OpenCv 人脸检测的学习

非花非雾 2016-01-22

最近公司要组织开发分享,但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,说下人脸检测,其实是通过openCv里边已经训练好的xml文件来进行的,我只是在学习。



我测试中我写了俩个Demo,其中一个是通过Carame来通过摄像头来进行人脸检测看看效果图:


可以看出检测出来的面部有线框。


第一个Dmeo是通过Jni编程来实现的人脸检测,

(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测了,后边我会把Demo上传。


第二个Demo其实与第一个很相似,我只是通过加载了训练方法以后对选择的图片进行人脸检测,你让你更加清楚的看到效果:

可以看到这个也检测出来了,但是要注意的一点是现在我用的这个训练文件检测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脸部检测";
	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引擎服务加载并初始化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目录中  
	        OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_9, this,  
	                callback);  
	}

}

对了,测试中使用的训练文件lbpcascade_frontalface.xml,你可以选择自己想用的



<?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>
          <leafValues>
            -0.7698410153388977 0.6592915654182434</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 106 -98110272 1610939566 -285484400 -850010381
            -189334372 -1671954433 -571026695 -262145</internalNodes>
          <leafValues>
            -0.7506558895111084 0.5444605946540833</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 48 -798690576 -131075 1095771153 -237144073 -65569 -1
            -216727745 -69206049</internalNodes>
          <leafValues>
            -0.7775990366935730 0.5465461611747742</leafValues></_></weakClassifiers></_>
    <!-- stage 2 -->
    <_>
      <maxWeakCount>4</maxWeakCount>
      <stageThreshold>-1.1592328548431396</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 47 -21585 -20549 -100818262 -738254174 -20561 -36865
            -151016790 -134238549</internalNodes>
          <leafValues>
            -0.5601882934570313 0.7743113040924072</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 12 -286003217 183435247 -268994614 -421330945
            -402686081 1090387966 -286785545 -402653185</internalNodes>
          <leafValues>
            -0.6124526262283325 0.6978127956390381</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 26 -50347012 970882927 -50463492 -1253377 -134218251
            -50364513 -33619992 -172490753</internalNodes>
          <leafValues>
            -0.6114496588706970 0.6537628173828125</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 8 -273 -135266321 1877977738 -2088243418 -134217987
            2146926575 -18910642 1095231247</internalNodes>
          <leafValues>
            -0.6854077577590942 0.5403239130973816</leafValues></_></weakClassifiers></_>
    <!-- stage 3 -->
    <_>
      <maxWeakCount>5</maxWeakCount>
      <stageThreshold>-0.7562355995178223</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 96 -1273 1870659519 -20971602 -67633153 -134250731
            2004875127 -250 -150995969</internalNodes>
          <leafValues>
            -0.4051094949245453 0.7584033608436585</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 33 -868162224 -76810262 -4262145 -257 1465211989
            -268959873 -2656269 -524289</internalNodes>
          <leafValues>
            -0.7388162612915039 0.5340843200683594</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 57 -12817 -49 -541103378 -152950 -38993 -20481 -1153876
            -72478976</internalNodes>
          <leafValues>
            -0.6582943797111511 0.5339496731758118</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 125 -269484161 -452984961 -319816180 -1594032130 -2111
            -990117891 -488975296 -520947741</internalNodes>
          <leafValues>
            -0.5981323719024658 0.5323504805564880</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 53 557787431 670265215 -1342193665 -1075892225
            1998528318 1056964607 -33570977 -1</internalNodes>
          <leafValues>
            -0.6498787999153137 0.4913350641727448</leafValues></_></weakClassifiers></_>
    <!-- stage 4 -->
    <_>
      <maxWeakCount>5</maxWeakCount>
      <stageThreshold>-0.8085358142852783</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 60 -536873708 880195381 -16842788 -20971521 -176687276
            -168427659 -16777260 -33554626</internalNodes>
          <leafValues>
            -0.5278195738792419 0.6946372389793396</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 7 -1 -62981529 -1090591130 805330978 -8388827 -41945787
            -39577 -531118985</internalNodes>
          <leafValues>
            -0.5206505060195923 0.6329920291900635</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 98 -725287348 1347747543 -852489 -16809993 1489881036
            -167903241 -1 -1</internalNodes>
          <leafValues>
            -0.7516061067581177 0.4232024252414703</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 44 -32777 1006582562 -65 935312171 -8388609 -1078198273
            -1 733886267</internalNodes>
          <leafValues>
            -0.7639313936233521 0.4123568832874298</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 24 -85474705 2138828511 -1036436754 817625855
            1123369029 -58796809 -1013468481 -194513409</internalNodes>
          <leafValues>
            -0.5123769044876099 0.5791834592819214</leafValues></_></weakClassifiers></_>
    <!-- stage 5 -->
    <_>
      <maxWeakCount>5</maxWeakCount>
      <stageThreshold>-0.5549971461296082</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 42 -17409 -20481 -268457797 -134239493 -17473 -1 -21829
            -21846</internalNodes>
          <leafValues>
