Unable to start activity ComponentInfo{com.magefitness.app/io.flutter.embedding.android.FlutterActivity}: java.lang.IllegalStateException: ensureInitializationComplete must be called after startInitialization
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Downloading https://storage.googleapis.com/tensorflow/mac/tensorflow-0.6.0-py3-none-any.whl (10.2MB) 100% |████████████████████████████████| 10.3MB 1.9MB/s Collecting numpy>=1.8.2 (from tensorflow==0.6.0) Downloading https://files.pythonhosted.org/packages/8e/75/7a8b7e3c073562563473f2a61bd53e75d0a1f5e2047e576ee61d44113c22/numpy-1.14.3-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (4.7MB) 100% |████████████████████████████████| 4.7MB 832kB/s Collecting protobuf==3.0.0a3 (from tensorflow==0.6.0) Downloading https://files.pythonhosted.org/packages/d7/92/34c5810fa05e98082d141048110db97d2f98d318fa96f8202bf146ab79de/protobuf-3.0.0a3.tar.gz (88kB) 100% |████████████████████████████████| 92kB 18.6MB/s Requirement not upgraded as not directly required: wheel>=0.26 in ./venv/lib/python3.6/site-packages (from tensorflow==0.6.0) (0.31.1) Collecting six>=1.10.0 (from tensorflow==0.6.0) Downloading https://files.pythonhosted.org/packages/67/4b/141a581104b1f6397bfa78ac9d43d8ad29a7ca43ea90a2d863fe3056e86a/six-1.11.0-py2.py3-none-any.whl Requirement not upgraded as not directly required: setuptools in ./venv/lib/python3.6/site-packages (from protobuf==3.0.0a3->tensorflow==0.6.0) (39.1.0) Building wheels for collected packages: protobuf Running setup.py bdist_wheel for protobuf ... done Stored in directory: /Users/zowee-laisc/Library/Caches/pip/wheels/07/0a/98/ca8fbec7368a85849700304bf0cf40d2d8e183f9a5dd136795 Successfully built protobuf Installing collected packages: numpy, protobuf, six, tensorflow Successfully installed numpy-1.14.3 protobuf-3.0.0a3 six-1.11.0 tensorflow-0.6.0 (venv) zowee-laiscdeMacBook-Pro:tensorflow zowee-laisc$
就这么简单，然后测试环境ok
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(venv) zowee-laiscdeMacBook-Pro:tensorflow zowee-laisc$ python Python 3.6.4 (v3.6.4:d48ecebad5, Dec 18 2017, 21:07:28) [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf >>> hello = tf.constant('hello tensorflow') >>> sess = tf.Session() I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4 I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4 >>> print(sess.run(hello)) b'hello tensorflow' >>> a = tf.constant(10) >>> b = tf.constant(32) >>> print(sess.run(a+b)) 42 >>> exit();
protected void setFragment() { String cameraId = chooseCamera();//选择合适的camera if (cameraId == null) { Toast.makeText(this, "No Camera Detected", Toast.LENGTH_SHORT).show(); finish(); }
Fragment fragment; if (useCamera2API) {//true 初始化fragment CameraConnectionFragment camera2Fragment = CameraConnectionFragment.newInstance( new CameraConnectionFragment.ConnectionCallback() { @Override public void onPreviewSizeChosen(final Size size, final int rotation) { Log.i("linlian","useCamera2API onPreviewSizeChosen="); previewHeight = size.getHeight(); previewWidth = size.getWidth(); CameraActivity.this.onPreviewSizeChosen(size, rotation); } }, this, getLayoutId(), getDesiredPreviewFrameSize());
camera2Fragment.setCamera(cameraId); fragment = camera2Fragment; } else { fragment = new LegacyCameraConnectionFragment(this, getLayoutId(), getDesiredPreviewFrameSize()); }
/** * {@link android.view.TextureView.SurfaceTextureListener} handles several lifecycle events on a * {@link TextureView}. */ private final TextureView.SurfaceTextureListener surfaceTextureListener = new TextureView.SurfaceTextureListener() { @Override public void onSurfaceTextureAvailable(//初始化 final SurfaceTexture texture, final int width, final int height) { openCamera(width, height); }
@Override public void onSurfaceTextureSizeChanged(//size 变化时候 final SurfaceTexture texture, final int width, final int height) { configureTransform(width, height); }
@Override public boolean onSurfaceTextureDestroyed(final SurfaceTexture texture) { return true; }
@Override public void onSurfaceTextureUpdated(final SurfaceTexture texture) {} };
打开摄像头，配置最合适的preview size
