4
此工作的目的是从视频场景中的现有图像中提取实时关键点processFrame由于匹配而执行不起作用 是我可以在实时使用FAST检测器实时识别Android上的物体
class Sample1View extends SampleViewBase {
public static final int VIEW_MODE_RGBA = 0;
public static final int VIEW_MODE_BLUE = 1;
public static final int VIEW_MODE_YELLOW = 2;
public static final int VIEW_MODE_DE = 3;
private Mat mYuv;
private Mat mRgba;
private Mat mGraySubmat;
private Mat mResult;
private Mat mIntermediateMat;
private Bitmap mBitmap;
private int mViewMode;
private Mat mColor;
private Mat mHsv;
TimingLogger timings;
private Mat img1;
private Mat descriptors;
private MatOfKeyPoint keypoints;
private FeatureDetector detector;
private DescriptorExtractor descriptor;
private DescriptorMatcher matcher;
private static final String TAG ="Sample::View";
public Sample1View(Context context) {
super(context);
mViewMode = VIEW_MODE_RGBA;
try {
img1=Utils.loadResource(getContext(), R.drawable.wings);
} catch (IOException e) {
// TODO Auto-generated catch block
Log.w("Activity::LoadResource","Unable to load resource R.drawable.wings");
e.printStackTrace();
}
descriptors = new Mat();
keypoints = new MatOfKeyPoint();
detector = FeatureDetector.create(FeatureDetector.FAST);
detector.detect(img1, keypoints);
descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);
descriptor.compute(img1, keypoints, descriptors);
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
}
@Override
protected void onPreviewStarted(int previewWidth, int previewHeight) {
Log.i(TAG, "preview Started");
synchronized (this) {
mYuv = new Mat(getFrameHeight() + getFrameHeight()/2, getFrameWidth(), CvType.CV_8UC1);
mGraySubmat = mYuv.submat(0, getFrameHeight(), 0, getFrameWidth());
mRgba = new Mat();
mIntermediateMat = new Mat();
mBitmap = Bitmap.createBitmap(previewWidth, previewHeight, Bitmap.Config.ARGB_8888);
mHsv = new Mat();
mColor = new Mat();
mResult = new Mat();
}
}
@Override
protected void onPreviewStopped() {
Log.i(TAG, "preview Stopped");
if(mBitmap != null) {
mBitmap.recycle();
}
synchronized (this) {
// Explicitly deallocate Mats
if (mYuv != null)
mYuv.release();
if (mRgba != null)
mRgba.release();
if (mGraySubmat != null)
mGraySubmat.release();
if (mIntermediateMat != null)
mIntermediateMat.release();
mYuv = null;
mRgba = null;
mGraySubmat = null;
mIntermediateMat = null;
if (mResult != null)
mResult.release();
if (mHsv != null)
mHsv.release();
if (mColor != null)
mColor.release();
mColor = null;
mResult = null;
mHsv = null;
}
}
// cvt_YUVtoRGBtoHSV显示对应的关键点在圆圈的形式在图像上：
@Override
protected Bitmap processFrame(byte[] data) {
mYuv.put(0, 0, data);
final int viewMode = mViewMode;
ColorDetection.cvt_YUVtoRGBtoHSV(mYuv,mGraySubmat);
MatOfKeyPoint mKeyPoints = new MatOfKeyPoint();
MatOfDMatch matches = new MatOfDMatch();
detector.detect(mGraySubmat, mKeyPoints);
descriptor.compute(mGraySubmat, mKeyPoints, mIntermediateMat);
matcher.match(mIntermediateMat, descriptors, matches);
mIntermediateMat2.create(resultSize, CvType.CV_8UC1);
Features2d.drawMatches(mGraySubmat, mKeyPoints, mGraySubmat, mKeyPoints, matches, mIntermediateMat2);
Imgproc.resize(mIntermediateMat2, mIntermediateMat2, mRgba.size());
Imgproc.cvtColor(mIntermediateMat2, mRgba, Imgproc.COLOR_RGBA2BGRA, 4);
break;
}
Bitmap bmp = mBitmap;
try {
Utils.matToBitmap(mRgba, bmp);
} catch(Exception e) {
Log.e("org.opencv.samples.*", "Utils.matToBitmap() throws an exception: " + e.getMessage());
bmp.recycle();
bmp = null;
}
return bmp;
}
public void setViewMode(int viewMode) {
mViewMode = viewMode;
}
}
，并在日志
CvException [org.opencv.core.CvException：/home/reports/ci/slave/50-SDK/opencv/modules/features2d/src/draw.cpp:207：error：（-215）i1> = 0 & & I1 <的static_cast（keypoints1.size（））在函数void CV :: drawMatches（常量CV ::垫&，常量性病::矢量&，常量CV ::垫&，常量性病::矢量&，常量性病： ：矢量&，CV ::垫&，const的标量&，const的标量&，常量的std ::矢量&，INT）