深入OpenCV Android应用开发中文版 – 第七章代码更新

本书中文版购书链接：京东当当

英文原版购书链接：亚马逊《Mastering OpenCV Android Application Programming》

原书所给代码以章为单位，针对的Android版本从API 19到API 21不等，同时使用的OpenCV库版本也有2.4.9和2.4.10两种。本文给出的代码是在原书代码的基础上，针对Android 7.0（API 24）与OpenCV 3.2进行了修改，使某些操作更为合理。

原书的完整代码可以在这里获取：https://www.packtpub.com/lcode_download/22299。

更新后的代码托管在GitHub：https://github.com/johnhany/MOAAP/tree/master/MOAAP-Chp7-r3。

关于在Android Studio上配置OpenCV开发环境的方法，请参考《在Android Studio上进行OpenCV 3.1开发》。

本章介绍如何在Android Studio 2上通过OpenCV使用两种基本的机器学习算法：支持向量机（SVM）和最近邻分类器（KNN），并开发一个简单的数字识别应用。关于这两种算法的原理可以参考《深入OpenCV Android应用开发》第七章。

本文需要用到MNIST数据集。如果官方网页打不开，可以在这里下载。下载好后，把train-images.idx3-ubyte和train-labels.idx1-ubyte两个文件拷贝到手机（或者模拟器）的SD卡根目录（没有单独SD卡的则拷贝到手机内存的根目录）。

开发环境

Windows 10 x64

Android Studio 2.3.3（Gradle 3.3，Android Plugin 2.3.3）

Android 7.0（API 24）

JDK 8u141

OpenCV 3.2.0 Android SDK

代码及简略解释

1. 创建Android Studio项目，包命名为net.johnhany.moaap_chp7。导入OpenCV-android-sdk\sdk\java到项目中，并为app模块加载模块依赖。

2. 将app\src\main\java\net\johnhany\moaap_chp7\MainActivity.java文件修改为如下内容：

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package net.johnhany.moaap_chp7;

import android.Manifest;

import android.content.pm.PackageManager;

import android.support.annotation.NonNull;

import android.support.v4.app.ActivityCompat;

import android.support.v4.content.ContextCompat;

import android.support.v7.app.AppCompatActivity;

import android.os.Bundle;

import android.util.Log;

import android.view.Menu;

import android.view.MenuItem;

import android.view.MotionEvent;

import android.view.SurfaceView;

import android.view.WindowManager;

import org.opencv.android.BaseLoaderCallback;

import org.opencv.android.CameraBridgeViewBase.CvCameraViewFrame;

import org.opencv.android.LoaderCallbackInterface;

import org.opencv.android.OpenCVLoader;

import org.opencv.core.Core;

import org.opencv.core.Mat;

import org.opencv.core.Point;

import org.opencv.core.Scalar;

import org.opencv.android.CameraBridgeViewBase;

import org.opencv.android.CameraBridgeViewBase.CvCameraViewListener2;

import org.opencv.imgproc.Imgproc;

import static org.opencv.core.Core.FONT_HERSHEY_SIMPLEX;

public class MainActivity extends AppCompatActivity implements CvCameraViewListener2 {

private CameraBridgeViewBase mOpenCvCameraView;

private DigitRecognizer mnist;

static int REQUEST_CAMERA = 0;

static int REQUEST_EXTERNAL_STROAGE = 0;

static boolean camera_granted = false;

static boolean read_external_storage_granted = false;

private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {

@Override

public void onManagerConnected(int status) {

switch (status) {

case LoaderCallbackInterface.SUCCESS:

{

Log.d("OCR", "Start loading MNIST and creating classifier");

mOpenCvCameraView.enableView();

mnist = new DigitRecognizer("train-images.idx3-ubyte", "train-labels.idx1-ubyte");

Log.d("OCR", "Loading MNIST done");

} break;

default:

{

super.onManagerConnected(status);

} break;

}

};

/** Called when the activity is first created. */

@Override

public void onCreate(Bundle savedInstanceState) {

super.onCreate(savedInstanceState);

getWindow().addFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON);

setContentView(R.layout.activity_main);

if (ContextCompat.checkSelfPermission(MainActivity.this, Manifest.permission.CAMERA)

!= PackageManager.PERMISSION_GRANTED) {

Log.i("permission", "request CAMERA");

ActivityCompat.requestPermissions(MainActivity.this, new String[]{Manifest.permission.CAMERA}, REQUEST_CAMERA);

}else {

Log.i("permission", "CAMERA already granted");

camera_granted = true;