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/** * Opens the camera specified by {@link CameraConnectionFragment#cameraId}. */ private void openCamera(final int width, final int height) { setUpCameraOutputs();//设置预览大小 configureTransform(width, height);//旋转偏移量 final Activity activity = getActivity(); final CameraManager manager = (CameraManager) activity.getSystemService(Context.CAMERA_SERVICE); try { if (!cameraOpenCloseLock.tryAcquire(2500, TimeUnit.MILLISECONDS)) { throw new RuntimeException("Time out waiting to lock camera opening."); } manager.openCamera(cameraId, stateCallback, backgroundHandler);//打开camera } catch (final CameraAccessException e) { LOGGER.e(e, "Exception!"); } catch (final InterruptedException e) { throw new RuntimeException("Interrupted while trying to lock camera opening.", e); } }
/** * {@link android.hardware.camera2.CameraDevice.StateCallback} * is called when {@link CameraDevice} changes its state. */ private final CameraDevice.StateCallback stateCallback = new CameraDevice.StateCallback() { @Override public void onOpened(final CameraDevice cd) { // This method is called when the camera is opened. We start camera preview here. cameraOpenCloseLock.release(); cameraDevice = cd; createCameraPreviewSession(); }
@Override public void onError(final CameraDevice cd, final int error) { cameraOpenCloseLock.release(); cd.close(); cameraDevice = null; final Activity activity = getActivity(); if (null != activity) { activity.finish(); } } };
backgroundThread
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/** * Starts a background thread and its {@link Handler}. */ private void startBackgroundThread() {//在onresume的时候被调用 backgroundThread = new HandlerThread("ImageListener"); backgroundThread.start(); backgroundHandler = new Handler(backgroundThread.getLooper()); }
/** * Creates a new {@link CameraCaptureSession} for camera preview. */ private void createCameraPreviewSession() { try { final SurfaceTexture texture = textureView.getSurfaceTexture(); assert texture != null;
// We configure the size of default buffer to be the size of camera preview we want. texture.setDefaultBufferSize(previewSize.getWidth(), previewSize.getHeight());
// This is the output Surface we need to start preview. final Surface surface = new Surface(texture);
// We set up a CaptureRequest.Builder with the output Surface. previewRequestBuilder = cameraDevice.createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW); previewRequestBuilder.addTarget(surface);
LOGGER.i("Opening camera preview: " + previewSize.getWidth() + "x" + previewSize.getHeight());
// Create the reader for the preview frames. previewReader = ImageReader.newInstance( previewSize.getWidth(), previewSize.getHeight(), ImageFormat.YUV_420_888, 2);
// Here, we create a CameraCaptureSession for camera preview. cameraDevice.createCaptureSession( Arrays.asList(surface, previewReader.getSurface()), new CameraCaptureSession.StateCallback() {
@Override public void onConfigured(final CameraCaptureSession cameraCaptureSession) { // The camera is already closed if (null == cameraDevice) { return; }
// When the session is ready, we start displaying the preview. captureSession = cameraCaptureSession; try { // Auto focus should be continuous for camera preview. previewRequestBuilder.set( CaptureRequest.CONTROL_AF_MODE, CaptureRequest.CONTROL_AF_MODE_CONTINUOUS_PICTURE); // Flash is automatically enabled when necessary. previewRequestBuilder.set( CaptureRequest.CONTROL_AE_MODE, CaptureRequest.CONTROL_AE_MODE_ON_AUTO_FLASH);
// Finally, we start displaying the camera preview. previewRequest = previewRequestBuilder.build(); captureSession.setRepeatingRequest( previewRequest, captureCallback, backgroundHandler); } catch (final CameraAccessException e) { LOGGER.e(e, "Exception!"); } }
/** * Callback for android.hardware.Camera API */ @Override public void onPreviewFrame(final byte[] bytes, final Camera camera) { Log.i("linlian","CameraActivity.onPreviewFrame()"); if (isProcessingFrame) { LOGGER.w("Dropping frame!");//如果正在处理，则丢掉这一frame return; }
try { // Initialize the storage bitmaps once when the resolution is known. if (rgbBytes == null) { Camera.Size previewSize = camera.getParameters().getPreviewSize(); previewHeight = previewSize.height; previewWidth = previewSize.width; rgbBytes = new int[previewWidth * previewHeight];//初始化 rgbBytes onPreviewSizeChosen(new Size(previewSize.width, previewSize.height), 90); } } catch (final Exception e) { LOGGER.e(e, "Exception!"); return; }
