.empty()) { //Draw the detected keypoints int flagDraw = Features2d.NOT_DRAW_SINGLE_POINTS; Features2d.drawKeypoints(frame, keypoints, frame, color, flagDraw); Utils.matToBitmap(frame, bitmap); }*/ imageView.setImageBitmap(bitmap); Log.e(TAG, "Final bitmap has been loaded!"); KeyPoint[] referenceKeypoints = keypoints.toArray(); Log.e(TAG, "Number of keypoints detected is " + String.valueOf(referenceKeypoints.length)); int iterationMax = referenceKeypoints.length; int iterate = 0; double xFeaturePoint, yFeaturePoint; double xSum = 0, ySum = 0; double totalResponse = 0; double keyPointResponse = 0; double xTemplateCentroid = 0, yTemplateCentroid = 0; DescriptorExtractor descriptorExtractor = DescriptorExtractor .create(DescriptorExtractor.ORB); Mat templateDescriptor = new Mat(); descriptorExtractor.compute(frame, keypoints, templateDescriptor); for (iterate = 0; iterate < iterationMax; iterate++) { xFeaturePoint = referenceKeypoints[iterate].pt.x; yFeaturePoint = referenceKeypoints[iterate].pt.y; keyPointResponse = referenceKeypoints[iterate].response; if (keyPointResponse > 0) { xSum = xSum + keyPointResponse * xFeaturePoint; ySum = ySum + keyPointResponse * yFeaturePoint; totalResponse = totalResponse + keyPointResponse; //Log.e(TAG, "Feature " + String.valueOf(iterate) + ":" + String.valueOf(referenceKeypoints[iterate])); } } xTemplateCentroid = xSum / totalResponse; yTemplateCentroid = ySum / totalResponse; Log.e(TAG, "Finished conversion of features to points!"); Log.e(TAG, "Centroid location is: (" + xTemplateCentroid + "," + yTemplateCentroid + ")"); double xSquareDistance = 0, ySquareDistance = 0; double distanceTemplateFeatures = 0; int numberOfPositiveResponses = 0; double[] colorValue; double rSum = 0, gSum = 0, bSum = 0; double rCentral, gCentral, bCentral; for (iterate = 0; iterate < iterationMax; iterate++) { xFeaturePoint = referenceKeypoints[iterate].pt.x; yFeaturePoint = referenceKeypoints[iterate].pt.y; keyPointResponse = referenceKeypoints[iterate].response; colorValue = colorFrame.get((int) yFeaturePoint, (int) xFeaturePoint); rSum = rSum + colorValue[0]; gSum = gSum + colorValue[1]; bSum = bSum + colorValue[2]; if (keyPointResponse > 0) { xSquareDistance = xSquareDistance + (xFeaturePoint - xTemplateCentroid) * (xFeaturePoint - xTemplateCentroid); ySquareDistance = ySquareDistance + (yFeaturePoint - yTemplateCentroid) * (yFeaturePoint - yTemplateCentroid); numberOfPositiveResponses++; } } rCentral = rSum / iterationMax; gCentral = gSum / iterationMax; bCentral = bSum / iterationMax; double deltaColor = 21; double rLow = rCentral - deltaColor; double rHigh = rCentral + deltaColor; double gLow = rCentral - deltaColor; double gHigh = rCentral + deltaColor; double bLow = rCentral - deltaColor; double bHigh = rCentral + deltaColor; Log.e(TAG, "Prominent color (R,G,B): (" + rCentral + "," + gCentral + "," + bCentral + ")"); distanceTemplateFeatures = Math .sqrt((xSquareDistance + ySquareDistance) / numberOfPositiveResponses); KeyPoint[] offsetCompensatedKeyPoints = keypoints.toArray(); double xMaxNormalisation, yMaxNormalisation; xMaxNormalisation = x1display - x0display; yMaxNormalisation = y1display - y0display; for (iterate = 0; iterate < iterationMax; iterate++) { offsetCompensatedKeyPoints[iterate].pt.x = offsetCompensatedKeyPoints[iterate].pt.x / xMaxNormalisation; offsetCompensatedKeyPoints[iterate].pt.y = offsetCompensatedKeyPoints[iterate].pt.y / yMaxNormalisation; //Log.e(TAG, "Compensated: (" + String.valueOf(offsetCompensatedKeyPoints[iterate].pt.x) + "," + String.valueOf(offsetCompensatedKeyPoints[iterate].pt.y) + ")"); } double xCentroidNormalised, yCentroidNormalised; xCentroidNormalised = (xTemplateCentroid - x0display) / xMaxNormalisation; yCentroidNormalised = (yTemplateCentroid - y0display) / yMaxNormalisation; Log.e(TAG, "Normalised Centroid: (" + String.valueOf(xCentroidNormalised) + "," + String.valueOf(yCentroidNormalised)); long timeEnd = (int) System.currentTimeMillis(); Log.e(TAG, "Time consumed is " + String.valueOf(timeEnd - timeBegin) + " milli-seconds!"); Log.e(TAG, "RMS distance is: " + distanceTemplateFeatures); globalVariable.setDistanceTemplateFeatures(distanceTemplateFeatures); globalVariable.setX0display(x0display); globalVariable.setY0display(y0display); globalVariable.setX1display(x1display); globalVariable.setY1display(y1display); globalVariable.setKeypoints(keypoints); globalVariable.setXtemplateCentroid(xTemplateCentroid); globalVariable.setYtemplateCentroid(yTemplateCentroid); globalVariable.setTemplateDescriptor(templateDescriptor); globalVariable.setNumberOfTemplateFeatures(iterationMax); globalVariable.setNumberOfPositiveTemplateFeatures(numberOfPositiveResponses); globalVariable.setRhigh(rHigh); globalVariable.setRlow(rLow); globalVariable.setGhigh(gHigh); globalVariable.setGlow(gLow); globalVariable.setBhigh(bHigh); globalVariable.setBlow(bLow); globalVariable.setXnormalisedCentroid(xCentroidNormalised); globalVariable.setYnormalisedCentroid(yCentroidNormalised); globalVariable.setNormalisedTemplateKeyPoints(offsetCompensatedKeyPoints); Log.e(TAG, "Finished setting the global variables!"); } catch (Exception e) { Log.e(TAG, "Please follow instructions!"); } } }); } catch (Exception e) { Log.e(TAG, "Template is not working!"); } }
From source file:eu.fpetersen.robobrain.behavior.followobject.OrbObjectDetector.java
License:Open Source License
public void process(Mat image) { Mat tempImage = new Mat(); Imgproc.cvtColor(image, tempImage, Imgproc.COLOR_RGBA2RGB); MatOfKeyPoint keypoints = detectInImage(tempImage); Mat descriptors = extractDescriptors(keypoints, tempImage); MatOfDMatch matches = new MatOfDMatch(); matcher.match(descriptors, originalDescriptors, matches); KeyPoint[] keypointArray = keypoints.toArray(); KeyPoint[] originalKeypointArray = originalKeypoints.toArray(); float min = 40.0f; float max = 1000.0f; for (DMatch match : matches.toList()) { if (match.distance < min) { min = match.distance;/*from w w w .j a v a 2s. c om*/ } else if (match.distance > max) { max = match.distance; } } float threshold = 1.5f * min; List<KeyPoint> matchedKeyPoints = new ArrayList<KeyPoint>(); List<Point> matchedPoints = new ArrayList<Point>(); List<Point> matchedOriginalPoints = new ArrayList<Point>(); for (DMatch match : matches.toList()) { if (match.distance < threshold) { KeyPoint matchedKeypoint = keypointArray[match.queryIdx]; matchedKeyPoints.add(matchedKeypoint); matchedPoints.add(matchedKeypoint.pt); KeyPoint matchedOriginalKeypoint = originalKeypointArray[match.trainIdx]; matchedOriginalPoints.add(matchedOriginalKeypoint.pt); } } if (matchedKeyPoints.size() > 10) { Mat H = Calib3d.findHomography( new MatOfPoint2f(matchedOriginalPoints.toArray(new Point[matchedOriginalPoints.size()])), new MatOfPoint2f(matchedPoints.toArray(new Point[matchedPoints.size()])), Calib3d.RANSAC, 10); List<Point> originalCorners = new ArrayList<Point>(); originalCorners.add(new Point(0, 0)); originalCorners.add(new Point(originalImage.cols(), 0)); originalCorners.add(new Point(originalImage.cols(), originalImage.rows())); originalCorners.add(new Point(0, originalImage.rows())); List<Point> corners = new ArrayList<Point>(); for (int i = 0; i < 4; i++) { corners.add(new Point(0, 0)); } Mat objectCorners = Converters.vector_Point2f_to_Mat(corners); Core.perspectiveTransform(Converters.vector_Point2f_to_Mat(originalCorners), objectCorners, H); corners.clear(); Converters.Mat_to_vector_Point2f(objectCorners, corners); Core.line(tempImage, corners.get(0), corners.get(1), new Scalar(0, 255, 0), 4); Core.line(tempImage, corners.get(1), corners.get(2), new Scalar(0, 255, 0), 4); Core.line(tempImage, corners.get(2), corners.get(3), new Scalar(0, 255, 0), 4); Core.line(tempImage, corners.get(3), corners.get(0), new Scalar(0, 255, 0), 4); } Features2d.drawKeypoints(tempImage, new MatOfKeyPoint(matchedKeyPoints.toArray(new KeyPoint[matchedKeyPoints.size()])), tempImage); Imgproc.cvtColor(tempImage, image, Imgproc.COLOR_RGB2RGBA); }
From source file:overwatchteampicker.OverwatchTeamPicker.java
