Android 中使用 dlib+opencv 实现动态人脸检测
2019-11-30

1 概述

完成 Android 相机预览功能以后,在此基础上我使用 dlib 与 opencv 库做了一个关于人脸检测的 demo。该 demo 在相机预览过程中对人脸进行实时检测,并将检测到的人脸用矩形框描绘出来。具体实现原理如下:

采用双层 View,底层的 TextureView 用于预览,程序从 TextureView 中获取预览帧数据,然后调用 dlib 库对帧数据进行处理,最后将检测结果绘制在顶层的 SurfaceView 中。

2 项目配置

由于项目中用到了 dlib 与 opencv 库,因此需要对其进行配置。主要涉及到以下几个方面:

2.1 C++支持

在项目创建过程中依次选择 Include C++ Support、C++11、Exceptions Support ( -fexceptions )以及 Runtime Type Information Support ( -frtti ) 。最后生成的 build.gradle 文件如下:

defaultConfig { applicationId "com.example.lightweh.facedetection" minSdkVersion 23 targetSdkVersion 28 versionCode 1 versionName "1.0" testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner" externalNativeBuild { cmake { arguments "-DCMAKE_BUILD_TYPE=Release" cppFlags "-std=c++11 -frtti -fexceptions" } }}

其中,arguments 参数是后添加上去的,主要用于指定 CMake 的编译模式为 Release,因为在 Debug 模式下 dlib 库中相关算法的运行速度非常慢。前期如果需要调试 C++ 代码,可先将 arguments 参数注释。

2.2 dlib 与 opencv 下载

到dlib官网下载最新版本的源码,解压后将文件夹中的dlib目录复制到 Android Studio 工程的 cpp 目录下。

到 sourceforge 下载最新的 opencv-android 库,解压后将文件夹中的 native 目录同样复制到 Android Studio 工程的 cpp 目录下,并改名为 opencv。

2.3 CMakeLists 配置

在 CMakeLists 文件中,我们首先包含 dlib 的 cmake 文件,接下来添加 opencv 的 include 文件夹并引入 opencv 的 so 库,同时将 jni_common 目录中的文件及人脸检测相关文件添加至 native-lib 库中,最后进行链接。

# 设置native目录set(NATIVE_DIR ${CMAKE_SOURCE_DIR}/src/main/cpp)# 设置dlibinclude(${NATIVE_DIR}/dlib/cmake)# 设置opencv include文件夹include_directories(${NATIVE_DIR}/opencv/jni/include)# 设置opencv的so库add_library( libopencv_java3 SHARED IMPORTED)set_target_properties( libopencv_java3 PROPERTIES IMPORTED_LOCATION ${NATIVE_DIR}/opencv/libs/${ANDROID_ABI}/libopencv_java3.so)# 将jni_common目录中所有文件名,存至SRC_LIST中AUX_SOURCE_DIRECTORY(${NATIVE_DIR}/jni_common SRC_LIST)add_library( # Sets the name of the library. native-lib # Sets the library as a shared library. SHARED # Provides a relative path to your source file(s). ${SRC_LIST} src/main/cpp/face_detector.h src/main/cpp/face_detector.cpp src/main/cpp/native-lib.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( # Specifies the target library. native-lib dlib libopencv_java3 jnigraphics # Links the target library to the log library # included in the NDK. ${log-lib})# 指定release编译选项set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -s -O3 -Wall")set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -s -O3 -Wall")

由于 C++ 代码中用到了头文件 "android/bitmap.h",所以链接时需要添加 jnigraphics 库。

3 JNI相关 Java 类定义

3.1 VisionDetRet 类

VisionDetRet 类的相关对象主要负责 C++ 与 Java 之间的数据传递。

public final class VisionDetRet { private int mLeft; private int mTop; private int mRight; private int mBottom; VisionDetRet() {} public VisionDetRet(int l, int t, int r, int b) { mLeft = l; mTop = t; mRight = r; mBottom = b; } public int getLeft() { return mLeft; } public int getTop() { return mTop; } public int getRight() { return mRight; } public int getBottom() { return mBottom; }}

3.2 FaceDet 类

FaceDet 类为 JNI 函数调用类,主要定义了一些需要 C++ 实现的 native 方法。

public class FaceDet { private static final String TAG = "FaceDet"; // accessed by native methods @SuppressWarnings("unused") private long mNativeFaceDetContext; static { try { // 预加载native方法库 System.loadLibrary("native-lib"); jniNativeClassInit(); Log.d(TAG, "jniNativeClassInit success"); } catch (UnsatisfiedLinkError e) { Log.e(TAG, "library not found"); } } public FaceDet() { jniInit(); } @Nullable @WorkerThread public List<VisionDetRet> detect(@NonNull Bitmap bitmap) { VisionDetRet[] detRets = jniBitmapDet(bitmap); return Arrays.asList(detRets); } @Override protected void finalize() throws Throwable { super.finalize(); release(); } public void release() { jniDeInit(); } @Keep private native static void jniNativeClassInit(); @Keep private synchronized native int jniInit(); @Keep private synchronized native int jniDeInit(); @Keep private synchronized native VisionDetRet[] jniBitmapDet(Bitmap bitmap);}

