Example usage for org.opencv.utils Converters vector_Point2f_to_Mat

List of usage examples for org.opencv.utils Converters vector_Point2f_to_Mat

Introduction

In this page you can find the example usage for org.opencv.utils Converters vector_Point2f_to_Mat.

Prototype

public static Mat vector_Point2f_to_Mat(List<Point> pts) 

Source Link

Usage

From source file:com.github.mbillingr.correlationcheck.ImageProcessor.java

License:Open Source License

void setPerspectiveCorrection(List<Point> refpoints) {
    List<Point> pts_in = new ArrayList<>();
    for (Point p : refpoints) {
        pts_in.add(new Point(p.x * raw_width, (1 - p.y) * raw_height));
    }/*w ww.  ja  v a2  s . c o m*/
    Mat mat_src = Converters.vector_Point2f_to_Mat(pts_in);

    List<Point> pts_out = new ArrayList<>();
    pts_out.add(new Point(0, work_height));
    pts_out.add(new Point(0, 0));
    pts_out.add(new Point(work_width, work_height));
    pts_out.add(new Point(work_width, 0));
    Mat mat_dst = Converters.vector_Point2f_to_Mat(pts_out);

    perspective_transform = Imgproc.getPerspectiveTransform(mat_src, mat_dst);
}

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 ww  w.  j  a  v a2 s. c  o m
        } 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:karthik.Barcode.CandidateMatrixBarcode.java

License:Open Source License

CandidateResult NormalizeCandidateRegion(double angle) {
    /* candidateRegion is the RotatedRect which contains a candidate region for the barcode
     // angle is the rotation angle or USE_ROTATED_RECT_ANGLE for this function to 
     // estimate rotation angle from the rect parameter
     // returns Mat containing cropped area(region of interest) with just the barcode 
     // The barcode region is from the *original* image, not the scaled image
     // the cropped area is also rotated as necessary to be horizontal or vertical rather than skewed        
     // Some parts of this function are from http://felix.abecassis.me/2011/10/opencv-rotation-deskewing/
     // and http://stackoverflow.com/questions/22041699/rotate-an-image-without-cropping-in-opencv-in-c
     *//*from  w w w .j  av a2  s  . c o  m*/

    double rotation_angle;
    CandidateResult result = new CandidateResult();

    // scale candidate region back up to original size to return cropped part from *original* image 
    // need the 1.0 there to force floating-point arithmetic from int values
    double scale_factor = img_details.src_original.rows() / (1.0 * img_details.src_grayscale.rows());

    // expand the region found - this helps capture the entire code including the border zone
    candidateRegion.size.width += 2 * params.RECT_WIDTH;
    candidateRegion.size.height += 2 * params.RECT_HEIGHT;

    // calculate location of rectangle in original image and its corner points
    RotatedRect scaledRegion = new RotatedRect(candidateRegion.center, candidateRegion.size,
            candidateRegion.angle);
    scaledRegion.center.x = scaledRegion.center.x * scale_factor;
    scaledRegion.center.y = scaledRegion.center.y * scale_factor;
    scaledRegion.size.height *= scale_factor;
    scaledRegion.size.width *= scale_factor;

    scaledRegion.points(img_details.scaledCorners);
    // lets get the coordinates of the ROI in the original image and save it

    result.ROI_coords = Arrays.copyOf(img_details.scaledCorners, 4);

    // get the bounding rectangle of the ROI by sorting its corner points
    // we do it manually because RotatedRect can generate corner points outside the Mat area
    Arrays.sort(img_details.scaledCorners, CandidateBarcode.get_x_comparator());
    int leftCol = (int) img_details.scaledCorners[0].x;
    int rightCol = (int) img_details.scaledCorners[3].x;
    leftCol = (leftCol < 0) ? 0 : leftCol;
    rightCol = (rightCol > img_details.src_original.cols() - 1) ? img_details.src_original.cols() - 1
            : rightCol;

    Arrays.sort(img_details.scaledCorners, CandidateBarcode.get_y_comparator());
    int topRow = (int) img_details.scaledCorners[0].y;
    int bottomRow = (int) img_details.scaledCorners[3].y;
    topRow = (topRow < 0) ? 0 : topRow;
    bottomRow = (bottomRow > img_details.src_original.rows() - 1) ? img_details.src_original.rows() - 1
            : bottomRow;

    Mat ROI_region = img_details.src_original.submat(topRow, bottomRow, leftCol, rightCol);

