How to Implement Object Detection with YOLO v3 on Mobil

    #!/bin/bash
    
    # Install required packages
    sudo apt update && sudo apt install -y \
    autoconf automake libtool curl make g++ unzip zip sqlite3 libc6-dev zlib1g-dev
    
    # Clone darknet repo
    git clone https://github.com/AlexeyAB/darknet
    
    # Enter darknet directory
    cd darknet
    
    # Compile dependencies
    make -j$(nproc)
    
    # Download weight file
    wget https://pjreddie.com/media/files/yolov3.weights
    
    # Set up android toolchain and NDK path
    export ANDROID_HOME=$HOME/Android/Sdk
    export PATH=$PATH:$ANDROID_HOME/tools:$ANDROID_HOME/platform-tools:${ANDROID_HOME}/ndk-bundle
    
    # Set up build environment variables for cmake
    export CXX=aarch64-linux-android-g++
    export CC=aarch64-linux-android-gcc
    export CMAKE_TOOLCHAIN_FILE=${ANDROID_HOME}/ndk-bundle/build/cmake/android.toolchain.cmake
    export ABI=arm64-v8a
    
    # Create build directory
    mkdir build && cd build
    
    # Generate Makefiles for building arm64 library
    cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=../jniLibs/..
    
    # Build shared object files for Android
    make -j$(nproc)
    
    # Build final AAR package
    cpack -G Android Gradle
    
    # Move built AAR package back to root folder
    mv./app/build/outputs/aar/*-debug.aar.
    
    # Exit from build script directory
    cd../../..
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import tensorflow as tf
    from absl import app, flags
    from absl.flags import FLAGS
    
    flags.DEFINE_string('input', None, 'Path to input weights file')
    flags.DEFINE_string('output', None, 'Path to output tflite model file')
    
    def main(_):
    
    # Load YOLO v3 architecture and load its pretrained weights
    yolo = tf.keras.models.load_model('/path/to/yolov3.h5', compile=False)
    
    # Define input and output shapes according to TensorFlow Lite requirements
    input_shape = (416, 416, 3)
    output_shapes = [(13, 13, 3, 85), (26, 26, 3, 85), (52, 52, 3, 85)]
    
    # Convert Keras model into a frozen graph
    converter = tf.lite.TFLiteConverter.from_keras_model(yolo)
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.representative_dataset = representative_data_gen
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter.inference_input_type = tf.int8
    converter.inference_output_type = tf.float32
    converter.experimental_new_converter = True
    
    # Fine tune TensorFlow Lite performance parameters for mobile devices
    converter.experimental_new_quantizer = False
    converter.target_spec.supported_types = [tf.uint8]
    converter.inference_input_type = tf.uint8
    converter.inference_output_type = tf.float32
    converter.allow_custom_ops = True
    converter.experimental_enable_resource_variables = True
    
    # Convert keras model into TFLite format
    tflite_model = converter.convert()
    
    # Save converted model to disk
    open(FLAGS.output, "wb").write(tflite_model)
    
    
    if __name__ == '__main__':
    try:
        app.run(main)
    except SystemExit:
        pass
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    implementation 'org.tensorflow:tensorflow-lite:+'

    implementation 'org.tensorflow:tensorflow-lite-gpu:+' // optional GPU acceleration if your device supports it
    implementation 'org.tensorflow:tensorflow-lite-support:+' // optional support libraries including optional TF Lite Task Library
    
         
         
    代码解读

    private static final String MODEL_PATH = "/path/to/yolov3.tflite";

    
    private Interpreter interpreter;
    
    @Override
    protected void onCreate(Bundle savedInstanceState) {
    super.onCreate(savedInstanceState);
    
    // Initialize TensorFlow Lite
            try {
                InputStream inputStream = getAssets().open(MODEL_PATH);
                byte[] model = new byte[inputStream.available()];
                inputStream.read(model);
                inputStream.close();
    
                ByteBuffer buffer = ByteBuffer.wrap(model);
                interpreter = new Interpreter(buffer);
    
            } catch (IOException e) {
                Log.d("TensorFlowLite", "Error reading model");
        }
    
    ...
    }
    
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
    代码解读