开发步骤
配置MainActivity的显示效果
首先通过 MainActivity extends Activity implements GLSurfaceView.Renderer创建MainActivity，在MainActivity的layout文件中新建一个GLSurfaceView，用于显示摄像头拍摄的实时画面，提供给AREngine进行识别：
在MainActivity的onCreate阶段进行SurfaceView的配置：
private GLSurfaceView mSurfaceView;
private static final int CONFIG_CHOOSER_RED_SIZE = 8;
private static final int CONFIG_CHOOSER_GREEN_SIZE = 8;
private static final int CONFIG_CHOOSER_BLUE_SIZE = 8;
private static final int CONFIG_CHOOSER_ALPHA_SIZE = 8;
private static final int CONFIG_CHOOSER_DEPTH_SIZE = 16;
private static final int CONFIG_CHOOSER_STENCIL_SIZE = 0;
private static final int OPENGLES_VERSION = 2;
mSurfaceView = findViewById(R.id.surfaceview);
mSurfaceView.setPreserveEGLContextOnPause(true);
mSurfaceView.setEGLContextClientVersion(OPENGLES_VERSION);
mSurfaceView.setEGLConfigChooser(CONFIG_CHOOSER_RED_SIZE, // Alpha used for plane blending.
CONFIG_CHOOSER_GREEN_SIZE,
CONFIG_CHOOSER_BLUE_SIZE,
CONFIG_CHOOSER_ALPHA_SIZE,
CONFIG_CHOOSER_DEPTH_SIZE,
CONFIG_CHOOSER_STENCIL_SIZE); // Alpha used for plane blending.
mSurfaceView.setRenderer(this);
mSurfaceView.setRenderMode(GLSurfaceView.RENDERMODE_CONTINUOUSLY);
加载一张框图文件，用于标记识别的位置，引导用户进行物体识别：
fitToScanView = findViewById(R.id.image_view_fit_to_scan);
InputStream bitmapstream;
try {
bitmapstream = getAssets().open("fit_to_scan.png");
} catch (IllegalArgumentException | IOException e) {
Log.d(TAG, "open bitmap failed!");
return;
}
Bitmap bitmap = BitmapFactory.decodeStream(bitmapstream);
fitToScanView.setImageBitmap(bitmap);AREngine检测和配置权限申请
物体识别功能的使用需要设备支持AREngine，并且已经安装了AREngine的APK包，所以要对运行的设备进行判断，是否支持该功能
rivate boolean arEngineAbilityCheck() {
boolean isInstallArEngineApk = AREnginesApk.isAREngineApkReady(this);
if (!isInstallArEngineApk && isRemindInstall) {
Toast.makeText(this, "Please agree to install.", Toast.LENGTH_LONG).show();
finish();
}
Log.d(TAG, "Is Install AR Engine Apk: " + isInstallArEngineApk);
if (!isInstallArEngineApk) {
//Code of jumping to AppGallery to download the APK
isRemindInstall = true;
}
return AREnginesApk.isAREngineApkReady(this);
}AREngine检测完成之后，再进行相机权限的动态申请
private static final String[] PERMISSIONS_ARRAYS = new String[]{Manifest.permission.CAMERA};
public static void requestPermission(final Activity activity) {
Log.d(TAG, "requestPermission >>");
for (String permission : PERMISSIONS_ARRAYS) {
if (ContextCompat.checkSelfPermission(activity, permission) != PackageManager.PERMISSION_GRANTED) {
permissionsList.add(permission);
}
}
ActivityCompat.requestPermissions(activity,
permissionsList.toArray(new String[permissionsList.size()]),
REQUEST_CODE_ASK_PERMISSIONS);
Log.d(TAG, "requestPermission <<");
}以上步骤完成之后，说明设备支持AREngine的物体识别功能，并且已经获得了相机的使用权限，接下来就可以创建ARSession，并进行识别数据库的配置
配置物体识别数据库
在进行物体识别之前，要将想识别的对象的图片添加到数据库中，使用的是ARAugmentedImageDatabase，创建一个boolean函数，进行数据库的图片添加，并给后续是否进行识别作判断：
private boolean setupAugmentedImageDatabase(ARWorldTrackingConfig config) {
ARAugmentedImageDatabase augmentedImageDatabase;
Optional augmentedImageBitmapOptional = loadAugmentedImageBitmap();
