NYC's Blog 上篇文章《机器视觉实战4：OpenCV Android环境搭建（喂饭版）》中介绍了如何使用Android Studio搭建OpenCV开发环境，本节基于之前搭建好的环境开发一个基于神经网络的目标检测App。
准备模型
首先从这里下载已经训练好的模型文件：
- deploy.prototxt：神经网络结构的描述文件
- mobilenet_iter_73000.caffemodel：神经网络的参数信息
这个模型是使用Caffe实现的Google MobileNet SSD检测模型。有个Caffe Zoo项目，收集了很多已经训练好的模型，有兴趣的可以看一下。下载好模型之后，在app/src/main/下面创建一个assets目录，把两个模型文件放进去。至此，模型的准备工作就完成了。
编写代码
布局文件activity_main.xml：
<?xml version="1.0" encoding="utf-8"?>
<androidx.constraintlayout.widget.ConstraintLayout 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">
<Button
android:id="@+id/imageSelect"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:layout_marginStart="32dp"
android:layout_marginLeft="32dp"
android:layout_marginTop="16dp"
android:text="@string/image_select"
app:layout_constraintStart_toStartOf="parent"
app:layout_constraintTop_toTopOf="parent" />
<Button
android:id="@+id/recognize"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:layout_marginStart="16dp"
android:layout_marginLeft="16dp"
android:layout_marginTop="16dp"
android:text="@string/recognize"
app:layout_constraintEnd_toEndOf="parent"
app:layout_constraintStart_toEndOf="@+id/imageSelect"
app:layout_constraintTop_toTopOf="parent" />
<ImageView
android:id="@+id/imageView"
android:layout_width="387dp"
android:layout_height="259dp"
android:layout_marginStart="8dp"
android:layout_marginLeft="8dp"
android:layout_marginTop="22dp"
android:layout_marginEnd="8dp"
android:layout_marginRight="8dp"
android:contentDescription="images"
app:layout_constraintEnd_toEndOf="parent"
app:layout_constraintStart_toStartOf="parent"
app:layout_constraintTop_toBottomOf="@+id/imageSelect" />
</androidx.constraintlayout.widget.ConstraintLayout>刚接触安卓开发没几天，布局是瞎写的，仅考虑了功能。
MainActivity.java代码：
package com.niyanchun.demo;
import androidx.annotation.Nullable;
import androidx.appcompat.app.AppCompatActivity;
import android.annotation.SuppressLint;
import android.content.Context;
import android.content.Intent;
import android.content.res.AssetManager;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.net.Uri;
import android.os.Bundle;
import android.util.Log;
import android.widget.Button;
import android.widget.EditText;
import android.widget.ImageView;
import android.widget.TextView;
import android.widget.Toast;
import org.opencv.android.OpenCVLoader;
import org.opencv.android.Utils;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.dnn.Dnn;
import org.opencv.dnn.Net;
import org.opencv.imgproc.Imgproc;
import java.io.BufferedInputStream;
import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
@SuppressLint("SetTextI18n")
public class MainActivity extends AppCompatActivity {
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
if (OpenCVLoader.initDebug()) {
Log.i("CV", "load OpenCV Library Successful.");
} else {
Log.i("CV", "load OpenCV Library Failed.");
}
imageView = findViewById(R.id.imageView);
imageView.setScaleType(ImageView.ScaleType.FIT_CENTER);
Button selectBtn = findViewById(R.id.imageSelect);
selectBtn.setOnClickListener(v -> {
Intent intent = new Intent();
intent.setType("image/*");
intent.setAction(Intent.ACTION_GET_CONTENT);
startActivityForResult(Intent.createChooser(intent, "选择图片"), PICK_IMAGE_REQUEST);
});
Button recognizeBtn = findViewById(R.id.recognize);
recognizeBtn.setOnClickListener(v -> {
// 确保加载完成
if (net == null) {
Toast.makeText(this, "正在加载模型，请稍后...", Toast.LENGTH_LONG).show();
while (net == null) {
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}
recognize();
});
}
@Override
protected void onResume() {
super.onResume();
loadModel();
}
@Override
protected void onActivityResult(int requestCode, int resultCode, @Nullable 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-decode", "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 rawWidth = options.outWidth;
int rawHeight = options.outHeight;
int max = Math.max(rawWidth, rawHeight);
int newWidth, newHeight;
float inSampleSize = 1.0f;
if (max > MAX_SIZE) {
newWidth = rawWidth / 2;
newHeight = rawHeight / 2;
while ((newWidth / inSampleSize) > MAX_SIZE || (newHeight / inSampleSize) > MAX_SIZE) {
inSampleSize *= 2;
}
}
options.inSampleSize = (int) inSampleSize;
options.inJustDecodeBounds = false;
