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Import the original TensorFlow Android sample
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‎android/AndroidManifest.xml‎

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<?xml version="1.0" encoding="UTF-8"?>
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<!--
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Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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<manifest xmlns:android="http://schemas.android.com/apk/res/android"
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package="org.tensorflow.demo">
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<uses-permission android:name="android.permission.CAMERA" />
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<uses-feature android:name="android.hardware.camera" />
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<uses-feature android:name="android.hardware.camera.autofocus" />
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<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
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<uses-sdk
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android:minSdkVersion="21"
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android:targetSdkVersion="23" />
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<application android:allowBackup="true"
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android:debuggable="true"
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android:label="@string/app_name"
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android:icon="@drawable/ic_launcher"
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android:theme="@style/MaterialTheme">
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<activity android:name="org.tensorflow.demo.ClassifierActivity"
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android:screenOrientation="portrait"
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android:label="@string/activity_name_classification">
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<intent-filter>
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<action android:name="android.intent.action.MAIN" />
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<category android:name="android.intent.category.LAUNCHER" />
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</intent-filter>
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</activity>
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<activity android:name="org.tensorflow.demo.DetectorActivity"
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android:screenOrientation="portrait"
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android:label="@string/activity_name_detection">
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<intent-filter>
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<action android:name="android.intent.action.MAIN" />
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<category android:name="android.intent.category.LAUNCHER" />
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</intent-filter>
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</activity>
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<activity android:name="org.tensorflow.demo.StylizeActivity"
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android:screenOrientation="portrait"
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android:label="@string/activity_name_stylize">
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<intent-filter>
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<action android:name="android.intent.action.MAIN" />
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<category android:name="android.intent.category.LAUNCHER" />
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</intent-filter>
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</activity>
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</application>
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</manifest>

‎android/BUILD‎

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# Description:
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# TensorFlow camera demo app for Android.
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package(default_visibility = ["//visibility:public"])
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licenses(["notice"]) # Apache 2.0
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load(
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"//tensorflow:tensorflow.bzl",
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"tf_copts",
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"tf_opts_nortti_if_android",
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)
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exports_files(["LICENSE"])
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LINKER_SCRIPT = "//tensorflow/contrib/android:jni/version_script.lds"
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# libtensorflow_demo.so contains the native code for image colorspace conversion
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# and object tracking used by the demo. It does not require TF as a dependency
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# to build if STANDALONE_DEMO_LIB is defined.
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# TF support for the demo is provided separately by libtensorflow_inference.so.
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cc_binary(
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name = "libtensorflow_demo.so",
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srcs = glob([
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"jni/**/*.cc",
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"jni/**/*.h",
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]),
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copts = tf_copts(),
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defines = ["STANDALONE_DEMO_LIB"],
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linkopts = [
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"-landroid",
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"-ljnigraphics",
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"-llog",
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"-lm",
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"-z defs",
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"-s",
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"-Wl,--version-script", # This line must be directly followed by LINKER_SCRIPT.
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LINKER_SCRIPT,
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],
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linkshared = 1,
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linkstatic = 1,
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tags = [
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"manual",
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"notap",
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],
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deps = [
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LINKER_SCRIPT,
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],
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)
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cc_library(
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name = "tensorflow_native_libs",
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srcs = [
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":libtensorflow_demo.so",
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"//tensorflow/contrib/android:libtensorflow_inference.so",
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],
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tags = [
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"manual",
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"notap",
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],
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)
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android_binary(
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name = "tensorflow_demo",
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srcs = glob([
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"src/**/*.java",
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]),
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# Package assets from assets dir as well as all model targets. Remove undesired models
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# (and corresponding Activities in source) to reduce APK size.
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assets = [
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"//tensorflow/examples/android/assets:asset_files",
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":external_assets",
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],
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assets_dir = "",
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custom_package = "org.tensorflow.demo",
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inline_constants = 1,
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manifest = "AndroidManifest.xml",
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resource_files = glob(["res/**"]),
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tags = [
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"manual",
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"notap",
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],
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deps = [
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":tensorflow_native_libs",
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"//tensorflow/contrib/android:android_tensorflow_inference_java",
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],
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)
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filegroup(
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name = "external_assets",
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srcs = [
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"@inception5h//:model_files",
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"@mobile_multibox//:model_files",
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"@stylize//:model_files",
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],
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)
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filegroup(
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name = "all_files",
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srcs = glob(
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["**/*"],
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exclude = [
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"**/METADATA",
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"**/OWNERS",
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"bin/**",
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"gen/**",
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"gradleBuild/**",
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"libs/**",
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],
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),
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visibility = ["//tensorflow:__subpackages__"],
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)
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filegroup(
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name = "java_files",
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srcs = glob(["src/**/*.java"]),
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)
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filegroup(
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name = "jni_files",
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srcs = glob([
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"jni/**/*.cc",
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"jni/**/*.h",
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]),
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)
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filegroup(
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name = "resource_files",
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srcs = glob(["res/**"]),
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)
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exports_files(["AndroidManifest.xml"])

