OpenCV4Android开发实录(2)： 使用OpenCV3.3.0库实现人脸检测
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OpenCV4Android系列：
1. OpenCV4Android开发实录(1)：移植OpenCV3.3.0库到Android Studio
2.OpenCV4Android开发实录(2)： 使用OpenCV3.3.0库实现人脸检测
上一篇文章OpenCV4Android开发实录(1)：移植OpenCV3.3.0库到Android Studio大概介绍了下OpenCV库的基本情况，阐述了将OpenCV库移植到Android Studio项目中的具体步骤。本文将在此文的基础上，通过对OpenCV框架中的人脸检测模块做相应介绍，然后实现人脸检测功能。
一、人脸检测模块移植
1.拷贝opencv-3.3.0-android-sdk\OpenCV-android-sdk\samples\face-detection\jni目录到工程app module的main目录下
2.修改jni目录下的Android.mk
(1) 将
#OPENCV_INSTALL_MODULES:=off
#OPENCV_LIB_TYPE:=SHARED
修改为：
OPENCV_INSTALL_MODULES:=on
OPENCV_LIB_TYPE:=SHARED
其中，OPENCV_INSTALL_MODULES的作用是在打包apk时加载OpenCV的动态库；OPENCV_LIB_TYPE的作用是指定OpenCV库的类型为动态库。
(2) 将
ifdef OPENCV_ANDROID_SDK
ifneq ("","$(wildcard $(OPENCV_ANDROID_SDK)/OpenCV.mk)")
include ${OPENCV_ANDROID_SDK}/OpenCV.mk
else
include ${OPENCV_ANDROID_SDK}/sdk/native/jni/OpenCV.mk
endif
include ../../sdk/native/jni/OpenCV.mk
endif
修改为：
include E:\Environment\opencv-3.3.0-android-sdk\OpenCV-android-sdk\sdk\native\jni\OpenCV.mk
其中，include包含的就是OpenCV SDK中OpenCV.mk文件所存储的绝对路径。最终Android.mk修改效果如下：
3.修改jni目录下Application.mk。由于在导入OpenCV libs时只拷贝了armeabi 、armeabi-v7a、arm64-v8a，因此这里指定编译平台也为上述三个；修改APP_PLaTFORM版本为android-16(可根据自身情况而定)，具体如下：
APP_STL := gnustl_static
APP_CPPFLAGS := -frtti –fexceptions
# 指定编译平台
APP_ABI := armeabi armeabi-v7a arm64-v8a
# 指定Android平台
APP_PLATFORM := android-16
4.修改DetectionBasedTracker_jni.h和DetectionBasedTracker_jni.cpp文件，将源文件中所有包含前缀“Java_org_opencv_samples_facedetect_”替换为“Java_com_jiangdg_opencv4android_natives_”，其中com.jiangdg.opencv4android.natives是Java层类DetectionBasedTracker.java所在的包路径，该类包含了人脸检测相关的native方法，否则，在调用自己编译生成的so库时会提示找不到该本地函数错误，以DetectionBasedTracker_jni.h为例：
/* DO NOT EDIT THIS FILE - it is machine generated */
#include <jni.h>
/* Header for class org_opencv_samples_fd_DetectionBasedTracker */
#ifndef _Included_org_opencv_samples_fd_DetectionBasedTracker
#define _Included_org_opencv_samples_fd_DetectionBasedTracker
#ifdef __cplusplus
extern "C" {
#endif
/*
* Class: org_opencv_samples_fd_DetectionBasedTracker
* Method: nativeCreateObject
* Signature: (Ljava/lang/String;F)J
*/
JNIEXPORT jlong JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeCreateObject
(JNIEnv *, jclass, jstring, jint);
/*
* Class: org_opencv_samples_fd_DetectionBasedTracker
* Method: nativeDestroyObject
* Signature: (J)V
*/
JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeDestroyObject
(JNIEnv *, jclass, jlong);
/*
* Class: org_opencv_samples_fd_DetectionBasedTracker
* Method: nativeStart