options.inPreferredConfig = Bitmap.Config.ARGB_8888;
image = BitmapFactory.decodeStream(getContentResolver().openInputStream(uri), null, options);
imageView.setImageBitmap(image);
} catch (Exception e) {
Log.e("image-decode", "decode image error", e);
}
}
}
/**
* 加载模型
*/
private void loadModel() {
if (net == null) {
Toast.makeText(this, "开始加载模型...", Toast.LENGTH_LONG).show();
String proto = getPath("MobileNetSSD_deploy.prototxt", this);
String weights = getPath("mobilenet_iter_73000.caffemodel", this);
net = Dnn.readNetFromCaffe(proto, weights);
Log.i("model", "load model successfully.");
Toast.makeText(this, "模型加载成功!", Toast.LENGTH_LONG).show();
}
}
/**
* 识别
*/
private void recognize() {
// 该网络的输入层要求的图片尺寸为 300*300
final int IN_WIDTH = 300;
final int IN_HEIGHT = 300;
final float WH_RATIO = (float) IN_WIDTH / IN_HEIGHT;
final double IN_SCALE_FACTOR = 0.007843;
final double MEAN_VAL = 127.5;
final double THRESHOLD = 0.2;
Mat imageMat = new Mat();
Utils.bitmapToMat(image, imageMat);
Imgproc.cvtColor(imageMat, imageMat, Imgproc.COLOR_RGBA2RGB);
Mat blob = Dnn.blobFromImage(imageMat, IN_SCALE_FACTOR,
new Size(IN_WIDTH, IN_HEIGHT),
new Scalar(MEAN_VAL, MEAN_VAL, MEAN_VAL),
false, false);
net.setInput(blob);
Mat detections = net.forward();
int cols = imageMat.cols();
int rows = imageMat.rows();
detections = detections.reshape(1, (int) detections.total() / 7);
boolean detected = false;
for (int i = 0; i < detections.rows(); ++i) {
double confidenceTmp = detections.get(i, 2)[0];
if (confidenceTmp > THRESHOLD) {
detected = true;
int classId = (int) detections.get(i, 1)[0];
int left = (int) (detections.get(i, 3)[0] * cols);
int top = (int) (detections.get(i, 4)[0] * rows);
int right = (int) (detections.get(i, 5)[0] * cols);
int bottom = (int) (detections.get(i, 6)[0] * rows);
// Draw rectangle around detected object.
Imgproc.rectangle(imageMat, new Point(left, top), new Point(right, bottom),
new Scalar(0, 255, 0), 4);
String label = classNames[classId] + ": " + confidenceTmp;
int[] baseLine = new int[1];
Size labelSize = Imgproc.getTextSize(label, Core.FONT_HERSHEY_COMPLEX, 0.5, 5, baseLine);
// Draw background for label.
Imgproc.rectangle(imageMat, new Point(left, top - labelSize.height),
new Point(left + labelSize.width, top + baseLine[0]),
new Scalar(255, 255, 255), Core.FILLED);
// Write class name and confidence.
Imgproc.putText(imageMat, label, new Point(left, top),
Core.FONT_HERSHEY_COMPLEX, 0.5, new Scalar(0, 0, 0));
}
}
if (!detected) {
Toast.makeText(this, "没有检测到目标！", Toast.LENGTH_LONG).show();
return;
}
Utils.matToBitmap(imageMat, image);
imageView.setImageBitmap(image);
}
// Upload file to storage and return a path.
private static String getPath(String file, Context context) {
Log.i("getPath", "start upload file " + file);
AssetManager assetManager = context.getAssets();
BufferedInputStream inputStream = null;
try {
// Read data from assets.
inputStream = new BufferedInputStream(assetManager.open(file));
byte[] data = new byte[inputStream.available()];
inputStream.read(data);
inputStream.close();
// Create copy file in storage.
File outFile = new File(context.getFilesDir(), file);
FileOutputStream os = new FileOutputStream(outFile);
os.write(data);
os.close();
Log.i("getPath", "upload file " + file + "done");
// Return a path to file which may be read in common way.
return outFile.getAbsolutePath();
} catch (IOException ex) {
Log.e("getPath", "Failed to upload a file");
}
return "";
}
private static final int MAX_SIZE = 1024;
private ImageView imageView;
private Bitmap image;
private Net net = null;
private int PICK_IMAGE_REQUEST = 1;
private static final String[] classNames = {"background",
"aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair",
"cow", "diningtable", "dog", "horse",
"motorbike", "person", "pottedplant",
"sheep", "sofa", "train", "tvmonitor"};
}代码中的一些关键点说明如下：
loadModel：实现了模型的加载，OpenCV提供了readNetFromCaffe方法用于加载Caffe训练的模型，其输入就是两个模型文件。onActivityResult：实现了选择图片后的图片处理和展示。recognize：实现利用加载的模型进行目标检测，并根据检测结果用框画出目标的位置。和之前的基于HOG特征的目标检测类似。
然后点击运行，效果如下：
可以看到，检测到了显示器、盆栽、猫、人等。对安卓还不太熟，后面有时间了弄一从摄像头视频中实时检测的App玩玩。
Reference:
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