4 Native 方法实现

4.1 定义 VisionDetRet 类对应的 C++ 类

#include <jni.h>#define CLASSNAME_VISION_DET_RET "com/lightweh/dlib/VisionDetRet"#define CONSTSIG_VISION_DET_RET "()V"#define CLASSNAME_FACE_DET "com/lightweh/dlib/FaceDet"class JNI_VisionDetRet {public: JNI_VisionDetRet(JNIEnv *env) { // 查找VisionDetRet类信息 jclass detRetClass = env->FindClass(CLASSNAME_VISION_DET_RET); // 获取VisionDetRet类成员变量 jID_left = env->GetFieldID(detRetClass, "mLeft", "I"); jID_top = env->GetFieldID(detRetClass, "mTop", "I"); jID_right = env->GetFieldID(detRetClass, "mRight", "I"); jID_bottom = env->GetFieldID(detRetClass, "mBottom", "I"); } void setRect(JNIEnv *env, jobject &jDetRet, const int &left, const int &top, const int &right, const int &bottom) { // 设置VisionDetRet类对象jDetRet的成员变量值 env->SetIntField(jDetRet, jID_left, left); env->SetIntField(jDetRet, jID_top, top); env->SetIntField(jDetRet, jID_right, right); env->SetIntField(jDetRet, jID_bottom, bottom); } // 创建VisionDetRet类实例 static jobject createJObject(JNIEnv *env) { jclass detRetClass = env->FindClass(CLASSNAME_VISION_DET_RET); jmethodID mid = env->GetMethodID(detRetClass, "<init>", CONSTSIG_VISION_DET_RET); return env->NewObject(detRetClass, mid); } // 创建VisionDetRet类对象数组 static jobjectArray createJObjectArray(JNIEnv *env, const int &size) { jclass detRetClass = env->FindClass(CLASSNAME_VISION_DET_RET); return (jobjectArray) env->NewObjectArray(size, detRetClass, NULL); }private: jfieldID jID_left; jfieldID jID_top; jfieldID jID_right; jfieldID jID_bottom;};

4.2 定义人脸检测类

人脸检测算法需要用大小位置不同的窗口在图像中进行滑动,然后判断窗口中是否存在人脸。本文采用的是 dlib 中的是HOG(histogram of oriented gradient)方法对人脸进行检测,其检测效果要好于 opencv。dlib 中同样提供了 CNN 方法来进行人脸检测,效果好于 HOG,不过需要使用 GPU 加速,不然程序运行会非常慢。

class FaceDetector {private: dlib::frontal_face_detector face_detector; std::vector<dlib::rectangle> det_rects;public: FaceDetector(); // 实现人脸检测算法 int Detect(const cv::Mat &image); // 返回检测结果 std::vector<dlib::rectangle> getDetResultRects();};

FaceDetector::FaceDetector() { // 定义人脸检测器 face_detector = dlib::get_frontal_face_detector();}int FaceDetector::Detect(const cv::Mat &image) { if (image.empty()) return 0; if (image.channels() == 1) { cv::cvtColor(image, image, CV_GRAY2BGR); } dlib::cv_image<dlib::bgr_pixel> dlib_image(image); det_rects.clear(); // 返回检测到的人脸矩形特征框 det_rects = face_detector(dlib_image); return det_rects.size();}std::vector<dlib::rectangle> FaceDetector::getDetResultRects() { return det_rects;}