* Signature: (J)V
*/
JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeStart
(JNIEnv *, jclass, jlong);
/*
* Class: org_opencv_samples_fd_DetectionBasedTracker
* Method: nativeStop
* Signature: (J)V
*/
JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeStop
(JNIEnv *, jclass, jlong);
/*
* Class: org_opencv_samples_fd_DetectionBasedTracker
* Method: nativeSetFaceSize
* Signature: (JI)V
*/
JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeSetFaceSize
(JNIEnv *, jclass, jlong, jint);
/*
* Class: org_opencv_samples_fd_DetectionBasedTracker
* Method: nativeDetect
* Signature: (JJJ)V
*/
JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeDetect
(JNIEnv *, jclass, jlong, jlong, jlong);
#ifdef __cplusplus
}
#endif
#endif
5.打开Android Studio中的Terminal窗口，使用cd命令切换到工程jni目录所在位置，并执行ndk-build命令，然后会自动在工程的app/src/main目录下生成libs和obj目录，其中libs目录存放的是目标动态库libdetection_based_tracker.so。
生成so库：
注意：如果执行ndk-build命令提示命令不存在，说明你的ndk环境变量没有配置好。
6.修改app模块build.gradle中的sourceSets字段，禁止自动调用ndk-build命令，设置目标so的存放路径，代码如下：android {
compileSdkVersion 25
defaultConfig {
applicationId "com.jiangdg.opencv4android"
minSdkVersion 15
targetSdkVersion 25
versionCode 1
versionName "1.0"
}
….// 代码省略
sourceSets {
main {
jni.srcDirs = [] //禁止自动调用ndk-build命令
jniLibs.srcDir 'src/main/libs' // 设置目标的so存放路径
}
}
….// 代码省略
}
其中，jni.srcDirs = []的作用是禁用gradle默认的ndk-build，防止AS自己生成android.mk编译jni工程，jniLibs.srcDir 'src/main/libs'的作用设置目标的so存放路径，以将自己生成的so组装到apk中。
二、源码解析使用OpenCV3.3.0库实现人脸检测功能主要包含以下四个步骤，即:
(1) 初始化加载OpenCV库引擎；
(2) 通过OpenCV库开启Camera渲染；
(3) 加载人脸检测模型；
(4) 调用人脸检测本地动态库实现人脸识别；
1.初始化加载OpenCV库引擎
OpenCV库的加载有两种方式，一种通过OpenCV Manager进行动态加载，也就是官方推荐的方式，这种方式需要另外安装OpenCV Manager，主要通过调用OpenCVLoader.initAsync()方法进行初始化；另一种为静态加载，也就是本文所使用的方法，即先将相关架构的so包拷贝到工程的libs目录，通过调用OpenCVLoader.initDebug()方法进行初始化，类似于调用system.loadLibrary("opencv_java")。
if (!OpenCVLoader.initDebug()) {
// 静态加载OpenCV失败，使用OpenCV Manager初始化
// 参数：OpenCV版本；上下文；加载结果回调接口
OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_3_3_0,
this, mLoaderCallback);
} else {
// 如果静态加载成功，直接调用onManagerConnected方法
mLoaderCallback.onManagerConnected(LoaderCallbackInterface.SUCCESS);
}
其中，mLoaderCallback为OpenCV库初始化状态回调接口，当OpenCV被初始化成功后其onManagerConnected(int status)方法会被调用，而我们就可以在该方法中处理本地动态库的加载、加载人脸检测模型文件、初始化人脸检测引擎以及开启Camera渲染等操作，具体代码如下：
private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
@Override
public void onManagerConnected(int status) {
switch (status) {
case LoaderCallbackInterface.SUCCESS:
// OpenCV初始化加载成功，再加载本地so库
System.loadLibrary("detection_based_tracker");
// 加载人脸检测模型
…..
// 初始化人脸检测引擎
…..
