近期公司要组织开发分享，可是自己还是新手真的不知道分享啥了，然后看了看前段时间研究过OpenCv，那么就分享他把。
openCv就不介绍了，说下人脸检測。事实上是通过openCv里边已经训练好的xml文件来进行的，我仅仅是在学习。
我測试中我写了俩个Demo。当中一个是通过Carame来通过摄像头来进行人脸检測看看效果图：
watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" width="507" height="301" alt="">
watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" width="507" height="301" alt="">
能够看出检測出来的面部有线框。
第一个Dmeo是通过Jni编程来实现的人脸检測，
（1）这是本地方法
package com.example.opencv.checkface2; import org.opencv.core.Mat;
import org.opencv.core.MatOfRect; public class DetectionBasedTracker
{
public DetectionBasedTracker(String cascadeName, int minFaceSize) {
mNativeObj = nativeCreateObject(cascadeName, minFaceSize);
} public void start() {
nativeStart(mNativeObj);
} public void stop() {
nativeStop(mNativeObj);
} public void setMinFaceSize(int size) {
nativeSetFaceSize(mNativeObj, size);
} public void detect(Mat imageGray, MatOfRect faces) {
nativeDetect(mNativeObj, imageGray.getNativeObjAddr(), faces.getNativeObjAddr());
} public void release() {
nativeDestroyObject(mNativeObj);
mNativeObj = 0;
} private long mNativeObj = 0; private static native long nativeCreateObject(String cascadeName, int minFaceSize);
private static native void nativeDestroyObject(long thiz);
private static native void nativeStart(long thiz);
private static native void nativeStop(long thiz);
private static native void nativeSetFaceSize(long thiz, int size);
private static native void nativeDetect(long thiz, long inputImage, long faces);
}
（2）通过jni调用人脸接触：
package com.example.opencv.checkface2; import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream; import org.opencv.android.BaseLoaderCallback;
import org.opencv.android.CameraBridgeViewBase;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewFrame;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewListener2;
import org.opencv.android.LoaderCallbackInterface;
import org.opencv.android.OpenCVLoader;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.objdetect.CascadeClassifier; import android.app.Activity;
import android.content.Context;
import android.os.Bundle;
import android.util.Log;
import android.view.Menu;
import android.view.MenuItem;
import android.view.WindowManager; public class MainActivity extends Activity implements CvCameraViewListener2 { private static final String TAG = "OCVSample::Activity";
private static final Scalar FACE_RECT_COLOR = new Scalar(0, 255, 0, 255);
public static final int JAVA_DETECTOR = 0;
public static final int NATIVE_DETECTOR = 1; private MenuItem mItemFace50;
private MenuItem mItemFace40;
private MenuItem mItemFace30;
private MenuItem mItemFace20;
private MenuItem mItemType; private Mat mRgba;
private Mat mGray;
private File mCascadeFile;
private CascadeClassifier mJavaDetector;
private DetectionBasedTracker mNativeDetector; private int mDetectorType = JAVA_DETECTOR;
private String[] mDetectorName; private float mRelativeFaceSize = 0.2f;
private int mAbsoluteFaceSize = 0; private CameraBridgeViewBase mOpenCvCameraView; private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
@Override
public void onManagerConnected(int status) {
switch (status) {
case LoaderCallbackInterface.SUCCESS:
{
Log.i(TAG, "OpenCV loaded successfully"); // Load native library after(!) OpenCV initialization
System.loadLibrary("detection_based_tracker"); try {
// load cascade file from application resources
InputStream is = getResources().openRawResource(R.raw.lbpcascade_frontalface);
File cascadeDir = getDir("cascade", Context.MODE_PRIVATE);
mCascadeFile = new File(cascadeDir, "lbpcascade_frontalface.xml");
FileOutputStream os = new FileOutputStream(mCascadeFile); byte[] buffer = new byte[4096];
int bytesRead;
while ((bytesRead = is.read(buffer)) != -1) {
os.write(buffer, 0, bytesRead);
}
is.close();
os.close(); mJavaDetector = new CascadeClassifier(mCascadeFile.getAbsolutePath());
