我开发了一个使用OpenCV光流检测头部手势的应用程序。我想优化我的计算方法。因为目前它很慢。你能建议我更好，快速，有效地做到这一点吗？
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。我想减少要检查的功能数量以找到光流方向。选择最能代表功能集的最小特征点。
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;
}
答案 0 :(得分：0)
您可以尝试使用此代码检查问题大小的时间：
http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/
（代码链接位于页面底部）
但这真的取决于你的意思。对于不同方法的某些相关时间，您可以在此处查看：
http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=flow
欢呼声