            -0.3763174116611481 0.7298233509063721</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 6 -805310737 -2098262358 -269504725 682502698
            2147483519 1740574719 -1090519233 -268472385</internalNodes>
          <leafValues>
            -0.5352765917778015 0.5659480094909668</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 61 -67109678 -6145 -8 -87884584 -20481 -1073762305
            -50856216 -16849696</internalNodes>
          <leafValues>
            -0.5678374171257019 0.4961479902267456</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 123 -138428633 1002418167 -1359008245 -1908670465
            -1346685918 910098423 -1359010520 -1346371657</internalNodes>
          <leafValues>
            -0.5706262588500977 0.4572288393974304</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 9 -89138513 -4196353 1256531674 -1330665426 1216308261
            -36190633 33498198 -151796633</internalNodes>
          <leafValues>
            -0.5344601869583130 0.4672054052352905</leafValues></_></weakClassifiers></_>
    <!-- stage 6 -->
    <_>
      <maxWeakCount>5</maxWeakCount>
      <stageThreshold>-0.8776460289955139</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 105 1073769576 206601725 -34013449 -33554433 -789514004
            -101384321 -690225153 -264193</internalNodes>
          <leafValues>
            -0.7700348496437073 0.5943940877914429</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 30 -1432340997 -823623681 -49153 -34291724 -269484035
            -1342767105 -1078198273 -1277955</internalNodes>
          <leafValues>
            -0.5043668746948242 0.6151274442672730</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 35 -1067385040 -195758209 -436748425 -134217731
            -50855988 -129 -1 -1</internalNodes>
          <leafValues>
            -0.6808040738105774 0.4667325913906097</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 119 832534325 -34111555 -26050561 -423659521 -268468364
            2105014143 -2114244 -17367185</internalNodes>
          <leafValues>
            -0.4927591383457184 0.5401885509490967</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 82 -1089439888 -1080524865 2143059967 -1114121
            -1140949004 -3 -2361356 -739516</internalNodes>
          <leafValues>
            -0.6445107460021973 0.4227822124958038</leafValues></_></weakClassifiers></_>
    <!-- stage 7 -->
    <_>
      <maxWeakCount>6</maxWeakCount>
      <stageThreshold>-1.1139287948608398</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 52 -1074071553 -1074003969 -1 -1280135430 -5324817 -1
            -335548482 582134442</internalNodes>
          <leafValues>
            -0.5307556986808777 0.6258179545402527</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 99 -706937396 -705364068 -540016724 -570495027
            -570630659 -587857963 -33628164 -35848193</internalNodes>
          <leafValues>
            -0.5227634310722351 0.5049746036529541</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 18 -2035630093 42119158 -268503053 -1671444 261017599
            1325432815 1954394111 -805306449</internalNodes>
          <leafValues>
            -0.4983572661876679 0.5106441378593445</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 111 -282529488 -1558073088 1426018736 -170526448
            -546832487 -5113037 -34243375 -570427929</internalNodes>
          <leafValues>
            -0.4990860521793366 0.5060507059097290</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 92 1016332500 -606301707 915094269 -1080086049
            -1837027144 -1361600280 2147318747 1067975613</internalNodes>
          <leafValues>
            -0.5695009231567383 0.4460467398166657</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 51 -656420166 -15413034 -141599534 -603435836
            1505950458 -787556946 -79823438 -1326199134</internalNodes>
          <leafValues>
            -0.6590405106544495 0.3616424500942230</leafValues></_></weakClassifiers></_>
    <!-- stage 8 -->
    <_>
      <maxWeakCount>7</maxWeakCount>
      <stageThreshold>-0.8243625760078430</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 28 -901591776 -201916417 -262 -67371009 -143312112
            -524289 -41943178 -1</internalNodes>
          <leafValues>
            -0.4972776770591736 0.6027074456214905</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 112 -4507851 -411340929 -268437513 -67502145 -17350859
            -32901 -71344315 -29377</internalNodes>
          <leafValues>
            -0.4383158981800079 0.5966237187385559</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 69 -75894785 -117379438 -239063587 -12538500 1485072126
            2076233213 2123118847 801906927</internalNodes>
          <leafValues>
            -0.6386105418205261 0.3977999985218048</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 19 -823480413 786628589 -16876049 -1364262914 242165211
            1315930109 -696268833 -455082829</internalNodes>
          <leafValues>
            -0.5512794256210327 0.4282079637050629</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 73 -521411968 6746762 -1396236286 -2038436114
            -185612509 57669627 -143132877 -1041235973</internalNodes>
          <leafValues>
            -0.6418755054473877 0.3549866080284119</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 126 -478153869 1076028979 -1645895615 1365298272
            -557859073 -339771473 1442574528 -1058802061</internalNodes>
          <leafValues>
