android 结合 opencv项目（NDK、OpenCV、android，官方Demo人脸识别）
第一个android结合 opencv项目（NDK、OpenCV、android人脸识别）
（2017.5.16更改，见绿色）
前提条件：已经搭建好jdk 和eclipse 开发android 和插件CDT (eclipse c++)的环境
在命令行窗口输入：java –version 如果显示相应版本 代表jdk环境搭建成功
在命令行窗口输入：android –version 如果显示相应版本 代表eclipseandroid环境搭建成功
第一步：配置eclipseNDK 环境
1) ：下载NDK
http://www.cnblogs.com/yaotong/archive/2011/01/25/1943615.html
我下载的版本是android-ndk-r10e
2) ：配置NDK
先配置环境变量，我的NDK安装在E盘
配置主要是执行ndk-build.cmd
谷歌改良了ndk的开发流程，对于Windows环境下NDK的开发，如果使用的NDK是r7之前的版本，必须要安装Cygwin才能使用NDK。而在NDKr7开始，Google的Windows版的NDK提供了一个ndk-build.cmd的脚本，这样，就可以直接利用这个脚本编译，而不需要使用Cygwin了。只需要为Eclipse Android工程添加一个Builders，而为Eclipse配置的builder，其实就是在执行Cygwin，然后传递ndk-build作为参数，这样就能让Eclipse自动编译NDK了。（摘自博客http://www.cnblogs.com/yejiurui/p/3476565.html）
3) :配置Builders（其实有两种方法配置，先说第一种，个人觉得第二种更加方便）
第一种
新建一个android project 我的是（Demo_NDK）
鼠标右键选中该工程选择下面的Properties
在弹出窗口选中
点击右边new按钮出现
直接单击Program 弹出
在切到Refresh 页面
并点击右侧Specify Resources
点击Finish
再去到Build Options 页面
同样点击右侧Specify Resources选中刚才新建的项目
最后点击OK，完成操作
第二种
选择Window下拉列表，选中Preferences,再作如下操作
然后选中本项目右键下拉选择Android Tools 选中 add native support 完成操作
以上步骤完成NDK环境搭建
第二步：配置进行opencv在android运行的环境
1）：到官网http://opencv.org/downloads.html下载sdk
我下载的是OpenCV-2.4.10-android-sdk
下载后在eclipse 导入android project
选中OpenCV Library -2.4.10 ----
当然你全部导入也行
导入完成后如下
再右键点击刚才新建的项目选中下方Properties
点击OK 后，IDE 会自动 builder project 如果出现以下代表环境搭建好了
第三步：
上面两个步骤完成后，我以官方demo opencv-sample-face-detection 来展示如何利用OPENCV来通过NDK和android相关联
建包，分别创建DetectionBasedTracker.java 和FdActity.java
结构如下
DetectionBasedTracker.java
用来写native方法（即本地方法，在C++环境运行）
代码如下
打开命令行进入这个项目的bin\classes目录（找到已经编译的.class文件）
输入命令：javah org.opencv.samples.facedetect.DetectionBasedTracker
org.opencv.samples.facedetect是类包路径
DetectionBasedTracker 是类名
将在bin\classes目录下生成org_opencv_samples_facedetect_DetectionBasedTracker.h文件，这个文件就是我们首先要拿到的文件
将其复制粘贴到项目的JNI 目录下
同时创建Android.mk 文件（配置文件）
LOCAL_MODULE :=Demo_NDK #标识你在Android.mk文件中描述的每个模板
这个标识着Java要调用的动态链接库
// Load native library after(!) OpenCV initialization
System.loadLibrary("Demo_NDK");
LOCAL_C_INCLUDES :=E:/OpenCV-2.4.10-android-sdk/sdk/native/jni/include
这将指定opencv包的路径，（这个很坑，我之前没有指定这段代码的时候，在下面.cpp文件导入openCV包总是报找不到包的错误）
再创建Application.mk 文件（配置文件）
再创建org_opencv_samples_facedetect_DetectionBasedTracker.cpp文件，在里面编写openCV代码，分别对DetectionBasedTracker.java6个Native方法实现，代码如下
#include
#include
#include
#include
#include
#include
#define LOG_TAG"FaceDetection/DetectionBasedTracker"
#define LOGD(...) ((void)__android_log_print(ANDROID_LOG_DEBUG,LOG_TAG, __VA_ARGS__))
usingnamespace std;
usingnamespace cv;
inlinevoidvector_Rect_to_Mat(vector<Rect>& v_rect,Mat& mat)
{
mat = Mat(v_rect, true);
}
JNIEXPORT jlongJNICALLJava_org_opencv_samples_facedetect_DetectionBasedTracker_nativeCreateObject
(JNIEnv* jenv,jclass,jstring jFileName,jint faceSize)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeCreateObjectenter");
constchar* jnamestr = jenv->GetStringUTFChars(jFileName, NULL);
string stdFileName(jnamestr);
jlong result = 0;
try
{
DetectionBasedTracker::Parameters DetectorParams;
if (faceSize > 0)
DetectorParams.minObjectSize = faceSize;
result = (jlong)newDetectionBasedTracker(stdFileName,DetectorParams);
}
catch(cv::Exception& e)
{