Bitmap augmentedImageBitmap = null;
if (augmentedImageBitmapOptional.isPresent()) {
augmentedImageBitmap = loadAugmentedImageBitmap().get();
} else {
return false;
}
if (augmentedImageBitmap == null) {
return false;
}
augmentedImageDatabase = new ARAugmentedImageDatabase(mSession);
augmentedImageDatabase.addImage("image_name", augmentedImageBitmap);
config.setAugmentedImageDatabase(augmentedImageDatabase);
String text = String.format(Locale.ROOT, "has set ImageNum: %d", augmentedImageDatabase.getNumImages());
messageSnackbarHelper.showMessage(this, text);
return true;
}所添加的识别图片源可以放置在Assets文件夹内，通过InputStream打开为Bitmap的格式
private Optional loadAugmentedImageBitmap() {
try (InputStream is = getAssets().open("cup.jpg")) {
return Optional.of(BitmapFactory.decodeStream(is));
} catch (IOException e) {
Log.e(TAG, "IO exception loading augmented image bitmap.", e);
}
return Optional.empty();
}
配置识别后显示效果
在MainActivity的onDrawFrame中配置识别到物体之后的显示效果，在检测到要识别的物体后，用一个方框将识别的物体标识出来。
public void onDrawFrame(GL10 unused) {
Log.d(TAG, "onDrawFrame >>");
GLES20.glClear(GLES20.GL_COLOR_BUFFER_BIT | GLES20.GL_DEPTH_BUFFER_BIT);
if (mSession == null) {
Log.d(TAG, "onDrawFrame mSession is null");
return;
}
try {
ARFrame frame = mSession.update();
ARCamera camera = frame.getCamera();
if (camera.getTrackingState() == ARTrackable.TrackingState.PAUSED) {
return;
}
Log.d(TAG, "ARTrackable.TrackingState" + camera.getTrackingState());
drawAugmentedImages(frame, projmtx, viewmtxs, colorCorrectionRgbas);
} catch (Throwable t) {
Log.e(TAG, "Exception on the OpenGL thread", t);
}
Log.d(TAG, "onDrawFrame <<");
}判断是否识别到物体时，调用ARAugmentedImage的getTrackingStatus方法，如果是TRACKING的话，则为识别到了
switch (augmentedImage.getTrackingState()) {
case TRACKING:
augmentedImageRenderer.draw(viewmtxs, projmtx, augmentedImage, centerAnchor, colorCorrectionRgbas);
break;
default:
break;
}之后通过getCenterPose获取物体的中心位置信息，将边框添加到显示中
anchorPose = augmentedImage.getCenterPose();
float scaleFactor = 1.0f;
float[] modelMatrixs = new float[MODEL_MATRIX_SIZE];
float[] tintColors = convertHexToColor(TINT_COLORS_HEXS[augmentedImage.getIndex() % TINT_COLORS_HEXS.length]);
worldBoundaryPoses[0].toMatrix(modelMatrixs, 0);
imageFrameUpperLeft.updateModelMatrix(modelMatrixs, scaleFactor);
imageFrameUpperLeft.draw(viewMatrix, projectionMatrix, colorCorrectionRgba, tintColors);
worldBoundaryPoses[1].toMatrix(modelMatrixs, 0);
imageFrameUpperRight.updateModelMatrix(modelMatrixs, scaleFactor);
imageFrameUpperRight.draw(viewMatrix, projectionMatrix, colorCorrectionRgba, tintColors);
worldBoundaryPoses[2].toMatrix(modelMatrixs, 0);
imageFrameLowerRight.updateModelMatrix(modelMatrixs, scaleFactor);
imageFrameLowerRight.draw(viewMatrix, projectionMatrix, colorCorrectionRgba, tintColors);
worldBoundaryPoses[3].toMatrix(modelMatrixs, 0);
imageFrameLowerLeft.updateModelMatrix(modelMatrixs, scaleFactor);
imageFrameLowerLeft.draw(viewMatrix, projectionMatrix, colorCorrectionRgba, tintColors);
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