            -0.4841901361942291 0.4668019413948059</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 45 -246350404 -1650402048 -1610612745 -788400696
            1467604861 -2787397 1476263935 -4481349</internalNodes>
          <leafValues>
            -0.5855734348297119 0.3879135847091675</leafValues></_></weakClassifiers></_>
    <!-- stage 9 -->
    <_>
      <maxWeakCount>7</maxWeakCount>
      <stageThreshold>-1.2237116098403931</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 114 -24819 1572863935 -16809993 -67108865 2146778388
            1433927541 -268608444 -34865205</internalNodes>
          <leafValues>
            -0.2518476545810700 0.7088654041290283</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 97 -1841359 -134271049 -32769 -5767369 -1116675 -2185
            -8231 -33603327</internalNodes>
          <leafValues>
            -0.4303432404994965 0.5283288359642029</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 25 -1359507589 -1360593090 -1073778729 -269553812
            -809512977 1744707583 -41959433 -134758978</internalNodes>
          <leafValues>
            -0.4259553551673889 0.5440809130668640</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 34 729753407 -134270989 -1140907329 -235200777
            658456383 2147467263 -1140900929 -16385</internalNodes>
          <leafValues>
            -0.5605589151382446 0.4220733344554901</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 134 -310380553 -420675595 -193005472 -353568129
            1205338070 -990380036 887604324 -420544526</internalNodes>
          <leafValues>
            -0.5192656517028809 0.4399855434894562</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 16 -1427119361 1978920959 -287119734 -487068946
            114759245 -540578051 -707510259 -671660453</internalNodes>
          <leafValues>
            -0.5013077259063721 0.4570254683494568</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 74 -738463762 -889949281 -328301948 -121832450
            -1142658284 -1863576559 2146417353 -263185</internalNodes>
          <leafValues>
            -0.4631414115428925 0.4790246188640595</leafValues></_></weakClassifiers></_>
    <!-- stage 10 -->
    <_>
      <maxWeakCount>7</maxWeakCount>
      <stageThreshold>-0.5544230937957764</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 113 -76228780 -65538 -1 -67174401 -148007 -33 -221796
            -272842924</internalNodes>
          <leafValues>
            -0.3949716091156006 0.6082032322883606</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 110 369147696 -1625232112 2138570036 -1189900 790708019
            -1212613127 799948719 -4456483</internalNodes>
          <leafValues>
            -0.4855885505676270 0.4785369932651520</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 37 784215839 -290015241 536832799 -402984963
            -1342414991 -838864897 -176769 -268456129</internalNodes>
          <leafValues>
            -0.4620285332202911 0.4989669024944305</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 41 -486418688 -171915327 -340294900 -21938 -519766032
            -772751172 -73096060 -585322623</internalNodes>
          <leafValues>
            -0.6420643329620361 0.3624351918697357</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 117 -33554953 -475332625 -1423463824 -2077230421
            -4849669 -2080505925 -219032928 -1071915349</internalNodes>
          <leafValues>
            -0.4820112884044647 0.4632140696048737</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 65 -834130468 -134217476 -1349314083 -1073803559
            -619913764 -1449131844 -1386890321 -1979118423</internalNodes>
          <leafValues>
            -0.4465552568435669 0.5061788558959961</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 56 -285249779 1912569855 -16530 -1731022870 -1161904146
            -1342177297 -268439634 -1464078708</internalNodes>
          <leafValues>
            -0.5190586447715759 0.4441480338573456</leafValues></_></weakClassifiers></_>
    <!-- stage 11 -->
    <_>
      <maxWeakCount>7</maxWeakCount>
      <stageThreshold>-0.7161560654640198</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 20 1246232575 1078001186 -10027057 60102 -277348353
            -43646987 -1210581153 1195769615</internalNodes>
          <leafValues>
            -0.4323809444904327 0.5663768053054810</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 15 -778583572 -612921106 -578775890 -4036478
            -1946580497 -1164766570 -1986687009 -12103599</internalNodes>
          <leafValues>
            -0.4588732719421387 0.4547033011913300</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 129 -1073759445 2013231743 -1363169553 -1082459201
            -1414286549 868185983 -1356133589 -1077936257</internalNodes>
          <leafValues>
            -0.5218553543090820 0.4111092388629913</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 102 -84148365 -2093417722 -1204850272 564290299
            -67121221 -1342177350 -1309195902 -776734797</internalNodes>
          <leafValues>
            -0.4920000731945038 0.4326725304126740</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 88 -25694458 67104495 -290216278 -168563037 2083877442
            1702788383 -144191964 -234882162</internalNodes>
          <leafValues>
            -0.4494568109512329 0.4448510706424713</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 59 -857980836 904682741 -1612267521 232279415
            1550862252 -574825221 -357380888 -4579409</internalNodes>