}

if (ContextCompat.checkSelfPermission(MainActivity.this, Manifest.permission.READ_EXTERNAL_STORAGE)

!= PackageManager.PERMISSION_GRANTED) {

Log.i("permission", "request EXTERNAL_STROAGE");

ActivityCompat.requestPermissions(MainActivity.this, new String[]{Manifest.permission.READ_EXTERNAL_STORAGE}, REQUEST_EXTERNAL_STROAGE);

}else {

Log.i("permission", "EXTERNAL_STROAGE already granted");

read_external_storage_granted = true;

}

OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_3_2_0, this, mLoaderCallback);

mOpenCvCameraView = (CameraBridgeViewBase) findViewById(R.id.java_surface_view);

mOpenCvCameraView.setVisibility(SurfaceView.VISIBLE);

mOpenCvCameraView.setCvCameraViewListener(this);

Log.d("OCR", "onCreate done");

}

@Override

public void onRequestPermissionsResult(int requestCode, @NonNull String permissions[], @NonNull int[] grantResults) {

if (requestCode == REQUEST_CAMERA) {

// If request is cancelled, the result arrays are empty.

if (grantResults.length > 0 && grantResults[0] == PackageManager.PERMISSION_GRANTED) {

// permission was granted

Log.i("permission", "CAMERA granted");

camera_granted = true;

} else {

// permission denied

Log.i("permission", "CAMERA denied");

}

} if (requestCode == REQUEST_EXTERNAL_STROAGE) {

// If request is cancelled, the result arrays are empty.

if (grantResults.length > 0 && grantResults[0] == PackageManager.PERMISSION_GRANTED) {

// permission was granted

Log.i("permission", "EXTERNAL_STROAGE granted");

read_external_storage_granted = true;

} else {

// permission denied

Log.i("permission", "EXTERNAL_STROAGE denied");

}

@Override

public void onPause()

{

super.onPause();

if (mOpenCvCameraView != null)

mOpenCvCameraView.disableView();

}

@Override

public void onResume()

{

super.onResume();

}

public void onDestroy() {

super.onDestroy();

if (mOpenCvCameraView != null)

mOpenCvCameraView.disableView();

}

@Override

public boolean onCreateOptionsMenu(Menu menu) {

return true;

}

@Override

public boolean onOptionsItemSelected(MenuItem item) {

return true;

}

public void onCameraViewStarted(int width, int height) {

}

public void onCameraViewStopped() {

}

public Mat onCameraFrame(CvCameraViewFrame inputFrame) {

//Get image size and draw a rectangle on the image for reference

Mat temp = inputFrame.rgba();

Imgproc.rectangle(temp, new Point(temp.cols()/2 - 200, temp.rows() / 2 - 200), new Point(temp.cols() / 2 + 200, temp.rows() / 2 + 200), new Scalar(255,255,255),1);

Mat digit = temp.submat(temp.rows()/2 - 180, temp.rows() / 2 + 180, temp.cols() / 2 - 180, temp.cols() / 2 + 180).clone();

Core.transpose(digit,digit);

int predict_result = mnist.FindMatch(digit);

Imgproc.putText(temp, Integer.toString(predict_result), new Point(50, 150), FONT_HERSHEY_SIMPLEX, 3.0, new Scalar(0, 0, 255), 5);

return temp;

}

public boolean onTouchEvent(MotionEvent event) {

return super.onTouchEvent(event);

}

3. 修改app\src\main\res\layout\activity_main.xml文件：

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

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

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

android:layout_height="fill_parent"

android:layout_width="fill_parent"

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

tools:context="net.johnhany.moaap_chp7.MainActivity"

android:id="@+id/ocr">

<org.opencv.android.JavaCameraView

android:layout_width="fill_parent"

android:layout_height="fill_parent"

android:visibility="gone"

android:id="@+id/java_surface_view"

opencv:camera_id="any" />

</LinearLayout>

4. 在app\src\main\java\net\johnhany\moaap_chp7下新建一个DigitRecognizer.java文件（非Activity类），其内容为：

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package net.johnhany.moaap_chp7;

import android.os.Environment;

import android.util.Log;

import org.opencv.core.Core;

import org.opencv.core.CvType;

import org.opencv.core.Mat;

import org.opencv.core.Size;

import org.opencv.imgproc.Imgproc;

import org.opencv.ml.KNearest;

import org.opencv.ml.SVM;

import java.io.File;

import java.io.FileInputStream;

import java.io.FileNotFoundException;

import java.io.IOException;

import java.nio.ByteBuffer;

import static org.opencv.ml.Ml.ROW_SAMPLE;

public class DigitRecognizer {

private String images_path = "";

private String labels_path = "";

private int total_images = 0;

private int width = 0;

private int height = 0;

private byte[] labels_data;

private KNearest knn = null;

private SVM svm = null;