public static ReturnValues findImage(String template, String source, int flag) { File lib = null;/*from w w w . jav a2s . c o 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:samples.GripVision.java
License:Open Source License
private Rect getTargetRect(GripPipeline pipeline, Mat image) { Rect targetRect = null;/*w w w. ja va 2 s .c o m*/ MatOfKeyPoint detectedTargets; pipeline.process(image); detectedTargets = pipeline.findBlobsOutput(); if (detectedTargets != null) { KeyPoint[] targets = detectedTargets.toArray(); if (targets.length > 1) { HalDashboard.getInstance().displayPrintf(15, "%s: %s", pipeline, targets[0]); double radius = targets[0].size / 2; targetRect = new Rect((int) (targets[0].pt.x - radius), (int) (targets[0].pt.y - radius), (int) targets[0].size, (int) targets[0].size); } detectedTargets.release(); } return targetRect; }
From source file:View.Signature.java
public static int sift(String routeVal, String route, String n_img1, String n_img2, String extension) { String bookObject = routeVal + n_img2 + extension; String bookScene = route + n_img1 + extension; //System.out.println("Iniciando SIFT"); //java.lang.System.out.print("Abriendo imagenes | "); Mat objectImage = Highgui.imread(bookObject, Highgui.CV_LOAD_IMAGE_COLOR); Mat sceneImage = Highgui.imread(bookScene, Highgui.CV_LOAD_IMAGE_COLOR); MatOfKeyPoint objectKeyPoints = new MatOfKeyPoint(); FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.SIFT); //java.lang.System.out.print("Encontrar keypoints con SIFT | "); featureDetector.detect(objectImage, objectKeyPoints); KeyPoint[] keypoints = objectKeyPoints.toArray(); MatOfKeyPoint objectDescriptors = new MatOfKeyPoint(); DescriptorExtractor descriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.SIFT); //java.lang.System.out.print("Computando descriptores | "); 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); //java.lang.System.out.print("Dibujando keypoints en imagen base | "); Features2d.drawKeypoints(objectImage, objectKeyPoints, outputImage, newKeypointColor, 0); // Match object image with the scene image MatOfKeyPoint sceneKeyPoints = new MatOfKeyPoint(); MatOfKeyPoint sceneDescriptors = new MatOfKeyPoint(); //java.lang.System.out.print("Detectando keypoints en imagen base | "); featureDetector.detect(sceneImage, sceneKeyPoints); //java.lang.System.out.print("Computando descriptores en imagen base | "); 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, 0); List<MatOfDMatch> matches = new LinkedList<MatOfDMatch>(); DescriptorMatcher descriptorMatcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED); //java.lang.System.out.print("Encontrando matches entre imagenes | "); descriptorMatcher.knnMatch(objectDescriptors, sceneDescriptors, matches, 2); //java.lang.System.out.println("Calculando buenos matches"); LinkedList<DMatch> goodMatchesList = new LinkedList<DMatch>(); float nndrRatio = 0.7f; java.lang.System.out.println(matches.size()); for (int i = 0; i < matches.size(); i++) { MatOfDMatch matofDMatch = matches.get(i); DMatch[] dmatcharray = matofDMatch.toArray(); DMatch m1 = dmatcharray[0];/*from w ww. jav a 2 s . co m*/ DMatch m2 = dmatcharray[1]; if (m1.distance <= m2.distance * nndrRatio) { goodMatchesList.addLast(m1); } } if (goodMatchesList.size() >= 7) { //java.lang.System.out.println("Match enontrado!!! Matches: "+goodMatchesList.size()); //if(goodMatchesList.size()>max){ //cambio = 1; //} 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() }); //System.out.println("Transforming object corners to scene corners..."); Core.perspectiveTransform(obj_corners, scene_corners, homography); Mat img = Highgui.imread(bookScene, 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, 0), 4); Core.line(img, new Point(scene_corners.get(1, 0)), new Point(scene_corners.get(2, 0)), new Scalar(0, 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); //java.lang.System.out.println("Dibujando imagen de coincidencias"); MatOfDMatch goodMatches = new MatOfDMatch(); goodMatches.fromList(goodMatchesList); Features2d.drawMatches(objectImage, objectKeyPoints, sceneImage, sceneKeyPoints, goodMatches, matchoutput, matchestColor, newKeypointColor, new