4.3 native 方法实现

JNI_VisionDetRet *g_pJNI_VisionDetRet;JavaVM *g_javaVM = NULL;// 该函数在加载本地库时被调用JNIEXPORT jint JNI_OnLoad(JavaVM *vm, void *reserved) { g_javaVM = vm; JNIEnv *env; vm->GetEnv((void **) &env, JNI_VERSION_1_6); // 初始化 g_pJNI_VisionDetRet g_pJNI_VisionDetRet = new JNI_VisionDetRet(env); return JNI_VERSION_1_6;}// 该函数用于执行清理操作void JNI_OnUnload(JavaVM *vm, void *reserved) { g_javaVM = NULL; delete g_pJNI_VisionDetRet;}namespace {#define JAVA_NULL 0 using DetPtr = FaceDetector *; // 用于存放人脸检测类对象的指针,关联Jave层对象与C++底层对象(相互对应) class JNI_FaceDet { public: JNI_FaceDet(JNIEnv *env) { jclass clazz = env->FindClass(CLASSNAME_FACE_DET); mNativeContext = env->GetFieldID(clazz, "mNativeFaceDetContext", "J"); env->DeleteLocalRef(clazz); } DetPtr getDetectorPtrFromJava(JNIEnv *env, jobject thiz) { DetPtr const p = (DetPtr) env->GetLongField(thiz, mNativeContext); return p; } void setDetectorPtrToJava(JNIEnv *env, jobject thiz, jlong ptr) { env->SetLongField(thiz, mNativeContext, ptr); } jfieldID mNativeContext; }; // Protect getting/setting and creating/deleting pointer between java/native std::mutex gLock; std::shared_ptr<JNI_FaceDet> getJNI_FaceDet(JNIEnv *env) { static std::once_flag sOnceInitflag; static std::shared_ptr<JNI_FaceDet> sJNI_FaceDet; std::call_once(sOnceInitflag, [env]() { sJNI_FaceDet = std::make_shared<JNI_FaceDet>(env); }); return sJNI_FaceDet; } // 从java对象获取它持有的c++对象指针 DetPtr const getDetPtr(JNIEnv *env, jobject thiz) { std::lock_guard<std::mutex> lock(gLock); return getJNI_FaceDet(env)->getDetectorPtrFromJava(env, thiz); } // The function to set a pointer to java and delete it if newPtr is empty // C++对象new以后,将指针转成long型返回给java对象持有 void setDetPtr(JNIEnv *env, jobject thiz, DetPtr newPtr) { std::lock_guard<std::mutex> lock(gLock); DetPtr oldPtr = getJNI_FaceDet(env)->getDetectorPtrFromJava(env, thiz); if (oldPtr != JAVA_NULL) { delete oldPtr; } getJNI_FaceDet(env)->setDetectorPtrToJava(env, thiz, (jlong) newPtr); }} // end unnamespace#ifdef __cplusplusextern "C" {#endif#define DLIB_FACE_JNI_METHOD(METHOD_NAME) Java_com_lightweh_dlib_FaceDet_##METHOD_NAMEvoid JNIEXPORTDLIB_FACE_JNI_METHOD(jniNativeClassInit)(JNIEnv *env, jclass _this) {}// 生成需要返回的结果数组jobjectArray getRecResult(JNIEnv *env, DetPtr faceDetector, const int &size) { // 根据检测到的人脸数创建相应大小的jobjectArray jobjectArray jDetRetArray = JNI_VisionDetRet::createJObjectArray(env, size); for (int i = 0; i < size; i++) { // 对检测到的每一个人脸创建对应的实例对象,然后插入数组 jobject jDetRet = JNI_VisionDetRet::createJObject(env); env->SetObjectArrayElement(jDetRetArray, i, jDetRet); dlib::rectangle rect = faceDetector->getDetResultRects()[i]; // 将人脸矩形框的值赋给对应的jobject实例对象 g_pJNI_VisionDetRet->setRect(env, jDetRet, rect.left(), rect.top(), rect.right(), rect.bottom()); } return jDetRetArray;}JNIEXPORT jobjectArray JNICALLDLIB_FACE_JNI_METHOD(jniBitmapDet)(JNIEnv *env, jobject thiz, jobject bitmap) { cv::Mat rgbaMat; cv::Mat bgrMat; jniutils::ConvertBitmapToRGBAMat(env, bitmap, rgbaMat, true); cv::cvtColor(rgbaMat, bgrMat, cv::COLOR_RGBA2BGR); // 获取人脸检测类指针 DetPtr mDetPtr = getDetPtr(env, thiz); // 调用人脸检测算法,返回检测到的人脸数 jint size = mDetPtr->Detect(bgrMat); // 返回检测结果 return getRecResult(env, mDetPtr, size);}jint JNIEXPORT JNICALLDLIB_FACE_JNI_METHOD(jniInit)(JNIEnv *env, jobject thiz) { DetPtr mDetPtr = new FaceDetector(); // 设置人脸检测类指针 setDetPtr(env, thiz, mDetPtr); return JNI_OK;}jint JNIEXPORT JNICALLDLIB_FACE_JNI_METHOD(jniDeInit)(JNIEnv *env, jobject thiz) { // 指针置0 setDetPtr(env, thiz, JAVA_NULL); return JNI_OK;}#ifdef __cplusplus}#endif

