Camera Face Detection
1. camera app, in packages/apps/Camera/src/com/android/camera/camera.java
startFaceDetection()
stopFaceDetection()
在initializeFirstTime()函数中会调用startFaceDetection() ，以开始做人脸识别的准备动作。
在startFaceDetection中会首先判断getMaxNumDetectedFaces是否大于0，若没有大于0，则表示硬件不支持人脸识别，直接退出函数，否则
mCameraDevice.setFaceDetectionListener(this);
mCameraDevice.startFaceDetection();
设置人脸识别的监听者为this(注意这边的参数，这意味着在mCameraDevice中的mFaceListener其实为这边的package中的camera类)，用于回调函数，并调用mCameraDevice的startFaceDetection。
在这文件中还有一个关键函数：
onFaceDetection()
这在mCameraDevice中会调用到，用作最后的显示动作。
这里的mCameraDevice就是framework层的camera类。
packages/apps/Camera/src/com/android/camera/ui/FaceView.java
public class FaceView extends View implements FocusIndicator{
....
}
In FaceView:
frameworks/base/media/java/android/media/FaceDetector.java
frameworks/base/graphics/java/android/graphics/Matrix.java
frameworks/base/graphics/java/android/graphics/RectF.java
frameworks/base/graphics/java/android/graphics/drawable/Drawable.java
主要是关于人脸识别后的view的一些操作。
2. 接着上面说mCameraDevice
位于frameworks/base/core/java/android/hardware/Camera.java
相关变量的定义：private FaceDetectionListener mFaceListener;
private boolean mFaceDetectionRunning = false;
private static final int CAMERA_FACE_DETECTION_HW = 0;
private static final int CAMERA_FACE_DETECTION_SW = 1;
public interface FaceDetectionListener{
void onFaceDetection(Face[] faces, Camera camera);//这里调用的就是package中camera类的函数
}
public final void setFaceDetectionListener(FaceDetectionListener listener){
mFaceListener = listener;//设置监听者
}
public final void startFaceDetection() {
if (mFaceDetectionRunning) {
throw new RuntimeException("Face detection is already running");
}
_startFaceDetection(CAMERA_FACE_DETECTION_HW);//真正开始FaceDetection的地方
mFaceDetectionRunning = true;
}
public final void stopFaceDetection() {
_stopFaceDetection();//停止FaceDetection
mFaceDetectionRunning = false;
}
private native final void _stopFaceDetection();
_startFaceDetection()和_stopFaceDetection()都是jni的方法，即用到本地调用，_startFaceDetection()会调用到android_hardware_Camera_startFaceDetection这个函数：
status_t rc = camera->sendCommand(CAMERA_CMD_START_FACE_DETECTION, type, 0);
这边会调用到Hal层的sendCommand方法，传递CAMERA_CMD_START_FACE_DETECTION消息，在Hal要对这个Message进行处理，以开始处理人脸识别。这边介绍一下ti的做法：
in camera hal:
status_t CameraHal::sendCommand(int32_t cmd, int32_t arg1, int32_t arg2)
{
……
switch(cmd)
{
case CAMERA_CMD_START_SMOOTH_ZOOM:
ret = mCameraAdapter->sendCommand(CameraAdapter::CAMERA_START_SMOOTH_ZOOM, arg1);
break;
case CAMERA_CMD_STOP_SMOOTH_ZOOM:
ret = mCameraAdapter->sendCommand(CameraAdapter::CAMERA_STOP_SMOOTH_ZOOM);
case CAMERA_CMD_START_FACE_DETECTION:
ret = mCameraAdapter->sendCommand(CameraAdapter::CAMERA_START_FD);
break;
case CAMERA_CMD_STOP_FACE_DETECTION:
ret = mCameraAdapter->sendCommand(CameraAdapter::CAMERA_STOP_FD);
break;
default:
break;
};
}
遇到CAMERA_CMD_START_FACE_DETECTION这个消息后，会继续调用mCameraAdapter->sendCommand，
之后会继续调用：
case CameraAdapter::CAMERA_START_FD:
ret = startFaceDetection();
break;
依然会去调用startFaceDetection这个函数，CameraAdapter中的startFaceDetection并未做什么事情，这里面主要是做一些初始化的设置动作，需要自己实现，我们可以参考OMXFD中的。
hardware/ti/omap4xxx/camera/OMXCameraAdapter/OMXFD.cpp //This file contains functionality for handling face detection.