if (mJavaDetector.empty()) {
Log.e(TAG, "Failed to load cascade classifier");
mJavaDetector = null;
} else
Log.i(TAG, "Loaded cascade classifier from " + mCascadeFile.getAbsolutePath()); mNativeDetector = new DetectionBasedTracker(mCascadeFile.getAbsolutePath(), 0); cascadeDir.delete(); } catch (IOException e) {
e.printStackTrace();
Log.e(TAG, "Failed to load cascade. Exception thrown: " + e);
} mOpenCvCameraView.enableView();
} break;
default:
{
super.onManagerConnected(status);
} break;
}
}
}; public MainActivity() {
mDetectorName = new String[2];
mDetectorName[JAVA_DETECTOR] = "Java";
mDetectorName[NATIVE_DETECTOR] = "Native (tracking)"; Log.i(TAG, "Instantiated new " + this.getClass());
} /** Called when the activity is first created. */
@Override
public void onCreate(Bundle savedInstanceState) {
Log.i(TAG, "called onCreate");
super.onCreate(savedInstanceState);
getWindow().addFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON); setContentView(R.layout.activity_main); mOpenCvCameraView = (CameraBridgeViewBase) findViewById(R.id.fd_activity_surface_view);
mOpenCvCameraView.setCvCameraViewListener(this);
} @Override
public void onPause()
{
super.onPause();
if (mOpenCvCameraView != null)
mOpenCvCameraView.disableView();
} @Override
public void onResume()
{
super.onResume();
OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_9, this, mLoaderCallback);
} public void onDestroy() {
super.onDestroy();
mOpenCvCameraView.disableView();
} public void onCameraViewStarted(int width, int height) {
mGray = new Mat();
mRgba = new Mat();
} public void onCameraViewStopped() {
mGray.release();
mRgba.release();
} public Mat onCameraFrame(CvCameraViewFrame inputFrame) { mRgba = inputFrame.rgba();
mGray = inputFrame.gray(); if (mAbsoluteFaceSize == 0) {
int height = mGray.rows();
if (Math.round(height * mRelativeFaceSize) > 0) {
mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
}
mNativeDetector.setMinFaceSize(mAbsoluteFaceSize);
} MatOfRect faces = new MatOfRect(); if (mDetectorType == JAVA_DETECTOR) {
if (mJavaDetector != null)
mJavaDetector.detectMultiScale(mGray, faces);
}
else if (mDetectorType == NATIVE_DETECTOR) {
if (mNativeDetector != null)
mNativeDetector.detect(mGray, faces);
}
else {
Log.e(TAG, "Detection method is not selected!");
} Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++)
Core.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3); return mRgba;
} @Override
public boolean onCreateOptionsMenu(Menu menu) {
Log.i(TAG, "called onCreateOptionsMenu");
mItemFace50 = menu.add("Face size 50%");
mItemFace40 = menu.add("Face size 40%");
mItemFace30 = menu.add("Face size 30%");
mItemFace20 = menu.add("Face size 20%");
mItemType = menu.add(mDetectorName[mDetectorType]);
return true;
} @Override
public boolean onOptionsItemSelected(MenuItem item) {
Log.i(TAG, "called onOptionsItemSelected; selected item: " + item);
if (item == mItemFace50)
setMinFaceSize(0.5f);
else if (item == mItemFace40)
setMinFaceSize(0.4f);
else if (item == mItemFace30)
setMinFaceSize(0.3f);
else if (item == mItemFace20)
setMinFaceSize(0.2f);
else if (item == mItemType) {
mDetectorType = (mDetectorType + 1) % mDetectorName.length;
item.setTitle(mDetectorName[mDetectorType]);
setDetectorType(mDetectorType);
}
return true;
} private void setMinFaceSize(float faceSize) {
mRelativeFaceSize = faceSize;
mAbsoluteFaceSize = 0;
} private void setDetectorType(int type) {
if (mDetectorType != type) {
mDetectorType = type; if (type == NATIVE_DETECTOR) {
Log.i(TAG, "Detection Based Tracker enabled");
mNativeDetector.start();
} else {
Log.i(TAG, "Cascade detector enabled");
mNativeDetector.stop();
}
}
}
}
（3）这是本地方法c++实现代码：
#include <DetectionBasedTracker_jni.h>
#include <opencv2/core/core.hpp>
#include <opencv2/contrib/detection_based_tracker.hpp> #include <string>