// For examining the actual TF input. if (SAVE_PREVIEW_BITMAP) { ImageUtils.saveBitmap(croppedBitmap); } //将原始图片进行剪切处理成需要的尺寸croppedBitmap runInBackground( new Runnable() { @Override public void run() { final long startTime = SystemClock.uptimeMillis(); //进行识别 final List<Classifier.Recognition> results = classifier.recognizeImage(croppedBitmap);
private static final int INPUT_SIZE = 224; private static final int IMAGE_MEAN = 117; private static final float IMAGE_STD = 1; private static final String INPUT_NAME = "input"; private static final String OUTPUT_NAME = "output";
@Override public List<Recognition> recognizeImage(final Bitmap bitmap) { Log.i("linlian","recognizeImage"); // Log this method so that it can be analyzed with systrace. Trace.beginSection("recognizeImage");
Trace.beginSection("preprocessBitmap"); // Preprocess the image data from 0-255 int to normalized float based // on the provided parameters. bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight()); Log.i("linlian","recognizeImage intValues.length="+intValues.length); for (int i = 0; i < intValues.length; ++i) { final int val = intValues[i]; floatValues[i * 3 + 0] = (((val >> 16) & 0xFF) - imageMean) / imageStd; floatValues[i * 3 + 1] = (((val >> 8) & 0xFF) - imageMean) / imageStd; floatValues[i * 3 + 2] = ((val & 0xFF) - imageMean) / imageStd; //Log.i("linlian"," i="+i+" "+floatValues[i * 3 + 0]+" "+floatValues[i * 3 + 0]+" "+floatValues[i * 3 + 0]); } Trace.endSection();
// Copy the input data into TensorFlow. 输入 Trace.beginSection("feed"); inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3); Trace.endSection();
// Run the inference call.运行 Trace.beginSection("run"); inferenceInterface.run(outputNames, logStats); Trace.endSection();
// Copy the output Tensor back into the output array. Trace.beginSection("fetch");输出 inferenceInterface.fetch(outputName, outputs); Trace.endSection();
// Find the best classifications. PriorityQueue<Recognition> pq = new PriorityQueue<Recognition>( 3, new Comparator<Recognition>() { @Override public int compare(Recognition lhs, Recognition rhs) { // Intentionally reversed to put high confidence at the head of the queue. return Float.compare(rhs.getConfidence(), lhs.getConfidence()); } }); for (int i = 0; i < outputs.length; ++i) { if (outputs[i] > THRESHOLD) { pq.add( new Recognition( "" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i], null)); } } final ArrayList<Recognition> recognitions = new ArrayList<Recognition>(); int recognitionsSize = Math.min(pq.size(), MAX_RESULTS); for (int i = 0; i < recognitionsSize; ++i) { recognitions.add(pq.poll()); } Trace.endSection(); // "recognizeImage" return recognitions; }
检测 detector
关于检测
加载的模型是由三种 可选 multi box，使用旧的API训练的模型
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private static final String MB_INPUT_NAME = "ResizeBilinear"; private static final String MB_OUTPUT_LOCATIONS_NAME = "output_locations/Reshape"; private static final String MB_OUTPUT_SCORES_NAME = "output_scores/Reshape"; private static final String MB_MODEL_FILE = "file:///android_asset/multibox_model.pb"; private static final String MB_LOCATION_FILE = "file:///android_asset/multibox_location_priors.txt";
另外一部分 tensor flow object detect
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private static final int TF_OD_API_INPUT_SIZE = 300; private static final String TF_OD_API_MODEL_FILE = "file:///android_asset/ssd_mobilenet_v1_android_export.pb"; private static final String TF_OD_API_LABELS_FILE = "file:///android_asset/coco_labels_list.txt";
还有yolo??
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// Configuration values for tiny-yolo-voc. Note that the graph is not included with TensorFlow and // must be manually placed in the assets/ directory by the user. // Graphs and models downloaded from http://pjreddie.com/darknet/yolo/ may be converted e.g. via // DarkFlow (https://github.com/thtrieu/darkflow). Sample command: // ./flow --model cfg/tiny-yolo-voc.cfg --load bin/tiny-yolo-voc.weights --savepb --verbalise private static final String YOLO_MODEL_FILE = "file:///android_asset/graph-tiny-yolo-voc.pb"; private static final int YOLO_INPUT_SIZE = 416; private static final String YOLO_INPUT_NAME = "input"; private static final String YOLO_OUTPUT_NAMES = "output"; private static final int YOLO_BLOCK_SIZE = 32;
yolo 是一个实时物体识别的
You only look once (YOLO) is a state-of-the-art, real-time object detection system. On a Pascal Titan X it processes images at 30 FPS and has a mAP of 57.9% on COCO test-dev.
