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 ${CMAKE_SOURCE_DIR}/src/main/cpp)
# 设置dlib
include(${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 __cplusplus
extern "C" {
#endif
#define DLIB_FACE_JNI_METHOD(METHOD_NAME) Java_com_lightweh_dlib_FaceDet_##METHOD_NAME
void JNIEXPORT
DLIB_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 JNICALL
DLIB_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 JNICALL
DLIB_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 JNICALL
DLIB_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:
@Override
public void onResume() {
super.onResume();
startBackgroundThread();
mFaceDet = new FaceDet();
if (mTextureView.isAvailable()) {
openCamera(mTextureView.getWidth(), mTextureView.getHeight());
} else {
mTextureView.setSurfaceTextureListener(mSurfaceTextureListener);
}
}
@Override
public 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:
@Override
public 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,960×720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:https://github.com/lightweh/FaceDetection
总结
以上所述是小编给大家介绍的Android 中使用 dlib+opencv 实现动态人脸检测功能,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对ZaLou.Cn网站的支持!