#include <vector> #include <android/log.h> #define LOG_TAG "FaceDetection/DetectionBasedTracker"
#define LOGD(...) ((void)__android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, __VA_ARGS__)) using namespace std;
using namespace cv; inline void vector_Rect_to_Mat(vector<Rect>& v_rect, Mat& mat)
{
mat = Mat(v_rect, true);
} JNIEXPORT jlong JNICALL Java_com_example_opencv_checkface2_DetectionBasedTracker_nativeCreateObject
(JNIEnv * jenv, jclass, jstring jFileName, jint faceSize)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeCreateObject enter");
const char* jnamestr = jenv->GetStringUTFChars(jFileName, NULL);//得到利用UTF-8编码的C/C++字符串，此字符串为存储分类器的路径。 string stdFileName(jnamestr);
jlong result = 0; try
{
DetectionBasedTracker::Parameters DetectorParams;
if (faceSize > 0)
DetectorParams.minObjectSize = faceSize;
result = (jlong)new DetectionBasedTracker(stdFileName, DetectorParams);
}
catch(cv::Exception& e)
{
LOGD("nativeCreateObject caught cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeCreateObject caught unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
return 0;
} LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeCreateObject exit");
return result;
} JNIEXPORT void JNICALL Java_com_example_opencv_checkface2_DetectionBasedTracker_nativeDestroyObject
(JNIEnv * jenv, jclass, jlong thiz)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeDestroyObject enter");
try
{
if(thiz != 0)
{
((DetectionBasedTracker*)thiz)->stop();
delete (DetectionBasedTracker*)thiz;
}
}
catch(cv::Exception& e)
{
LOGD("nativeestroyObject caught cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeDestroyObject caught unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeDestroyObject exit");
} JNIEXPORT void JNICALL Java_com_example_opencv_checkface2_DetectionBasedTracker_nativeStart
(JNIEnv * jenv, jclass, jlong thiz)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeStart enter");
try
{
((DetectionBasedTracker*)thiz)->run();
}
catch(cv::Exception& e)
{
LOGD("nativeStart caught cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeStart caught unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeStart exit");
} JNIEXPORT void JNICALL Java_com_example_opencv_checkface2_DetectionBasedTracker_nativeStop
(JNIEnv * jenv, jclass, jlong thiz)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeStop enter");
try
{
((DetectionBasedTracker*)thiz)->stop();
}
catch(cv::Exception& e)
{
LOGD("nativeStop caught cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeStop caught unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeStop exit");
} JNIEXPORT void JNICALL Java_com_example_opencv_checkface2_DetectionBasedTracker_nativeSetFaceSize
(JNIEnv * jenv, jclass, jlong thiz, jint faceSize)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeSetFaceSize enter");
try
{
if (faceSize > 0)
{
DetectionBasedTracker::Parameters DetectorParams = \
((DetectionBasedTracker*)thiz)->getParameters();
DetectorParams.minObjectSize = faceSize;
((DetectionBasedTracker*)thiz)->setParameters(DetectorParams);
}
}
catch(cv::Exception& e)
{
LOGD("nativeStop caught cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeSetFaceSize caught unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeSetFaceSize exit");
} JNIEXPORT void JNICALL Java_com_example_opencv_checkface2_DetectionBasedTracker_nativeDetect
(JNIEnv * jenv, jclass, jlong thiz, jlong imageGray, jlong faces)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeDetect enter");
try
{
vector<Rect> RectFaces;
((DetectionBasedTracker*)thiz)->process(*((Mat*)imageGray));
((DetectionBasedTracker*)thiz)->getObjects(RectFaces);
vector_Rect_to_Mat(RectFaces, *((Mat*)faces));
}
catch(cv::Exception& e)
{
LOGD("nativeCreateObject caught cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeDetect caught unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeDetect exit");