DigitRecognizer(String images, String labels)

{

images_path = images;

labels_path = labels;

try{

ReadMNISTData();

}

catch (IOException e)

{

Log.i("Read error:", "" + e.getMessage());

}

private void ReadMNISTData() throws FileNotFoundException {

File external_storage = Environment.getExternalStorageDirectory(); //Get SD Card's path

//Read images

Mat training_images;

File mnist_images_file = new File(external_storage, images_path);

FileInputStream images_reader = new FileInputStream(mnist_images_file);

try{

//Read the file headers which contain the total number of images and dimensions. First 16 bytes hold the header

//byte 0 -3 : Magic Number (Not to be used)

//byte 4 - 7: Total number of images in the dataset

//byte 8 - 11: width of each image in the dataset

//byte 12 - 15: height of each image in the dataset

byte [] header = new byte[16];

images_reader.read(header, 0, 16);

//Combining the bytes to form an integer

ByteBuffer temp = ByteBuffer.wrap(header, 4, 12);

total_images = temp.getInt();

width = temp.getInt();

height = temp.getInt();

//Total number of pixels in each image

int px_count = width * height;

training_images = new Mat(total_images, px_count, CvType.CV_8U);

//images_data = new byte[total_images][px_count];

//Read each image and store it in an array.

for (int i = 0 ; i < total_images ; i++)

{

byte[] image = new byte[px_count];

images_reader.read(image, 0, px_count);

training_images.put(i,0,image);

}

training_images.convertTo(training_images, CvType.CV_32FC1);

images_reader.close();

}

catch (IOException e)

{

Log.i("MNIST Read Error:", "" + e.getMessage());

}

//Read Labels

Mat training_labels;

labels_data = new byte[total_images];

File mnist_labels_file = new File(external_storage, labels_path);

FileInputStream labels_reader = new FileInputStream(mnist_labels_file);

try{

training_labels = new Mat(total_images, 1, CvType.CV_8U);

Mat temp_labels = new Mat(1, total_images, CvType.CV_8U);

byte[] header = new byte[8];

//Read the header

labels_reader.read(header, 0, 8);

//Read all the labels at once

labels_reader.read(labels_data,0,total_images);

temp_labels.put(0,0, labels_data);

//Take a transpose of the image

Core.transpose(temp_labels, training_labels);

training_labels.convertTo(training_labels, CvType.CV_32S);

labels_reader.close();

}

catch (IOException e)

{

Log.i("MNIST Read Error:", "" + e.getMessage());

}

//K-NN Classifier

//knn = KNearest.create();

//knn.setDefaultK(10);

//knn.train(training_images, ROW_SAMPLE, training_labels);

//SVM Classifier

//svm = SVM.create();

//svm.train(training_images, ROW_SAMPLE, training_labels);

File mnist_svm_file = new File(external_storage, "SVM_MNIST.xml");

svm = SVM.load(mnist_svm_file.getAbsolutePath());

Log.d("MNIST SVM:", "var_count: " + svm.getVarCount());

}

int FindMatch(Mat test_image)

{

//Dilate the image

Imgproc.dilate(test_image, test_image, Imgproc.getStructuringElement(Imgproc.CV_SHAPE_CROSS, new Size(3,3)));

//Resize the image to match it with the sample image size

Imgproc.resize(test_image, test_image, new Size(width, height));

//Convert the image to grayscale

Imgproc.cvtColor(test_image, test_image, Imgproc.COLOR_RGB2GRAY);

//Adaptive Threshold

Imgproc.adaptiveThreshold(test_image,test_image,255,Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY_INV,15, 2);

Mat test = new Mat(1, test_image.rows() * test_image.cols(), CvType.CV_32FC1);

int count = 0;

for(int i = 0 ; i < test_image.rows(); i++)

{

for(int j = 0 ; j < test_image.cols(); j++) {

test.put(0, count, test_image.get(i, j)[0]);

count++;

}

Mat results = new Mat(1, 1, CvType.CV_8U);

//K-NN Prediction

//return (int)knn.findNearest(test, 10, results);

//SVM Prediction

return (int)svm.predict(test);