          <leafValues>
            -0.5180826783180237 0.3888972699642181</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 27 -98549440 -137838400 494928389 -246013630 939541351
            -1196072350 -620603549 2137216273</internalNodes>
          <leafValues>
            -0.6081240773200989 0.3333222270011902</leafValues></_></weakClassifiers></_>
    <!-- stage 12 -->
    <_>
      <maxWeakCount>8</maxWeakCount>
      <stageThreshold>-0.6743940711021423</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 29 -150995201 2071191945 -1302151626 536934335
            -1059008937 914128709 1147328110 -268369925</internalNodes>
          <leafValues>
            -0.1790193915367127 0.6605972051620483</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 128 -134509479 1610575703 -1342177289 1861484541
            -1107833788 1577058173 -333558568 -136319041</internalNodes>
          <leafValues>
            -0.3681024610996246 0.5139749646186829</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 70 -1 1060154476 -1090984524 -630918524 -539492875
            779616255 -839568424 -321</internalNodes>
          <leafValues>
            -0.3217232525348663 0.6171553134918213</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 4 -269562385 -285029906 -791084350 -17923776 235286671
            1275504943 1344390399 -966276889</internalNodes>
          <leafValues>
            -0.4373284578323364 0.4358185231685638</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 76 17825984 -747628419 595427229 1474759671 575672208
            -1684005538 872217086 -1155858277</internalNodes>
          <leafValues>
            -0.4404836893081665 0.4601220190525055</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 124 -336593039 1873735591 -822231622 -355795238
            -470820869 -1997537409 -1057132384 -1015285005</internalNodes>
          <leafValues>
            -0.4294152259826660 0.4452161788940430</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 54 -834212130 -593694721 -322142257 -364892500
            -951029539 -302125121 -1615106053 -79249765</internalNodes>
          <leafValues>
            -0.3973052501678467 0.4854526817798615</leafValues></_>
        <!-- tree 7 -->
        <_>
          <internalNodes>
            0 -1 95 1342144479 2147431935 -33554561 -47873 -855685912 -1
            1988052447 536827383</internalNodes>
          <leafValues>
            -0.7054683566093445 0.2697997391223908</leafValues></_></weakClassifiers></_>
    <!-- stage 13 -->
    <_>
      <maxWeakCount>9</maxWeakCount>
      <stageThreshold>-1.2042298316955566</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 39 1431368960 -183437936 -537002499 -137497097
            1560590321 -84611081 -2097193 -513</internalNodes>
          <leafValues>
            -0.5905947685241699 0.5101932883262634</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 120 -1645259691 2105491231 2130706431 1458995007
            -8567536 -42483883 -33780003 -21004417</internalNodes>
          <leafValues>
            -0.4449204802513123 0.4490709304809570</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 89 -612381022 -505806938 -362027516 -452985106
            275854917 1920431639 -12600561 -134221825</internalNodes>
          <leafValues>
            -0.4693818688392639 0.4061094820499420</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 14 -805573153 -161 -554172679 -530519488 -16779441
            2000682871 -33604275 -150997129</internalNodes>
          <leafValues>
            -0.3600351214408875 0.5056326985359192</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 67 6192 435166195 1467449341 2046691505 -1608493775
            -4755729 -1083162625 -71365637</internalNodes>
          <leafValues>
            -0.4459891915321350 0.4132415652275085</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 86 -41689215 -3281034 1853357967 -420712635 -415924289
            -270209208 -1088293113 -825311232</internalNodes>
          <leafValues>
            -0.4466069042682648 0.4135067760944367</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 80 -117391116 -42203396 2080374461 -188709 -542008165
            -356831940 -1091125345 -1073796897</internalNodes>
          <leafValues>
            -0.3394956290721893 0.5658645033836365</leafValues></_>
        <!-- tree 7 -->
        <_>
          <internalNodes>
            0 -1 75 -276830049 1378714472 -1342181951 757272098
            1073740607 -282199241 -415761549 170896931</internalNodes>
          <leafValues>
            -0.5346512198448181 0.3584479391574860</leafValues></_>
        <!-- tree 8 -->
        <_>
          <internalNodes>
            0 -1 55 -796075825 -123166849 2113667055 -217530421
            -1107432194 -16385 -806359809 -391188771</internalNodes>
          <leafValues>
            -0.4379335641860962 0.4123645126819611</leafValues></_></weakClassifiers></_>
    <!-- stage 14 -->
    <_>
      <maxWeakCount>10</maxWeakCount>
      <stageThreshold>-0.8402050137519836</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 71 -890246622 15525883 -487690486 47116238 -1212319899
            -1291847681 -68159890 -469829921</internalNodes>
          <leafValues>
            -0.2670986354351044 0.6014143228530884</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 31 -1361180685 -1898008841 -1090588811 -285410071
            -1074016265 -840443905 2147221487 -262145</internalNodes>
          <leafValues>
