android端使用openCV实现车牌检测

task nativeLibsToJar(type: Jar, description: 'create a jar archive of the native libs') {
 destinationDir file("$buildDir/native-libs")
 baseName 'native-libs'
 from fileTree(dir: 'libs', include: '**/*.so')
 into 'lib/'
}
tasks.withType(JavaCompile) {
 compileTask -> compileTask.dependsOn(nativeLibsToJar)
}
 
dependencies {
 compile fileTree(include: ['*.jar'], dir: 'libs')
 compile fileTree(dir: "$buildDir/native-libs", include: 'native-libs.jar')
 ......
}

private void initOpenCV(){
 boolean result = OpenCVLoader.initDebug();
 if(result){
 Log.i(TAG, "initOpenCV success...");
 //初始化车牌检测器
 mPlateDetector = new ObjectDetector(this, R.raw.haarcascade_license_plate,
  3, new Scalar(255, 0, 0, 0));
 mObject = new MatOfRect();
 }else {
 Log.e(TAG, "initOpenCV fail...");
 }
 }

/**
 * 创建级联分类器
 * @param context 上下文
 * @param id 级联分类器ID
 * @return 级联分类器
 */
 private CascadeClassifier createDetector(Context context, int id) {
 CascadeClassifier javaDetector;
 InputStream is = null;
 FileOutputStream os = null;
 try {
 is = context.getResources().openRawResource(id);
 File cascadeDir = context.getDir(LICENSE_PLATE_MODEL, Context.MODE_PRIVATE);
 File cascadeFile = new File(cascadeDir, id + ".xml");
 os = new FileOutputStream(cascadeFile);
 
 byte[] buffer = new byte[4096];
 int bytesRead;
 while ((bytesRead = is.read(buffer)) != -1) {
 os.write(buffer, 0, bytesRead);
 }
 
 javaDetector = new CascadeClassifier(cascadeFile.getAbsolutePath());
 if (javaDetector.empty()) {
 javaDetector = null;
 }
 
 boolean delete = cascadeDir.delete();
 Log.i("ObjectDetector", "deleteResult=" + delete);
 return javaDetector;
 } catch (IOException e) {
 e.printStackTrace();
 return null;
 } finally {
 try {
 if (null != is) {
  is.close();
 }
 if (null != os) {
  os.close();
 }
 } catch (IOException e) {
 e.printStackTrace();
 }
 }
 }

/**
 * 执行车牌检测
 * @param bitmap bitmap
 * @return 车牌检测后的bitmap
 */
 private Bitmap doPlateDetecting(Bitmap bitmap){
 if(mPlateDetector != null && bitmap != null){
 Mat mRgba = new Mat();
 Mat mGray = new Mat();
 //bitmap转成map
 Utils.bitmapToMat(bitmap, mRgba);
 //rgba转成灰度图
 Imgproc.cvtColor(mRgba, mGray, Imgproc.COLOR_RGBA2GRAY);
 // 检测车牌
 Rect[] object = mPlateDetector.detectObject(mGray, mObject);
 if(object != null && object.length > 0){
 //检测到车牌区域
 Rect rect = object[0];
 //矩形标识
 Imgproc.rectangle(mRgba, rect.tl(), rect.br(), mPlateDetector.getRectColor(), 3);
 }
 //mat转回bitmap
 Utils.matToBitmap(mRgba, bitmap);
 }
 return bitmap;
 }

public Rect[] detectObject(Mat gray, MatOfRect object) {
 mCascadeClassifier.detectMultiScale(
 gray, // 要检查的灰度图像
 object, // 检测到的车牌
 1.1, // 表示在前后两次相继的扫描中，搜索窗口的比例系数
 mMinNeighbors, // 默认是3
 Objdetect.CASCADE_SCALE_IMAGE,
 getSize(gray, 80), // 检测目标最小值
 getSize(gray, 800)); // 检测目标最大值
 
 return object.toArray();
 }