OMXCameraAdapter::setParametersFD()
OMXCameraAdapter::startFaceDetection()
OMXCameraAdapter::stopFaceDetection()
OMXCameraAdapter::pauseFaceDetection()
OMXCameraAdapter::setFaceDetection()
OMXCameraAdapter::detectFaces()
OMXCameraAdapter::encodeFaceCoordinates()
hardware/ti/omap4xxx/camera/OMXCameraAdapter/OMXAlgo.cpp //This file contains functionality for handling algorithm configurations.
OMXCameraAdapter::setAlgoPriority()
hardware/ti/omap4xxx/domx/omx_core/inc/OMX_Core.h //depend on hardware/ti/omap4xxx/domx modules.
OMX_SetConfig()
hardware/ti/omap4xxx/domx/omx_core/inc/OMX_TI_IVCommon.h
typedef struct OMX_CONFIG_EXTRADATATYPE {
OMX_U32 nSize;
OMX_VERSIONTYPE nVersion;
OMX_U32 nPortIndex;
OMX_EXT_EXTRADATATYPE eExtraDataType;
OMX_TI_CAMERAVIEWTYPE eCameraView;
OMX_BOOL bEnable;
} OMX_CONFIG_EXTRADATATYPE;
typedef struct OMX_CONFIG_OBJDETECTIONTYPE {
OMX_U32 nSize;
OMX_VERSIONTYPE nVersion;
OMX_U32 nPortIndex;
OMX_BOOL bEnable;
OMX_BOOL bFrameLimited;
OMX_U32 nFrameLimit;
OMX_U32 nMaxNbrObjects;
OMX_S32 nLeft;
OMX_S32 nTop;
OMX_U32 nWidth;
OMX_U32 nHeight;
OMX_OBJDETECTQUALITY eObjDetectQuality;
OMX_U32 nPriority;
OMX_U32 nDeviceOrientation;
} OMX_CONFIG_OBJDETECTIONTYPE;
在notifyEvent中，
case CameraHalEvent::EVENT_FACE:
faceEvtData = evt->mEventData->faceEvent;
if ( ( NULL != mCameraHal ) &&
( NULL != mNotifyCb) &&
( mCameraHal->msgTypeEnabled(CAMERA_MSG_PREVIEW_METADATA) ) )
{
// WA for an issue inside CameraService
camera_memory_t *tmpBuffer = mRequestMemory(-1, 1, 1, NULL);
mDataCb(CAMERA_MSG_PREVIEW_METADATA,
tmpBuffer,
0,
faceEvtData->getFaceResult(),
mCallbackCookie);
faceEvtData.clear();
if ( NULL != tmpBuffer ) {
tmpBuffer->release(tmpBuffer);
}
}
break;
一旦有人脸识别的数据，就回传 faceEvtData->getFaceResult(),给上层的监听者，消息是CAMERA_MSG_PREVIEW_METADATA。
NotificationThread->mAppCallbackNotifier->notificationThread()->
if(mEventQ.hasMsg()) {
///Received an event from one of the event providers
CAMHAL_LOGDA("Notification Thread received an event from event provider (CameraAdapter)");
notifyEvent();
}
它实在Callbacknotifier中初始化中另起一个线程用来监听事件，如果有人脸识别的动作的化，则回传相应的结果给上层的监听者以做显示，这跟preview(notifyEvent())是分开，是另外的一份单独的数据。
而在framework层的camera里，会有如下函数进行消息处理：
public void handleMessage(Message msg) {
switch(msg.what) {
......
case CAMERA_MSG_PREVIEW_METADATA:
if (mFaceListener != null) {
mFaceListener.onFaceDetection((Face[])msg.obj, mCamera);
}
return;
}
这样就可以在预览的界面上看到人脸识别的窗口了。
public static class Face{
public Face() {
}
public Rect rect;
public int score;
public int id = -1;
public Point leftEye = null;
public Point rightEye = null;
public Point mouth = null;
}
public class Parameters { //new camera parameters.
private static final String KEY_MAX_NUM_DETECTED_FACES_HW = "max-num-detected-faces-hw";
private static final String KEY_MAX_NUM_DETECTED_FACES_SW = "max-num-detected-faces-sw";
public int getMaxNumDetectedFaces() {
return getInt(KEY_MAX_NUM_DETECTED_FACES_HW, 0);
}
}
}
3.总结
Camera app 和 android api已经准备好，我们的工作主要集中在camera的Hal层。我们需要了解所有的face detection的相关方法以实现它。