/Tflite_Javacv_Rtmp_App

手机摄像头数据实时推流到服务器、app获取推流的byte数组进行货车车斗装载货物识别，并利用opencv检测出当前车辆的运输状态（装载、倾倒、运输） 最后把每个过程的数据存储到本地 并上传服务器

Primary LanguageJava

TF Lite Android App

本程序实现功能

1，摄像头数据实时推流到服务器，在本程序中，服务器设置的是边缘设备（android系统开发板）使用前需要配置sys

2，获取推流时的byte[]数组 转成图片后 利用自训练的Tflite分类模型和opencv算法，综合分析出当前货车的载物类别和行驶行为

Building from Source with Bazel

  1. Follow the Bazel steps for the TF Demo App:

  2. Install Bazel and Android Prerequisites. It's easiest with Android Studio.

    • You'll need at least SDK version 23.
    • Bazel requires Android Build Tools 26.0.1 or higher.
    • You also need to install the Android Support Repository, available through Android Studio under Android SDK Manager -> SDK Tools -> Android Support Repository.

  3. Edit your WORKSPACE to add SDK and NDK targets.

    • Make sure the api_level in WORKSPACE is set to an SDK version that you have installed.
    • By default, Android Studio will install the SDK to ~/Android/Sdk and the NDK to ~/Android/Sdk/ndk-bundle.

  4. Build the app with Bazel. The demo needs C++11:

bazel build -c opt --cxxopt='--std=c++11' \
  //tensorflow/contrib/lite/java/demo/app/src/main:TfLiteCameraDemo
  1. Install the demo on a debug-enabled device:
adb install bazel-bin/tensorflow/contrib/lite/java/demo/app/src/main/TfLiteCameraDemo.apk