NDK OpenCV人脸定位
NDK系列之OpenCV人脸定位技术实战，本节主要是通过OpenCV C++库，实现识别人脸定位，并对识别到的人脸画面增加红框显示。
实现效果：
实现逻辑：
1.初始化CameraX，绑定图片分析器ImageAnalysis，监听相机数据；
2.加载OpenCV提供的人脸识别训练数据lbpcascade_frontalface到本地；
3.初始化人脸跟踪中转站FaceTracker，将人脸识别训练数据路径传递到Native层；
4.Native读取人脸识别训练数据，创建人脸检测跟踪器Ptr<DetectionBasedTracker> tracker；
5.通过中转站FaceTracker，调用Native层tracker开启人脸跟踪；
6.通过中转站FaceTracker，实例化Native层播放窗口ANativeWindow，关联surfaceView；
7.获取相机数据，传递Native层，人脸定位，绘制人脸框，渲染画面到屏幕。
本节主要内容：
1.OpenCV库导入；
2.Java层CameraX使用；
3.Native层识别人脸和画面渲染；
源码：
NdkHeadTest: NDK OpenCV人脸定位
一、OpenCV库导入
1）复制OpenCV源文件到cpp目录下，动态库文件复制到jniLibs目录下：
2）在CMakeLists文件中，导入源文件和库文件
二、Java层CameraX使用
1）初始化CameraX，绑定图片分析器ImageAnalysis，监听相机数据；
private void initCamera() {/*** CameraX*/cameraProviderFuture = ProcessCameraProvider.getInstance(this);cameraProviderFuture.addListener(() -> {try {ProcessCameraProvider cameraProvider = cameraProviderFuture.get();bindAnalysis(cameraProvider);} catch (Exception e) {e.printStackTrace();}}, ContextCompat.getMainExecutor(this));
}private void bindAnalysis(ProcessCameraProvider cameraProvider) {//STRATEGY_KEEP_ONLY_LATEST :非阻塞模式，每次获得最新帧//STRATEGY_BLOCK_PRODUCER : 阻塞模式，处理不及时会导致降低帧率//图片分析：得到摄像头图像数据ImageAnalysis imageAnalysis =new ImageAnalysis.Builder().setTargetResolution(new Size(640, 480)).setBackpressureStrategy(ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST).build();imageAnalysis.setAnalyzer(ContextCompat.getMainExecutor(this), this);cameraProvider.unbindAll();//绑定生命周期cameraProvider.bindToLifecycle(this,CameraSelector.DEFAULT_BACK_CAMERA, imageAnalysis);
}2）相机数据会通过ImageAnalysis.Analyzer接口回调到analyze(@NonNull ImageProxy image)
@Override
public void analyze(@NonNull ImageProxy image) {byte[] bytes = Utils.getDataFromImage(image);// 定位人脸，并且显示摄像头的图像faceTracker.detect(bytes, image.getWidth(), image.getHeight(), image.getImageInfo().getRotationDegrees());image.close();
}三、Native层识别人脸和画面渲染
1）加载OpenCV提供的人脸识别训练数据lbpcascade_frontalface到本地；
String path = Utils.copyAsset2Dir(this, "lbpcascade_frontalface.xml");2）初始化人脸跟踪中转站FaceTracker，将人脸识别训练数据路径传递到Native层；
faceTracker = new FaceTracker(path);public FaceTracker(String model) {mNativeObj = nativeCreateObject(model);
}private static native long nativeCreateObject(String model);Native层接收到人脸识别训练数据路径，初始化FaceTracker.cpp
extern "C"
JNIEXPORT jlong JNICALL
Java_com_ndk_head_FaceTracker_nativeCreateObject(JNIEnv *env, jclass clazz, jstring model_) {// 转换人脸训练模型数据为char *const char *model = env->GetStringUTFChars(model_, 0);FaceTracker *tracker = new FaceTracker(model);env->ReleaseStringUTFChars(model_, model);// 返回tracker地址给Java层return (jlong) tracker;
} 3）Native读取人脸识别训练数据，创建人脸检测跟踪器Ptr<DetectionBasedTracker> tracker；
FaceTracker::FaceTracker(const char *model) {// 初始化互斥锁pthread_mutex_init(&mutex, 0);// 创建检测器适配器Ptr<CascadeDetectorAdapter> mainDetector = makePtr<CascadeDetectorAdapter>(makePtr<CascadeClassifier>(model));Ptr<CascadeDetectorAdapter> trackingDetector = makePtr<CascadeDetectorAdapter>(makePtr<CascadeClassifier>(model));//跟踪器DetectionBasedTracker::Parameters DetectorParams;tracker = makePtr<DetectionBasedTracker>(DetectionBasedTracker(mainDetector, trackingDetector,DetectorParams));
}4）通过中转站FaceTracker，调用Native层tracker开启人脸跟踪；
faceTracker.start();public void start() {nativeStart(mNativeObj);
}private static native void nativeStart(long thiz); Native层开启人脸跟踪
extern "C"
JNIEXPORT void JNICALL