MatOfByte(), 2); String n_outputImage = route + "results\\" + n_img2 + "_outputImage_sift" + extension; String n_matchoutput = route + "results\\" + n_img2 + "_matchoutput_sift" + extension; String n_img = route + "results\\" + n_img2 + "_sift" + extension; Highgui.imwrite(n_outputImage, outputImage); Highgui.imwrite(n_matchoutput, matchoutput); //Highgui.imwrite(n_img, img); java.lang.System.out.println(goodMatches.size().height); double result = goodMatches.size().height * 100 / matches.size(); java.lang.System.out.println((int) result); //double result =goodMatches.size().height; if (result > 100) { return 100; } else if (result <= 100 && result > 85) { return 85; } else if (result <= 85 && result > 50) { return 50; } else if (result <= 50 && result > 25) { return 25; } else { return 0; } } else { //java.lang.System.out.println("Firma no encontrada"); } return 0; //System.out.println("Terminando SIFT"); }
From source file:View.SignatureLib.java
public static int sift(String routeRNV, String routeAdherent) { String bookObject = routeAdherent; String bookScene = routeRNV;/*from w w w. jav a 2 s .c om*/ //System.out.println("Iniciando SIFT"); //java.lang.System.out.print("Abriendo imagenes | "); Mat objectImage = Highgui.imread(bookObject, Highgui.CV_LOAD_IMAGE_COLOR); Mat sceneImage = Highgui.imread(bookScene, Highgui.CV_LOAD_IMAGE_COLOR); MatOfKeyPoint objectKeyPoints = new MatOfKeyPoint(); FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.SIFT); //java.lang.System.out.print("Encontrar keypoints con SIFT | "); featureDetector.detect(objectImage, objectKeyPoints); KeyPoint[] keypoints = objectKeyPoints.toArray(); MatOfKeyPoint objectDescriptors = new MatOfKeyPoint(); DescriptorExtractor descriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.SIFT); //java.lang.System.out.print("Computando descriptores | "); 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); //java.lang.System.out.print("Dibujando keypoints en imagen base | "); Features2d.drawKeypoints(objectImage, objectKeyPoints, outputImage, newKeypointColor, 0); // Match object image with the scene image MatOfKeyPoint sceneKeyPoints = new MatOfKeyPoint(); MatOfKeyPoint sceneDescriptors = new MatOfKeyPoint(); //java.lang.System.out.print("Detectando keypoints en imagen base | "); featureDetector.detect(sceneImage, sceneKeyPoints); //java.lang.System.out.print("Computando descriptores en imagen base | "); 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, 0); List<MatOfDMatch> matches = new LinkedList<MatOfDMatch>(); DescriptorMatcher descriptorMatcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED); //java.lang.System.out.println(sceneDescriptors); if (sceneDescriptors.empty()) { java.lang.System.out.println("Objeto no encontrado"); return 0; } descriptorMatcher.knnMatch(objectDescriptors, sceneDescriptors, matches, 2); //java.lang.System.out.println("Calculando buenos matches"); LinkedList<DMatch> goodMatchesList = new LinkedList<DMatch>(); float nndrRatio = 0.7f; 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() >= 7) { max = goodMatchesList.size(); 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() }); //System.out.println("Transforming object corners to scene corners..."); Core.perspectiveTransform(obj_corners, scene_corners, homography); Mat img = Highgui.imread(bookScene, 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, 0), 4); Core.line(img, new Point(scene_corners.get(1, 0)), new Point(scene_corners.get(2, 0)), new Scalar(0, 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); //java.lang.System.out.println("Dibujando imagen de coincidencias"); MatOfDMatch goodMatches = new MatOfDMatch(); goodMatches.fromList(goodMatchesList); Features2d.drawMatches(objectImage, objectKeyPoints, sceneImage, sceneKeyPoints, goodMatches, matchoutput, matchestColor, newKeypointColor, new MatOfByte(), 2); String n_outputImage = "../pre/outputImage_sift.jpg"; String n_matchoutput = "../pre/matchoutput_sift.jpg"; String n_img = "../pre/sift.jpg"; Highgui.imwrite(n_outputImage, outputImage); Highgui.imwrite(n_matchoutput, matchoutput); Highgui.imwrite(n_img, img); java.lang.System.out.println(goodMatches.size().height); double result = goodMatches.size().height;//*100/matches.size(); int score = 0; if (result > 26) { score = 100; } else if (result <= 26 && result > 22) { score = 85; } else if (result <= 22 && result > 17) { score = 50; } else if (result <= 17 && result > 11) { score = 25; } else { score = 0; } java.lang.System.out.println("Score: " + score); return score; } else { java.lang.System.out.println("Objeto no encontrado"); return 0; } //System.out.println("Terminando SIFT"); }
From source file:vinylsleevedetection.Analyze.java
public void Check() { count = 1;//from w w w .j a va 2 s. c o m //load openCV library System.loadLibrary(Core.NATIVE_LIBRARY_NAME); //for loop to compare source images to user image for (int j = 1; j < 4; j++) { //source image location (record sleeve) String Object = "E:\\Users\\Jamie\\Documents\\NetBeansProjects\\VinylSleeveDetection\\Source\\" + j + ".jpg"; //user image location String Scene = "E:\\Users\\Jamie\\Documents\\NetBeansProjects\\VinylSleeveDetection\\Output\\camera.jpg"; //load images Mat objectImage = Imgcodecs.imread(Object, Imgcodecs.CV_LOAD_IMAGE_COLOR); Mat sceneImage = Imgcodecs.imread(Scene, Imgcodecs.CV_LOAD_IMAGE_COLOR); //use BRISK feature detection MatOfKeyPoint objectKeyPoints = new MatOfKeyPoint(); FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.BRISK); //perform feature detection on source image featureDetector.detect(objectImage, objectKeyPoints); KeyPoint[] keypoints = objectKeyPoints.toArray(); //use descriptor extractor MatOfKeyPoint objectDescriptors = new MatOfKeyPoint(); DescriptorExtractor descriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.BRISK); descriptorExtractor.compute(objectImage, objectKeyPoints, objectDescriptors); Mat outputImage = new Mat(objectImage.rows(), objectImage.cols(), Imgcodecs.CV_LOAD_IMAGE_COLOR); Scalar newKeypointColor = new Scalar(255, 0, 0); Features2d.drawKeypoints(objectImage, objectKeyPoints, outputImage, newKeypointColor, 0); 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, Imgcodecs.CV_LOAD_IMAGE_COLOR); Scalar matchestColor = new Scalar(0, 255, 0); List<MatOfDMatch> matches = new LinkedList<>(); DescriptorMatcher descriptorMatcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE); descriptorMatcher.knnMatch(objectDescriptors, sceneDescriptors, matches, 2); LinkedList<DMatch> goodMatchesList = new LinkedList<DMatch>(); float nndrRatio = 0.7f; 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 the number of good mathces is more than 150 a match is found if (goodMatchesList.size() > 150) { System.out.println("Object Found"); 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 = Imgcodecs.imread(Scene, Imgcodecs.CV_LOAD_IMAGE_COLOR); //draw a green square around the matched object Imgproc.line(img, new Point(scene_corners.get(0, 0)), new Point(scene_corners.get(1, 0)), new Scalar(0, 255, 0), 10); Imgproc.line(img, new Point(scene_corners.get(1, 0)), new Point(scene_corners.get(2, 0)), new Scalar(0, 255, 0), 10); Imgproc.line(img, new Point(scene_corners.get(2, 0)), new Point(scene_corners.get(3, 0)), new Scalar(0, 255, 0), 10); Imgproc.line(img, new Point(scene_corners.get(3, 0)), new Point(scene_corners.get(0, 0)), new Scalar(0, 255, 0), 10); MatOfDMatch goodMatches = new MatOfDMatch(); goodMatches.fromList(goodMatchesList); Features2d.drawMatches(objectImage, objectKeyPoints, sceneImage, sceneKeyPoints, goodMatches, matchoutput, matchestColor, newKeypointColor, new MatOfByte(), 2); //output image with match, image of the match locations and keypoints image String folder = "E:\\Users\\Jamie\\Documents\\NetBeansProjects\\VinylSleeveDetection\\Output\\"; Imgcodecs.imwrite(folder + "outputImage.jpg", outputImage); Imgcodecs.imwrite(folder + "matchoutput.jpg", matchoutput); Imgcodecs.imwrite(folder + "found.jpg", img); count = j; break; } else { System.out.println("Object Not Found"); count = 0; } } }