This folder contains a simple camera-based demo application utilizing TensorFlow.

This demo uses a Google Inception model to classify camera frames in real-time, displaying the top results in an overlay on the camera image.

As a prerequisite, Bazel, the Android NDK, and the Android SDK must all be installed on your system.

1. Get the recommended Bazel version listed at: https://www.tensorflow.org/versions/master/get_started/os_setup.html#source
2. The Android NDK may be obtained from: http://developer.android.com/tools/sdk/ndk/index.html
3. The Android SDK and build tools may be obtained from: https://developer.android.com/tools/revisions/build-tools.html

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.

The TensorFlow GraphDef that contains the model definition and weights is not packaged in the repo because of its size. It will be downloaded automatically via a new_http_archive defined in WORKSPACE.

Optional: If you wish to place the model in your assets manually (E.g. for non-Bazel builds), remove the inception_5 entry in BUILD and download the archive yourself to the assets directory in the source tree:

$ curl -L https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip -o /tmp/inception5h.zip

$ unzip /tmp/inception5h.zip -d tensorflow/examples/android/assets/

The labels file describing the possible classification will also be in the assets directory.

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

$ bazel build //tensorflow/examples/android:tensorflow_demo

If you get build errors about protocol buffers, run git submodule update --init and build again.

If adb debugging is enabled on your Android 5.0 or later device, you may then use the following command from your workspace root to install the APK once built:

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

Some older versions of adb might complain about the -g option (returning: "Error: Unknown option: -g"). In this case, if your device runs Android 6.0 or later, then make sure you update to the latest adb version before trying the install command again. If your device runs earlier versions of Android, however, you can issue the install command without the -g option.

Alternatively, a streamlined means of building, installing and running in one command is:

$ bazel mobile-install //tensorflow/examples/android:tensorflow_demo --start_app

If camera permission errors are encountered (possible on Android Marshmallow or above), then the adb install command above should be used instead, as it automatically grants the required camera permissions with -g. The permission errors may not be obvious if the app halts immediately, so if you installed with bazel and the app doesn't come up, then the easiest thing to do is try installing with adb.

Once the app is installed it will be named "TensorFlow Demo" and have the orange TensorFlow logo as its icon.