// 开启渲染Camera
mCameraView.enableView();
break;
default:
super.onManagerConnected(status);
break;
}
}
};
2. 通过OpenCV库开启Camera渲染
在OpenCV中与Camera紧密相关的主要有两个类，即CameraBridgeViewBase和JavaCameraView，其中，CameraBridgeViewBase是一个基类，继承于SuarfaceView和SurafaceHolder.Callback接口，用于实现Camera与OpenCV库之间的交互，它主要的作用是控制Camera、处理视频帧以及调用相关内部接口对视频帧做相关调整，然后将调整后的视频帧数据渲染到手机屏幕上。比如enableView()方法、disableView()方法用于连接到Camera和断开与Camera的连接，代码如下：
public void enableView() {
synchronized(mSyncObject) {
mEnabled = true;
checkCurrentState();
}
}
public void disableView() {
synchronized(mSyncObject) {
mEnabled = false;
checkCurrentState();
}
}其中，checkCurrentState()方法用于更新Camera的渲染状态，它调用了processEnterState()方法来启动或停用Camera，以及将Camera的状态对外回调。为了方便开发者实时获取Camera的连接状态，CameraBridgeViewBase还提供了一个setCvCameraViewListener(CvCameraViewListener2 listener)方法，参数listener其一个内部接口，它包括三个方法：onCameraViewStarted(int width, int height)、void onCameraViewStopped()、Mat onCameraFrame(CvCameraViewFrame inputFrame)，分别用于对外回调Camera连接状态和传递Camera的实时视频帧数据。
private void checkCurrentState() {
Log.d(TAG, "call checkCurrentState");
int targetState;
if (mEnabled && mSurfaceExist && getVisibility() == VISIBLE) {
targetState = STARTED;
} else {
targetState = STOPPED;
}
if (targetState != mState) {
/* The state change detected. Need to exit the current state and enter target state */
processExitState(mState);
mState = targetState;
processEnterState(mState);
}
}
private void processEnterState(int state) {
Log.d(TAG, "call processEnterState: " + state);
switch(state) {
case STARTED:
// 调用connectCamera()抽象方法，启动Camera
onEnterStartedState();
// 调用连接成功监听器接口方法
if (mListener != null) {
mListener.onCameraViewStarted(mFrameWidth, mFrameHeight);
}
break;
case STOPPED:
// 调用disconnectCamera()抽象方法，停用Camera
onEnterStoppedState();
// 调用断开连接监听器接口方法
if (mListener != null) {
mListener.onCameraViewStopped();
}
break;
};
}
既然CameraBridgeViewBase是一个基类，与Camera紧密相关的connectCamera()和disconnectCamera()又是抽象方法，那么就必定会有一个子类来实现这两个方法，而这个子类就是JavaCameraView。JavaCameraView继承于CameraBridgeViewBase和PreviewCallback接口，是衔接OpenCV和Camera的桥梁，是Camera启动、禁止的实际实现者，在这个类里我们可以看到关于Camera很多熟悉的操作。源码如下：
@Override
protected boolean connectCamera(int width, int height) {
// 初始化Camera，连接到Camera
if (!initializeCamera(width, height))
return false;
mCameraFrameReady = false;
// 开启一个与Camera相关的工作线程CameraWorker
Log.d(TAG, "Starting processing thread");
mStopThread = false;
mThread = new Thread(new CameraWorker());
mThread.start();
return true;
}
@Override
protected void disconnectCamera() {
// 断开Camera连接，释放相关资源
try {
mStopThread = true;
Log.d(TAG, "Notify thread");
synchronized (this) {
this.notify();
}
// 停止工作线程
if (mThread != null)
mThread.join();