    // create a container that is a square with side = diagonal of ROI.
    // this is large enough to accommodate the ROI region with rotation without cropping it

    int orig_rows = bottomRow - topRow;
    int orig_cols = rightCol - leftCol;
    int diagonal = (int) Math.sqrt(orig_rows * orig_rows + orig_cols * orig_cols);

    int newWidth = diagonal + 1;
    int newHeight = diagonal + 1;

    int offsetX = (newWidth - orig_cols) / 2;
    int offsetY = (newHeight - orig_rows) / 2;

    Mat enlarged_ROI_container = new Mat(newWidth, newHeight, img_details.src_original.type());
    enlarged_ROI_container.setTo(ZERO_SCALAR);

    // copy ROI to centre of container and rotate it
    ROI_region.copyTo(enlarged_ROI_container.rowRange(offsetY, offsetY + orig_rows).colRange(offsetX,
            offsetX + orig_cols));
    Point enlarged_ROI_container_centre = new Point(enlarged_ROI_container.rows() / 2.0,
            enlarged_ROI_container.cols() / 2.0);
    Mat rotated = Mat.zeros(enlarged_ROI_container.size(), enlarged_ROI_container.type());

    if (angle == Barcode.USE_ROTATED_RECT_ANGLE)
        rotation_angle = estimate_barcode_orientation();
    else
        rotation_angle = angle;

    // perform the affine transformation
    img_details.rotation_matrix = Imgproc.getRotationMatrix2D(enlarged_ROI_container_centre, rotation_angle,
            1.0);
    img_details.rotation_matrix.convertTo(img_details.rotation_matrix, CvType.CV_32F); // convert type so matrix multip. works properly

    img_details.newCornerCoord.setTo(ZERO_SCALAR);

    // convert scaledCorners to contain locations of corners in enlarged_ROI_container Mat
    img_details.scaledCorners[0] = new Point(offsetX, offsetY);
    img_details.scaledCorners[1] = new Point(offsetX, offsetY + orig_rows);
    img_details.scaledCorners[2] = new Point(offsetX + orig_cols, offsetY);
    img_details.scaledCorners[3] = new Point(offsetX + orig_cols, offsetY + orig_rows);
    // calculate the new location for each corner point of the rectangle ROI after rotation
    for (int r = 0; r < 4; r++) {
        img_details.coord.put(0, 0, img_details.scaledCorners[r].x);
        img_details.coord.put(1, 0, img_details.scaledCorners[r].y);
        Core.gemm(img_details.rotation_matrix, img_details.coord, 1, img_details.delta, 0,
                img_details.newCornerCoord);
        updatePoint(img_details.newCornerPoints.get(r), img_details.newCornerCoord.get(0, 0)[0],
                img_details.newCornerCoord.get(1, 0)[0]);
    }
    rotated.setTo(ZERO_SCALAR);
    Imgproc.warpAffine(enlarged_ROI_container, rotated, img_details.rotation_matrix,
            enlarged_ROI_container.size(), Imgproc.INTER_CUBIC);
    // sort rectangles points in order by first sorting all 4 points based on x
    // we then sort the first two based on y and then the next two based on y
    // this leaves the array in order top-left, bottom-left, top-right, bottom-right
    Collections.sort(img_details.newCornerPoints, CandidateBarcode.get_x_comparator());
    Collections.sort(img_details.newCornerPoints.subList(0, 2), CandidateBarcode.get_y_comparator());
    Collections.sort(img_details.newCornerPoints.subList(2, 4), CandidateBarcode.get_y_comparator());

    // calc height and width of rectangular region

    double height = length(img_details.newCornerPoints.get(1), img_details.newCornerPoints.get(0));
    double width = length(img_details.newCornerPoints.get(2), img_details.newCornerPoints.get(0));

    // create destination points for warpPerspective to map to
    updatePoint(img_details.transformedPoints.get(0), 0, 0);
    updatePoint(img_details.transformedPoints.get(1), 0, height);
    updatePoint(img_details.transformedPoints.get(2), width, 0);
    updatePoint(img_details.transformedPoints.get(3), width, height);

