@Override public Mat render(CameraBridgeViewBase.CvCameraViewFrame inputFrame) { Mat undistortedFrame = new Mat(inputFrame.rgba().size(), inputFrame.rgba().type()); Imgproc.undistort(inputFrame.rgba(), undistortedFrame, mCalibrator.getCameraMatrix(), mCalibrator.getDistortionCoefficients()); Mat comparisonFrame = inputFrame.rgba(); undistortedFrame.colRange(new Range(0, mWidth / 2)).copyTo(comparisonFrame.colRange(new Range(mWidth / 2, mWidth))); List<MatOfPoint> border = new ArrayList<MatOfPoint>(); final int shift = (int)(mWidth * 0.005); border.add(new MatOfPoint(new Point(mWidth / 2 - shift, 0), new Point(mWidth / 2 + shift, 0), new Point(mWidth / 2 + shift, mHeight), new Point(mWidth / 2 - shift, mHeight))); Imgproc.fillPoly(comparisonFrame, border, new Scalar(255, 255, 255)); Imgproc.putText(comparisonFrame, mResources.getString(R.string.original), new Point(mWidth * 0.1, mHeight * 0.1), Core.FONT_HERSHEY_SIMPLEX, 1.0, new Scalar(255, 255, 0)); Imgproc.putText(comparisonFrame, mResources.getString(R.string.undistorted), new Point(mWidth * 0.6, mHeight * 0.1), Core.FONT_HERSHEY_SIMPLEX, 1.0, new Scalar(255, 255, 0)); return comparisonFrame; }
@Override public Mat render(CameraBridgeViewBase.CvCameraViewFrame inputFrame) { Mat undistortedFrame = new Mat(inputFrame.rgba().size(), inputFrame.rgba().type()); Imgproc.undistort(inputFrame.rgba(), undistortedFrame, mCalibrator.getCameraMatrix(), mCalibrator.getDistortionCoefficients()); Mat comparisonFrame = inputFrame.rgba(); undistortedFrame.colRange(new Range(0, mWidth / 2)).copyTo(comparisonFrame.colRange(new Range(mWidth / 2, mWidth))); List<MatOfPoint> border = new ArrayList<MatOfPoint>(); final int shift = (int)(mWidth * 0.005); border.add(new MatOfPoint(new Point(mWidth / 2 - shift, 0), new Point(mWidth / 2 + shift, 0), new Point(mWidth / 2 + shift, mHeight), new Point(mWidth / 2 - shift, mHeight))); Core.fillPoly(comparisonFrame, border, new Scalar(255, 255, 255)); Core.putText(comparisonFrame, mResources.getString(R.string.original), new Point(mWidth * 0.1, mHeight * 0.1), Core.FONT_HERSHEY_SIMPLEX, 1.0, new Scalar(255, 255, 0)); Core.putText(comparisonFrame, mResources.getString(R.string.undistorted), new Point(mWidth * 0.6, mHeight * 0.1), Core.FONT_HERSHEY_SIMPLEX, 1.0, new Scalar(255, 255, 0)); return comparisonFrame; }
public void onCameraViewStarted(int width, int height) { mRgba = new Mat(height, width, CvType.CV_8UC3); mHSV = new Mat(); mIntermediateMat = new Mat(); mGray = new Mat(height, width, CvType.CV_8UC1); mask2 = Mat.zeros( mRgba.rows() + 2, mRgba.cols() + 2, CvType.CV_8UC1 ); newVal = new Scalar( 120, 120, 120 ); loDiff = new Scalar( lo, lo, lo ); upDiff = new Scalar( up, up, up ); rowRange = new Range( 1, mask2.rows() - 1 ); colRange = new Range( 1, mask2.cols() - 1 ); mBitmap = Bitmap.createBitmap(width, height, Bitmap.Config.ARGB_8888); }
private Mat cropFingerprint(Mat src) { int rowStart = (int) (CameraOverlayView.PADDING * src.rows()); int rowEnd = (int) ((1 - CameraOverlayView.PADDING) * src.rows()); int colStart = (int) (CameraOverlayView.PADDING * src.cols()); int colEnd = (int) ((1 - CameraOverlayView.PADDING) * src.cols()); Range rowRange = new Range(rowStart, rowEnd); Range colRange = new Range(colStart, colEnd); return src.submat(rowRange, colRange); }