} catch (InterruptedException e) {
e.printStackTrace();
} finally {
mThread = null;
}
/* Now release camera */
releaseCamera();
mCameraFrameReady = false;
} CameraWorker是一个工作线程，用于处理从onPreviewFrame获得的视频帧数据，其存储在一个Mat类型的数组中。它会不断调用父类CameraBridgeViewBase的deliverAndDrawFrame方法，将处理后的视频帧数据流通过调用内部接口CvCameraViewListener2的onCameraFrame(CvCameraViewFrame frame)对外回调。
private class CameraWorker implements Runnable {
@Override
public void run() {
do {
…..//代码省略
if (!mStopThread && hasFrame) {
if (!mFrameChain[1 - mChainIdx].empty())
deliverAndDrawFrame(mCameraFrame[1 - mChainIdx]);
}
} while (!mStopThread);
}
}
3. 加载人脸检测模型
为了得到更好的人脸检测性能，OpenCV在SDK中提供了多个frontface检测器(人脸模型)，存放在..\opencv-3.3.0-android-sdk\OpenCV-android-sdk\sdk\etc\目录下，这篇对OpenCV自带的人脸检测模型做了比较，结果显示LBP实时性要好些。因此，本文选用目lbpcascades录下lbpcascade_frontalface.xml模型，该模型包括了3000个正样本和1500个负样本，我们将其拷贝到AS工程的res/raw目录下，并通过getDir方法保存到/data/data/com.jiangdg.opencv4android/ cascade目录下。
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 byteesRead;
while ((byteesRead = is.read(buffer)) != -1) {
os.write(buffer, 0, byteesRead);
}
is.close();
os.close();注：关于模型的训练在以后的博文中会讨论到。
4. 人脸检测
在opencv-3.3.0-android-sdk的face-detection示例项目中，提供了CascadeClassifier和
DetectionBasedTracker两种方式来实现人脸检测，其中，CascadeClassifier是OpenCV用于人脸检测的一个级联分类器，DetectionBasedTracker是通过JNI编程实现的人脸检测。两种方式我都试用了下，发现DetectionBasedTracker方式还是比CascadeClassifier稳定些，CascadeClassifier会存在一定频率的误检。
public class DetectionBasedTracker {
private long mNativeObj = 0;
// 构造方法：初始化人脸检测引擎
public DetectionBasedTracker(String cascadeName, int minFaceSize) {
mNativeObj = nativeCreateObject(cascadeName, minFaceSize);
}
// 开始人脸检测
public void start() {
nativeStart(mNativeObj);
}
// 停止人脸检测
public void stop() {
nativeStop(mNativeObj);
}
// 设置人脸最小尺寸
public void setMinFaceSize(int size) {
nativeSetFaceSize(mNativeObj, size);
}
// 检测
public void detect(Mat imageGray, MatOfRect faces) {
nativeDetect(mNativeObj, imageGray.getNativeObjAddr(), faces.getNativeObjAddr());
}
// 释放资源
public void release() {
nativeDestroyObject(mNativeObj);
mNativeObj = 0;
}
// native方法
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);
}
初始化DetectionBasedTracker后，我们只需要在CvCameraViewListener2接口的onCameraFrame方法中对每帧图片进行人脸检测即可。
@Override
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
….// 代码省略
// 获取检测到的脸部数据
MatOfRect faces = new MatOfRect();
…// 代码省略
if (mNativeDetector != null) {
mNativeDetector.detect(mGray, faces);
}
// 绘制检测框
Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++) {
Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);
}
return mRgba;
}
注：由于篇幅原因，关于人脸检测的C/C++实现代码(原理)，我们将在后续文章中讨论。
三、效果演示
1. FaceDetectActivity.class
/**
* 人脸检测
*
* Created by jiangdongguo on 2018/1/4.