    Mat perspectiveTransform = Imgproc.getPerspectiveTransform(
            Converters.vector_Point2f_to_Mat(img_details.newCornerPoints),
            Converters.vector_Point2f_to_Mat(img_details.transformedPoints));
    Mat perspectiveOut = Mat.zeros((int) height + 2, (int) width + 2, CvType.CV_32F);
    Imgproc.warpPerspective(rotated, perspectiveOut, perspectiveTransform, perspectiveOut.size(),
            Imgproc.INTER_CUBIC);

    result.ROI = perspectiveOut;
    return result;
}

From source file:karthiknr.TextID.ProcessAsyncActivity.java

License:Apache License

public Mat findWarpedMat(Mat imgSource) {

    //convert the image to black and white does (8 bit)
    Imgproc.Canny(imgSource, imgSource, 50, 50);

    //apply gaussian blur to smoothen lines of dots
    Imgproc.GaussianBlur(imgSource, imgSource, new org.opencv.core.Size(5, 5), 5);

    //find the contours
    List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
    Imgproc.findContours(imgSource, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);

    double maxArea = -1;
    int maxAreaIdx = -1;
    Log.d("size", Integer.toString(contours.size()));
    MatOfPoint temp_contour = contours.get(0); //the largest is at the index 0 for starting point
    MatOfPoint2f approxCurve = new MatOfPoint2f();
    MatOfPoint largest_contour = contours.get(0);
    //largest_contour.ge
    List<MatOfPoint> largest_contours = new ArrayList<MatOfPoint>();
    //Imgproc.drawContours(imgSource,contours, -1, new Scalar(0, 255, 0), 1);

    for (int idx = 0; idx < contours.size(); idx++) {
        temp_contour = contours.get(idx);
        double contourarea = Imgproc.contourArea(temp_contour);
        //compare this contour to the previous largest contour found
        if (contourarea > maxArea) {
            //check if this contour is a square
            MatOfPoint2f new_mat = new MatOfPoint2f(temp_contour.toArray());
            int contourSize = (int) temp_contour.total();
            MatOfPoint2f approxCurve_temp = new MatOfPoint2f();
            Imgproc.approxPolyDP(new_mat, approxCurve_temp, contourSize * 0.05, true);
            if (approxCurve_temp.total() == 4) {
                maxArea = contourarea;/*from w  ww. j a v  a 2s .c o  m*/
                maxAreaIdx = idx;
                approxCurve = approxCurve_temp;
                largest_contour = temp_contour;
            }
        }
    }

    Imgproc.cvtColor(imgSource, imgSource, Imgproc.COLOR_BayerBG2RGB);
    //Mat sourceImage =Imgcodecs.imread(Environment.getExternalStorageDirectory().getAbsolutePath()+"/TextID/"+"/oocr.png");
    double[] temp_double;
    temp_double = approxCurve.get(0, 0);
    Point p1 = new Point(temp_double[0], temp_double[1]);
    //Core.circle(imgSource,p1,55,new Scalar(0,0,255));
    //Imgproc.warpAffine(sourceImage, dummy, rotImage,sourceImage.size());
    temp_double = approxCurve.get(1, 0);
    Point p2 = new Point(temp_double[0], temp_double[1]);
    // Core.circle(imgSource,p2,150,new Scalar(255,255,255));
    temp_double = approxCurve.get(2, 0);
    Point p3 = new Point(temp_double[0], temp_double[1]);
    //Core.circle(imgSource,p3,200,new Scalar(255,0,0));
    temp_double = approxCurve.get(3, 0);
    Point p4 = new Point(temp_double[0], temp_double[1]);
    // Core.circle(imgSource,p4,100,new Scalar(0,0,255));
    List<Point> source = new ArrayList<Point>();
    source.add(p1);
    source.add(p2);
    source.add(p3);
    source.add(p4);
    Mat startM = Converters.vector_Point2f_to_Mat(source);
    return startM;
}

From source file:karthiknr.TextID.ProcessAsyncActivity.java

License:Apache License

public Mat warpImage(Mat inputMat, Mat startM) {
    int resultWidth = 1000;
    int resultHeight = 1000;

    Mat outputMat = new Mat(resultWidth, resultHeight, CvType.CV_8UC4);

    Point ocvPOut1 = new Point(0, 0);
    Point ocvPOut2 = new Point(0, resultHeight);
    Point ocvPOut3 = new Point(resultWidth, resultHeight);
    Point ocvPOut4 = new Point(resultWidth, 0);
    List<Point> dest = new ArrayList<Point>();
    dest.add(ocvPOut1);/*  w w w  .  j  a v  a2  s  . co  m*/
    dest.add(ocvPOut2);
    dest.add(ocvPOut3);
    dest.add(ocvPOut4);
    Mat endM = Converters.vector_Point2f_to_Mat(dest);

