Android开发—基于OpenCV实现相机实时图像识别跟踪，android项目开发实训总结

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.onOptionsItemSelect

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ed(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 matchesList = mMatches.toList();

if (matchesList.size() < 4) {

// There are too few matches to find the homography.

return;

}

final List referenceKeypointsList = mReferenceKeypoints.toList();

final List 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 goodReferencePointsList = new ArrayList();

final ArrayList goodScenePointsList = new ArrayList();

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

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