Android开发—基于OpenCV实现相机实时图像识别跟踪

缺乏、安全感 2023-01-01 08:54 302阅读 0赞

### 利用OpenCV实现实时图像识别和图像跟踪 ###

*  图像识别
 *   *  什么是图像识别
     *  图像识别的研究现状
 *  Android图像识别相关技术
 *  基于OpenCV实现
 *   *  实现思路
     *  代码部分
     *   *  权限设置
         *   *  AndroidMainifest.xml
             *  权限提示方法
         *  界面设计
         *   *  activity\_img\_recognition.xml
         *  主要逻辑代码
         *   *  CameraActivity.java 【相机启动获取图像和包装MAT相关】
             *  ImageRecognitionFilter.java【图像特征过滤比对及绘制追踪绿框】
     *  实现效果图
 *  结语

# 图像识别 #

## 什么是图像识别 ##

图像识别，是指利用计算机对图像进行处理、分析和理解，以识别各种不同模式的目标和对像的技术。根据观测到的图像，对其中的物体分辨其类别，做出有意义的判断。利用现代信息处理与计算技术来模拟和完成人类的认识、理解过程。一般而言，一个图像识别系统主要由三个部分组成，分别是：图像分割、图像特征提取以及分类器的识别分类。

其中，图像分割将图像划分为多个有意义的区域，然后将每个区域的图像进行特征提取，最后分类器根据提取的图像特征对图像进行相对应的分类。实际上，图像识别和图像分割并不存在严格的界限。从某种意义上，图像分割的过程就是图像识别的过程。图像分割着重于对象和背景的关系，研究的是对象在特定背景下所表现出来的整体属性，而图像识别则着重于对象本身的属性。

## 图像识别的研究现状 ##

图像识别的发展经历了三个阶段：文字识别、数字图像处理与识别、物体识别。

图像识别作为计算视觉技术体系中的重要一环，一直备受重视。微软在两年前就公布了一项里程碑式的成果：它的图像系统识别图片的错误率比人类还要低。如今，图像识别技术又发展到一个新高度。这有赖于更多数据的开放、更多基础工具的开源、产业链的更新迭代，以及高性能的AI计算芯片、深度摄像头和优秀的深度学习算法等的进步，这些都为图像识别技术向更深处发展提供了源源不断的动力。

其实对于图像识别技术，大家已经不陌生，人脸识别、虹膜识别、指纹识别等都属于这个范畴，但是图像识别远不只如此，它涵盖了生物识别、物体与场景识别、视频识别三大类。发展至今，尽管与理想还相距甚远，但日渐成熟的图像识别技术已开始探索在各类行业的应用。![图像分析系统流程][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyODcxOTEx_size_16_color_FFFFFF_t_70]

# Android图像识别相关技术 #

1.  **[OpenCV][]**  
    基于BSD许可（开源）发行的跨平台计算机视觉库，可以运行在Linux、Windows、Android和Mac OS操作系统上。  
    轻量级而且高效——由一系列 C 函数和少量 C++ 类构成，同时提供了Python、Ruby、MATLAB等语言的接口，实现了图像处理和计算机视觉方面的很多通用算法
2.  **[TensorFlow][]**  
    TensorFlow是一个深度学习框架，支持Linux平台，Windows平台，Mac平台，甚至手机移动设备等各种平台。  
    TensorFlow提供了非常丰富的深度学习相关的API，可以说目前所有深度学习框架里，提供的API最全的，包括基本的向量矩阵计算、各种优化算法、各种卷积神经网络和循环神经网络基本单元的实现、以及可视化的辅助工具、等等。
3.  **[YOLO][]**  
    YOLO (You Only Look Once)是一种快速和准确的实时对象检测算法。  
    YOLOv3 在 TensorFlow 中实现的完整数据管道。它可用在数据集上来训练和评估自己的目标检测模型。
4.  ……

# 基于OpenCV实现 #

介绍使用OpenCV来实现指定图像识别的DEMO：

## 实现思路 ##

①打开应用的同时开启摄像头  
②对实时摄像头拍摄的图像封装成MAT对象进行逐帧比对：

1.  获取目标特征并针对各特征集获取描述符
2.  获取两个描述符集合间的匹配项
3.  获取参考图像和空间匹配图像间的单应性
4.  当图像矩阵符合单应性时，绘制跟踪图像的轮廓线

## 代码部分 ##

### 权限设置 ###

#### AndroidMainifest.xml ####

<uses-permission android:name="android.permission.CAMERA" />
        <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />
        <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />
    
