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
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。
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,更多相关Android OpenCV 人脸检测APP内容请搜索编程学习网以前的文章希望大家以后多多支持编程学习网!
沃梦达教程
本文标题为:Android 利用OpenCV制作人脸检测APP
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