LOGD("nativeCreateObject caughtcv::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 ofDetectionBasedTracker.nativeCreateObject()");
return 0;
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeCreateObjectexit");
return result;
}
JNIEXPORT voidJNICALLJava_org_opencv_samples_facedetect_DetectionBasedTracker_nativeDestroyObject
(JNIEnv* jenv,jclass,jlong thiz)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeDestroyObjectenter");
try
{
if(thiz != 0)
{
((DetectionBasedTracker*)thiz)->stop();
delete (DetectionBasedTracker*)thiz;
}
}
catch(cv::Exception& e)
{
LOGD("nativeestroyObject caughtcv::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 ofDetectionBasedTracker.nativeDestroyObject()");
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeDestroyObjectexit");
}
JNIEXPORT voidJNICALLJava_org_opencv_samples_facedetect_DetectionBasedTracker_nativeStart
(JNIEnv* jenv,jclass,jlong thiz)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeStartenter");
try
{
((DetectionBasedTracker*)thiz)->run();
}
catch(cv::Exception& e)
{
LOGD("nativeStart caughtcv::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 ofDetectionBasedTracker.nativeStart()");
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeStartexit");
}
JNIEXPORT voidJNICALLJava_org_opencv_samples_facedetect_DetectionBasedTracker_nativeStop
(JNIEnv* jenv,jclass,jlong thiz)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeStopenter");
try
{
((DetectionBasedTracker*)thiz)->stop();
}
catch(cv::Exception& e)
{
LOGD("nativeStop caughtcv::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 ofDetectionBasedTracker.nativeStop()");
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeStopexit");
}
JNIEXPORT voidJNICALLJava_org_opencv_samples_facedetect_DetectionBasedTracker_nativeSetFaceSize
(JNIEnv* jenv,jclass,jlong thiz,jint faceSize)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeSetFaceSizeenter");
try
{
if (faceSize > 0)
{
DetectionBasedTracker::Parameters DetectorParams = \
((DetectionBasedTracker*)thiz)->getParameters();
DetectorParams.minObjectSize = faceSize;
((DetectionBasedTracker*)thiz)->setParameters(DetectorParams);
}
}
catch(cv::Exception& e)
{
LOGD("nativeStop caughtcv::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 ofDetectionBasedTracker.nativeSetFaceSize()");
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeSetFaceSizeexit");
}
JNIEXPORT voidJNICALLJava_org_opencv_samples_facedetect_DetectionBasedTracker_nativeDetect
(JNIEnv* jenv,jclass,jlong thiz,jlong imageGray,jlong faces)
{
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeDetectenter");
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 caughtcv::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 DetectionBasedTracker.nativeDetect()");
}
LOGD("Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativeDetectexit");
}
然后完善DetectionBasedTracker.java 文件
packageorg.opencv.samples.facedetect;
import org.opencv.core.Mat;
importorg.opencv.core.MatOfRect;
publicclass DetectionBasedTracker
{
public DetectionBasedTracker(String cascadeName,int minFaceSize) {
mNativeObj =nativeCreateObject(cascadeName,minFaceSize);
}
publicvoid start() {
nativeStart(mNativeObj);
}
publicvoid stop() {
nativeStop(mNativeObj);
}
publicvoid setMinFaceSize(int size) {
nativeSetFaceSize(mNativeObj, size);
