Java Video.calcOpticalFlowPyrLK方法代码示例

import org.opencv.video.Video; //导入方法依赖的package包/类
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
        final int viewMode = mViewMode;
        switch (viewMode) {
            case VIEW_MODE_OPTICAL_FLOW:
                mGray = inputFrame.gray();
                if(features.toArray().length==0){
                    int rowStep = 50, colStep = 100;
                    int nRows = mGray.rows()/rowStep, nCols = mGray.cols()/colStep;

//                    Log.d(TAG, "\nRows: "+nRows+"\nCols: "+nCols+"\n");

                    Point points[] = new Point[nRows*nCols];
                    for(int i=0; i<nRows; i++){
                        for(int j=0; j<nCols; j++){
                            points[i*nCols+j]=new Point(j*colStep, i*rowStep);
//                            Log.d(TAG, "\nRow: "+i*rowStep+"\nCol: "+j*colStep+"\n: ");
                        }
                    }

                    features.fromArray(points);

                    prevFeatures.fromList(features.toList());
                    mPrevGray = mGray.clone();
                    break;
                }

                nextFeatures.fromArray(prevFeatures.toArray());
                Video.calcOpticalFlowPyrLK(mPrevGray, mGray, prevFeatures, nextFeatures, status, err);

                List<Point> prevList=features.toList(), nextList=nextFeatures.toList();
                Scalar color = new Scalar(255);

                for(int i = 0; i<prevList.size(); i++){
//                    Core.circle(mGray, prevList.get(i), 5, color);
                    Imgproc.line(mGray, prevList.get(i), nextList.get(i), color);
                }

                mPrevGray = mGray.clone();
                break;
            case VIEW_MODE_KLT_TRACKER:
                mGray = inputFrame.gray();

                if(features.toArray().length==0){
                    Imgproc.goodFeaturesToTrack(mGray, features, 10, 0.01, 10);
                    Log.d(TAG, features.toList().size()+"");
                    prevFeatures.fromList(features.toList());
                    mPrevGray = mGray.clone();
//                    prevFeatures.fromList(nextFeatures.toList());
                    break;
                }

//                OpticalFlow(mPrevGray.getNativeObjAddr(), mGray.getNativeObjAddr(), prevFeatures.getNativeObjAddr(), nextFeatures.getNativeObjAddr());
                Video.calcOpticalFlowPyrLK(mPrevGray, mGray, prevFeatures, nextFeatures, status, err);
                List<Point> drawFeature = nextFeatures.toList();
//                Log.d(TAG, drawFeature.size()+"");
                for(int i = 0; i<drawFeature.size(); i++){
                    Point p = drawFeature.get(i);
                    Imgproc.circle(mGray, p, 5, new Scalar(255));
                }
                mPrevGray = mGray.clone();
                prevFeatures.fromList(nextFeatures.toList());
                break;
            default: mViewMode = VIEW_MODE_KLT_TRACKER;
        }

        return mGray;
    }
 

import org.opencv.video.Video; //导入方法依赖的package包/类
/**
 * @return Pair of new, FILTERED, last and current POINTS, or null if it hasn't managed to track anything.
 */
Pair<Point[], Point[]> track(final Mat lastImg, final Mat currentImg, Point[] lastPoints){
	final int size = lastPoints.length;
	final MatOfPoint2f currentPointsMat = new MatOfPoint2f();
	final MatOfPoint2f pointsFBMat = new MatOfPoint2f();
	final MatOfByte statusMat = new MatOfByte();
	final MatOfFloat errSimilarityMat = new MatOfFloat();
	final MatOfByte statusFBMat = new MatOfByte();
	final MatOfFloat errSimilarityFBMat = new MatOfFloat();
	
	//Forward-Backward tracking
	Video.calcOpticalFlowPyrLK(lastImg, currentImg, new MatOfPoint2f(lastPoints), currentPointsMat, 
			statusMat, errSimilarityMat, WINDOW_SIZE, MAX_LEVEL, termCriteria, 0, LAMBDA);
	Video.calcOpticalFlowPyrLK(currentImg, lastImg, currentPointsMat, pointsFBMat, 
			statusFBMat, errSimilarityFBMat, WINDOW_SIZE, MAX_LEVEL, termCriteria, 0, LAMBDA);
	
	final byte[] status = statusMat.toArray();
	float[] errSimilarity = new float[lastPoints.length]; 
	//final byte[] statusFB = statusFBMat.toArray();
	final float[] errSimilarityFB = errSimilarityFBMat.toArray();	
	
	// compute the real FB error (relative to LAST points not the current ones...
	final Point[] pointsFB = pointsFBMat.toArray();
	for(int i = 0; i < size; i++){
		errSimilarityFB[i] = Util.norm(pointsFB[i], lastPoints[i]);
	}
	
	final Point[] currPoints = currentPointsMat.toArray();
	// compute real similarity error
	errSimilarity = normCrossCorrelation(lastImg, currentImg, lastPoints, currPoints, status);
	
	
	//TODO  errSimilarityFB has problem != from C++
	// filter out points with fwd-back error > the median AND points with similarity error > median
	return filterPts(lastPoints, currPoints, errSimilarity, errSimilarityFB, status);
}