android - OpenCV:光流计算优化

Question

我开发了一个使用 OpenCV 光流检测头部姿势的应用程序。我想优化我的计算方法。因为目前它很慢。你能建议我更好、更快、更有效的方法吗？

目前我正在比较两帧之间每个特征点的 X 和 Y 坐标以确定光流方向。我想减少要检查的特征数量以找到光流方向。选择最能代表特征集的最小特征点。

这是我的代码:

    @Override
    public Mat onCameraFrame(Mat inputFrame) {
        up.value = 0;
        down.value = 0;
        left.value = 0;
        right.value = 0;
        pq.clear();

        // start the timing counter to put the frame rate on screen
        // and make sure the start time is up to date, do
        // a reset every 10 seconds
        if (lMilliStart == 0)
            lMilliStart = System.currentTimeMillis();

        if ((lMilliNow - lMilliStart) > 10000) {
            lMilliStart = System.currentTimeMillis();
            lFrameCount = 0;
        }

        inputFrame.copyTo(mRgba);
        sMatSize.width = mRgba.width();
        sMatSize.height = mRgba.height();

        switch (viewMode) {

        case VIEW_MODE_OPFLOW:

            if (mMOP2fptsPrev.rows() == 0) {

                // Log.d("Baz", "First time opflow");
                // first time through the loop so we need prev and this mats
                // plus prev points
                // get this mat
                Imgproc.cvtColor(mRgba, matOpFlowThis, Imgproc.COLOR_RGBA2GRAY);

                // copy that to prev mat
                matOpFlowThis.copyTo(matOpFlowPrev);

                // get prev corners
                Imgproc.goodFeaturesToTrack(matOpFlowPrev, MOPcorners, iGFFTMax, 0.05, 20);
                mMOP2fptsPrev.fromArray(MOPcorners.toArray());

                // get safe copy of this corners
                mMOP2fptsPrev.copyTo(mMOP2fptsSafe);
            } else {
                // Log.d("Baz", "Opflow");
                // we've been through before so
                // this mat is valid. Copy it to prev mat
                matOpFlowThis.copyTo(matOpFlowPrev);

                // get this mat
                Imgproc.cvtColor(mRgba, matOpFlowThis, Imgproc.COLOR_RGBA2GRAY);

                // get the corners for this mat
                Imgproc.goodFeaturesToTrack(matOpFlowThis, MOPcorners, iGFFTMax, 0.05, 20);
                mMOP2fptsThis.fromArray(MOPcorners.toArray());

                // retrieve the corners from the prev mat
                // (saves calculating them again)
                mMOP2fptsSafe.copyTo(mMOP2fptsPrev);

                // and save this corners for next time through
                mMOP2fptsThis.copyTo(mMOP2fptsSafe);
            }

            /*
             * Parameters: prevImg first 8-bit input image nextImg second input
             * image prevPts vector of 2D points for which the flow needs to be
             * found; point coordinates must be single-precision floating-point
             * numbers. nextPts output vector of 2D points (with
             * single-precision floating-point coordinates) containing the
             * calculated new positions of input features in the second image;
             * when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must
             * have the same size as in the input. status output status vector
             * (of unsigned chars); each element of the vector is set to 1 if
             * the flow for the corresponding features has been found,
             * otherwise, it is set to 0. err output vector of errors; each
             * element of the vector is set to an error for the corresponding
             * feature, type of the error measure can be set in flags parameter;
             * if the flow wasn't found then the error is not defined (use the
             * status parameter to find such cases).
             */
            Video.calcOpticalFlowPyrLK(matOpFlowPrev, matOpFlowThis, mMOP2fptsPrev, mMOP2fptsThis, mMOBStatus, mMOFerr);

            cornersPrev = mMOP2fptsPrev.toList();
            cornersThis = mMOP2fptsThis.toList();
            byteStatus = mMOBStatus.toList();

            y = byteStatus.size() - 1;

            for (x = 0; x < y; x++) {
                if (byteStatus.get(x) == 1) {
                    pt = cornersThis.get(x);
                    pt2 = cornersPrev.get(x);
                    double m = Math.abs(pt2.y - pt.y ) / Math.abs(pt2.x - pt.x);

                    double distance= Math.sqrt(Math.pow((pt.x - pt2.x),2) + Math.pow((pt.y - pt2.y),2));

                    if(distance < NOISE)
                        continue;

                    if (pt.x < pt2.x && pt2.y < pt.y)

                        if (m > 1)
                            up.value++;
                        else
                            right.value++;

                    else if (pt.x < pt2.x && pt2.y == pt.y)
                        right.value++;

                    else if (pt.x < pt2.x && pt2.y > pt.y)
                        if (m > 1)
                            down.value++;
                        else
                            right.value++;

                    else if (pt.x == pt2.x && pt2.y > pt.y)
                        down.value++;

                    else if (pt.x > pt2.x && pt2.y > pt.y)
                        if (m > 1)
                            down.value++;
                        else
                            left.value++;

                    else if (pt.x > pt2.x && pt2.y == pt.y)
                        left.value++;

                    else if (pt.x > pt2.x && pt2.y < pt.y)
                        if (m > 1)
                            up.value++;
                        else
                            left.value++;

                    else if (pt.x == pt2.x && pt2.y < pt.y)
                        up.value++;

                    Core.circle(mRgba, pt, 5, colorRed, iLineThickness - 1);
                    Core.line(mRgba, pt, pt2, colorRed, iLineThickness);
                }
            }//end of for

            Direction r1, r2, r3;

            if(up.value == 0 && left.value == 0 && right.value == 0 && down.value == 0) {
                string = String.format("Direction: ---");
                showTitle(string, 3, colorRed);

            }else{

                if (left.value < right.value) {
                    r1 = right;
                } else r1 = left;

                if (up.value < down.value) {
                    r2 = down;
                } else r2 = up;

                if (r1.value < r2.value) {
                    r3 = r2;
                } else r3 = r1;

                string = String.format("Direction: %s", r3.name);

                for (HeadGestureListener listener : listeners) {
                    listener.onHeadGestureDetected(r3.name);
                }

                showTitle(string, 3, colorRed);
            }

            //Log.d("Mukcay",pq.poll().name );
            // Log.d("Baz", "Opflow feature count: "+x);
            if (bDisplayTitle)
                showTitle("Optical Flow", 1, colorGreen);
                break;
        }

        // get the time now in every frame
        lMilliNow = System.currentTimeMillis();

        // update the frame counter
        lFrameCount++;

        if (bDisplayTitle) {
            string = String.format("FPS: %2.1f", (float) (lFrameCount * 1000) / (float) (lMilliNow - lMilliStart));
            showTitle(string, 2, colorGreen);
        }

        if (System.currentTimeMillis() - lMilliShotTime < 1500)
            showTitle(sShotText, 3, colorRed);

        return mRgba;
    }

Answer 1

您可以尝试使用此代码来检查问题大小的时间:

http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/

(代码链接在页面底部)

但这真的取决于你所说的快速是什么意思。对于不同方法的一些相对时间，您可以在此处查看:

http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=flow

干杯