}
publicvoid detect(Mat imageGray, MatOfRect faces) {
nativeDetect(mNativeObj,imageGray.getNativeObjAddr(), faces.getNativeObjAddr());
}
publicvoid release() {
nativeDestroyObject(mNativeObj);
mNativeObj = 0;
}
privatelongmNativeObj = 0;
privatestaticnativelongnativeCreateObject(String cascadeName, int minFaceSize);
privatestaticnativevoidnativeDestroyObject(long thiz);
privatestaticnativevoidnativeStart(longthiz);
privatestaticnativevoidnativeStop(longthiz);
privatestaticnativevoidnativeSetFaceSize(longthiz,intsize);
privatestaticnativevoidnativeDetect(longthiz,longinputImage,longfaces);
}
接下来是FdActivity.java主界面，代码如下
packageorg.opencv.samples.facedetect;
packageorg.opencv.samples.facedetect;
import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
importorg.opencv.android.BaseLoaderCallback;
importorg.opencv.android.CameraBridgeViewBase;
importorg.opencv.android.CameraBridgeViewBase.CvCameraViewFrame;
importorg.opencv.android.CameraBridgeViewBase.CvCameraViewListener2;
importorg.opencv.android.LoaderCallbackInterface;
importorg.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.core.Size;
import org.opencv.objdetect.CascadeClassifier;
import android.app.Activity;
import android.content.Context;
importandroid.content.pm.ActivityInfo;
import android.os.Bundle;
import android.util.Log;
import android.view.Menu;
import android.view.MenuItem;
import android.view.WindowManager;
import com.example.demo_ndk.R;
public class FdActivity extendsActivity implements CvCameraViewListener2 {
private static final String TAG ="OCVSample::Activity";
private static final Scalar FACE_RECT_COLOR = newScalar(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;
//在 mOpenCvCameraView 的回调接口onCameraFrame函数里面处理每一帧从相机获取到的图片。
private CameraBridgeViewBase mOpenCvCameraView;
private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
@Override
public void onManagerConnected(intstatus) {
switch (status) {
caseLoaderCallbackInterface.SUCCESS:
{
Log.i(TAG, "OpenCVloaded successfully");
// Load native libraryafter(!) OpenCV initialization
System.loadLibrary("Demo_NDK");
try {
// load cascade filefrom application resources
InputStream is =getResources().openRawResource(R.raw.lbpcascade_frontalface);
//这里去加载人脸识别分类文件（lbpcascade_frontalface.XML 是XML文件，这都是利用Opencv给我们提供好的XML人脸识别分类文件，在opencv/source/data/目录下,这里把那个文件拉到了Raw资源文件里面，方便Android调用，如果要自己实现一个XML人脸识别分类文件的话，需要用到opencv_haartraining，来训练大量数据，最终生成XML人脸识别分类文件）
File cascadeDir =getDir("cascade", Context.MODE_PRIVATE);
mCascadeFile = new File(cascadeDir,"lbpcascade_frontalface.xml");
FileOutputStream os =new FileOutputStream(mCascadeFile);
byte[] buffer = newbyte[4096];
int bytesRead;
while ((bytesRead =is.read(buffer)) != -1) {
os.write(buffer, 0,bytesRead);
}
is.close();
os.close();
mJavaDetector = newCascadeClassifier(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 = newDetectionBasedTracker(mCascadeFile.getAbsolutePath(), 0);
cascadeDir.delete();
} catch (IOException e) {
e.printStackTrace();
Log.e(TAG, "Failedto load cascade. Exception thrown: " + e);
}
mOpenCvCameraView.enableView();
} break;
default:
{
super.onManagerConnected(status);
} break;
}
}
};
public FdActivity() {
mDetectorName = newString[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, "calledonCreate");