            -0.4149844348430634 0.4670888185501099</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 40 1426190596 1899364271 2142731795 -142607505
            -508232452 -21563393 -41960001 -65</internalNodes>
          <leafValues>
            -0.4985891580581665 0.3719584941864014</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 109 -201337965 10543906 -236498096 -746195597
            1974565825 -15204415 921907633 -190058309</internalNodes>
          <leafValues>
            -0.4568729996681213 0.3965812027454376</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 130 -595026732 -656401928 -268649235 -571490699
            -440600392 -133131 -358810952 -2004088646</internalNodes>
          <leafValues>
            -0.4770836830139160 0.3862601518630981</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 66 941674740 -1107882114 1332789109 -67691015
            -1360463693 -1556612430 -609108546 733546933</internalNodes>
          <leafValues>
            -0.4877715110778809 0.3778986334800720</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 49 -17114945 -240061474 1552871558 -82775604 -932393844
            -1308544889 -532635478 -99042357</internalNodes>
          <leafValues>
            -0.3721654713153839 0.4994400143623352</leafValues></_>
        <!-- tree 7 -->
        <_>
          <internalNodes>
            0 -1 133 -655906006 1405502603 -939205164 1884929228
            -498859222 559417357 -1928559445 -286264385</internalNodes>
          <leafValues>
            -0.3934195041656494 0.4769641458988190</leafValues></_>
        <!-- tree 8 -->
        <_>
          <internalNodes>
            0 -1 0 -335837777 1860677295 -90 -1946186226 931096183
            251612987 2013265917 -671232197</internalNodes>
          <leafValues>
            -0.4323300719261169 0.4342164099216461</leafValues></_>
        <!-- tree 9 -->
        <_>
          <internalNodes>
            0 -1 103 37769424 -137772680 374692301 2002666345 -536176194
            -1644484728 807009019 1069089930</internalNodes>
          <leafValues>
            -0.4993278682231903 0.3665378093719482</leafValues></_></weakClassifiers></_>
    <!-- stage 15 -->
    <_>
      <maxWeakCount>9</maxWeakCount>
      <stageThreshold>-1.1974394321441650</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 43 -5505 2147462911 2143265466 -4511070 -16450 -257
            -201348440 -71333206</internalNodes>
          <leafValues>
            -0.3310225307941437 0.5624626278877258</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 90 -136842268 -499330741 2015250980 -87107126
            -641665744 -788524639 -1147864792 -134892563</internalNodes>
          <leafValues>
            -0.5266560912132263 0.3704403042793274</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 104 -146800880 -1780368555 2111170033 -140904684
            -16777551 -1946681885 -1646463595 -839131947</internalNodes>
          <leafValues>
            -0.4171888828277588 0.4540435671806335</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 85 -832054034 -981663763 -301990281 -578814081
            -932319000 -1997406723 -33555201 -69206017</internalNodes>
          <leafValues>
            -0.4556705355644226 0.3704262077808380</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 24 -118492417 -1209026825 1119023838 -1334313353
            1112948738 -297319313 1378887291 -139469193</internalNodes>
          <leafValues>
            -0.4182529747486115 0.4267231225967407</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 78 -1714382628 -2353704 -112094959 -549613092
            -1567058760 -1718550464 -342315012 -1074972227</internalNodes>
          <leafValues>
            -0.3625369668006897 0.4684656262397766</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 5 -85219702 316836394 -33279 1904970288 2117267315
            -260901769 -621461759 -88607770</internalNodes>
          <leafValues>
            -0.4742925167083740 0.3689507246017456</leafValues></_>
        <!-- tree 7 -->
        <_>
          <internalNodes>
            0 -1 11 -294654041 -353603585 -1641159686 -50331921
            -2080899877 1145569279 -143132713 -152044037</internalNodes>
          <leafValues>
            -0.3666271567344666 0.4580127298831940</leafValues></_>
        <!-- tree 8 -->
        <_>
          <internalNodes>
            0 -1 32 1887453658 -638545712 -1877976819 -34320972
            -1071067983 -661345416 -583338277 1060190561</internalNodes>
          <leafValues>
            -0.4567637443542481 0.3894708156585693</leafValues></_></weakClassifiers></_>
    <!-- stage 16 -->
    <_>
      <maxWeakCount>9</maxWeakCount>
      <stageThreshold>-0.5733128190040588</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 122 -994063296 1088745462 -318837116 -319881377
            1102566613 1165490103 -121679694 -134744129</internalNodes>
          <leafValues>
            -0.4055117964744568 0.5487945079803467</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 68 -285233233 -538992907 1811935199 -369234005 -529
            -20593 -20505 -1561401854</internalNodes>
          <leafValues>
            -0.3787897229194641 0.4532003402709961</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 58 -1335245632 1968917183 1940861695 536816369
            -1226071367 -570908176 457026619 1000020667</internalNodes>
          <leafValues>
            -0.4258328974246979 0.4202791750431061</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 94 -1360318719 -1979797897 -50435249 -18646473