Java_com_ndk_head_FaceTracker_nativeStart(JNIEnv *env, jclass clazz, jlong thiz) {if (thiz != 0) {FaceTracker *tracker = reinterpret_cast<FaceTracker *>(thiz);tracker->tracker->run();}
}5）通过中转站FaceTracker，实例化Native层播放窗口ANativeWindow，关联surfaceView；
@Override
public void surfaceChanged(@NonNull SurfaceHolder holder, int format, int width, int height) {if (faceTracker != null)faceTracker.setSurface(holder.getSurface());
}public void setSurface(Surface surface) {nativeSetSurface(mNativeObj, surface);
}private static native void nativeSetSurface(long thiz, Surface surface);
Native层实例化ANativeWindow，关联surfaceView
extern "C"
JNIEXPORT void JNICALL
Java_com_ndk_head_FaceTracker_nativeSetSurface(JNIEnv *env, jclass clazz, jlong thiz,jobject surface) {if (thiz != 0) {FaceTracker *tracker = reinterpret_cast<FaceTracker *>(thiz);if (!surface) {tracker->setANativeWindow(0);return;}tracker->setANativeWindow(ANativeWindow_fromSurface(env, surface));}
}6）获取相机数据，传递Native层，人脸定位，绘制人脸框，渲染画面到屏幕。
@Override
public void analyze(@NonNull ImageProxy image) {byte[] bytes = Utils.getDataFromImage(image);// 定位人脸，并且显示摄像头的图像faceTracker.detect(bytes, image.getWidth(), image.getHeight(), image.getImageInfo().getRotationDegrees());image.close();
}public void detect(byte[] inputImage, int width, int height, int rotationDegrees) {nativeDetect(mNativeObj, inputImage, width, height, rotationDegrees);
}private static native void nativeDetect(long thiz, byte[] inputImage, int width, int height, int rotationDegrees);Native层识别人脸，绘制人脸红框
extern "C"
JNIEXPORT void JNICALL
Java_com_ndk_head_FaceTracker_nativeDetect(JNIEnv *env, jclass clazz, jlong thiz,jbyteArray inputImage_, jint width, jint height,jint rotationDegrees) {if (thiz == 0) {return;}FaceTracker *tracker = reinterpret_cast<FaceTracker *>(thiz);// 将图片数据转化为jbytejbyte *inputImage = env->GetByteArrayElements(inputImage_, 0);// 根据I420宽高，设置Mat（OpenCV支持的图片格式）的宽高，并赋值 srcMat src(height * 3 / 2, width, CV_8UC1, inputImage);// YUV转为RGBAcvtColor(src, src, CV_YUV2RGBA_I420);// 旋转图片if (rotationDegrees == 90) {rotate(src, src, ROTATE_90_CLOCKWISE);} else if (rotationDegrees == 270) {rotate(src, src, ROTATE_90_COUNTERCLOCKWISE);}Mat gray; // 存储降噪后的图片（灰度图）// 灰度化cvtColor(src, gray, CV_RGBA2GRAY);// 增强对比度equalizeHist(gray, gray);// 人脸定位tracker->tracker->process(gray);std::vector<Rect> faces; // 人脸集合tracker->tracker->getObjects(faces);for (Rect face:faces) {// 找到人脸，画红色矩形框rectangle(src, face, Scalar(255, 0, 0));}tracker->draw(src);env->ReleaseByteArrayElements(inputImage_, inputImage, 0);
}将最终定位完成的图片绘制到屏幕
void FaceTracker::draw(Mat img) {pthread_mutex_lock(&mutex);do {if (!window) {break;}// 设置window格式ANativeWindow_setBuffersGeometry(window, img.cols, img.rows,WINDOW_FORMAT_RGBA_8888);// 把需要显示的数据设置给bufferANativeWindow_Buffer buffer;if (ANativeWindow_lock(window, &buffer, 0)) {ANATIVEWINDOW_RELEASE(window);break;}// 把视频数据刷新到buffer中uint8_t *dstData = static_cast<uint8_t *>(buffer.bits);int dstlineSize = buffer.stride * 4;// 视频图形rgba数据uint8_t *srcData = img.data;int srclineSize = img.cols * 4;// 一行一行的拷贝for (int i = 0; i < buffer.height; ++i) {memcpy(dstData + i * dstlineSize, srcData + i * srclineSize, srclineSize);}// 提交渲染ANativeWindow_unlockAndPost(window);} while (0);pthread_mutex_unlock(&mutex);
}至此，OpenCV人脸识别定位技术项目已完成；同时人眼识别定位等相关实现也是雷同的，都可以通过OpenCV实现，后续会通过OpenCV与OpenGL实现大眼萌特效。
源码：
NdkHeadTest: NDK OpenCV人脸定位