Android调用系统摄像头 android摄像头实时识别

随着直播渐渐的火起来，像抱着直播大腿的其他功能也渐渐的火起来了，比如说人脸识别。说起人脸识别用处甚广，比如说有以这个功能为核心的app：美颜相机、美图秀秀、SNOW等等，但是美颜相机和美图秀秀是用的国内SDK《Face++》来做的，这个sdk呢好像是他们自己的后台进行识别并不是app本身做识别。这样就跟我们今天要了解的动态识别不是很对路，肯定不能拿到摄像头的一帧画面去调一次接口再接回参数吧，这样性能肯定不行。所以今天就拿SNOW的例子来说，虽然我不知道他是用什么做的，但是我们可以用openCV也能实现。

我们先看看效果图：

实现步骤如下：

2、然后新建个项目我这里以studio里为基准，在main目录里面新建jniLibs文件夹，为什么叫jniLibs呢，因为这是调用c库的默认文件夹命名，当然你也可以命名其他的，但是需要在build里面指定这个文件夹。好了，打开我们刚才下载的文件，然后一次打开sdk\native\libs，最后把libs目录里面的所有文件夹拷贝到jniLibs里面去。请看图：

3、加好jniLibs之后呢还需要导入一个module，在studio里面点击file->new->import module->导入module目录是刚才下载的sdk\java这个目录。请看图：

4、导入之后呢右键项目打开open module setting选项，在app选项里点击Dependencies这个，然后点击最右边的+号把刚刚导入的module加进去。请看图：