            -608879292 -805306691 -269304244 -17840167</internalNodes>
          <leafValues>
            -0.4561023116111755 0.4002747833728790</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 87 2062765935 -16449 -1275080721 -16406 45764335
            -1090552065 -772846337 -570464322</internalNodes>
          <leafValues>
            -0.4314672648906708 0.4086346626281738</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 127 -536896021 1080817663 -738234288 -965478709
            -2082767969 1290855887 1993822934 -990381609</internalNodes>
          <leafValues>
            -0.4174543321132660 0.4249868988990784</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 3 -818943025 168730891 -293610428 -79249354 669224671
            621166734 1086506807 1473768907</internalNodes>
          <leafValues>
            -0.4321364760398865 0.4090838730335236</leafValues></_>
        <!-- tree 7 -->
        <_>
          <internalNodes>
            0 -1 79 -68895696 -67107736 -1414315879 -841676168
            -619843344 -1180610531 -1081990469 1043203389</internalNodes>
          <leafValues>
            -0.5018386244773865 0.3702533841133118</leafValues></_>
        <!-- tree 8 -->
        <_>
          <internalNodes>
            0 -1 116 -54002134 -543485719 -2124882422 -1437445858
            -115617074 -1195787391 -1096024366 -2140472445</internalNodes>
          <leafValues>
            -0.5037505626678467 0.3564981222152710</leafValues></_></weakClassifiers></_>
    <!-- stage 17 -->
    <_>
      <maxWeakCount>9</maxWeakCount>
      <stageThreshold>-0.4892596900463104</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 132 -67113211 2003808111 1862135111 846461923 -2752
            2002237273 -273154752 1937223539</internalNodes>
          <leafValues>
            -0.2448196411132813 0.5689709186553955</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 62 1179423888 -78064940 -611839555 -539167899
            -1289358360 -1650810108 -892540499 -1432827684</internalNodes>
          <leafValues>
            -0.4633283913135529 0.3587929606437683</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 23 -285212705 -78450761 -656212031 -264050110 -27787425
            -1334349961 -547662981 -135796924</internalNodes>
          <leafValues>
            -0.3731099069118500 0.4290455579757690</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 77 341863476 403702016 -550588417 1600194541
            -1080690735 951127993 -1388580949 -1153717473</internalNodes>
          <leafValues>
            -0.3658909499645233 0.4556473195552826</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 22 -586880702 -204831512 -100644596 -39319550
            -1191150794 705692513 457203315 -75806957</internalNodes>
          <leafValues>
            -0.5214384198188782 0.3221037387847900</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 72 -416546870 545911370 -673716192 -775559454
            -264113598 139424 -183369982 -204474641</internalNodes>
          <leafValues>
            -0.4289036989212036 0.4004956185817719</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 50 -1026505020 -589692154 -1740499937 -1563770497
            1348491006 -60710713 -1109853489 -633909413</internalNodes>
          <leafValues>
            -0.4621542394161224 0.3832748532295227</leafValues></_>
        <!-- tree 7 -->
        <_>
          <internalNodes>
            0 -1 108 -1448872304 -477895040 -1778390608 -772418127
            -1789923416 -1612057181 -805306693 -1415842113</internalNodes>
          <leafValues>
            -0.3711548447608948 0.4612701535224915</leafValues></_>
        <!-- tree 8 -->
        <_>
          <internalNodes>
            0 -1 92 407905424 -582449988 52654751 -1294472 -285103725
            -74633006 1871559083 1057955850</internalNodes>
          <leafValues>
            -0.5180652141571045 0.3205870389938355</leafValues></_></weakClassifiers></_>
    <!-- stage 18 -->
    <_>
      <maxWeakCount>10</maxWeakCount>
      <stageThreshold>-0.5911940932273865</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 81 4112 -1259563825 -846671428 -100902460 1838164148
            -74153752 -90653988 -1074263896</internalNodes>
          <leafValues>
            -0.2592592537403107 0.5873016119003296</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 1 -285216785 -823206977 -1085589 -1081346 1207959293
            1157103471 2097133565 -2097169</internalNodes>
          <leafValues>
            -0.3801195919513702 0.4718827307224274</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 121 -12465 -536875169 2147478367 2130706303 -37765492
            -866124467 -318782328 -1392509185</internalNodes>
          <leafValues>
            -0.3509117066860199 0.5094807147979736</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 38 2147449663 -20741 -16794757 1945873146 -16710 -1
            -8406341 -67663041</internalNodes>
          <leafValues>
            -0.4068757295608521 0.4130136370658875</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 17 -155191713 866117231 1651407483 548272812 -479201468
            -447742449 1354229504 -261884429</internalNodes>
          <leafValues>
            -0.4557141065597534 0.3539792001247406</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 100 -225319378 -251682065 -492783986 -792341777
            -1287261695 1393643841 -11274182 -213909521</internalNodes>
          <leafValues>
