在Android中将图片传递给TFLite模型,可以按照以下步骤进行:
try {
Interpreter interpreter = new Interpreter(loadModelFile());
} catch (IOException e) {
e.printStackTrace();
}
private MappedByteBuffer loadModelFile() throws IOException {
AssetFileDescriptor fileDescriptor = getAssets().openFd("model.tflite");
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);
}
run()
方法将图片传递给模型进行推理。以下是一个示例代码:// 获取输入和输出Tensor
Interpreter interpreter = new Interpreter(loadModelFile());
Tensor inputTensor = interpreter.getInputTensor(0);
Tensor outputTensor = interpreter.getOutputTensor(0);
// 预处理图片
Bitmap bitmap = BitmapFactory.decodeFile("path/to/image.jpg");
bitmap = Bitmap.createScaledBitmap(bitmap, inputTensor.shape()[1], inputTensor.shape()[2], true);
ByteBuffer inputBuffer = ByteBuffer.allocateDirect(4 * inputTensor.shape()[1] * inputTensor.shape()[2] * inputTensor.shape()[3]);
inputBuffer.order(ByteOrder.nativeOrder());
inputBuffer.rewind();
for (int i = 0; i < inputTensor.shape()[1]; i++) {
for (int j = 0; j < inputTensor.shape()[2]; j++) {
int pixelValue = bitmap.getPixel(i, j);
inputBuffer.putFloat((pixelValue >> 16 & 0xFF) / 255.0f);
inputBuffer.putFloat((pixelValue >> 8 & 0xFF) / 255.0f);
inputBuffer.putFloat((pixelValue & 0xFF) / 255.0f);
}
}
// 运行推理
float[][] output = new float[1][outputTensor.shape()[1]];
interpreter.run(inputBuffer, output);
// 处理输出结果
// ...
以上代码示例仅为演示目的,实际情况中可能需要根据具体模型和应用场景进行适当的修改。
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