        <uses-feature android:name="android.hardware.camera" />
        <uses-feature
            android:name="android.hardware.camera.autofocus"
            android:required="false" />
        <uses-feature
            android:name="android.hardware.camera.flash"
            android:required="false" />

#### 权限提示方法 ####

private void requestPermissions() {
        
            final int REQUEST_CODE = 1;
            if (ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) != PackageManager.PERMISSION_GRANTED) {
        
                ActivityCompat.requestPermissions(this, new String[]{
        
                                Manifest.permission.CAMERA, Manifest.permission.WRITE_EXTERNAL_STORAGE},
                        REQUEST_CODE);
            }
        }

### 界面设计 ###

#### activity\_img\_recognition.xml ####

<?xml version="1.0" encoding="utf-8"?>
    <RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android"
        xmlns:opencv="http://schemas.android.com/apk/res-auto"
        xmlns:tools="http://schemas.android.com/tools"
        android:id="@+id/activity_img_recognition"
        android:layout_width="match_parent"
        android:layout_height="match_parent"
        tools:context="com.sueed.imagerecognition.CameraActivity">
    
        <org.opencv.android.JavaCameraView
            android:id="@+id/jcv"
            android:layout_width="match_parent"
            android:layout_height="match_parent"
            android:visibility="gone"
            opencv:camera_id="any"
            opencv:show_fps="true" />
    </RelativeLayout>

### 主要逻辑代码 ###

#### CameraActivity.java 【相机启动获取图像和包装MAT相关】 ####

因为OpenCV中JavaCameraView继承自SurfaceView，若有需要可以自定义编写extends SurfaceView implements SurfaceHolder.Callback的xxxSurfaceView替换使用。

package com.sueed.imagerecognition;
    
    import android.Manifest;
    import android.content.Intent;
    import android.content.pm.PackageManager;
    import android.os.Bundle;
    import android.util.Log;
    import android.view.Menu;
    import android.view.MenuItem;
    import android.view.SurfaceView;
    import android.view.View;
    import android.view.WindowManager;
    import android.widget.ImageView;
    import android.widget.RelativeLayout;
    import android.widget.Toast;
    
    import androidx.appcompat.app.AppCompatActivity;
    import androidx.core.app.ActivityCompat;
    import androidx.core.content.ContextCompat;
    
    import com.sueed.imagerecognition.filters.Filter;
    import com.sueed.imagerecognition.filters.NoneFilter;
    import com.sueed.imagerecognition.filters.ar.ImageDetectionFilter;
    import com.sueed.imagerecognition.imagerecognition.R;
    
    import org.opencv.android.CameraBridgeViewBase;
    import org.opencv.android.CameraBridgeViewBase.CvCameraViewFrame;
    import org.opencv.android.CameraBridgeViewBase.CvCameraViewListener2;
    import org.opencv.android.JavaCameraView;
    import org.opencv.android.OpenCVLoader;
    import org.opencv.core.Mat;
    
    import java.io.IOException;
    
    // Use the deprecated Camera class.
    @SuppressWarnings("deprecation")
    public final class CameraActivity extends AppCompatActivity implements CvCameraViewListener2 {
        
    
        // A tag for log output.
        private static final String TAG = CameraActivity.class.getSimpleName();
    
        // The filters.
        private Filter[] mImageDetectionFilters;
    
        // The indices of the active filters.
        private int mImageDetectionFilterIndex;
    
        // The camera view.
        private CameraBridgeViewBase mCameraView;
    
        @Override
        protected void onCreate(final Bundle savedInstanceState) {
        
            super.onCreate(savedInstanceState);
            getWindow().addFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON);
            //init CameraView
            mCameraView = new JavaCameraView(this, 0);
            mCameraView.setMaxFrameSize(size.MaxWidth, size.MaxHeight);
            mCameraView.setCvCameraViewListener(this);
            setContentView(mCameraView);
            requestPermissions();
            mCameraView.enableView();
        }
    
        @Override
        public void onPause() {
        
            if (mCameraView != null) {
        
                mCameraView.disableView();
            }
            super.onPause();
        }
    
        @Override
        public void onResume() {
        
            super.onResume();
            OpenCVLoader.initDebug();
        }
    
        @Override
        public void onDestroy() {
        
            if (mCameraView != null) {
        
                mCameraView.disableView();
            }
            super.onDestroy();
        }
    
        @Override
        public boolean onCreateOptionsMenu(final Menu menu) {
        
            getMenuInflater().inflate(R.menu.activity_camera, menu);
            return true;
        }
    