5 Java端调用人脸检测算法

在开启人脸检测之前,需要在相机 AutoFitTextureView 上覆盖一层自定义 BoundingBoxView 用于绘制检测到的人脸矩形框,该 View 的具体实现如下:

public class BoundingBoxView extends SurfaceView implements SurfaceHolder.Callback { protected SurfaceHolder mSurfaceHolder; private Paint mPaint; private boolean mIsCreated; public BoundingBoxView(Context context, AttributeSet attrs) { super(context, attrs); mSurfaceHolder = getHolder(); mSurfaceHolder.addCallback(this); mSurfaceHolder.setFormat(PixelFormat.TRANSPARENT); setZOrderOnTop(true); mPaint = new Paint(); mPaint.setAntiAlias(true); mPaint.setColor(Color.RED); mPaint.setStrokeWidth(5f); mPaint.setStyle(Paint.Style.STROKE); } @Override public void surfaceChanged(SurfaceHolder surfaceHolder, int format, int width, int height) { } @Override public void surfaceCreated(SurfaceHolder surfaceHolder) { mIsCreated = true; } @Override public void surfaceDestroyed(SurfaceHolder surfaceHolder) { mIsCreated = false; } public void setResults(List<VisionDetRet> detRets) { if (!mIsCreated) { return; } Canvas canvas = mSurfaceHolder.lockCanvas(); //清除掉上一次的画框。 canvas.drawColor(Color.TRANSPARENT, PorterDuff.Mode.CLEAR); canvas.drawColor(Color.TRANSPARENT); for (VisionDetRet detRet : detRets) { Rect rect = new Rect(detRet.getLeft(), detRet.getTop(), detRet.getRight(), detRet.getBottom()); canvas.drawRect(rect, mPaint); } mSurfaceHolder.unlockCanvasAndPost(canvas); }}

同时,需要在布局文件中添加对应的 BoundingBoxView 层,保证与 AutoFitTextureView 完全重合:

<?xml version="1.0" encoding="utf-8"?><RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android" xmlns:tools="http://schemas.android.com/tools" android:layout_width="match_parent" android:layout_height="match_parent" tools:context=".CameraFragment"> <com.lightweh.facedetection.AutoFitTextureView android:id="@+id/textureView" android:layout_width="wrap_content" android:layout_height="wrap_content" android:layout_centerVertical="true" android:layout_centerHorizontal="true" /> <com.lightweh.facedetection.BoundingBoxView android:id="@+id/boundingBoxView" android:layout_width="wrap_content" android:layout_height="wrap_content" android:layout_alignLeft="@+id/textureView" android:layout_alignTop="@+id/textureView" android:layout_alignRight="@+id/textureView" android:layout_alignBottom="@+id/textureView" /></RelativeLayout>

BoundingBoxView 添加完成以后,即可在 CameraFragment 中添加对应的人脸检测代码:

private class detectAsync extends AsyncTask<Bitmap, Void, List<VisionDetRet>> { @Override protected void onPreExecute() { mIsDetecting = true; super.onPreExecute(); } protected List<VisionDetRet> doInBackground(Bitmap... bp) { List<VisionDetRet> results; // 返回检测结果 results = mFaceDet.detect(bp[0]); return results; } protected void onPostExecute(List<VisionDetRet> results) { // 绘制检测到的人脸矩形框 mBoundingBoxView.setResults(results); mIsDetecting = false; }}

然后,分别在 onResume 与 onPause 函数中完成人脸检测类对象的初始化和释放:

@Overridepublic void onResume() { super.onResume(); startBackgroundThread(); mFaceDet = new FaceDet(); if (mTextureView.isAvailable()) { openCamera(mTextureView.getWidth(), mTextureView.getHeight()); } else { mTextureView.setSurfaceTextureListener(mSurfaceTextureListener); }}@Overridepublic void onPause() { closeCamera(); stopBackgroundThread(); if (mFaceDet != null) { mFaceDet.release(); } super.onPause();}

最后,在 TextureView 的回调函数 onSurfaceTextureUpdated 完成调用:

@Overridepublic void onSurfaceTextureUpdated(SurfaceTexture texture) { if (!mIsDetecting) { Bitmap bp = mTextureView.getBitmap(); // 保证图片方向与预览方向一致 bp = Bitmap.createBitmap(bp, 0, 0, bp.getWidth(), bp.getHeight(), mTextureView.getTransform(null), true ); new detectAsync().execute(bp); }}

6 测试结果

经测试,960x720的 bitmap 图片在华为手机(Android 6.0,8核1.2GHz,2G内存)上执行一次检测约耗时800~850ms。Demo 运行效果如下:

7 Demo 源码

Github:FaceDetection

8. 参考

https://github.com/tzutalin/dlib-androidhttps://github.com/gv22ga/dlib-face-recognition-androidhttps://blog.csdn.net/yanzi1225627/article/details/7934710https://blog.csdn.net/hjimce/article/details/64127654