}
这样就能实现人脸检測了，后边我会把Demo上传。
第二个Demo事实上与第一个非常相似，我仅仅是通过载入了训练方法以后对选择的图片进行人脸检測。你让你更加清楚的看到效果：
watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" width="301" height="500" alt="">
能够看到这个也检測出来了。可是要注意的一点是如今我用的这个训练文件检測人脸的时候人必须是正的，不能到
package com.example.opencv_checkface_1; import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream; import org.opencv.android.BaseLoaderCallback;
import org.opencv.android.LoaderCallbackInterface;
import org.opencv.android.OpenCVLoader;
import org.opencv.android.Utils;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.objdetect.CascadeClassifier; import android.app.Activity;
import android.content.Context;
import android.content.Intent;
import android.database.Cursor;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.graphics.BitmapFactory.Options;
import android.os.Bundle;
import android.os.Environment;
import android.provider.MediaStore.Images.ImageColumns;
import android.util.Log;
import android.view.Menu;
import android.view.View;
import android.view.View.OnClickListener;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.TextView; public class MainActivity extends Activity {
private static final String TAG = "OpenCv脸部检測";
private ImageView mIv_img;
private TextView mTv_faction;
private Button mBtn_check;
private Button mBtn_choose;
private Bitmap mBitmap = null;
private String mPath = Environment.getExternalStorageDirectory().getAbsolutePath() + "/haarcascade_frontalface_alt2.xml";
private File mCascadeFile;
private CascadeClassifier mJavaDetector;
private CascadeClassifier cascadeClassifier;
private BaseLoaderCallback callback = new BaseLoaderCallback(this) {
public void onManagerConnected(int status) { switch (status) {
case LoaderCallbackInterface.SUCCESS: {
try {
// load cascade file from application resources
InputStream is = getResources().openRawResource(R.raw.lbpcascade_frontalface);
File cascadeDir = getDir("cascade", Context.MODE_PRIVATE);
mCascadeFile = new File(cascadeDir, "lbpcascade_frontalface.xml");
FileOutputStream os = new FileOutputStream(mCascadeFile); byte[] buffer = new byte[4096];
int bytesRead;
while ((bytesRead = is.read(buffer)) != -1) {
os.write(buffer, 0, bytesRead);
}
is.close();
os.close(); mJavaDetector = new CascadeClassifier(mCascadeFile.getAbsolutePath());
if (mJavaDetector.empty()) {
Log.e(TAG, "Failed to load cascade classifier");
mJavaDetector = null;
} else
Log.i(TAG, "Loaded cascade classifier from " + mCascadeFile.getAbsolutePath()); cascadeClassifier = new CascadeClassifier(mCascadeFile.getAbsolutePath()); cascadeDir.delete(); } catch (IOException e) {
e.printStackTrace();
Log.e(TAG, "Failed to load cascade. Exception thrown: " + e);
}
}
break;
default: {
super.onManagerConnected(status);
}
break;
}
};
};
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
init();
} public void init(){
Log.e(TAG, mPath);
File file = new File(mPath);
if (file.exists()) {
Log.e(TAG, "文件存在");
}else
Log.e(TAG, "文件不存在");
mIv_img = (ImageView)findViewById(R.id.iv_img);
mTv_faction = (TextView)findViewById(R.id.tv_faction);
mBtn_check = (Button)findViewById(R.id.btn_check);
mBtn_choose = (Button)findViewById(R.id.btn_choose);
mBtn_check.setOnClickListener(new OnClickListener() { @Override
public void onClick(View v) { Mat testmat = new Mat(); Utils.bitmapToMat(mBitmap, testmat);
MatOfRect facedetect = new MatOfRect(); cascadeClassifier.detectMultiScale(testmat, facedetect); int facenum = 0;
for (Rect rect : facedetect.toArray()) {
Core.rectangle(testmat, new Point(rect.x, rect.y), new Point(
rect.x + rect.width, rect.y + rect.height), new Scalar(
255, 0, 0));
++facenum;
}
Utils.matToBitmap(testmat, mBitmap);
mIv_img.setImageBitmap(mBitmap);
mTv_faction.setText("Facecount:" + facenum);
}
}); mBtn_choose.setOnClickListener(new OnClickListener() { @Override
public void onClick(View v) {