/** * 其主要思路为： 1、求取源图I的平均灰度，并记录rows和cols； 2、按照一定大小，分为N*M个方块，求出每块的平均值，得到子块的亮度矩阵D； 3、用矩阵D的每个元素减去源图的平均灰度，得到子块的亮度差值矩阵E； 4、用双立方差值法，将矩阵E差值成与源图一样大小的亮度分布矩阵R； 5、得到矫正后的图像result=I-R； * @Title: unevenLightCompensate * @Description: 光线补偿 * @param image * @param blockSize * void * @throws */ public static void unevenLightCompensate(Mat image, int blockSize) { if(image.channels() == 3) { Imgproc.cvtColor(image, image, 7); } double average = Core.mean(image).val[0]; Scalar scalar = new Scalar(average); int rowsNew = (int) Math.ceil((double)image.rows() / (double)blockSize); int colsNew = (int) Math.ceil((double)image.cols() / (double)blockSize); Mat blockImage = new Mat(); blockImage = Mat.zeros(rowsNew, colsNew, CvType.CV_32FC1); for(int i = 0; i < rowsNew; i ++) { for(int j = 0; j < colsNew; j ++) { int rowmin = i * blockSize; int rowmax = (i + 1) * blockSize; if(rowmax > image.rows()) rowmax = image.rows(); int colmin = j * blockSize; int colmax = (j +1) * blockSize; if(colmax > image.cols()) colmax = image.cols(); Range rangeRow = new Range(rowmin, rowmax); Range rangeCol = new Range(colmin, colmax); Mat imageROI = new Mat(image, rangeRow, rangeCol); double temaver = Core.mean(imageROI).val[0]; blockImage.put(i, j, temaver); } } Core.subtract(blockImage, scalar, blockImage); Mat blockImage2 = new Mat(); int INTER_CUBIC = 2; Imgproc.resize(blockImage, blockImage2, image.size(), 0, 0, INTER_CUBIC); Mat image2 = new Mat(); image.convertTo(image2, CvType.CV_32FC1); Mat dst = new Mat(); Core.subtract(image2, blockImage2, dst); dst.convertTo(image, CvType.CV_8UC1); }
public static Rect detectBorder(Mat original){ Mat src = original.clone(); Log.d(TAG, "1 original: " + src.toString()); Imgproc.GaussianBlur(src, src, new Size(3, 3), 0); Log.d(TAG, "2.1 --> Gaussian blur done\n blur: " + src.toString()); Imgproc.cvtColor(src, src, Imgproc.COLOR_RGBA2GRAY); Log.d(TAG, "2.2 --> Grayscaling done\n gray: " + src.toString()); Mat sobelX = new Mat(); Mat sobelY = new Mat(); Imgproc.Sobel(src, sobelX, CvType.CV_32FC1, 2, 0, 5, 1, 0); Log.d(TAG, "3.1 --> Sobel done.\n X: " + sobelX.toString()); Imgproc.Sobel(src, sobelY, CvType.CV_32FC1, 0, 2, 5, 1, 0); Log.d(TAG, "3.2 --> Sobel done.\n Y: " + sobelY.toString()); Mat sum_img = new Mat(); Core.addWeighted(sobelX, 0.5, sobelY, 0.5, 0.5, sum_img); //Core.add(sobelX, sobelY, sum_img); Log.d(TAG, "4 --> Addition done. sum: " + sum_img.toString()); sobelX.release(); sobelY.release(); Mat gray = new Mat(); Core.normalize(sum_img, gray, 0, 255, Core.NORM_MINMAX, CvType.CV_8UC1); Log.d(TAG, "5 --> Normalization done. gray: " + gray.toString()); sum_img.release(); Mat row_proj = new Mat(); Mat col_proj = new Mat(); Core.reduce(gray, row_proj, 1, Core.REDUCE_AVG, CvType.CV_8UC1); Log.d(TAG, "6.1 --> Reduce done. row: " + row_proj.toString()); Core.reduce(gray, col_proj, 0, Core.REDUCE_AVG, CvType.CV_8UC1); Log.d(TAG, "6.2 --> Reduce done. col: " + col_proj.toString()); gray.release(); Imgproc.Sobel(row_proj, row_proj, CvType.CV_8UC1, 0, 2); Log.d(TAG, "7.1 --> Sobel done. row: " + row_proj.toString()); Imgproc.Sobel(col_proj, col_proj, CvType.CV_8UC1, 2, 0); Log.d(TAG, "7.2 --> Sobel done. col: " + col_proj.toString()); Rect result = new Rect(); int half_pos = (int) (row_proj.total()/2); Mat row_sub = new Mat(row_proj, new Range(0, half_pos), new Range(0, 1)); Log.d(TAG, "8.1 --> Copy sub matrix done. row: " + row_sub.toString()); result.y = (int) Core.minMaxLoc(row_sub).maxLoc.y; Log.d(TAG, "8.2 --> Minmax done. Y: " + result.y); row_sub.release(); Mat row_sub2 = new Mat(row_proj, new Range(half_pos, (int) row_proj.total()), new Range(0, 1)); Log.d(TAG, "8.3 --> Copy sub matrix done. row: " + row_sub2.toString()); result.height = (int) (Core.minMaxLoc(row_sub2).maxLoc.y + half_pos - result.y); Log.d(TAG, "8.4 --> Minmax done. Height: " + result.height); row_sub2.release(); half_pos = (int) (col_proj.total()/2); Mat col_sub = new Mat(col_proj, new Range(0, 1), new Range(0, half_pos)); Log.d(TAG, "9.1 --> Copy sub matrix done. col: " + col_sub.toString()); result.x = (int) Core.minMaxLoc(col_sub).maxLoc.x; Log.d(TAG, "9.2 --> Minmax done. X: " + result.x); col_sub.release(); Mat col_sub2 = new Mat(col_proj, new Range(0, 1), new Range(half_pos, (int) col_proj.total())); Log.d(TAG, "9.3 --> Copy sub matrix done. col: " + col_sub2.toString()); result.width = (int) (Core.minMaxLoc(col_sub2).maxLoc.x + half_pos - result.x); Log.d(TAG, "9.4 --> Minmax done. Width: " + result.width); col_sub2.release(); row_proj.release(); col_proj.release(); src.release(); return result; }