‎android/README.md‎

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# TensorFlow Android Camera Demo
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This folder contains an example application utilizing TensorFlow for Android
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devices.
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## Description
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The demos in this folder are designed to give straightforward samples of using
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TensorFlow in mobile applications.
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Inference is done using the [TensorFlow Android Inference Interface](../../../tensorflow/contrib/android),
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which may be built separately if you want a standalone library to drop into your
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existing application. Object tracking and YUV -> RGB conversion is handled by
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libtensorflow_demo.so.
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A device running Android 5.0 (API 21) or higher is required to run the demo due
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to the use of the camera2 API, although the native libraries themselves can run
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on API >= 14 devices.
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## Current samples:
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1. [TF Classify](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/ClassifierActivity.java):
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Uses the [Google Inception](https://arxiv.org/abs/1409.4842)
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model to classify camera frames in real-time, displaying the top results
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in an overlay on the camera image.
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2. [TF Detect](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/DetectorActivity.java):
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Demonstrates a model based on [Scalable Object Detection
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using Deep Neural Networks](https://arxiv.org/abs/1312.2249) to
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localize and track people in the camera preview in real-time.
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3. [TF Stylize](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java):
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Uses a model based on [A Learned Representation For Artistic Style]
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(https://arxiv.org/abs/1610.07629) to restyle the camera preview image
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to that of a number of different artists.
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<img src="sample_images/classify1.jpg" width="30%">
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<img src="sample_images/stylize1.jpg" width="30%">
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<img src="sample_images/detect1.jpg" width="30%">
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## Prebuilt APK:
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If you just want the fastest path to trying the demo, you may download the
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nightly build
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[here](https://ci.tensorflow.org/view/Nightly/job/nightly-android/). Expand the
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"View" and then the "out" folders under "Last Successful Artifacts" to find
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tensorflow_demo.apk. Also available are precompiled native libraries that you
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may drop into your own applications. See
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[tensorflow/contrib/android/README.md](../../../tensorflow/contrib/android/README.md)
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for more details.
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## Running the Demo
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Once the app is installed it can be started via the "TF Classify", "TF Detect"
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and "TF Stylize" icons, which have the orange TensorFlow logo as their icon.
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While running the activities, pressing the volume keys on your device will
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toggle debug visualizations on/off, rendering additional info to the screen
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that may be useful for development purposes.
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## Building the Demo from Source
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Pick your preferred approach below. At the moment, we have full support for
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Bazel, and partial support for gradle, cmake, make, and Android Studio.
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As a first step for all build types, clone the TensorFlow repo with:
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```
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git clone --recurse-submodules https://github.com/tensorflow/tensorflow.git
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```
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Note that `--recurse-submodules` is necessary to prevent some issues with
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protobuf compilation.
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### Bazel
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NOTE: Bazel does not currently support building for Android on Windows. Full
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support for gradle/cmake builds is coming soon, but in the meantime we suggest
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that Windows users download the
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[prebuilt binaries](https://ci.tensorflow.org/view/Nightly/job/nightly-android/)
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instead.
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##### Install Bazel and Android Prerequisites
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Bazel is the primary build system for TensorFlow. To build with Bazel,
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it and the Android NDK and SDK must be installed on your system.
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1. Get the recommended Bazel version listed in [os_setup.html](https://www.tensorflow.org/versions/master/get_started/os_setup.html#source)
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2. The Android NDK is required to build the native (C/C++) TensorFlow code.
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The current recommended version is 12b, which may be found
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[here](https://developer.android.com/ndk/downloads/older_releases.html#ndk-12b-downloads).
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3. The Android SDK and build tools may be obtained
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[here](https://developer.android.com/tools/revisions/build-tools.html),
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or alternatively as part of
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[Android Studio](https://developer.android.com/studio/index.html). Build
