安卓端实现实时图像识别完整代码
时间: 2023-11-26 19:06:23 浏览: 28
安卓端实时图像识别可以使用TensorFlow Lite库来实现。下面是一个简单的示例代码:
1. 首先,在build.gradle文件中添加依赖项:
```
implementation 'org.tensorflow:tensorflow-lite:2.4.0'
```
2. 创建一个模型文件(.tflite文件),并将其放在assets目录中。
3. 在Activity中加载模型文件,并准备相机:
```
private Interpreter tflite;
private CameraView cameraView;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
// 加载模型文件
try {
tflite = new Interpreter(loadModelFile());
} catch (IOException e) {
e.printStackTrace();
}
// 准备相机
cameraView = findViewById(R.id.camera_view);
cameraView.setFacing(CameraView.FACING_BACK);
cameraView.setLifecycleOwner(this);
cameraView.addFrameProcessor(new FrameProcessor() {
@Override
public void process(@NonNull Frame frame) {
// 在这里进行图像识别
}
});
}
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);
}
```
4. 在FrameProcessor中进行图像识别:
```
@Override
public void process(@NonNull Frame frame) {
Bitmap bitmap = frame.getBitmap();
if (bitmap == null) {
return;
}
// 将图像转换为Tensor
TensorImage inputImageBuffer = new TensorImage(DataType.FLOAT32);
inputImageBuffer.load(bitmap);
TensorBuffer outputProbabilityBuffer = TensorBuffer.createFixedSize(new int[]{1, NUM_CLASSES}, DataType.FLOAT32);
// 运行模型
tflite.run(inputImageBuffer.getBuffer(), outputProbabilityBuffer.getBuffer().rewind());
// 处理结果
float[] results = outputProbabilityBuffer.getFloatArray();
int maxIndex = 0;
for (int i = 1; i < results.length; i++) {
if (results[i] > results[maxIndex]) {
maxIndex = i;
}
}
// 在UI线程更新UI
runOnUiThread(new Runnable() {
@Override
public void run() {
// 显示识别结果
textView.setText(labels[maxIndex]);
}
});
}
private static final int NUM_CLASSES = 10;
private static final String[] labels = {"0", "1", "2", "3", "4", "5", "6", "7", "8", "9"};
```
这里的示例代码是用来识别手写数字的,如果要识别其他类型的图像,需要将模型文件和标签文件替换成对应的文件。