public static Classifier create( final AssetManager assetManager, final String modelFilename, final String labelFilename, final int inputSize) throws IOException { final TensorFlowObjectDetectionAPIModel d = new TensorFlowObjectDetectionAPIModel();
d.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFilename);
final Graph g = d.inferenceInterface.graph();
d.inputName = "image_tensor";//输入的shap定义 // The inputName node has a shape of [N, H, W, C], where // N is the batch size // H = W are the height and width // C is the number of channels (3 for our purposes - RGB) final Operation inputOp = g.operation(d.inputName); if (inputOp == null) { throw new RuntimeException("Failed to find input Node '" + d.inputName + "'"); } d.inputSize = inputSize; // The outputScoresName node has a shape of [N, NumLocations], where N // is the batch size. 三个输出 final Operation outputOp1 = g.operation("detection_scores"); if (outputOp1 == null) { throw new RuntimeException("Failed to find output Node 'detection_scores'"); } final Operation outputOp2 = g.operation("detection_boxes"); if (outputOp2 == null) { throw new RuntimeException("Failed to find output Node 'detection_boxes'"); } final Operation outputOp3 = g.operation("detection_classes"); if (outputOp3 == null) { throw new RuntimeException("Failed to find output Node 'detection_classes'"); }
// Pre-allocate buffers. d.outputNames = new String[] {"detection_boxes", "detection_scores", "detection_classes", "num_detections"}; d.intValues = new int[d.inputSize * d.inputSize]; d.byteValues = new byte[d.inputSize * d.inputSize * 3]; d.outputScores = new float[MAX_RESULTS]; d.outputLocations = new float[MAX_RESULTS * 4]; d.outputClasses = new float[MAX_RESULTS]; d.outputNumDetections = new float[1]; return d; }
@Override public List<Recognition> recognizeImage(final Bitmap bitmap) { // Log this method so that it can be analyzed with systrace. Trace.beginSection("recognizeImage");
Trace.beginSection("preprocessBitmap"); // Preprocess the image data from 0-255 int to normalized float based // on the provided parameters. bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
// Copy the input data into TensorFlow.输入 Trace.beginSection("feed"); inferenceInterface.feed(inputName, byteValues, 1, inputSize, inputSize, 3); Trace.endSection();
// Run the inference call.运行 Trace.beginSection("run"); inferenceInterface.run(outputNames, logStats); Trace.endSection();
// Copy the output Tensor back into the output array.结果输出 Trace.beginSection("fetch"); outputLocations = new float[MAX_RESULTS * 4]; outputScores = new float[MAX_RESULTS]; outputClasses = new float[MAX_RESULTS]; outputNumDetections = new float[1]; inferenceInterface.fetch(outputNames[0], outputLocations); inferenceInterface.fetch(outputNames[1], outputScores); inferenceInterface.fetch(outputNames[2], outputClasses); inferenceInterface.fetch(outputNames[3], outputNumDetections); Trace.endSection();
// Find the best detections. final PriorityQueue<Recognition> pq = new PriorityQueue<Recognition>( 1, new Comparator<Recognition>() { @Override public int compare(final Recognition lhs, final Recognition rhs) { // Intentionally reversed to put high confidence at the head of the queue. return Float.compare(rhs.getConfidence(), lhs.getConfidence()); } });
// Scale them back to the input size. for (int i = 0; i < outputScores.length; ++i) { final RectF detection = new RectF( outputLocations[4 * i + 1] * inputSize, outputLocations[4 * i] * inputSize, outputLocations[4 * i + 3] * inputSize, outputLocations[4 * i + 2] * inputSize); pq.add( new Recognition("" + i, labels.get((int) outputClasses[i]), outputScores[i], detection)); }
final ArrayList<Recognition> recognitions = new ArrayList<Recognition>(); for (int i = 0; i < Math.min(pq.size(), MAX_RESULTS); ++i) { recognitions.add(pq.poll()); } Trace.endSection(); // "recognizeImage" return recognitions; }
训练模型
运行手写识别
只需要直接运行fully_connected_feed.py文件，就可以开始训练：
python fully_connected_feed.py
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Traceback (most recent call last):
File "fully_connected_feed.py", line 279, in <module>
#编译添加自己的jni 库 add_library( # Sets the name of the library. detection_based_tracker
# Sets the library as a shared library. SHARED
# Provides a relative path to your source file(s). src/main/cpp/DetectionBasedTracker_jni.cpp )
# Searches for a specified prebuilt library and stores the path as a # variable. Because CMake includes system libraries in the search path by # default, you only need to specify the name of the public NDK library # you want to add. CMake verifies that the library exists before # completing its build.