/** * <p>Predicts a response for an input sample.</p> * * <p>The method predicts the response corresponding to the given sample (see * "Predicting with GBT"). * The result is either the class label or the estimated function value. The * "CvGBTrees.predict" method enables using the parallel version of the GBT * model prediction if the OpenCV is built with the TBB library. In this case, * predictions of single trees are computed in a parallel fashion.</p> * * @param sample Input feature vector that has the same format as every training * set element. If not all the variables were actually used during training, * <code>sample</code> contains forged values at the appropriate places. * @param missing Missing values mask, which is a dimensional matrix of the same * size as <code>sample</code> having the <code>CV_8U</code> type. * <code>1</code> corresponds to the missing value in the same position in the * <code>sample</code> vector. If there are no missing values in the feature * vector, an empty matrix can be passed instead of the missing mask. * @param slice Parameter defining the part of the ensemble used for prediction. * <p>If <code>slice = Range.all()</code>, all trees are used. Use this parameter * to get predictions of the GBT models with different ensemble sizes learning * only one model.</p> * @param k Number of tree ensembles built in case of the classification problem * (see "Training GBT"). Use this parameter to change the output to sum of the * trees' predictions in the <code>k</code>-th ensemble only. To get the total * GBT model prediction, <code>k</code> value must be -1. For regression * problems, <code>k</code> is also equal to -1. * * @see <a href="http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html#cvgbtrees-predict">org.opencv.ml.CvGBTrees.predict</a> */ public float predict(Mat sample, Mat missing, Range slice, int k) { float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj, slice.start, slice.end, k); return retVal; }
/** * <p>Predicts a response for an input sample.</p> * * <p>The method runs the sample through the trees in the ensemble and returns the * output class label based on the weighted voting.</p> * * @param sample Input sample. * @param missing Optional mask of missing measurements. To handle missing * measurements, the weak classifiers must include surrogate splits (see * <code>CvDTreeParams.use_surrogates</code>). * @param slice Continuous subset of the sequence of weak classifiers to be used * for prediction. By default, all the weak classifiers are used. * @param rawMode Normally, it should be set to <code>false</code>. * @param returnSum If <code>true</code> then return sum of votes instead of the * class label. * * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-predict">org.opencv.ml.CvBoost.predict</a> */ public float predict(Mat sample, Mat missing, Range slice, boolean rawMode, boolean returnSum) { float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj, slice.start, slice.end, rawMode, returnSum); return retVal; }
/** * <p>Removes the specified weak classifiers.</p> * * <p>The method removes the specified weak classifiers from the sequence.</p> * * <p>Note: Do not confuse this method with the pruning of individual decision * trees, which is currently not supported.</p> * * @param slice Continuous subset of the sequence of weak classifiers to be * removed. * * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-prune">org.opencv.ml.CvBoost.prune</a> */ public void prune(Range slice) { prune_0(nativeObj, slice.start, slice.end); return; }
