Java Code Examples for org.opencv.core.Range

The following are top voted examples for showing how to use org.opencv.core.Range. These examples are extracted from open source projects. You can vote up the examples you like and your votes will be used in our system to generate more good examples.
Example 1
Project: OpenCV-AndroidSamples-master   File: ComparisonFrameRender.java   View source code 6 votes vote down vote up
@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;
}
 
Example 2
Project: Aruco-Marker-Tracking-Android   File: ComparisonFrameRender.java   View source code 6 votes vote down vote up
@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;
}
 
Example 3
Project: OpenCV-AndroidSamples   File: ComparisonFrameRender.java   View source code 6 votes vote down vote up
@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;
}
 
Example 4
Project: OpenCV-BackProjection   File: BackProjectionActivity.java   View source code 6 votes vote down vote up
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);        
}
 
Example 5
Project: fingerblox   File: ImageProcessing.java   View source code 5 votes vote down vote up
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);
}
 
Example 6
Project: classchecks   File: ImgprocessUtils.java   View source code 5 votes vote down vote up
/**
 * 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);
}
 
Example 7
Project: CVScanner   File: CVProcessor.java   View source code 4 votes vote down vote up
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;
}
 
Example 8
Project: TinyPlanetMaker   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 9
Project: TinyPlanetMaker   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 10
Project: TinyPlanetMaker   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 11
Project: android-things-drawbot   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 12
Project: android-things-drawbot   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 13
Project: android-things-drawbot   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 14
Project: Android-Crop-Receipt   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 15
Project: Android-Crop-Receipt   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 16
Project: Android-Crop-Receipt   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 17
Project: AndroidCameraSudokuSolver   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 18
Project: AndroidCameraSudokuSolver   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 19
Project: AndroidCameraSudokuSolver   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 20
Project: OpenCV_Android_Plus   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 21
Project: OpenCV_Android_Plus   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 22
Project: OpenCV_Android_Plus   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 23
Project: MemeVision   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 24
Project: MemeVision   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 25
Project: MemeVision   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 26
Project: RectangleDetection   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 27
Project: RectangleDetection   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 28
Project: RectangleDetection   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 29
Project: cardboardAR-lib   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 30
Project: cardboardAR-lib   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 31
Project: cardboardAR-lib   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 32
Project: Aruco-Marker-Tracking-Android   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 33
Project: Aruco-Marker-Tracking-Android   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 34
Project: Aruco-Marker-Tracking-Android   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 35
Project: ColorDetector   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 36
Project: ColorDetector   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 37
Project: ColorDetector   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 38
Project: MIME   File: CvGBTrees.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 39
Project: MIME   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }
 
Example 40
Project: MIME   File: CvBoost.java   View source code 3 votes vote down vote up
/**
* <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;
   }