阿里云企业网站搭建,制作网站怎么用图片做背景,python 自动 wordpress,搜索引擎网站的搜素结果有何区别在上图通过手机的相机拍摄到的物体识别出具体的名称#xff0c;这个需要通过TensorFlow 训练的模型引用到项目中#xff1b;以下就是详细地集成 TensorFlow步骤#xff0c;请按照以下步骤进行操作#xff1a; 在项目的根目录下的 build.gradle 文件中添加 TensorFlow 的 Ma… 在上图通过手机的相机拍摄到的物体识别出具体的名称这个需要通过TensorFlow 训练的模型引用到项目中以下就是详细地集成 TensorFlow步骤请按照以下步骤进行操作 在项目的根目录下的 build.gradle 文件中添加 TensorFlow 的 Maven 仓库。在 repositories 部分添加以下行 allprojects {repositories {// 其他仓库...maven {url https://google.bintray.com/tensorflow}}
} 在应用的 build.gradle 文件中添加 TensorFlow Lite 的依赖。在 dependencies 部分添加以下行 implementation org.tensorflow:tensorflow-lite:2.5.0 将 TensorFlow Lite 模型文件添加到你的 Android 项目中。将模型文件.tflite复制到 app/src/main/assets 目录下。如果 assets 目录不存在可以手动创建。创建一个 TFLiteObjectDetectionAPIModel类用于加载和运行 TensorFlow Lite 模型。以下是一个示例代码 import org.tensorflow.lite.Interpreter; public class TFLiteObjectDetectionAPIModel implements Classifier {private static final Logger LOGGER new Logger();// Only return this many results.private static final int NUM_DETECTIONS 10;// Float modelprivate static final float IMAGE_MEAN 128.0f;private static final float IMAGE_STD 128.0f;// Number of threads in the java appprivate static final int NUM_THREADS 4;private boolean isModelQuantized;// Config values.private int inputSize;// Pre-allocated buffers.private VectorString labels new VectorString();private int[] intValues;// outputLocations: array of shape [Batchsize, NUM_DETECTIONS,4]// contains the location of detected boxesprivate float[][][] outputLocations;// outputClasses: array of shape [Batchsize, NUM_DETECTIONS]// contains the classes of detected boxesprivate float[][] outputClasses;// outputScores: array of shape [Batchsize, NUM_DETECTIONS]// contains the scores of detected boxesprivate float[][] outputScores;// numDetections: array of shape [Batchsize]// contains the number of detected boxesprivate float[] numDetections;private ByteBuffer imgData;private Interpreter tfLite;private TFLiteObjectDetectionAPIModel() {}/** Memory-map the model file in Assets. */private static MappedByteBuffer loadModelFile(AssetManager assets, String modelFilename)throws IOException {AssetFileDescriptor fileDescriptor assets.openFd(modelFilename);FileInputStream inputStream new FileInputStream(fileDescriptor.getFileDescriptor());FileChannel fileChannel inputStream.getChannel();long startOffset fileDescriptor.getStartOffset();long declaredLength fileDescriptor.getDeclaredLength();return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);}/*** Initializes a native TensorFlow session for classifying images.** param assetManager The asset manager to be used to load assets.* param modelFilename The filepath of the model GraphDef protocol buffer.* param labelFilename The filepath of label file for classes.* param inputSize The size of image input* param isQuantized Boolean representing model is quantized or not*/public static Classifier create(final AssetManager assetManager,final String modelFilename,final String labelFilename,final int inputSize,final boolean isQuantized)throws IOException {final TFLiteObjectDetectionAPIModel d new TFLiteObjectDetectionAPIModel();InputStream labelsInput null;String actualFilename labelFilename.split(file:///android_asset/)[1];labelsInput assetManager.open(actualFilename);BufferedReader br null;br new BufferedReader(new InputStreamReader(labelsInput));String line;while ((line br.readLine()) ! null) {LOGGER.w(line);d.labels.add(line);}br.close();d.inputSize inputSize;try {d.tfLite new Interpreter(loadModelFile(assetManager, modelFilename));} catch (Exception e) {throw new RuntimeException(e);}d.isModelQuantized isQuantized;// Pre-allocate buffers.int numBytesPerChannel;if (isQuantized) {numBytesPerChannel 1; // Quantized} else {numBytesPerChannel 4; // Floating point}d.imgData ByteBuffer.allocateDirect(1 * d.inputSize * d.inputSize * 3 * numBytesPerChannel);d.imgData.order(ByteOrder.nativeOrder());d.intValues new int[d.inputSize * d.inputSize];d.tfLite.setNumThreads(NUM_THREADS);d.outputLocations new float[1][NUM_DETECTIONS][4];d.outputClasses new float[1][NUM_DETECTIONS];d.outputScores new float[1][NUM_DETECTIONS];d.numDetections new float[1];return d;}Overridepublic ListRecognition 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());imgData.rewind();for (int i 0; i inputSize; i) {for (int j 0; j inputSize; j) {int pixelValue intValues[i * inputSize j];if (isModelQuantized) {// Quantized modelimgData.put((byte) ((pixelValue 16) 0xFF));imgData.put((byte) ((pixelValue 8) 0xFF));imgData.put((byte) (pixelValue 0xFF));} else { // Float modelimgData.putFloat((((pixelValue 16) 0xFF) - IMAGE_MEAN) / IMAGE_STD);imgData.putFloat((((pixelValue 8) 0xFF) - IMAGE_MEAN) / IMAGE_STD);imgData.putFloat(((pixelValue 0xFF) - IMAGE_MEAN) / IMAGE_STD);}}}Trace.endSection(); // preprocessBitmap// Copy the input data into TensorFlow.Trace.beginSection(feed);outputLocations new float[1][NUM_DETECTIONS][4];outputClasses new float[1][NUM_DETECTIONS];outputScores new float[1][NUM_DETECTIONS];numDetections new float[1];Object[] inputArray {imgData};MapInteger, Object outputMap new HashMap();outputMap.put(0, outputLocations);outputMap.put(1, outputClasses);outputMap.put(2, outputScores);outputMap.put(3, numDetections);Trace.endSection();// Run the inference call.Trace.beginSection(run);tfLite.runForMultipleInputsOutputs(inputArray, outputMap);Trace.endSection();// Show the best detections.// after scaling them back to the input size.final ArrayListRecognition recognitions new ArrayList(NUM_DETECTIONS);for (int i 0; i NUM_DETECTIONS; i) {final RectF detection new RectF(outputLocations[0][i][1] * inputSize,outputLocations[0][i][0] * inputSize,outputLocations[0][i][3] * inputSize,outputLocations[0][i][2] * inputSize);// SSD Mobilenet V1 Model assumes class 0 is background class// in label file and class labels start from 1 to number_of_classes1,// while outputClasses correspond to class index from 0 to number_of_classesint labelOffset 1;recognitions.add(new Recognition( i,labels.get((int) outputClasses[0][i] labelOffset),outputScores[0][i],detection));}Trace.endSection(); // recognizeImagereturn recognitions;}Overridepublic void enableStatLogging(final boolean logStats) {}Overridepublic String getStatString() {return ;}Overridepublic void close() {}public void setNumThreads(int num_threads) {if (tfLite ! null) tfLite.setNumThreads(num_threads);}Overridepublic void setUseNNAPI(boolean isChecked) {if (tfLite ! null) tfLite.setUseNNAPI(isChecked);}
} 确保替换 modelPath 参数为你的模型文件在 assets 目录中的路径。 在你的应用程序中使用 TFLiteObjectDetectionAPIModel 类进行推理。以下是一个简单的示例 Override
public void onPreviewSizeChosen(final Size size, final int rotation) {final float textSizePx TypedValue.applyDimension(TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, getResources().getDisplayMetrics());borderedText new BorderedText(textSizePx);borderedText.setTypeface(Typeface.MONOSPACE);tracker new MultiBoxTracker(this);int cropSize TF_OD_API_INPUT_SIZE;try {detector TFLiteObjectDetectionAPIModel.create(getAssets(),TF_OD_API_MODEL_FILE,TF_OD_API_LABELS_FILE,TF_OD_API_INPUT_SIZE,TF_OD_API_IS_QUANTIZED);cropSize TF_OD_API_INPUT_SIZE;} catch (final IOException e) {e.printStackTrace();LOGGER.e(e, Exception initializing classifier!);Toast toast Toast.makeText(getApplicationContext(), Classifier could not be initialized, Toast.LENGTH_SHORT);toast.show();finish();} // 解析输出数据
// ... 根据你的模型和任务你可能需要根据模型的规范和文档来解析输出数据。 输出解析文本数据 需要项目源码私聊