super.onCreate(savedInstanceState);
getWindow().addFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON);
//设置拍摄方向
/*
系统默认
ActivityInfo.SCREEN_ORIENTATION_UNSPECIFIED
锁定直式
ActivityInfo.SCREEN_ORIENTATION_PORTRAIT
锁定横式
ActivityInfo.SCREEN_ORIENTATION_LANDSCAPE
随使用者当下
ActivityInfo.SCREEN_ORIENTATION_USER
与活动线程下相同的设定
ActivityInfo.SCREEN_ORIENTATION_BEHIND
不随SENSOR改变
ActivityInfo.SCREEN_ORIENTATION_NOSENSOR
随SENSOR改变
ActivityInfo.SCREEN_ORIENTATION_SENSOR
*/
this.setRequestedOrientation(ActivityInfo.SCREEN_ORIENTATION_LANDSCAPE);
setContentView(R.layout.face_detect_surface_view);
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_3, 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) {
//这里获取相机拍摄到的原图，彩色图
mRgba = inputFrame.rgba();
//这里获取相机拍摄到的灰度图，用来给下面检测人脸使用。
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)
//调用opencv的detectMultiScale（）检测函数，参数意义如下
/*
mGray表示的是要检测的输入图像，faces表示检测到的目标序列,存储检测结果（坐标位置，长，宽），1.1表示
每次图像尺寸减小的比例为1.1，2表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大
小都可以检测到目标),2（其实是一个常量：CV_HAAR_SCALE_IMAGE）表示不是缩放分类器来检测，而是缩放图像，最后两个size()为检测目标的
最小最大尺寸
*/
mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO:objdetect.CV_HAAR_SCALE_IMAGE
newSize(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
}
else if (mDetectorType ==NATIVE_DETECTOR) {
if (mNativeDetector != null)
mNativeDetector.detect(mGray,faces);
}
else {
Log.e(TAG, "Detection methodis not selected!");
}
Rect[] facesArray = faces.toArray();
for (int i = 0; i //在原图mRgba上为每个检测到的人脸画一个绿色矩形 Core.rectangle(mRgba,facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3); //返回处理好的图像，返回后会直接显示在JavaCameraView上。 return mRgba; } @Override public boolean onCreateOptionsMenu(Menu menu) { Log.i(TAG, "calledonCreateOptionsMenu"); mItemFace50 = menu.add("Face size50%"); mItemFace40 = menu.add("Face size40%"); mItemFace30 = menu.add("Face size30%"); mItemFace20 = menu.add("Face size20%"); mItemType = menu.add(mDetectorName[mDetectorType]); return true; } @Override public boolean onOptionsItemSelected(MenuItem item) { Log.i(TAG, "calledonOptionsItemSelected; 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) { int tmpDetectorType =(mDetectorType + 1) % mDetectorName.length; item.setTitle(mDetectorName[tmpDetectorType]); setDetectorType(tmpDetectorType); } 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, "DetectionBased Tracker enabled"); mNativeDetector.start(); } else { Log.i(TAG, "Cascadedetector enabled"); mNativeDetector.stop(); } } } } 最后配置AndroidManifest.xml文件 <supports-screensandroid:resizeable="true" android:smallScreens="true" android:normalScreens="true" android:largeScreens="true" android:anyDensity="true" /> <uses-permissionandroid:name="android.permission.CAMERA"/> <uses-featureandroid:name="android.hardware.camera"android:required="false"/> <uses-featureandroid:name="android.hardware.camera.autofocus"android:required="false"/> <uses-featureandroid:name="android.hardware.camera.front"android:required="false"/> <uses-featureandroid:name="android.hardware.camera.front.autofocus"android:required="false"/> 到此大功告成 运行结果如下 源代码已经上传至： http://download.csdn.net/detail/wjb820728252/9725892 至此，也是我这段时间走过的坑，希望能帮助到刚入门这门槛的朋友，谢谢！
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