        @Override
        public boolean onOptionsItemSelected(final MenuItem item) {
        
            switch (item.getItemId()) {
        
                case R.id.menu_next_image_detection_filter:
                    mImageDetectionFilterIndex++;
                    if (mImageDetectionFilters != null && mImageDetectionFilterIndex == mImageDetectionFilters.length) {
        
                        mImageDetectionFilterIndex = 0;
                    }
                    return true;
    
                default:
                    return super.onOptionsItemSelected(item);
            }
        }
    
        @Override
        public void onCameraViewStarted(final int width, final int height) {
        
            Filter Enkidu = null;
            try {
        
                Enkidu = new ImageDetectionFilter(CameraActivity.this, R.drawable.enkidu);
            } catch (IOException e) {
        
                e.printStackTrace();
            }
    
    
            Filter akbarHunting = null;
            try {
        
                akbarHunting = new ImageDetectionFilter(CameraActivity.this, R.drawable.akbar_hunting_with_cheetahs);
            } catch (IOException e) {
        
                Log.e(TAG, "Failed to load drawable: " + "akbar_hunting_with_cheetahs");
                e.printStackTrace();
            }
    
            mImageDetectionFilters = new Filter[]{
        
                    new NoneFilter(),
                    Enkidu,
                    akbarHunting
            };
        }
    
        @Override
        public void onCameraViewStopped() {
        
        }
    
        @Override
        public Mat onCameraFrame(final CvCameraViewFrame inputFrame) {
        
            final Mat rgba = inputFrame.rgba();
    
            if (mImageDetectionFilters != null) {
        
                mImageDetectionFilters[mImageDetectionFilterIndex].apply(rgba, rgba);
            }
    
            return rgba;
        }
    
    }

#### ImageRecognitionFilter.java【图像特征过滤比对及绘制追踪绿框】 ####

package com.nummist.secondsight.filters.ar;
    
    import java.io.IOException;
    import java.util.ArrayList;
    import java.util.List;
    
    import org.opencv.android.Utils;
    import org.opencv.calib3d.Calib3d;
    import org.opencv.core.Core;
    import org.opencv.core.CvType;
    import org.opencv.core.DMatch;
    import org.opencv.core.KeyPoint;
    import org.opencv.core.Mat;
    import org.opencv.core.MatOfDMatch;
    import org.opencv.core.MatOfKeyPoint;
    import org.opencv.core.MatOfPoint;
    import org.opencv.core.MatOfPoint2f;
    import org.opencv.core.Point;
    import org.opencv.core.Scalar;
    import org.opencv.features2d.DescriptorExtractor;
    import org.opencv.features2d.DescriptorMatcher;
    import org.opencv.features2d.FeatureDetector;
    import org.opencv.imgcodecs.Imgcodecs;
    import org.opencv.imgproc.Imgproc;
    
    import android.content.Context;
    
    import com.nummist.secondsight.filters.Filter;
    
    public final class ImageDetectionFilter implements Filter {
        
    
        // The reference image (this detector's target).
        private final Mat mReferenceImage;
        // Features of the reference image.
        private final MatOfKeyPoint mReferenceKeypoints = new MatOfKeyPoint();
        // Descriptors of the reference image's features.
        private final Mat mReferenceDescriptors = new Mat();
        // The corner coordinates of the reference image, in pixels.
        // CvType defines the color depth, number of channels, and
        // channel layout in the image. Here, each point is represented
        // by two 32-bit floats.
        private final Mat mReferenceCorners = new Mat(4, 1, CvType.CV_32FC2);
    
        // Features of the scene (the current frame).
        private final MatOfKeyPoint mSceneKeypoints = new MatOfKeyPoint();
        // Descriptors of the scene's features.
        private final Mat mSceneDescriptors = new Mat();
        // Tentative corner coordinates detected in the scene, in
        // pixels.
        private final Mat mCandidateSceneCorners = new Mat(4, 1, CvType.CV_32FC2);
        // Good corner coordinates detected in the scene, in pixels.
        private final Mat mSceneCorners = new Mat(0, 0, CvType.CV_32FC2);
        // The good detected corner coordinates, in pixels, as integers.
        private final MatOfPoint mIntSceneCorners = new MatOfPoint();
    
        // A grayscale version of the scene.
        private final Mat mGraySrc = new Mat();
        // Tentative matches of scene features and reference features.
        private final MatOfDMatch mMatches = new MatOfDMatch();
    
