12-06 07:03 阅读 198

Android 利用OpenCV制作人脸检测APP

本篇文章手把手教大家使用OpenCV来实现一个能在安卓手机上运行的人脸检测APP。其实不仅仅是能检测人脸，还能检测鼻子，嘴巴，眼睛和耳朵。需要的朋友可以参考一下

前言

本篇文章手把手教大家使用OpenCV来实现一个能在安卓手机上运行的人脸检测APP。其实不仅仅是能检测人脸，还能检测鼻子，嘴巴，眼睛和耳朵。花了不少精力写这篇文章，希望点赞收藏关注。

无图无真相，先把APP运行的结果给大家看看。

如上图所示，APP运行后，点击“选择图片”，从手机中选择一张图片，然后点击“处理”，APP会将人脸用矩形给框起来，同时把鼻子也给检测出来了。由于目的是给大家做演示，所以APP设计得很简单，而且也只实现了检测人脸和鼻子，没有实现对其他五官的检测。而且这个APP也只能检测很简单的图片，如果图片中背景太复杂就无法检测出人脸。

下面我将一步一步教大家如何实现上面的APP！

第一步：下载并安装Android studio

为了保证大家能下载到和我相同版本的Android Studio，我把安装包上传了到微云。地址是：https://share.weiyun.com/bHVOWGC9

下载后，一路点击下一步就安装好了。当然，安装过程中要联网，所以可能会中途失败，如果失败了，重试几次，如果还是有问题，那么可能要开启VPN。

第二步：下载SDK tools

打开Android studio后，点击“File”->“Settings”

点击“Appearance & Behavior”->“System Settings”->“Android SDK”->“SDK Tools”。

然后选中“NDK”和“CMake”，点击“OK”。下载这两个工具可能要花一点时间，如果失败了请重试或开启VPN。

第三步：新建一个Android APP项目

点击“File”->“New”->“New Project”

选中“Empty Activity”，点击“Next”

“Language”选择“Java”，Minimum SDK选择“API 21”。点击“Finish”

第四步：下载Opencv

下载地址

下载后解压。

第五步：导入OpenCV

将opencv-4.5.4-android-sdk\OpenCV-android-sdk下面的sdk复制到你在第三步创建的Android项目下面。就是第三步图中的D:\programming\MyApplication下面。然后将sdk文件夹改名为openCVsdk。

选择“Project”->“settings.gradle”。在文件中添加include ‘:openCVsdk'

选择“Project”->“openCVsdk”->“build.gradle”。

将apply plugin: 'kotlin-android'改为//apply plugin: ‘kotlin-android'

将compileSdkVersion和minSdkVersion，targetSdkVersion改为31，21，31。

点击“File”->“Project Structure”

点击“Dependencies”->“app”->“+”->“Module Dependency”

选中“openCVsdk”，点击“OK”，以及母窗口的“OK”

在Android项目文件夹的app\src里面创建一个新文件夹jniLibs，然后把openCV文件夹的opencv-4.5.4-android-sdk\OpenCV-android-sdk\sdk\native\staticlibs里面的东西都copy到jniLibs文件夹中。

下载分类器。解压后，将下图中的文件都复制到项目文件夹的app\src\main\res\raw文件夹下。

第六步：添加代码

双击“Project”->“app”-》“main”-》“res”下面的“activity_main.xml”。然后点击右上角的“code”。

然后将里面的代码都换成下面的代码

<?xml version="1.0" encoding="utf-8"?>

<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"

    xmlns:app="http://schemas.android.com/apk/res-auto"

    xmlns:tools="http://schemas.android.com/tools"

    android:layout_width="match_parent"

    android:layout_height="match_parent"

    tools:context=".MainActivity"

    android:orientation="vertical"

