安卓光流与opencv

Question

我正在尝试使用openCV在android中实现光流http://code.google.com/p/android-opencv/.基本上我想建造这样的东西http://www.youtube.com/watch?v=P_Sjn67jIJY。无论如何，因为我是安卓开发的新手，有人能指导我在某个地方构建视频中的东西吗？我已经在android上安装了opencv端口，并使用eclipse成功构建了cvcamera示例。谢谢，Thanos

Answer 1

请参阅此Stanford OpenCV光流链接。除了由于1.x与2.x的C与C++API问题，调用可能略有不同之外，事情应该基本上以相同的方式工作。

只需编辑CVCamera示例，它应该很快。我在CVCamera开始工作的大约一个小时内制作了一个实时人脸检测应用程序。

Answer 2

访问http://opencv.willowgarage.com/wiki/Android2.3.0，OpenCV 2.3.0 Release Candidate对Android有很好的支持。里面有光流。使用它

Answer 3

虽然我也在尝试做同样的事情，但现在OpenCV4Android中似乎对光流有了更多的支持。看看org.opencv.video中的APIOpenCV Java文档我看到calcOpticalFlowPyrLK和calcOpticalFlowFarneback。我能够让calcOpticalFlowFarne恢复工作（尽管结果似乎不太好，可能需要调整参数）calcOpticalFlowPyrLK被证明是一个棘手的问题。我似乎无法将FeatureDetector类（MatOfKeyPoint）返回的关键点转换为calcOpticalFlowFarneback（MatOfPoint2f）其他线程所需的点

Answer 4

此代码将帮助您获取光学矢量。它会追踪他们

@Overridepublic Mat onCameraFrame（CvCameraViewFrame inputFrame）｛

    mRgba = inputFrame.rgba();
    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);
            Core.circle(mRgba, pt, 5, colorRed, iLineThickness - 1);
            Core.line(mRgba, pt, pt2, colorRed, iLineThickness);
            }
        }
return mRgba;
    }