*/
public class FaceDetectActivity extends AppCompatActivity implements CameraBridgeViewBase.CvCameraViewListener2 {
private static final int JAVA_DETECTOR = 0;
private static final int NATIVE_DETECTOR = 1;
private static final String TAG = "FaceDetectActivity";
@BindView(R.id.cameraView_face)
CameraBridgeViewBase mCameraView;
private Mat mGray;
private Mat mRgba;
private int mDetectorType = NATIVE_DETECTOR;
private int mAbsoluteFaceSize = 0;
private float mRelativeFaceSize = 0.2f;
private DetectionBasedTracker mNativeDetector;
private CascadeClassifier mJavaDetector;
private static final Scalar FACE_RECT_COLOR = new Scalar(0, 255, 0, 255);
private File mCascadeFile;
private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
@Override
public void onManagerConnected(int status) {
switch (status) {
case LoaderCallbackInterface.SUCCESS:
// OpenCV初始化加载成功，再加载本地so库
System.loadLibrary("detection_based_tracker");
try {
// 加载人脸检测模式文件
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 byteesRead;
while ((byteesRead = is.read(buffer)) != -1) {
os.write(buffer, 0, byteesRead);
}
is.close();
os.close();
// 使用模型文件初始化人脸检测引擎
mJavaDetector = new CascadeClassifier(mCascadeFile.getAbsolutePath());
if (mJavaDetector.empty()) {
Log.e(TAG, "加载cascade classifier失败");
mJavaDetector = null;
} else {
Log.d(TAG, "Loaded cascade classifier from " + mCascadeFile.getAbsolutePath());
}
mNativeDetector = new DetectionBasedTracker(mCascadeFile.getAbsolutePath(), 0);
cascadeDir.delete();
} catch (FileNotFoundException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
}
// 开启渲染Camera
mCameraView.enableView();
break;
default:
super.onManagerConnected(status);
break;
}
}
};
@Override
protected void onCreate(@Nullable Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
getWindow().addFlags(WindowManager.LayoutParams.FLAG_FULLSCREEN);
setContentView(R.layout.activity_facedetect);
// 绑定View
ButterKnife.bind(this);
mCameraView.setVisibility(CameraBridgeViewBase.VISIBLE);
// 注册Camera渲染事件监听器
mCameraView.setCvCameraViewListener(this);
}
@Override
protected void onResume() {
super.onResume();
// 静态初始化OpenCV
if (!OpenCVLoader.initDebug()) {
Log.d(TAG, "无法加载OpenCV本地库，将使用OpenCV Manager初始化");
OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_3_3_0, this, mLoaderCallback);
} else {
Log.d(TAG, "成功加载OpenCV本地库");
mLoaderCallback.onManagerConnected(LoaderCallbackInterface.SUCCESS);
}
}
@Override
protected void onPause() {
super.onPause();
// 停止渲染Camera
if (mCameraView != null) {
mCameraView.disableView();
}
}
@Override
protected void onDestroy() {
super.onDestroy();
// 停止渲染Camera
if (mCameraView != null) {
mCameraView.disableView();
}
}
@Override
public void onCameraViewStarted(int width, int height) {
// 灰度图像
mGray = new Mat();
// R、G、B彩色图像
mRgba = new Mat();
}
@Override
public void onCameraViewStopped() {
mGray.release();
mRgba.release();
}
@Override
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
mRgba = inputFrame.rgba();
mGray = inputFrame.gray();
// 设置脸部大小
if (mAbsoluteFaceSize == 0) {
int height = mGray.rows();
if (Math.round(height * mRelativeFaceSize) > 0) {
mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
}
mNativeDetector.setMinFaceSize(mAbsoluteFaceSize);
}
// 获取检测到的脸部数据
MatOfRect faces = new MatOfRect();
if (mDetectorType == JAVA_DETECTOR) {
if (mJavaDetector != null) {
mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2,
new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());
}
} else if (mDetectorType == NATIVE_DETECTOR) {
if (mNativeDetector != null) {
mNativeDetector.detect(mGray, faces);
}
} else {
Log.e(TAG, "Detection method is not selected!");
}
// 绘制检测框
Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++) {
Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);
}
Log.i(TAG, "共检测到 " + faces.toArray().length + " 张脸");
return mRgba;
}
}
2. activity_facedetect.xml
<?xml version="1.0" encoding="utf-8"?>
<RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:opencv="http://schemas.android.com/apk/res-auto"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:orientation="vertical">
<org.opencv.android.JavaCameraView
android:id="@+id/cameraView_face"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:visibility="gone"
opencv:camera_id="any"
opencv:show_fps="true" />
</RelativeLayout>
3. AndroidMnifest.xml
<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
package="com.jiangdg.opencv4android">
<uses-permission android:name="android.permission.CAMERA"/>
<uses-feature android:name="android.hardware.camera" android:required="false"/>
<uses-feature android:name="android.hardware.camera.autofocus" android:required="false"/>
<uses-feature android:name="android.hardware.camera.front" android:required="false"/>
<uses-feature android:name="android.hardware.camera.front.autofocus" android:required="false"/>
<supports-screens android:resizeable="true"
android:smallScreens="true"
android:normalScreens="true"
android:largeScreens="true"
android:anyDensity="true" />
<application
android:allowBackup="true"
android:icon="@mipmap/ic_launcher"
android:label="@string/app_name"
android:roundIcon="@mipmap/ic_launcher_round"
android:supportsRtl="true"
android:theme="@style/AppTheme">
<activity android:name=".MainActivity">
<intent-filter>
<action android:name="android.intent.action.MAIN" />
<category android:name="android.intent.category.LAUNCHER" />
</intent-filter>
</activity>
<activity android:name=".HelloOpenCVActivity"
android:screenOrientation="landscape"
android:configChanges="keyboardHidden|orientation"/>
<activity android:name=".FaceDetectActivity"
android:screenOrientation="landscape"
android:configChanges="keyboardHidden|orientation"/>
</application>
</manifest>
效果演示：
源码下载：https://github.com/jiangdongguo/OpenCV4Android（欢迎star & fork）
更新于2018-3-13
四、使用Cmake方式编译和升级到OpenCV3.4.1
在上面工程中，有两个不好的体验：(1)每次编译so时必须手动调用ndk-build命令；(2)在编写C/C++代码时没有代码提示，也没有报错警告。我想这两种情况无论是哪个都是让人感觉很不爽的，因此，今天打算在OpenCV3.4.1版本的基础上对项目进行重构下，使用Cmake方式来进行编译。
1. 新建main/app/cpp目录。将jni目录下的"DetectionBasedTracker_jni.cpp" 和"DetectionBasedTracker_jni.h" 文件拷贝到该目录下，并将opencv-3.4.1源码..\opencv-3.4.1-android-sdk\OpenCV-android-sdk\sdk\native\jni\目录下的整个include目录拷贝到该cpp目录下，同时可以删除整个app/src/main/jni目录
2. 新建main/app/jniLibs目录，将opencv-3.4.1源码中..\opencv-3.4.1-android-sdk\OpenCV-android-sdk\sdk\native\libs或staticlibs相关架构的动态库(.so)和静态库(.a)文件拷贝到该jniLibs目录下，同时删除libs、obj目录。
3. 在app目录下新建脚本文件CMakeLists.txt，该文件用于编写Cmake编译运行需要的脚本。需要注意的是，你在jniLibs目录导入了哪些静态库和动态库，在CmakeList.txt编写自动编译脚本时只能导入和链接这些库：
# Sets the minimum version of CMake required to build the native
# library. You should either keep the default value or only pass a
# value of 3.4.0 or lower.
cmake_minimum_required(VERSION 3.4.1)