>

    <Button

        android:id="@+id/select_btn"

        android:layout_width="wrap_content"

        android:layout_height="wrap_content"

        android:text="选择图片" />

    <Button

        android:id="@+id/process_btn"

        android:layout_width="wrap_content"

        android:layout_height="wrap_content"

        android:text="处理" />

    <ImageView

        android:id="@+id/imageView"

        android:layout_width="wrap_content"

        android:layout_height="wrap_content" />

</LinearLayout>

双击“Project”->“app”-》“main”-》“java”-》“com.example…”下面的“MainActivity”。然后把里面的代码都换成下面的代码（保留原文件里的第一行代码）

import androidx.appcompat.app.AppCompatActivity;

import android.os.Bundle;

import android.content.Intent;

import android.graphics.Bitmap;

import android.graphics.BitmapFactory;

import android.net.Uri;

import android.util.Log;

import android.view.View;

import android.widget.Button;

import android.widget.ImageView;

import org.opencv.android.OpenCVLoader;

import org.opencv.android.Utils;

import org.opencv.core.CvType;

import org.opencv.core.Mat;

import org.opencv.core.Point;

import org.opencv.imgproc.Imgproc;

import java.io.File;

import java.io.FileOutputStream;

import java.io.IOException;

import java.io.InputStream;

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 android.content.Context;

public class MainActivity extends AppCompatActivity {

    private double max_size = 1024;

    private int PICK_IMAGE_REQUEST = 1;

    private ImageView myImageView;

    private Bitmap selectbp;

    private static final String    TAG                 = "OCVSample::Activity";

    private static final Scalar    FACE_RECT_COLOR     = new Scalar(0, 255, 0, 255);

    public static final int        JAVA_DETECTOR       = 0;

    public static final int        NATIVE_DETECTOR     = 1;

    private Mat                    mGray;

    private File                   mCascadeFile;

    private CascadeClassifier      mJavaDetector,mNoseDetector;

    private int                    mDetectorType       = JAVA_DETECTOR;

    private float                  mRelativeFaceSize   = 0.2f;

    private int                    mAbsoluteFaceSize   = 0;

    @Override

    protected void onCreate(Bundle savedInstanceState) {

        super.onCreate(savedInstanceState);

        setContentView(R.layout.activity_main);

        staticLoadCVLibraries();

        myImageView = (ImageView)findViewById(R.id.imageView);

        myImageView.setScaleType(ImageView.ScaleType.FIT_CENTER);

        Button selectImageBtn = (Button)findViewById(R.id.select_btn);

        selectImageBtn.setOnClickListener(new View.OnClickListener() {

            @Override

            public void onClick(View v) {

                // makeText(MainActivity.this.getApplicationContext(), "start to browser image", Toast.LENGTH_SHORT).show();

                selectImage();

}

            private void selectImage() {

                Intent intent = new Intent();

                intent.setType("image/*");

                intent.setAction(Intent.ACTION_GET_CONTENT);

                startActivityForResult(Intent.createChooser(intent,"选择图像..."), PICK_IMAGE_REQUEST);

}

});

        Button processBtn = (Button)findViewById(R.id.process_btn);

        processBtn.setOnClickListener(new View.OnClickListener() {

            @Override

            public void onClick(View v) {

                // makeText(MainActivity.this.getApplicationContext(), "hello, image process", Toast.LENGTH_SHORT).show();

                convertGray();

}

});

}

    private void staticLoadCVLibraries() {

        boolean load = OpenCVLoader.initDebug();

        if(load) {

            Log.i("CV", "Open CV Libraries loaded...");

}

}

    private void convertGray() {

        Mat src = new Mat();

        Mat temp = new Mat();

        Mat dst = new Mat();

        Utils.bitmapToMat(selectbp, src);

        Imgproc.cvtColor(src, temp, Imgproc.COLOR_BGRA2BGR);

        Log.i("CV", "image type:" + (temp.type() == CvType.CV_8UC3));

        Imgproc.cvtColor(temp, dst, Imgproc.COLOR_BGR2GRAY);

        Utils.matToBitmap(dst, selectbp);

        myImageView.setImageBitmap(selectbp);

        mGray = dst;

        mJavaDetector = loadDetector(R.raw.lbpcascade_frontalface,"lbpcascade_frontalface.xml");

        mNoseDetector = loadDetector(R.raw.haarcascade_mcs_nose,"haarcascade_mcs_nose.xml");

        if (mAbsoluteFaceSize == 0) {

            int height = mGray.rows();

            if (Math.round(height * mRelativeFaceSize) > 0) {

                mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize);

}

}

        MatOfRect faces = new MatOfRect();

        if (mJavaDetector != null) {

            mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO: objdetect.CV_HAAR_SCALE_IMAGE

