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diff --git a/tensorflow/contrib/lite/g3doc/demo_android.md b/tensorflow/contrib/lite/g3doc/demo_android.md new file mode 100644 index 0000000000..d79a2696b4 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/demo_android.md @@ -0,0 +1,149 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + +# Android Demo App + +An example Android application using TensorFLow Lite is available +[on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo). +The demo is a sample camera app that classifies images continuously +using either a quantized Mobilenet model or a floating point Inception-v3 model. +To run the demo, a device running Android 5.0 ( API 21) or higher is required. + +In the demo app, inference is done using the TensorFlow Lite Java API. The demo +app classifies frames in real-time, displaying the top most probable +classifications. It also displays the time taken to detect the object. + +There are three ways to get the demo app to your device: + +* Download the [prebuilt binary APK](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk). +* Use Android Studio to build the application. +* Download the source code for TensorFlow Lite and the demo and build it using + bazel. + + +## Download the pre-built binary + +The easiest way to try the demo is to download the +[pre-built binary APK](https://storage.googleapis.com/download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk) + +Once the APK is installed, click the app icon to start the program. The first +time the app is opened, it asks for runtime permissions to access the device +camera. The demo app opens the back-camera of the device and recognizes objects +in the camera's field of view. At the bottom of the image (or at the left +of the image if the device is in landscape mode), it displays top three objects +classified and the classification latency. + + +## Build in Android Studio with TensorFlow Lite AAR from JCenter + +Use Android Studio to try out changes in the project code and compile the demo +app: + +* Install the latest version of + [Android Studio](https://developer.android.com/studio/index.html). +* Make sure the Android SDK version is greater than 26 and NDK version is greater + than 14 (in the Android Studio settings). +* Import the `tensorflow/contrib/lite/java/demo` directory as a new + Android Studio project. +* Install all the Gradle extensions it requests. + +Now you can build and run the demo app. + +The build process downloads the quantized [Mobilenet TensorFlow Lite model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip), and unzips it into the assets directory: `tensorflow/contrib/lite/java/demo/app/src/main/assets/`. + +Some additional details are available on the +[TF Lite Android App page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/README.md). + +### Using other models + +To use a different model: +* Download the floating point [Inception-v3 model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip). +* Unzip and copy `inceptionv3_non_slim_2015.tflite` to the assets directory. +* Change the chosen classifier in [Camera2BasicFragment.java](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java)<br> + from: `classifier = new ImageClassifierQuantizedMobileNet(getActivity());`<br> + to: `classifier = new ImageClassifierFloatInception(getActivity());`. + + +## Build TensorFlow Lite and the demo app from source + +### Clone the TensorFlow repo + +```sh +git clone https://github.com/tensorflow/tensorflow +``` + +### Install Bazel + +If `bazel` is not installed on your system, see +[Installing Bazel](https://bazel.build/versions/master/docs/install.html). + +Note: Bazel does not currently support Android builds on Windows. Windows users +should download the +[prebuilt binary](https://storage.googleapis.com/download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk). + +### Install Android NDK and SDK + +The Android NDK is required to build the native (C/C++) TensorFlow Lite code. The +current recommended version is *14b* and can be found on the +[NDK Archives](https://developer.android.com/ndk/downloads/older_releases.html#ndk-14b-downloads) +page. + +The Android SDK and build tools can be +[downloaded separately](https://developer.android.com/tools/revisions/build-tools.html) +or used as part of +[Android Studio](https://developer.android.com/studio/index.html). To build the +TensorFlow Lite Android demo, build tools require API >= 23 (but it will run on +devices with API >= 21). + +In the root of the TensorFlow repository, update the `WORKSPACE` file with the +`api_level` and location of the SDK and NDK. If you installed it with +Android Studio, the SDK path can be found in the SDK manager. The default NDK +path is:`{SDK path}/ndk-bundle.` For example: + +``` +android_sdk_repository ( + name = "androidsdk", + api_level = 23, + build_tools_version = "23.0.2", + path = "/home/xxxx/android-sdk-linux/", +) + +android_ndk_repository( + name = "androidndk", + path = "/home/xxxx/android-ndk-r10e/", + api_level = 19, +) +``` + +Some additional details are available on the +[TF Lite Android App page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/README.md). + +### Build the source code + +To build the demo app, run `bazel`: + +``` +bazel build --cxxopt=--std=c++11 //tensorflow/contrib/lite/java/demo/app/src/main:TfLiteCameraDemo +``` + +Caution: Because of an bazel bug, we only support building the Android demo app +within a Python 2 environment. + + +## About the demo + +The demo app is resizing each camera image frame (224 width * 224 height) to +match the quantized MobileNets model (299 * 299 for Inception-v3). The resized +image is converted—row by row—into a +[ByteBuffer](https://developer.android.com/reference/java/nio/ByteBuffer.html). +Its size is 1 * 224 * 224 * 3 bytes, where 1 is the number of images in a batch. +224 * 224 (299 * 299) is the width and height of the image. 3 bytes represents +the 3 colors of a pixel. + +This demo uses the TensorFlow Lite Java inference API +for models which take a single input and provide a single output. This outputs a +two-dimensional array, with the first dimension being the category index and the +second dimension being the confidence of classification. Both models have 1001 +unique categories and the app sorts the probabilities of all the categories and +displays the top three. The model file must be downloaded and bundled within the +assets directory of the app. |