# Creates and names a library, sets it as either STATIC
# or SHARED, and provides the relative paths to its source code.
# You can define multiple libraries, and CMake builds it for you.
# Gradle automatically packages shared libraries with your APK.
set(CMAKE_VERBOSE_MAKEFILE on)
set(libs "${CMAKE_SOURCE_DIR}/src/main/jniLibs")
include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/include)
#--------------------------------------------------- import ---------------------------------------------------#
add_library(libopencv_java3 SHARED IMPORTED )
set_target_properties(libopencv_java3 PROPERTIES
IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_java3.so")
add_library(libopencv_core STATIC IMPORTED )
set_target_properties(libopencv_core PROPERTIES
IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_core.a")
add_library(libopencv_highgui STATIC IMPORTED )
set_target_properties(libopencv_highgui PROPERTIES
IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_highgui.a")
add_library(libopencv_imgcodecs STATIC IMPORTED )
set_target_properties(libopencv_imgcodecs PROPERTIES
IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_imgcodecs.a")
add_library(libopencv_imgproc STATIC IMPORTED )
set_target_properties(libopencv_imgproc PROPERTIES
IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_imgproc.a")
add_library(libopencv_objdetect STATIC IMPORTED )
set_target_properties(libopencv_objdetect PROPERTIES
IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_objdetect.a")
#add_library(libopencv_calib3d STATIC IMPORTED )
#set_target_properties(libopencv_calib3d PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_calib3d.a")
#
#
#add_library(libopencv_dnn STATIC IMPORTED )
#set_target_properties(libopencv_core PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_dnn.a")
#
#add_library(libopencv_features2d STATIC IMPORTED )
#set_target_properties(libopencv_features2d PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_features2d.a")
#
#add_library(libopencv_flann STATIC IMPORTED )
#set_target_properties(libopencv_flann PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_flann.a")
#
#add_library(libopencv_ml STATIC IMPORTED )
#set_target_properties(libopencv_ml PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_ml.a")
#
#add_library(libopencv_photo STATIC IMPORTED )
#set_target_properties(libopencv_photo PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_photo.a")
#
#add_library(libopencv_shape STATIC IMPORTED )
#set_target_properties(libopencv_shape PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_shape.a")
#
#add_library(libopencv_stitching STATIC IMPORTED )
#set_target_properties(libopencv_stitching PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_stitching.a")
#
#add_library(libopencv_superres STATIC IMPORTED )
#set_target_properties(libopencv_superres PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_superres.a")
#
#add_library(libopencv_video STATIC IMPORTED )
#set_target_properties(libopencv_video PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_video.a")
#
#add_library(libopencv_videoio STATIC IMPORTED )
#set_target_properties(libopencv_videoio PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_videoio.a")
#
#add_library(libopencv_videostab STATIC IMPORTED )
#set_target_properties(libopencv_videostab PROPERTIES
# IMPORTED_LOCATION "${libs}/${ANDROID_ABI}/libopencv_videostab.a")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=gnu++11 -fexceptions -frtti")
add_library( # Sets the name of the library.
opencv341
# Sets the library as a shared library.
SHARED
# Provides a relative path to your source file(s).
# Associated headers in the same location as their source
# file are automatically included.
src/main/cpp/DetectionBasedTracker_jni.cpp)
find_library( # Sets the name of the path variable.
log-lib
# Specifies the name of the NDK library that
# you want CMake to locate.
log)
target_link_libraries(opencv341 android log
libopencv_java3 #used for java sdk
#17 static libs in total
#libopencv_calib3d
libopencv_core
#libopencv_dnn
#libopencv_features2d
#libopencv_flann
libopencv_highgui
libopencv_imgcodecs
libopencv_imgproc
#libopencv_ml
libopencv_objdetect
#libopencv_photo
#libopencv_shape
#libopencv_stitching
#libopencv_superres
#libopencv_video
#libopencv_videoio
#libopencv_videostab
${log-lib}
)
4. 右击选中app/src/main/cpp目录，选择"Link C++ Project with Gradle"并浏览选择本项目中的CmakeLists.txt文件，将C++环境关联到gradle
5. 将工程中的openCVLibrary330 module更新到openCVLibrary341，修改app目录下的gradle.build文件
apply plugin: 'com.android.application'
android {
compileSdkVersion 25
defaultConfig {
//...代码省略
externalNativeBuild {
cmake {
arguments "-DANDROID_ARM_NEON=TRUE", "-DANDROID_TOOLCHAIN=clang","-DCMAKE_BUILD_TYPE=Release"
//'-DANDROID_STL=gnustl_static'
cppFlags "-std=c++11","-frtti", "-fexceptions"
}
}
// 设置输出指定目标平台so
ndk{
abiFilters 'armeabi-v7a','arm64-v8a','armeabi'
}
}
// ...代码省略
externalNativeBuild {
cmake {
path 'CMakeLists.txt'
}
}
}
dependencies {
//.. 代码省略
implementation project(':openCVLibrary341')
} 至此，该工程重构完毕，切换到cpp文件中，码个Mat看下成效~