                        new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size());

}

        Rect[] facesArray = faces.toArray();

        for (int i = 0; i < facesArray.length; i++) {

            Log.e(TAG, "start to detect nose!");

            Mat faceROI = mGray.submat(facesArray[i]);

            MatOfRect noses = new MatOfRect();

            mNoseDetector.detectMultiScale(faceROI, noses, 1.1, 2, 2,

                    new Size(30, 30));

            Rect[] nosesArray = noses.toArray();

            Imgproc.rectangle(src,

                    new Point(facesArray[i].tl().x + nosesArray[0].tl().x, facesArray[i].tl().y + nosesArray[0].tl().y) ,

                    new Point(facesArray[i].tl().x + nosesArray[0].br().x, facesArray[i].tl().y + nosesArray[0].br().y) ,

                    FACE_RECT_COLOR, 3);

            Imgproc.rectangle(src, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);

}

        Utils.matToBitmap(src, selectbp);

        myImageView.setImageBitmap(selectbp);

}

    private CascadeClassifier loadDetector(int rawID,String fileName) {

        CascadeClassifier classifier = null;

        try {

            // load cascade file from application resources

            InputStream is = getResources().openRawResource(rawID);

            File cascadeDir = getDir("cascade", Context.MODE_PRIVATE);

            mCascadeFile = new File(cascadeDir, fileName);

            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();

            Log.e(TAG, "start to load file:  " + mCascadeFile.getAbsolutePath());

            classifier = new CascadeClassifier(mCascadeFile.getAbsolutePath());

            if (classifier.empty()) {

                Log.e(TAG, "Failed to load cascade classifier");

                classifier = null;

            } else

                Log.i(TAG, "Loaded cascade classifier from " + mCascadeFile.getAbsolutePath());

            cascadeDir.delete();

        } catch (IOException e) {

            e.printStackTrace();

            Log.e(TAG, "Failed to load cascade. Exception thrown: " + e);

}

        return classifier;

}

    @Override

    protected void onActivityResult(int requestCode, int resultCode, Intent data) {

        super.onActivityResult(requestCode, resultCode, data);

        if (requestCode == PICK_IMAGE_REQUEST && resultCode == RESULT_OK && data != null && data.getData() != null) {

            Uri uri = data.getData();

            try {

                Log.d("image-tag", "start to decode selected image now...");

                InputStream input = getContentResolver().openInputStream(uri);

                BitmapFactory.Options options = new BitmapFactory.Options();

                options.inJustDecodeBounds = true;

                BitmapFactory.decodeStream(input, null, options);

                int raw_width = options.outWidth;

                int raw_height = options.outHeight;

                int max = Math.max(raw_width, raw_height);

                int newWidth = raw_width;

                int newHeight = raw_height;

                int inSampleSize = 1;

                if (max > max_size) {

                    newWidth = raw_width / 2;

                    newHeight = raw_height / 2;

                    while ((newWidth / inSampleSize) > max_size || (newHeight / inSampleSize) > max_size) {

                        inSampleSize *= 2;

}

}

                options.inSampleSize = inSampleSize;

                options.inJustDecodeBounds = false;

                options.inPreferredConfig = Bitmap.Config.ARGB_8888;

                selectbp = BitmapFactory.decodeStream(getContentResolver().openInputStream(uri), null, options);

                myImageView.setImageBitmap(selectbp);

            } catch (Exception e) {

                e.printStackTrace();

}

}

}

}

第七步：连接手机运行程序

首先要打开安卓手机的开发者模式，每个手机品牌的打开方式不一样，你自行百度一下就知道了。例如在百度中搜索“小米手机如何开启开发者模式”。

然后用数据线将手机和电脑连接起来。成功后，Android studio里面会显示你的手机型号。如下图中显示的是“Xiaomi MI 8 UD”，本例中的开发手机是小米手机。

点击上图中的“Run”-》“Run ‘app'”就可以将APP运行到手机上面了，注意手机屏幕要处于打开状态。你自拍的图片可以检测不成功，可以下载我的测试图片试试。

到此这篇关于Android 利用OpenCV制作人脸检测APP的文章就介绍到这了

原文链接：https://blog.csdn.net/jiangjunshow/article/details/121722047