5、现在开始写代码了，这里我把需要写的代码文件会一一贴出来，下面请看图：

首先是MainActivity的代码：

package com.wyw.facedemo;
import android.content.Context;
import android.os.Bundle;
import android.support.v7.app.AppCompatActivity;
import android.util.Log;
import android.view.View;
import android.view.WindowManager;
import android.widget.Button;
import android.widget.RelativeLayout;
import org.opencv.android.CameraBridgeViewBase;
import org.opencv.android.JavaCameraView;
import org.opencv.android.OpenCVLoader;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.objdetect.CascadeClassifier;
import java.io.File;
import java.io.FileOutputStream;
import java.io.InputStream;
public class MainActivity extends AppCompatActivity implements CameraBridgeViewBase.CvCameraViewListener {
private CameraBridgeViewBase openCvCameraView;
private CascadeClassifier cascadeClassifier;
//图像人脸小于高度的多少就不检测
private int absoluteFaceSize;
//临时图像对象
private Mat matLin;
//最终图像对象
private Mat mat;
//前置摄像头
public static int CAMERA_FRONT = 0;
//后置摄像头
public static int CAMERA_BACK = 1;
private int camera_scene = CAMERA_BACK;
private void initializeOpenCVDependencies() {
try {
// Copy the resource into a temp file so OpenCV can load it
InputStream is = getResources().openRawResource(R.raw.lbpcascade_frontalface);
File cascadeDir = getDir("cascade", Context.MODE_PRIVATE);
File 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();
// Load the cascade classifier
cascadeClassifier = new CascadeClassifier(mCascadeFile.getAbsolutePath());
} catch (Exception e) {
Log.e("OpenCVActivity", "Error loading cascade", e);
}
// And we are ready to go
openCvCameraView.enableView();
}
@Override
public void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
getWindow().addFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON);
setContentView(R.layout.activity_main);
final RelativeLayout relativeLayout = (RelativeLayout) findViewById(R.id.relative);
openCvCameraView = new JavaCameraView(this, CameraBridgeViewBase.CAMERA_ID_FRONT);
openCvCameraView.setCvCameraViewListener(this);
final Button button = new Button(MainActivity.this);
button.setText("切换摄像头");
button.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
if (camera_scene == CAMERA_FRONT) {//如果是前置摄像头就切换成后置
relativeLayout.removeAllViews();
openCvCameraView.disableView();
openCvCameraView = null;
cascadeClassifier = null;
openCvCameraView = new JavaCameraView(MainActivity.this, CameraBridgeViewBase.CAMERA_ID_BACK);
openCvCameraView.setCvCameraViewListener(MainActivity.this);
openCvCameraView.setCameraIndex(CameraBridgeViewBase.CAMERA_ID_BACK);//后置摄像头
camera_scene = CAMERA_BACK;
relativeLayout.addView(openCvCameraView);
relativeLayout.addView(button);
initializeOpenCVDependencies();
} else {
relativeLayout.removeAllViews();
openCvCameraView.disableView();
openCvCameraView = null;
cascadeClassifier = null;
openCvCameraView = new JavaCameraView(MainActivity.this, CameraBridgeViewBase.CAMERA_ID_FRONT);
openCvCameraView.setCvCameraViewListener(MainActivity.this);
openCvCameraView.setCameraIndex(CameraBridgeViewBase.CAMERA_ID_FRONT);//前置摄像头
camera_scene = CAMERA_FRONT;
relativeLayout.addView(openCvCameraView);
relativeLayout.addView(button);
initializeOpenCVDependencies();
}
}
});
relativeLayout.addView(openCvCameraView);
relativeLayout.addView(button);
if (camera_scene == CAMERA_FRONT) {
openCvCameraView.setCameraIndex(CameraBridgeViewBase.CAMERA_ID_FRONT);//前置摄像头
} else if (camera_scene == CAMERA_BACK) {
openCvCameraView.setCameraIndex(CameraBridgeViewBase.CAMERA_ID_BACK);//后置摄像头
}
}
@Override
public void onCameraViewStarted(int width, int height) {
matLin = new Mat(height, width, CvType.CV_8UC4);//临时图像
// 人脸小于高度的百分之30就不检测
absoluteFaceSize = (int) (height * 0.3);
}
@Override
public void onCameraViewStopped() {
}
@Override
public Mat onCameraFrame(Mat aInputFrame) {
//转置函数,将图像翻转(顺时针90度)
Core.transpose(aInputFrame, matLin);
if (camera_scene == CAMERA_FRONT) {//前置摄像头
//转置函数,将图像翻转(对换)
Core.flip(matLin, aInputFrame, 1);
//转置函数,将图像顺时针顺转(对换)
Core.flip(aInputFrame, matLin, 0);
mat = matLin;
} else if (camera_scene == CAMERA_BACK) {//后置摄像头
//转置函数,将图像翻转(对换)
Core.flip(matLin, aInputFrame, 1);
mat = aInputFrame;
}
MatOfRect faces = new MatOfRect();
Log.i("123456", "absoluteFaceSize = " + absoluteFaceSize);
// Use the classifier to detect faces
if (cascadeClassifier != null) {
cascadeClassifier.detectMultiScale(mat, faces, 1.1, 1, 1,
new Size(absoluteFaceSize, absoluteFaceSize), new Size());
}
// 检测出多少个
Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++) {
Log.i("123456", "facesArray[i].tl()坐上坐标 == " + facesArray[i].tl() + " facesArray[i].br() == 右下坐标" + facesArray[i].br());
Core.rectangle(mat, facesArray[i].tl(), facesArray[i].br(), new Scalar(0, 255, 0, 255), 3);
}
return mat;
}
@Override
public void onResume() {
super.onResume();
if (!OpenCVLoader.initDebug()) {
Log.e("log_wons", "OpenCV init error");
// Handle initialization error
}
initializeOpenCVDependencies();
//OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_6, this, mLoaderCallback);
}
}

然后是layout的xml代码：

现在是raw文件夹里面的xml(这个xml是图片解析出来进行对比校验人脸的模型库)由于这个文件有一千多行就不贴了，如有需要请去下载本demo查看！当然也可以去你下载的openCV的sdk里面拿，目录是\samples\face-detection\res\raw。请看图：

最后就是AndroidManifest文件了：

做到这一步就赶紧把你的代码运行起来吧！！本篇博客就到这里，如果有有疑问的欢迎留言讨论。