            -0.4117803275585175 0.4118592441082001</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 63 -382220122 -2002072729 -51404800 -371201558
            -923011069 -2135301457 -2066104743 -1042557441</internalNodes>
          <leafValues>
            -0.4008397758007050 0.4034757018089294</leafValues></_>
        <!-- tree 7 -->
        <_>
          <internalNodes>
            0 -1 101 -627353764 -48295149 1581203952 -436258614
            -105268268 -1435893445 -638126888 -1061107126</internalNodes>
          <leafValues>
            -0.5694189667701721 0.2964762747287750</leafValues></_>
        <!-- tree 8 -->
        <_>
          <internalNodes>
            0 -1 118 -8399181 1058107691 -621022752 -251003468 -12582915
            -574619739 -994397789 -1648362021</internalNodes>
          <leafValues>
            -0.3195341229438782 0.5294018983840942</leafValues></_>
        <!-- tree 9 -->
        <_>
          <internalNodes>
            0 -1 92 -348343812 -1078389516 1717960437 364735981
            -1783841602 -4883137 -457572354 -1076950384</internalNodes>
          <leafValues>
            -0.3365339040756226 0.5067458748817444</leafValues></_></weakClassifiers></_>
    <!-- stage 19 -->
    <_>
      <maxWeakCount>10</maxWeakCount>
      <stageThreshold>-0.7612916231155396</stageThreshold>
      <weakClassifiers>
        <!-- tree 0 -->
        <_>
          <internalNodes>
            0 -1 10 -1976661318 -287957604 -1659497122 -782068 43591089
            -453637880 1435470000 -1077438561</internalNodes>
          <leafValues>
            -0.4204545319080353 0.5165745615959168</leafValues></_>
        <!-- tree 1 -->
        <_>
          <internalNodes>
            0 -1 131 -67110925 14874979 -142633168 -1338923040
            2046713291 -2067933195 1473503712 -789579837</internalNodes>
          <leafValues>
            -0.3762553930282593 0.4075302779674530</leafValues></_>
        <!-- tree 2 -->
        <_>
          <internalNodes>
            0 -1 83 -272814301 -1577073 -1118685 -305156120 -1052289
            -1073813756 -538971154 -355523038</internalNodes>
          <leafValues>
            -0.4253497421741486 0.3728055357933044</leafValues></_>
        <!-- tree 3 -->
        <_>
          <internalNodes>
            0 -1 135 -2233 -214486242 -538514758 573747007 -159390971
            1994225489 -973738098 -203424005</internalNodes>
          <leafValues>
            -0.3601998090744019 0.4563256204128265</leafValues></_>
        <!-- tree 4 -->
        <_>
          <internalNodes>
            0 -1 115 -261031688 -1330369299 -641860609 1029570301
            -1306461192 -1196149518 -1529767778 683139823</internalNodes>
          <leafValues>
            -0.4034293889999390 0.4160816967487335</leafValues></_>
        <!-- tree 5 -->
        <_>
          <internalNodes>
            0 -1 64 -572993608 -34042628 -417865 -111109 -1433365268
            -19869715 -1920939864 -1279457063</internalNodes>
          <leafValues>
            -0.3620899617671967 0.4594142735004425</leafValues></_>
        <!-- tree 6 -->
        <_>
          <internalNodes>
            0 -1 36 -626275097 -615256993 1651946018 805366393
            2016559730 -430780849 -799868165 -16580645</internalNodes>
          <leafValues>
            -0.3903816640377045 0.4381459355354309</leafValues></_>
        <!-- tree 7 -->
        <_>
          <internalNodes>
            0 -1 93 1354797300 -1090957603 1976418270 -1342502178
            -1851873892 -1194637077 -1153521668 -1108399474</internalNodes>
          <leafValues>
            -0.3591445386409760 0.4624078869819641</leafValues></_>
        <!-- tree 8 -->
        <_>
          <internalNodes>
            0 -1 91 68157712 1211368313 -304759523 1063017136 798797750
            -275513546 648167355 -1145357350</internalNodes>
          <leafValues>
            -0.4297670423984528 0.4023293554782867</leafValues></_>
        <!-- tree 9 -->
        <_>
          <internalNodes>
            0 -1 107 -546318240 -1628569602 -163577944 -537002306
            -545456389 -1325465645 -380446736 -1058473386</internalNodes>
          <leafValues>
            -0.5727006793022156 0.2995934784412384</leafValues></_></weakClassifiers></_></stages>
  <features>
    <_>
      <rect>
        0 0 3 5</rect></_>
    <_>
      <rect>
        0 0 4 2</rect></_>
    <_>
      <rect>
        0 0 6 3</rect></_>
    <_>
      <rect>
        0 1 2 3</rect></_>
    <_>
      <rect>
        0 1 3 3</rect></_>
    <_>
      <rect>
        0 1 3 7</rect></_>
    <_>
      <rect>
        0 4 3 3</rect></_>
    <_>
      <rect>
        0 11 3 4</rect></_>
    <_>
      <rect>
        0 12 8 4</rect></_>
    <_>
      <rect>
        0 14 4 3</rect></_>
    <_>
      <rect>
        1 0 5 3</rect></_>
    <_>
      <rect>
        1 1 2 2</rect></_>
    <_>
      <rect>
        1 3 3 1</rect></_>
    <_>
      <rect>
        1 7 4 4</rect></_>
    <_>
      <rect>
        1 12 2 2</rect></_>
    <_>
      <rect>
        1 13 4 1</rect></_>
    <_>
      <rect>
        1 14 4 3</rect></_>
    <_>
      <rect>
        1 17 3 2</rect></_>
    <_>
      <rect>
        2 0 2 3</rect></_>
    <_>
      <rect>
        2 1 2 2</rect></_>
    <_>
      <rect>
        2 2 4 6</rect></_>
    <_>
      <rect>
        2 3 4 4</rect></_>
    <_>
      <rect>
        2 7 2 1</rect></_>
    <_>
      <rect>
        2 11 2 3</rect></_>
    <_>
      <rect>
        2 17 3 2</rect></_>
    <_>
      <rect>