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tools API >= 23 is required to build the TF Android demo (though it will
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run on API >= 21 devices).
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##### Edit WORKSPACE
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The Android entries in [`<workspace_root>/WORKSPACE`](../../../WORKSPACE#L2-L13)
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must be uncommented with the paths filled in appropriately depending on where
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you installed the NDK and SDK. Otherwise an error such as:
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"The external label '//external:android/sdk' is not bound to anything" will
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be reported.
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Also edit the API levels for the SDK in WORKSPACE to the highest level you
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have installed in your SDK. This must be >= 23 (this is completely independent
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of the API level of the demo, which is defined in AndroidManifest.xml).
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The NDK API level may remain at 14.
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##### Install Model Files (optional)
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The TensorFlow `GraphDef`s that contain the model definitions and weights
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are not packaged in the repo because of their size. They are downloaded
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automatically and packaged with the APK by Bazel via a new_http_archive defined
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in `WORKSPACE` during the build process.
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**Optional**: If you wish to place the models in your assets manually (E.g. for
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non-Bazel builds), remove all of the `model_files` entries from the `assets`
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list in `tensorflow_demo` found in the `[BUILD](BUILD)` file. Then download
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and extract the archives yourself to the `assets` directory in the source tree:
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```bash
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BASE_URL=https://storage.googleapis.com/download.tensorflow.org/models
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for MODEL_ZIP in inception5h.zip mobile_multibox_v1a.zip stylize_v1.zip
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do
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curl -L ${BASE_URL}/${MODEL_ZIP} -o /tmp/${MODEL_ZIP}
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unzip /tmp/${MODEL_ZIP} -d tensorflow/examples/android/assets/
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done
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```
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This will extract the models and their associated metadata files to the local
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assets/ directory.
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##### Build
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After editing your WORKSPACE file to update the SDK/NDK configuration,
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you may build the APK. Run this from your workspace root:
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```bash
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bazel build -c opt //tensorflow/examples/android:tensorflow_demo
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```
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If you get build errors about protocol buffers, run
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`git submodule update --init` and make sure that you've modified your WORKSPACE
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file as instructed, then try building again.
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##### Install
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Make sure that adb debugging is enabled on your Android 5.0 (API 21) or
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later device, then after building use the following command from your workspace
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root to install the APK:
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```bash
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adb install -r bazel-bin/tensorflow/examples/android/tensorflow_demo.apk
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```
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### Android Studio
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Android Studio may be used to build the demo in conjunction with Bazel. First,
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make sure that you can build with Bazel following the above directions. Then,
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look at [build.gradle](build.gradle) and make sure that the path to Bazel
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matches that of your system.
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At this point you can add the tensorflow/examples/android directory as a new
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Android Studio project. Click through installing all the Gradle extensions it
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requests, and you should be able to have Android Studio build the demo like any
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other application (it will call out to Bazel to build the native code with the
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NDK).
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### CMake
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Full CMake support for the demo is coming soon, but for now it is possible to
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build the TensorFlow Android Inference library using
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[tensorflow/contrib/android/cmake](../../../tensorflow/contrib/android/cmake).

‎android/__init__.py‎

Whitespace-only changes.

‎android/assets/BUILD‎

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package(default_visibility = ["//visibility:public"])
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licenses(["notice"]) # Apache 2.0
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# It is necessary to use this filegroup rather than globbing the files in this
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# folder directly the examples/android:tensorflow_demo target due to the fact
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# that assets_dir is necessarily set to "" there (to allow using other
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# arbitrary targets as assets).
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filegroup(
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name = "asset_files",
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srcs = glob(
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["**/*"],
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exclude = ["BUILD"],
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),
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)

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