利用OpenCV實現實時圖像識別和圖像跟蹤

圖像識別

什么是圖像識別

圖像識別，是指利用計算機對圖像進行處理、分析和理解，以識別各種不同模式的目標和對像的技術。根據觀測到的圖像，對其中的物體分辨其類別，做出有意義的判斷。利用現代信息處理與計算技術來模擬和完成人類的認識、理解過程。一般而言，一個圖像識別系統主要由三個部分組成，分別是：圖像分割、圖像特征提取以及分類器的識別分類。

其中，圖像分割將圖像劃分為多個有意義的區域，然后將每個區域的圖像進行特征提取，最后分類器根據提取的圖像特征對圖像進行相對應的分類。實際上，圖像識別和圖像分割并不存在嚴格的界限。從某種意義上，圖像分割的過程就是圖像識別的過程。圖像分割著重于對象和背景的關系，研究的是對象在特定背景下所表現出來的整體屬性，而圖像識別則著重于對象本身的屬性。

圖像識別的研究現狀

圖像識別的發展經歷了三個階段：文字識別、數字圖像處理與識別、物體識別。

圖像識別作為計算視覺技術體系中的重要一環，一直備受重視。微軟在兩年前就公布了一項里程碑式的成果：它的圖像系統識別圖片的錯誤率比人類還要低。如今，圖像識別技術又發展到一個新高度。這有賴于更多數據的開放、更多基礎工具的開源、產業鏈的更新迭代，以及高性能的AI計算芯片、深度攝像頭和優秀的深度學習算法等的進步，這些都為圖像識別技術向更深處發展提供了源源不斷的動力。

其實對于圖像識別技術，大家已經不陌生，人臉識別、虹膜識別、指紋識別等都屬于這個范疇，但是圖像識別遠不只如此，它涵蓋了生物識別、物體與場景識別、視頻識別三大類。發展至今，盡管與理想還相距甚遠，但日漸成熟的圖像識別技術已開始探索在各類行業的應用。

Android圖像識別相關技術

OpenCV
基于BSD許可（開源）發行的跨平臺計算機視覺庫，可以運行在Linux、Windows、Android和Mac OS操作系統上。
輕量級而且高效——由一系列 C 函數和少量 C++ 類構成，同時提供了Python、Ruby、MATLAB等語言的接口，實現了圖像處理和計算機視覺方面的很多通用算法

TensorFlow
TensorFlow是一個深度學習框架，支持Linux平臺，Windows平臺，Mac平臺，甚至手機移動設備等各種平臺。
TensorFlow提供了非常豐富的深度學習相關的API，可以說目前所有深度學習框架里，提供的API最全的，包括基本的向量矩陣計算、各種優化算法、各種卷積神經網絡和循環神經網絡基本單元的實現、以及可視化的輔助工具、等等。

YOLO
YOLO (You Only Look Once)是一種快速和準確的實時對象檢測算法。
YOLOv3 在 TensorFlow 中實現的完整數據管道。它可用在數據集上來訓練和評估自己的目標檢測模型。

……

基于OpenCV實現

介紹使用OpenCV來實現指定圖像識別的DEMO：

實現思路

①打開應用的同時開啟攝像頭
②對實時攝像頭拍攝的圖像封裝成MAT對象進行逐幀比對：

獲取目標特征并針對各特征集獲取描述符

獲取兩個描述符集合間的匹配項

獲取參考圖像和空間匹配圖像間的單應性

當圖像矩陣符合單應性時，繪制跟蹤圖像的輪廓線

代碼部分

權限設置

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" />

權限提示方法

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);

}

}

界面設計

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>

主要邏輯代碼

CameraActivity.java 【相機啟動獲取圖像和包裝MAT相關】

因為OpenCV中JavaCameraView繼承自SurfaceView，若有需要可以自定義編寫extends SurfaceView implements SurfaceHolder.Callback的xxxSurfaceView替換使用。

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【圖像特征過濾比對及繪制追蹤綠框】

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);

}

}

實現效果圖

確認允許權限：

實時追蹤指定圖像

結語

本文只實現了需要提供完整原圖進行比對才能實現圖像識別，還有許多更加智能方便的識別技術和方法，比如：HOG、SIFT、SURF 等方法經由正負樣本庫進行訓練后可以從圖像中提取一些特征，并通過特征確定物體類別。OpenCV庫中也仍有很大一部分的功能在本文中未能進行實踐，亟待今后繼續探索和研究。更多Python知識請關注我分享更多！

本文轉載于：Android開發-基于OpenCV實現相機實時圖像識別跟蹤_Sueed-CSDN博客

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