        3 0 2 2</rect></_>
    <_>
      <rect>
        3 1 7 3</rect></_>
    <_>
      <rect>
        3 7 2 1</rect></_>
    <_>
      <rect>
        3 7 2 4</rect></_>
    <_>
      <rect>
        3 18 2 2</rect></_>
    <_>
      <rect>
        4 0 2 3</rect></_>
    <_>
      <rect>
        4 3 2 1</rect></_>
    <_>
      <rect>
        4 6 2 1</rect></_>
    <_>
      <rect>
        4 6 2 5</rect></_>
    <_>
      <rect>
        4 7 5 2</rect></_>
    <_>
      <rect>
        4 8 4 3</rect></_>
    <_>
      <rect>
        4 18 2 2</rect></_>
    <_>
      <rect>
        5 0 2 2</rect></_>
    <_>
      <rect>
        5 3 4 4</rect></_>
    <_>
      <rect>
        5 6 2 5</rect></_>
    <_>
      <rect>
        5 9 2 2</rect></_>
    <_>
      <rect>
        5 10 2 2</rect></_>
    <_>
      <rect>
        6 3 4 4</rect></_>
    <_>
      <rect>
        6 4 4 3</rect></_>
    <_>
      <rect>
        6 5 2 3</rect></_>
    <_>
      <rect>
        6 5 2 5</rect></_>
    <_>
      <rect>
        6 5 4 3</rect></_>
    <_>
      <rect>
        6 6 4 2</rect></_>
    <_>
      <rect>
        6 6 4 4</rect></_>
    <_>
      <rect>
        6 18 1 2</rect></_>
    <_>
      <rect>
        6 21 2 1</rect></_>
    <_>
      <rect>
        7 0 3 7</rect></_>
    <_>
      <rect>
        7 4 2 3</rect></_>
    <_>
      <rect>
        7 9 5 1</rect></_>
    <_>
      <rect>
        7 21 2 1</rect></_>
    <_>
      <rect>
        8 0 1 4</rect></_>
    <_>
      <rect>
        8 5 2 2</rect></_>
    <_>
      <rect>
        8 5 3 2</rect></_>
    <_>
      <rect>
        8 17 3 1</rect></_>
    <_>
      <rect>
        8 18 1 2</rect></_>
    <_>
      <rect>
        9 0 5 3</rect></_>
    <_>
      <rect>
        9 2 2 6</rect></_>
    <_>
      <rect>
        9 5 1 1</rect></_>
    <_>
      <rect>
        9 11 1 1</rect></_>
    <_>
      <rect>
        9 16 1 1</rect></_>
    <_>
      <rect>
        9 16 2 1</rect></_>
    <_>
      <rect>
        9 17 1 1</rect></_>
    <_>
      <rect>
        9 18 1 1</rect></_>
    <_>
      <rect>
        10 5 1 2</rect></_>
    <_>
      <rect>
        10 5 3 3</rect></_>
    <_>
      <rect>
        10 7 1 5</rect></_>
    <_>
      <rect>
        10 8 1 1</rect></_>
    <_>
      <rect>
        10 9 1 1</rect></_>
    <_>
      <rect>
        10 10 1 1</rect></_>
    <_>
      <rect>
        10 10 1 2</rect></_>
    <_>
      <rect>
        10 14 3 3</rect></_>
    <_>
      <rect>
        10 15 1 1</rect></_>
    <_>
      <rect>
        10 15 2 1</rect></_>
    <_>
      <rect>
        10 16 1 1</rect></_>
    <_>
      <rect>
        10 16 2 1</rect></_>
    <_>
      <rect>
        10 17 1 1</rect></_>
    <_>
      <rect>
        10 21 1 1</rect></_>
    <_>
      <rect>
        11 3 2 2</rect></_>
    <_>
      <rect>
        11 5 1 2</rect></_>
    <_>
      <rect>
        11 5 3 3</rect></_>
    <_>
      <rect>
        11 5 4 6</rect></_>
    <_>
      <rect>
        11 6 1 1</rect></_>
    <_>
      <rect>
        11 7 2 2</rect></_>
    <_>
      <rect>
        11 8 1 2</rect></_>
    <_>
      <rect>
        11 10 1 1</rect></_>
    <_>
      <rect>
        11 10 1 2</rect></_>
    <_>
      <rect>
        11 15 1 1</rect></_>
    <_>
      <rect>
        11 17 1 1</rect></_>
    <_>
      <rect>
        11 18 1 1</rect></_>
    <_>
      <rect>
        12 0 2 2</rect></_>
    <_>
      <rect>
        12 1 2 5</rect></_>
    <_>
      <rect>
        12 2 4 1</rect></_>
    <_>
      <rect>
        12 3 1 3</rect></_>
    <_>
      <rect>
        12 7 3 4</rect></_>
    <_>
      <rect>
        12 10 3 2</rect></_>
    <_>
      <rect>
        12 11 1 1</rect></_>
    <_>
      <rect>
        12 12 3 2</rect></_>
    <_>
      <rect>
        12 14 4 3</rect></_>
    <_>
      <rect>
        12 17 1 1</rect></_>
    <_>
      <rect>
        12 21 2 1</rect></_>
    <_>
      <rect>
        13 6 2 5</rect></_>
    <_>
      <rect>
        13 7 3 5</rect></_>
    <_>
      <rect>
        13 11 3 2</rect></_>
    <_>
      <rect>
        13 17 2 2</rect></_>
    <_>
      <rect>
        13 17 3 2</rect></_>
    <_>
      <rect>
        13 18 1 2</rect></_>
    <_>
      <rect>
        13 18 2 2</rect></_>
    <_>
      <rect>
        14 0 2 2</rect></_>
    <_>
      <rect>
        14 1 1 3</rect></_>
    <_>
      <rect>
        14 2 3 2</rect></_>
    <_>
      <rect>
        14 7 2 1</rect></_>
    <_>
      <rect>
        14 13 2 1</rect></_>
    <_>
      <rect>
        14 13 3 3</rect></_>
    <_>
      <rect>
        14 17 2 2</rect></_>
    <_>
      <rect>
        15 0 2 2</rect></_>
    <_>
      <rect>
        15 0 2 3</rect></_>
    <_>
      <rect>
        15 4 3 2</rect></_>
    <_>
      <rect>
        15 4 3 6</rect></_>
    <_>
      <rect>
        15 6 3 2</rect></_>
    <_>
      <rect>
        15 11 3 4</rect></_>
    <_>
      <rect>
        15 13 3 2</rect></_>
    <_>
      <rect>
        15 17 2 2</rect></_>
    <_>
      <rect>
        15 17 3 2</rect></_>
    <_>
      <rect>
        16 1 2 3</rect></_>
    <_>
      <rect>
        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里边包含了openCv的代码,但是没有mangager的安装包,如果提示不能使用下载安装相应的apk就能使用:

Demo:http://download.csdn.net/detail/u012808234/9414664

登录 后评论
下一篇
弹性计算秉林
43人浏览
2020-07-08
相关推荐
OpenCV资料
1870人浏览
2017-12-04 00:33:00
【OpenCv小白入门】人脸检测
1285人浏览
2018-01-26 15:20:00
Opencv-Python学习笔记(二)
1289人浏览
2018-03-14 12:42:18
基于 OpenCV 的人脸识别
3789人浏览
2016-07-29 17:13:00
0
0
0
4285