}

在这个DigitRecognizer包含了大部分核心代码。如果想使用SVM，注释掉134-135行，并取消132-133行的注释，这样运行时就要先读入训练数据并训练SVM，然后从摄像头读入图像，并对图像中央区域的数字进行分类。由于SVM训练过程计算量比较大，特别是移动平台上缺少多线程等优化时性能更是直线下降。推荐不更改代码，直接读取我训练好的SVM分类器。预训练的分类器文件在这里下载：http://pan.baidu.com/s/1nuS3M9F，下载好后拷贝到手机SD卡根目录。如果想要自己训练SVM，推荐在电脑上进行训练，然后把训练好的分类器文件提供给Android应用使用。训练SVM的C++代码在这里。在Windows平台式配置OpenCV开发环境的方法可以参考《Windows7+VS2012下64位OpenCV3.0+CUDA7.5的编译和部署》。我实验的结果是，迭代训练500次，精度74%，远不能达到实际运用的要求，至少比胡乱猜准一些-_-

如果想要使用KNN，注释掉134-135行、169行，取消掉127-129行、166行的注释。与SVM正相反，KNN的“训练”过程非常快，分类过程反而计算量非常大，因为OpenCV的实现只是把所有样本保留下来，然后通过所有已知样本与新样本计算相似度，在实机上测试时能明显感到卡顿。不过让人惊喜的是，KNN的测试精度能够达到96.65%（C++版本代码在这里），比某些缺乏优化的CNN模型效果还要好。

5. 修改app\src\main\res\AndroidManifest.xml文件为如下内容：

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

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

package="net.johnhany.moaap_chp7">

<supports-screens android:resizeable="true"

android:smallScreens="true"

android:normalScreens="true"

android:largeScreens="true"

android:anyDensity="true" />

<uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />

<uses-permission android:name="android.permission.CAMERA"/>

<uses-feature android:name="android.hardware.camera" android:required="false"/>

<uses-feature android:name="android.hardware.camera.autofocus" android:required="false"/>

<uses-feature android:name="android.hardware.camera.front" android:required="false"/>

<uses-feature android:name="android.hardware.camera.front.autofocus" android:required="false"/>

<application

android:allowBackup="true"

android:icon="@mipmap/ic_launcher"

android:label="@string/app_name"

android:supportsRtl="true"

android:theme="@style/AppTheme">

<activity android:name=".MainActivity"

android:screenOrientation="landscape">

<intent-filter>

</intent-filter>

</activity>

</application>

</manifest>

6. 修改app\src\main\res\values\strings.xml文件：

<string name="app_name">第七章 - 深入OpenCV Android应用开发</string>

</resources>

7. 修改app\src\main\res\values\styles.xml文件：

<item name="android:windowActionBar">false</item>

<item name="colorPrimary">@color/colorPrimary</item>

<item name="colorPrimaryDark">@color/colorPrimaryDark</item>

<item name="colorAccent">@color/colorAccent</item>

</style>

</resources>

8. app\build.gradle文件修改为：

apply plugin: 'com.android.application'

android {

compileSdkVersion 24

buildToolsVersion "26.0.1"

defaultConfig {

applicationId "net.johnhany.moaap_chp7"

minSdkVersion 16

targetSdkVersion 24

versionCode 1

versionName "1.0"

testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"

}

buildTypes {

release {

minifyEnabled false

proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'

}

dependencies {

compile fileTree(include: ['*.jar'], dir: 'libs')

androidTestCompile('com.android.support.test.espresso:espresso-core:2.2.2', {

exclude group: 'com.android.support', module: 'support-annotations'

})

compile 'com.android.support:appcompat-v7:24.2.1'

compile 'com.android.support.constraint:constraint-layout:1.0.2'

testCompile 'junit:junit:4.12'

compile project(':openCVLibrary320')

}

9. openCVLibrary320\build.gradle文件修改为：

apply plugin: 'com.android.library'

android {

compileSdkVersion 24

buildToolsVersion "26.0.1"

defaultConfig {

minSdkVersion 16

targetSdkVersion 24

}

buildTypes {

release {

minifyEnabled false

proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.txt'

}

10. 检查一下项目根目录的build.gradle文件是否为如下内容：

// Top-level build file where you can add configuration options common to all sub-projects/modules.

buildscript {

repositories {

jcenter()

}

dependencies {

classpath 'com.android.tools.build:gradle:2.3.3'

// NOTE: Do not place your application dependencies here; they belong

// in the individual module build.gradle files

}

allprojects {

repositories {

jcenter()

}

task clean(type: Delete) {

delete rootProject.buildDir

}

运行效果

SVM运行效果：

KNN运行效果：

参考

http://answers.opencv.org/question/93682/svm-model-fails-to-load/?answer=93683#post-id-93683

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