find_library( # Sets the name of the path variable. log-lib
# Specifies the name of the NDK library that # you want CMake to locate. log )
# Specifies libraries CMake should link to your target library. You # can link multiple libraries, such as libraries you define in this # build script, prebuilt third-party libraries, or system libraries.
target_link_libraries( # Specifies the target library. detection_based_tracker
lib_opencv
# Links the target library to the log library # included in the NDK. ${log-lib} )
/Measurebox2/facedection/src/main/cpp/DetectionBasedTracker_jni.cpp Error:(36) undefined reference to `cv::CascadeClassifier::detectMultiSc Error:error: linker command failed with exit code 1 (use -v to see invocation)
或者 Error:(36) undefined reference to `cv::CascadeClassi
Error:Execution failed for task ':facedection:transformNativeLibsWithStripDebugSymbolForDebug'. > A problem occurred starting process 'command '/Users/zowee-laisc/lynn/sdk/android-sdk-macosx/ndk-bundle/toolchains/mips64el-linux-android-4.9/prebuilt/darwin-x86_64/bin/mips64el-linux-android-strip''
import scrapy class ChandaoItem(scrapy.Item): # define the fields for your item here like: #bug title title = scrapy.Field() #严重等级 severity = scrapy.Field() #bug 发现者 founder = scrapy.Field() #当前责任人 current = scrapy.Field()
@Override public final Float evaluate(float fraction, Number startValue, Number endValue){ float t = mDuration * fraction; float b = startValue.floatValue(); float c = endValue.floatValue() - startValue.floatValue(); float d = mDuration; float result = calculate(t,b,c,d); for(EasingListener l : mListeners){ l.on(t,result,b,c,d); } return result; }
/** * Loading the svg from the resources. * * @param context Context object to get the resources. * @param svgResource int resource id of the svg. */ public void load(Context context, int svgResource) { if (mSvg != null) return; try { mSvg = SVG.getFromResource(context, svgResource);//通过 com.caverock.androidsvg api加载 mSvg.setDocumentPreserveAspectRatio(PreserveAspectRatio.UNSCALED); } catch (SVGParseException e) { Log.e(LOG_TAG, "Could not load specified SVG resource", e); } }
/** * Render the svg to canvas and catch all the paths while rendering. * * @param width - the width to scale down the view to, * @param height - the height to scale down the view to, * @return All the paths from the svg. */ public List<SvgPath> getPathsForViewport(final int width, final int height) { final float strokeWidth = mSourcePaint.getStrokeWidth(); Canvas canvas = new Canvas() { private final Matrix mMatrix = new Matrix();
@Override public int getWidth() { return width; }
@Override public int getHeight() { return height; }
@Override public void drawPath(Path path, Paint paint) { Path dst = new Path();
/** * Rescale the canvas with specific width and height. * * @param width The width of the canvas. * @param height The height of the canvas. * @param strokeWidth Width of the path to add to scaling. * @param canvas The canvas to be drawn. */ private void rescaleCanvas(int width, int height, float strokeWidth, Canvas canvas) { if (mSvg == null) return; final RectF viewBox = mSvg.getDocumentViewBox();
/** * Default constructor. * * @param pathView The view that must be animated. */ public AnimatorBuilder(final PathView pathView) { anim = ObjectAnimator.ofFloat(pathView, "percentage", 0.0f, 1.0f); }
通过影响percentage的值，从而影响
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/** * Animate this property. It is the percentage of the path that is drawn. * It must be [0,1]. * * @param percentage float the percentage of the path. */ public void setPercentage(float percentage) { if (percentage < 0.0f || percentage > 1.0f) { throw new IllegalArgumentException("setPercentage not between 0.0f and 1.0f"); } progress = percentage; synchronized (mSvgLock) { updatePathsPhaseLocked();//更新path } invalidate();再重新绘制 }
根据progress，更新svgPath
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/** * This refreshes the paths before draw and resize. */ private void updatePathsPhaseLocked() { final int count = paths.size(); for (int i = 0; i < count; i++) { SvgUtils.SvgPath svgPath = paths.get(i); svgPath.path.reset(); svgPath.measure.getSegment(0.0f, svgPath.length * progress, svgPath.path, true); //Given a start and stop distance, return in dst the intervening segment(s). If the segment is zero-length, return false, else return true. startD and stopD are pinned to legal values (0..getLength()). If startD <= stopD then return false (and leave dst untouched). Begin the segment with a moveTo if startWithMoveTo is true // Required only for Android 4.4 and earlier svgPath.path.rLineTo(0.0f, 0.0f); } }