    Mat perspectiveTransform = Imgproc.getPerspectiveTransform(startM, endM);

    Imgproc.warpPerspective(inputMat, outputMat, perspectiveTransform, new Size(resultWidth, resultHeight),
            Imgproc.INTER_CUBIC);

    return outputMat;
}

From source file:mineshcvit.opendocscanner.CropImage.java

License:Apache License

private void onSaveClicked() throws Exception {
    // TODO this code needs to change to use the decode/crop/encode single
    // step api so that we don't require that the whole (possibly large)
    // bitmap doesn't have to be read into memory
    if (mSaving)//from   w ww .  j  a va  2 s .c  om
        return;

    if (mCrop == null) {

        return;
    }

    mSaving = true;

    //   Rect r = mCrop.getCropRect();
    final float[] trapezoid = mCrop.getTrapezoid();
    Log.w("myApp", "onsaveclicekd, trap[0] is " + trapezoid[0]);
    Log.w("myApp", "onsaveclicekd, trap[1] is " + trapezoid[1]);

    Log.w("myApp", "onsaveclicekd, trap[2] is " + trapezoid[2]);

    Log.w("myApp", "onsaveclicekd, trap[3] is " + trapezoid[3]);

    Log.w("myApp", "onsaveclicekd, trap[4] is " + trapezoid[4]);

    Log.w("myApp", "onsaveclicekd, trap[5] is " + trapezoid[5]);

    Log.w("myApp", "onsaveclicekd, trap[6] is " + trapezoid[6]);
    Log.w("myApp", "onsaveclicekd, trap[7] is " + trapezoid[7]);

    /// refer this for perspective correction

    //minesh:
    //find the bounding rectangle of the quadilateral
    //new image to whiche perspective corrected matrix to be plotted will be made in this size
    final RectF perspectiveCorrectedBoundingRect = new RectF(mCrop.getPerspectiveCorrectedBoundingRect());

    //dimension of the new image
    int result_width = (int) perspectiveCorrectedBoundingRect.width();
    int result_height = (int) perspectiveCorrectedBoundingRect.height();

    Log.w("myApp", "bounding rect width is " + result_width);
    Log.w("myApp", "bounding rect height " + result_height);

    Mat inputMat = new Mat(mBitmap.getHeight(), mBitmap.getHeight(), CvType.CV_8UC4);
    Utils.bitmapToMat(mBitmap, inputMat);
    final Mat outputMat = new Mat(result_width, result_height, CvType.CV_8UC4);

    //the 4 points of the quad,
    Point ocvPIn1 = new Point((int) trapezoid[0], (int) trapezoid[1]);//left top
    Point ocvPIn2 = new Point((int) trapezoid[6], (int) trapezoid[7]);//left bottom
    Point ocvPIn3 = new Point((int) trapezoid[4], (int) trapezoid[5]); //bottom right
    Point ocvPIn4 = new Point((int) trapezoid[2], (int) trapezoid[3]);//right top

    List<Point> source = new ArrayList<Point>();
    source.add(ocvPIn1);
    source.add(ocvPIn2);
    source.add(ocvPIn3);
    source.add(ocvPIn4);

    Mat startM = Converters.vector_Point2f_to_Mat(source);

    //points in the destination imafge
    Point ocvPOut1 = new Point(0, 0);// lfet top
    Point ocvPOut2 = new Point(0, result_height);//left bottom
    Point ocvPOut3 = new Point(result_width, result_height); //bottom right
    Point ocvPOut4 = new Point(result_width, 0);//right top

    List<Point> dest = new ArrayList<Point>();
    dest.add(ocvPOut1);
    dest.add(ocvPOut2);
    dest.add(ocvPOut3);
    dest.add(ocvPOut4);
    Mat endM = Converters.vector_Point2f_to_Mat(dest);

    Mat perspectiveTransform = Imgproc.getPerspectiveTransform(startM, endM);
    Imgproc.warpPerspective(inputMat, outputMat, perspectiveTransform, new Size(result_width, result_height),
            Imgproc.INTER_CUBIC);

    //
    Imgcodecs.imwrite(mImagePath, outputMat);

    //     }
    //  }, mHandler);
    //  }

    Intent intent = new Intent();
    setResult(RESULT_OK, intent);
    finish();
}