From 56c7861736f5322cc0e10433f62b7522926273f7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 8 Jun 2020 16:32:25 -0700
Subject: [PATCH] Add more docs on using TFLite through C++

PiperOrigin-RevId: 315378099
Change-Id: I1ab60a8a2bbed768c2714444f48f173b006fe540
---
 tensorflow/lite/g3doc/guide/android.md | 25 +++++++++++++++++++++++--
 1 file changed, 23 insertions(+), 2 deletions(-)

diff --git a/tensorflow/lite/g3doc/guide/android.md b/tensorflow/lite/g3doc/guide/android.md
index b22ea13d722..ba1bc46e8b4 100644
--- a/tensorflow/lite/g3doc/guide/android.md
+++ b/tensorflow/lite/g3doc/guide/android.md
@@ -262,9 +262,24 @@ Note that the `0.1.100` version here is purely for the sake of
 testing/development. With the local AAR installed, you can use the standard
 [TensorFlow Lite Java inference APIs](../guide/inference.md) in your app code.
 
-##### Build C++ libraries
+## Build Android app using C++
 
-If you want to use TFLite through C++ libraries, you can build the shared
+There are two ways to use TFLite through C++ if you build your app with the NDK:
+
+### Use TFLite C API
+
+This is the *recommended* approach. Download the
+[TensorFlow Lite AAR hosted at JCenter](https://bintray.com/google/tensorflow/tensorflow-lite),
+rename it to `tensorflow-lite-*.zip`, and unzip it. You must include the three
+header files in `headers/tensorflow/lite/c/` folder and the relevant
+`libtensorflowlite_jni.so` dynamic library in `jni/` folder in your NDK project.
+
+The `c_api.h` header file contains basic documentation about using the TFLite C
+API.
+
+### Use TFLite C++ API
+
+If you want to use TFLite through C++ API, you can build the C++ shared
 libraries:
 
 32bit armeabi-v7a:
@@ -278,3 +293,9 @@ bazel build -c opt --config=android_arm //tensorflow/lite:libtensorflowlite.so
 ```sh
 bazel build -c opt --config=android_arm64 //tensorflow/lite:libtensorflowlite.so
 ```
+
+Currently, there is no straightforward way to extract all header files needed,
+so you must include all header files in `tensorflow/lite/` from the TensorFlow
+repository. Additionally, you will need header files from
+[FlatBUffers](https://github.com/google/flatbuffers) and
+[Abseil](https://github.com/abseil/abseil-cpp).