        // A feature detector, which finds features in images.
        private final FeatureDetector mFeatureDetector = FeatureDetector.create(FeatureDetector.ORB);
        // A descriptor extractor, which creates descriptors of
        // features.
        private final DescriptorExtractor mDescriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
        // A descriptor matcher, which matches features based on their
        // descriptors.
        private final DescriptorMatcher mDescriptorMatcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
    
        // The color of the outline drawn around the detected image.
        private final Scalar mLineColor = new Scalar(0, 255, 0);
    
        public ImageDetectionFilter(final Context context, final int referenceImageResourceID) throws IOException {
        
    
            // Load the reference image from the app's resources.
            // It is loaded in BGR (blue, green, red) format.
            mReferenceImage = Utils.loadResource(context, referenceImageResourceID, Imgcodecs.CV_LOAD_IMAGE_COLOR);
    
            // Create grayscale and RGBA versions of the reference image.
            final Mat referenceImageGray = new Mat();
            Imgproc.cvtColor(mReferenceImage, referenceImageGray, Imgproc.COLOR_BGR2GRAY);
            Imgproc.cvtColor(mReferenceImage, mReferenceImage, Imgproc.COLOR_BGR2RGBA);
    
            // Store the reference image's corner coordinates, in pixels.
            mReferenceCorners.put(0, 0, new double[]{
        0.0, 0.0});
            mReferenceCorners.put(1, 0, new double[]{
        referenceImageGray.cols(), 0.0});
            mReferenceCorners.put(2, 0, new double[]{
        referenceImageGray.cols(), referenceImageGray.rows()});
            mReferenceCorners.put(3, 0, new double[]{
        0.0, referenceImageGray.rows()});
    
            // Detect the reference features and compute their
            // descriptors.
            mFeatureDetector.detect(referenceImageGray, mReferenceKeypoints);
            mDescriptorExtractor.compute(referenceImageGray, mReferenceKeypoints, mReferenceDescriptors);
        }
    
        @Override
        public void apply(final Mat src, final Mat dst) {
        
    
            // Convert the scene to grayscale.
            Imgproc.cvtColor(src, mGraySrc, Imgproc.COLOR_RGBA2GRAY);
    
            // Detect the scene features, compute their descriptors,
            // and match the scene descriptors to reference descriptors.
            mFeatureDetector.detect(mGraySrc, mSceneKeypoints);
            mDescriptorExtractor.compute(mGraySrc, mSceneKeypoints, mSceneDescriptors);
            mDescriptorMatcher.match(mSceneDescriptors, mReferenceDescriptors, mMatches);
    
            // Attempt to find the target image's corners in the scene.
            findSceneCorners();
    
            // If the corners have been found, draw an outline around the
            // target image.
            // Else, draw a thumbnail of the target image.
            draw(src, dst);
        }
    
        private void findSceneCorners() {
        
    
            final List<DMatch> matchesList = mMatches.toList();
            if (matchesList.size() < 4) {
        
                // There are too few matches to find the homography.
                return;
            }
    
            final List<KeyPoint> referenceKeypointsList = mReferenceKeypoints.toList();
            final List<KeyPoint> sceneKeypointsList = mSceneKeypoints.toList();
    
            // Calculate the max and min distances between keypoints.
            double maxDist = 0.0;
            double minDist = Double.MAX_VALUE;
            for (final DMatch match : matchesList) {
        
                final double dist = match.distance;
                if (dist < minDist) {
        
                    minDist = dist;
                }
                if (dist > maxDist) {
        
                    maxDist = dist;
                }
            }
    
            // The thresholds for minDist are chosen subjectively
            // based on testing. The unit is not related to pixel
            // distances; it is related to the number of failed tests
            // for similarity between the matched descriptors.
            if (minDist > 50.0) {
        
                // The target is completely lost.
                // Discard any previously found corners.
                mSceneCorners.create(0, 0, mSceneCorners.type());
                return;
            } else if (minDist > 25.0) {
        
                // The target is lost but maybe it is still close.
                // Keep any previously found corners.
                return;
            }
    
            // Identify "good" keypoints based on match distance.
            final ArrayList<Point> goodReferencePointsList = new ArrayList<Point>();
            final ArrayList<Point> goodScenePointsList = new ArrayList<Point>();
            final double maxGoodMatchDist = 1.75 * minDist;
            for (final DMatch match : matchesList) {
        
                if (match.distance < maxGoodMatchDist) {
        
                    goodReferencePointsList.add(referenceKeypointsList.get(match.trainIdx).pt);
                    goodScenePointsList.add(sceneKeypointsList.get(match.queryIdx).pt);
                }
            }
    
            if (goodReferencePointsList.size() < 4 || goodScenePointsList.size() < 4) {
        
                // There are too few good points to find the homography.
                return;
            }
    
            // There are enough good points to find the homography.
            // (Otherwise, the method would have already returned.)
    
