概述
基于前两篇JNI的准备，这次尝试集成一个Opencv的sdk，参照demo实现一个人脸检测功能。人脸检测与人脸识别是两种不同的概念。人脸识别更加复杂，从数据采集到获取样品特征值，里面涉及到的数据处理包含了很多高等数学的概念。希望以后能实现一个吧。
今天主要是实现人脸检测功能。
《JNI（一）：基础理论知识》
《JNI（二）：环境配置和简单使用》
看下效果
具体步骤
下载OpenCV的SDK
点我下载最新的SDK
新建Android项目
在新项目下将sdk的module导进来
项目配置
需要在app的gradle里添加以下1
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36defaultConfig {
applicationId "com.zhouk.opencvdemo"
minSdkVersion 21
targetSdkVersion 26
versionCode 1
versionName "1.0"
testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
externalNativeBuild {
cmake {
arguments "-DOpenCV_DIR=" + project(':sdk').projectDir + "/native/jni",
"-DANDROID_TOOLCHAIN=clang",
"-DANDROID_STL=c++_static"
targets "detection_based_tracker"
}
}
}
buildTypes {
release {
minifyEnabled false
proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
}
}
sourceSets {
main {
java.srcDirs = ['src/main/java'] //指定编译源码的目录
aidl.srcDirs = ['src/main/java']
res.srcDirs = ['src/main/res'] //指定编译资源的目录
manifest.srcFile 'AndroidManifest.xml'
}
}
externalNativeBuild {
cmake {
path 'jni/CMakeLists.txt' //提供cmake的路径
}
}
初始化：1
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4if (OpenCVLoader.initDebug()) {
Log.d(TAG, "找到opencv库");
mLoaderCallback.onManagerConnected(LoaderCallbackInterface.SUCCESS);
}
初始化成功后回调开始加载opencv库1
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51private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
public void onManagerConnected(int status) {
switch (status) {
case LoaderCallbackInterface.SUCCESS:
{
Log.e(TAG, "OpenCV 加载成功");
// Load native library after(!) OpenCV initialization
System.loadLibrary("detection_based_tracker");
try {
// load cascade file from application resources
InputStream is = getResources().openRawResource(R.raw.lbpcascade_frontalface);//人脸特征数据
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 bytesRead;
while ((bytesRead = is.read(buffer)) != -1) {
os.write(buffer, 0, bytesRead);
}
is.close();
os.close();
mJavaDetector = new CascadeClassifier(mCascadeFile.getAbsolutePath());
if (mJavaDetector.empty()) {
Log.e(TAG, "cascade classifier加载失败");
mJavaDetector = null;
} else
Log.i(TAG, "Loaded cascade classifier from " + mCascadeFile.getAbsolutePath());
mNativeDetector = new DetectionBasedTracker(mCascadeFile.getAbsolutePath(), 0);
cascadeDir.delete();
} catch (IOException e) {
e.printStackTrace();
Log.e(TAG, "加载cascade失败：" + e);
}
mOpenCvCameraView.enableView();
} break;
default:
{
super.onManagerConnected(status);
} break;
}
}
};
摄像头返回的Mat回调1
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33public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
mRgba = inputFrame.rgba();
mGray = inputFrame.gray();//灰度处理
if (mAbsoluteFaceSize == 0) {
int height = mGray.rows();
if (Math.round(height * mRelativeFaceSize) > 0) {
mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);
}
mNativeDetector.setMinFaceSize(mAbsoluteFaceSize);
}
MatOfRect faces = new MatOfRect();
//把检测到的人脸圈出来
if (mDetectorType == JAVA_DETECTOR) {
if (mJavaDetector != null)
mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO: objdetect.CV_HAAR_SCALE_IMAGE
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!");
}
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;
}
结果