Java Code Examples for org.opencv.core.Core#reduce()

The following examples show how to use org.opencv.core.Core#reduce() . These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may want to check out the right sidebar which shows the related API usage.
Example 1
/**
 * Calculate the meanFeatureVector for each StudentImageCollectionEvent using the extracted featureVectors
 */
public synchronized void trainClassifier(){
    Log.i(getClass().getName(), "trainClassifier");
    // Initiate training if a StudentImageCollectionEvent has not been trained yet but all corresponding StudentImage's features have been extracted
    List<StudentImageCollectionEvent> studentImageCollectionEvents = studentImageCollectionEventDao.queryBuilder().where(StudentImageCollectionEventDao.Properties.MeanFeatureVector.isNull()).list();
    Log.i(getClass().getName(), "Count of StudentImageCollectionEvents where MeanFeatureVector is null: " + studentImageCollectionEvents.size());
    if (studentImageCollectionEvents.size() > 0){
        for (StudentImageCollectionEvent studentImageCollectionEvent : studentImageCollectionEvents){
            Long studentImagesWithoutExtractedFeatures = studentImageDao.queryBuilder()
                    .where(StudentImageDao.Properties.StudentImageCollectionEventId.eq(studentImageCollectionEvent.getId()))
                    .where(StudentImageDao.Properties.StudentImageFeatureId.eq(0))
                    .count();
            // Skip calculation of meanFeatureVector if not all features have been extracted yet
            if (studentImagesWithoutExtractedFeatures == 0){
                Mat allFeatureVectors = new Mat();
                List<StudentImage> studentImages = studentImageCollectionEvent.getStudentImages();
                for (StudentImage studentImage : studentImages){
                    List<Float> featureVectorList = gson.fromJson(studentImage.getStudentImageFeature().getFeatureVector(), new TypeToken<List<Float>>(){}.getType());
                    Mat featureVector = Converters.vector_float_to_Mat(featureVectorList);
                    allFeatureVectors.push_back(featureVector.reshape(1, 1));
                }

                Mat meanFeatureVector = new Mat();
                Core.reduce(allFeatureVectors, meanFeatureVector, 0, Core.REDUCE_AVG);
                List<Float> meanFeatureVectorList = new ArrayList<>();
                Converters.Mat_to_vector_float(meanFeatureVector.reshape(1, meanFeatureVector.cols()), meanFeatureVectorList);
                String meanFeatureVectorString = gson.toJson(meanFeatureVectorList);
                studentImageCollectionEvent.setMeanFeatureVector(meanFeatureVectorString);

                Student student = createStudent(studentImages);

                studentImageCollectionEvent.setStudent(student);
                studentImageCollectionEventDao.update(studentImageCollectionEvent);
                Log.i(getClass().getName(), "StudentImageCollectionEvent with Id " + studentImageCollectionEvent.getId() + " has been trained in classifier");
            } else {
                Log.i(getClass().getName(), "trainClassifier: Calculation of meanFeatureVector has been skipped for the StudentImageCollectionEvent: " + studentImageCollectionEvent.getId() + " studentImagesWithoutExtractedFeatures: " + studentImagesWithoutExtractedFeatures);
            }
        }
    }
}
 
Example 2
/**
 * Calculate the meanFeatureVector for each StudentImageCollectionEvent using the extracted featureVectors
 */
public synchronized void trainClassifier(){
    Log.i(getClass().getName(), "trainClassifier");
    // Initiate training if a StudentImageCollectionEvent has not been trained yet but all corresponding StudentImage's features have been extracted
    List<StudentImageCollectionEvent> studentImageCollectionEvents = studentImageCollectionEventDao.queryBuilder().where(StudentImageCollectionEventDao.Properties.MeanFeatureVector.isNull()).list();
    Log.i(getClass().getName(), "Count of StudentImageCollectionEvents where MeanFeatureVector is null: " + studentImageCollectionEvents.size());
    if (studentImageCollectionEvents.size() > 0){
        for (StudentImageCollectionEvent studentImageCollectionEvent : studentImageCollectionEvents){
            Long studentImagesWithoutExtractedFeatures = studentImageDao.queryBuilder()
                    .where(StudentImageDao.Properties.StudentImageCollectionEventId.eq(studentImageCollectionEvent.getId()))
                    .where(StudentImageDao.Properties.StudentImageFeatureId.eq(0))
                    .count();
            // Skip calculation of meanFeatureVector if not all features have been extracted yet
            if (studentImagesWithoutExtractedFeatures == 0){
                Mat allFeatureVectors = new Mat();
                List<StudentImage> studentImages = studentImageCollectionEvent.getStudentImages();
                for (StudentImage studentImage : studentImages){
                    List<Float> featureVectorList = gson.fromJson(studentImage.getStudentImageFeature().getFeatureVector(), new TypeToken<List<Float>>(){}.getType());
                    Mat featureVector = Converters.vector_float_to_Mat(featureVectorList);
                    allFeatureVectors.push_back(featureVector.reshape(1, 1));
                }

                Mat meanFeatureVector = new Mat();
                Core.reduce(allFeatureVectors, meanFeatureVector, 0, Core.REDUCE_AVG);
                List<Float> meanFeatureVectorList = new ArrayList<>();
                Converters.Mat_to_vector_float(meanFeatureVector.reshape(1, meanFeatureVector.cols()), meanFeatureVectorList);
                String meanFeatureVectorString = gson.toJson(meanFeatureVectorList);
                studentImageCollectionEvent.setMeanFeatureVector(meanFeatureVectorString);

                Student student = createStudent(studentImages);

                studentImageCollectionEvent.setStudent(student);
                studentImageCollectionEventDao.update(studentImageCollectionEvent);
                Log.i(getClass().getName(), "StudentImageCollectionEvent with Id " + studentImageCollectionEvent.getId() + " has been trained in classifier");
            } else {
                Log.i(getClass().getName(), "trainClassifier: Calculation of meanFeatureVector has been skipped for the StudentImageCollectionEvent: " + studentImageCollectionEvent.getId() + " studentImagesWithoutExtractedFeatures: " + studentImagesWithoutExtractedFeatures);
            }
        }
    }
}
 
Example 3
private void computePsi(){
    Core.reduce(Gamma, Psi, 0, Core.REDUCE_AVG);
}
 
Example 4
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;
}