            // Convert the matched points to MatOfPoint2f format, as
            // required by the Calib3d.findHomography function.
            final MatOfPoint2f goodReferencePoints = new MatOfPoint2f();
            goodReferencePoints.fromList(goodReferencePointsList);
            final MatOfPoint2f goodScenePoints = new MatOfPoint2f();
            goodScenePoints.fromList(goodScenePointsList);
    
            // Find the homography.
            final Mat homography = Calib3d.findHomography(goodReferencePoints, goodScenePoints);
    
            // Use the homography to project the reference corner
            // coordinates into scene coordinates.
            Core.perspectiveTransform(mReferenceCorners, mCandidateSceneCorners, homography);
    
            // Convert the scene corners to integer format, as required
            // by the Imgproc.isContourConvex function.
            mCandidateSceneCorners.convertTo(mIntSceneCorners, CvType.CV_32S);
    
            // Check whether the corners form a convex polygon. If not,
            // (that is, if the corners form a concave polygon), the
            // detection result is invalid because no real perspective can
            // make the corners of a rectangular image look like a concave
            // polygon!
            if (Imgproc.isContourConvex(mIntSceneCorners)) {
        
                // The corners form a convex polygon, so record them as
                // valid scene corners.
                mCandidateSceneCorners.copyTo(mSceneCorners);
            }
        }
    
        protected void draw(final Mat src, final Mat dst) {
        
    
            if (dst != src) {
        
                src.copyTo(dst);
            }
    
            if (mSceneCorners.height() < 4) {
        
                // The target has not been found.
    
                // Draw a thumbnail of the target in the upper-left
                // corner so that the user knows what it is.
    
                // Compute the thumbnail's larger dimension as half the
                // video frame's smaller dimension.
                int height = mReferenceImage.height();
                int width = mReferenceImage.width();
                final int maxDimension = Math.min(dst.width(), dst.height()) / 2;
                final double aspectRatio = width / (double) height;
                if (height > width) {
        
                    height = maxDimension;
                    width = (int) (height * aspectRatio);
                } else {
        
                    width = maxDimension;
                    height = (int) (width / aspectRatio);
                }
    
                // Select the region of interest (ROI) where the thumbnail
                // will be drawn.
                final Mat dstROI = dst.submat(0, height, 0, width);
    
                // Copy a resized reference image into the ROI.
                Imgproc.resize(mReferenceImage, dstROI, dstROI.size(), 0.0, 0.0, Imgproc.INTER_AREA);
    
                return;
            }
    
            // Outline the found target in green.
            Imgproc.line(dst, new Point(mSceneCorners.get(0, 0)), new Point(mSceneCorners.get(1, 0)), mLineColor, 4);
            Imgproc.line(dst, new Point(mSceneCorners.get(1, 0)), new Point(mSceneCorners.get(2, 0)), mLineColor, 4);
            Imgproc.line(dst, new Point(mSceneCorners.get(2, 0)), new Point(mSceneCorners.get(3, 0)), mLineColor, 4);
            Imgproc.line(dst, new Point(mSceneCorners.get(3, 0)), new Point(mSceneCorners.get(0, 0)), mLineColor, 4);
        }
    }

## 实现效果图 ##

**确认允许权限：**  
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**实时追踪指定图像**  
\[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-apk42FOU-1609501715045)(https://i.ibb.co/fXDKgtk/bo3.jpg =400x800)\]\[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-3S9pNQm0-1609501715048)(https://i.ibb.co/BCk9gXs/bo4.jpg =400x800)\]

# 结语 #

本文只实现了需要提供完整原图进行比对才能实现图像识别，还有许多更加智能方便的识别技术和方法，比如：HOG、SIFT、SURF 等方法经由正负样本库进行训练后可以从图像中提取一些特征，并通过特征确定物体类别。OpenCV库中也仍有很大一部分的功能在本文中未能进行实践，亟待今后继续探索和研究。

作者：陈舒婷

原文链接：https://blog.csdn.net/qq\_42871911/article/details/111460677

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[OpenCV]: https://mp.csdn.net/console/configBlog
[TensorFlow]: https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0
[YOLO]: https://github.com/wizyoung/YOLOv3_TensorFlow