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私 void classifyFrame() {// 获取BitmapBitmap bitmap = textureView.getBitmap(classifier.getImageSizeX(), classifier.getImageSizeY());// 感兴趣的文本String textToShow = classifier.classifyFrame(bitmap);}

// 输入数据的保存空间ByteBuffer=imgData=ByteBuffer.allocateDirect(DIM_BATCH_SIZE//1* getImageSizeX()* getImageSizeY()* DIM_PIXEL_SIZE//3* getNumBytesPerChannel());

// 将Bitmap转换为ByteBufferprivate void convertBitmapToByteBuffer(Bitmap bitmap) {imgData.rewind();bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());

long startTime = SystemClock.uptimeMillis();pixel=0;for (int i=0; i

}

protected void addPixelValue(int pixelValue) {imgData.put((byte)((pixelValue >> 16) & 0xFF));imgData.put((byte)((pixelValue >> 8) & 0xFF));imgData.put((byte)(pixelValue & 0xFF));}

// TensorFlow Lite引擎protected Interpreter tflite;// 输入结果存储空间private ByteBuffer imgData;

// 模型加载与预处理tflite= new Interpreter(loadModelFile(activity));imgData=ByteBuffer.allocateDirect(DIM_BATCH_SIZE//1* getImageSizeX()* getImageSizeY()* DIM_PIXEL_SIZE//3* getNumBytesPerChannel());

// 模型运行public void run(Object input) {Object[] inputs = {input};Map<Integer, Object> outputs = new HashMap<>();outputs.put(0, output);runForMultipleInputsOutputs(inputs, outputs);}

public void runForMultipleInputsOutputs(Object[] inputs, Map<Integer, Object> outputs) {Tensor[] tensors = wrapper.run(inputs);for (Integer idx : outputs.keySet()) {tensors[idx].copyTo(outputs.get(idx));}}

// 定义模型输入维度private static native int[] getInputDims(long interpreterHandle, int inputIdx, int numBytes);

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