Pre-Collision Assist with Pedestrian Detection - Honda Civic

Combine real-time hazard detection and classification from Camera with Walabot RF radar, powered by TensorFlow with YOLO approach.

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Building the Android Applictaion from Source

You can choose to run it standalone but functioanlity will be limited. Please see this https://www.hackster.io/asadzia/pre-collision-assist-with-pedestrian-detection-honda-civic-db91cb for information on setting up other parts.

Pick your preferred approach below. At the moment, we have full support for Bazel, and partial support for gradle, cmake, make, and Android Studio.

As a first step for all build types, clone the TensorFlow repo with:

git clone --recurse-submodules https://github.com/tensorflow/tensorflow.git

Note that --recurse-submodules is necessary to prevent some issues with protobuf compilation.

Bazel

NOTE: Bazel does not currently support building for Android on Windows. Full support for gradle/cmake builds is coming soon, but in the meantime we suggest that Windows users download the prebuilt binaries instead.

Install Bazel and Android Prerequisites

Bazel is the primary build system for TensorFlow. To build with Bazel, it and the Android NDK and SDK must be installed on your system.

1. Install the latest version of Bazel as per the instructions on the Bazel website.
2. The Android NDK is required to build the native (C/C++) TensorFlow code. The current recommended version is 12b, which may be found here.
3. The Android SDK and build tools may be obtained here, or alternatively as part of Android Studio. Build tools API >= 23 is required to build the TF Android demo (though it will run on API >= 21 devices).

Edit WORKSPACE

The Android entries in <workspace_root>/WORKSPACE must be uncommented with the paths filled in appropriately depending on where you installed the NDK and SDK. Otherwise an error such as: "The external label '//external:android/sdk' is not bound to anything" will be reported.

Also edit the API levels for the SDK in WORKSPACE to the highest level you have installed in your SDK. This must be >= 23 (this is completely independent of the API level of the demo, which is defined in AndroidManifest.xml). The NDK API level may remain at 14.

Install Model Files (optional)

The TensorFlow GraphDefs that contain the model definitions and weights are not packaged in the repo because of their size. They are downloaded automatically and packaged with the APK by Bazel via a new_http_archive defined in WORKSPACE during the build process, and by Gradle via download-models.gradle.

Optional: If you wish to place the models in your assets manually, remove all of the model_files entries from the assets list in tensorflow_demo found in the [BUILD](BUILD) file. Then download and extract the archives yourself to the assets directory in the source tree:

BASE_URL=https://storage.googleapis.com/download.tensorflow.org/models
for MODEL_ZIP in inception5h.zip mobile_multibox_v1a.zip stylize_v1.zip
do
  curl -L ${BASE_URL}/${MODEL_ZIP} -o /tmp/${MODEL_ZIP}
  unzip /tmp/${MODEL_ZIP} -d tensorflow/examples/android/assets/
done

This will extract the models and their associated metadata files to the local assets/ directory.

If you are using Gradle, make sure to remove download-models.gradle reference from build.gradle after your manually download models; otherwise gradle might download models again and overwrite your models.

Build

After editing your WORKSPACE file to update the SDK/NDK configuration, you may build the APK. Run this from your workspace root:

bazel build -c opt //tensorflow/examples/android:tensorflow_demo

If you get build errors about protocol buffers, run git submodule update --init and make sure that you've modified your WORKSPACE file as instructed, then try building again.

Install

Make sure that adb debugging is enabled on your Android 5.0 (API 21) or later device, then after building use the following command from your workspace root to install the APK:

adb install -r bazel-bin/tensorflow/examples/android/tensorflow_demo.apk

Android Studio

Android Studio may be used to build the demo in conjunction with Bazel. First, make sure that you can build with Bazel following the above directions. Then, look at build.gradle and make sure that the path to Bazel matches that of your system.

At this point you can add the tensorflow/examples/android directory as a new Android Studio project. Click through installing all the Gradle extensions it requests, and you should be able to have Android Studio build the demo like any other application (it will call out to Bazel to build the native code with the NDK).

CMake

Full CMake support for the demo is coming soon, but for now it is possible to build the TensorFlow Android Inference library using tensorflow/contrib/android/cmake.