Intent intent = new Intent();
intent.setAction(Intent.ACTION_PICK);
intent.setType("image/*");
startActivityForResult(intent, 1);
}
}); } @Override
public boolean onCreateOptionsMenu(Menu menu) {
getMenuInflater().inflate(R.menu.main, menu);
return true;
} @Override
protected void onActivityResult(int requestCode, int resultCode, Intent data) {
// TODO Auto-generated method stub
super.onActivityResult(requestCode, resultCode, data); // the image picker callback
if (requestCode == 1) {
if (data != null) { Cursor cursor = getContentResolver().query(data.getData(),
null, null, null, null);
cursor.moveToFirst();
int idx = cursor.getColumnIndex(ImageColumns.DATA);
String fileSrc = cursor.getString(idx); Options options = new Options();
options.inJustDecodeBounds = true;
mBitmap = BitmapFactory.decodeFile(fileSrc, options); options.inSampleSize = Math.max(1, (int) Math.ceil(Math.max(
(double) options.outWidth / 1024f,
(double) options.outHeight / 1024f)));
options.inJustDecodeBounds = false;
mBitmap = BitmapFactory.decodeFile(fileSrc, options);
mTv_faction.setText("Clik Detect. ==>"); mIv_img.setImageBitmap(mBitmap);
mBtn_check.setVisibility(View.VISIBLE);
} else {
Log.d(TAG, "idButSelPic Photopicker canceled");
}
}
} @Override
protected void onResume() {
// TODO Auto-generated method stub
super.onResume();
// 通过OpenCV引擎服务载入并初始化OpenCV类库，所谓OpenCV引擎服务即是
// OpenCV_2.4.9.2_Manager_2.4_*.apk程序包。存在于OpenCV安装包的apk文件夹中
OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_9, this,
callback);
} }
对了，測试中使用的训练文件lbpcascade_frontalface.xml。你能够选择自己想用的
watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" alt="">
<?xml version="1.0"? >
<!--
number of positive samples 3000
number of negative samples 1500
-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier">
<stageType>BOOST</stageType>
<featureType>LBP</featureType>
<height>24</height>
<width>24</width>
<stageParams>
<boostType>GAB</boostType>
<minHitRate>0.9950000047683716</minHitRate>
<maxFalseAlarm>0.5000000000000000</maxFalseAlarm>
<weightTrimRate>0.9500000000000000</weightTrimRate>
<maxDepth>1</maxDepth>
<maxWeakCount>100</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>256</maxCatCount></featureParams>
<stageNum>20</stageNum>
<stages>
<!-- stage 0 -->
<_>
<maxWeakCount>3</maxWeakCount>
<stageThreshold>-0.7520892024040222</stageThreshold>
<weakClassifiers>
<!-- tree 0 -->
<_>
<internalNodes>
0 -1 46 -67130709 -21569 -1426120013 -1275125205 -21585
-16385 587145899 -24005</internalNodes>
<leafValues>
-0.6543210148811340 0.8888888955116272</leafValues></_>
<!-- tree 1 -->
<_>
<internalNodes>
0 -1 13 -163512766 -769593758 -10027009 -262145 -514457854
-193593353 -524289 -1</internalNodes>
<leafValues>
-0.7739216089248657 0.7278633713722229</leafValues></_>
<!-- tree 2 -->
<_>
<internalNodes>
0 -1 2 -363936790 -893203669 -1337948010 -136907894
1088782736 -134217726 -741544961 -1590337</internalNodes>
<leafValues>
-0.7068563103675842 0.6761534214019775</leafValues></_></weakClassifiers></_>
<!-- stage 1 -->
<_>
<maxWeakCount>4</maxWeakCount>
<stageThreshold>-0.4872078299522400</stageThreshold>
<weakClassifiers>
<!-- tree 0 -->
<_>
<internalNodes>
0 -1 84 2147483647 1946124287 -536870913 2147450879
738132490 1061101567 243204619 2147446655</internalNodes>
<leafValues>
-0.8083735704421997 0.7685696482658386</leafValues></_>
<!-- tree 1 -->
<_>
<internalNodes>
0 -1 21 2147483647 263176079 1879048191 254749487 1879048191
-134252545 -268435457 801111999</internalNodes>
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16 3 2 4</rect></_>
<_>
<rect>
16 6 1 1</rect></_>
<_>
<rect>
16 16 2 2</rect></_>
<_>
<rect>
17 1 2 2</rect></_>
<_>
<rect>
17 1 2 5</rect></_>
<_>
<rect>
17 12 2 2</rect></_>
<_>
<rect>
18 0 2 2</rect></_></features></cascade>
</opencv_storage>
Demo里边包括了openCv的代码，可是没有mangager的安装包，假设提示不能使用下载安装对应的apk就能使用：