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TensorFlow Lite Object Detection Android Demo

Overview

NOTE: This project was originally taken (and modified) from the official TensorFlow examples repository at https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android. This README is exactly the same from the project.

This is a camera app that continuously detects the objects (bounding boxes and classes) in the frames seen by your device's back camera, using a quantized MobileNet SSD model trained on the COCO dataset. These instructions walk you through building and running the demo on an Android device.

The model files are downloaded via Gradle scripts when you build and run. You don't need to do any steps to download TFLite models into the project explicitly.

Application can run either on device or emulator.

Build the demo using Android Studio

Prerequisites

If you don't have already, install

You need an Android device and Android development environment with minimum API 21.

Android Studio 3.2 or later.

Building

Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.

From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/object_detection/android directory from wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.

If it asks you to do a Gradle Sync, click OK.

You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-21'" and similar.

Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.

If it asks you to use Instant Run, click Proceed Without Instant Run.

Also, you need to have an Android device plugged in with developer options enabled at this point. See

Model used

Downloading, extraction and placing it in assets folder has been managed automatically by download.gradle.

If you explicitly want to download the model, you can download from

Custom model used

This example shows you how to perform TensorFlow Lite object detection using a custom model.

Clone the TensorFlow models GitHub repository to your computer.

git clone https://github.com/tensorflow/models/

Build and install this repository.

cd models/research

python3 setup.py build && python3 setup.py install

Download the MobileNet SSD trained on

Go to models/research directory and execute this code to get the frozen TensorFlow Lite graph.

python3 object_detection/export_tflite_ssd_graph.py \

--pipeline_config_path object_detection/samples/configs/ssd_mobilenet_v2_oid_v4.config \

--trained_checkpoint_prefix /model.ckpt \

--output_directory exported_model

Convert the frozen graph to the TFLite model.

tflite_convert \

--input_shape=1,300,300,3 \

--input_arrays=normalized_input_image_tensor \

--output_arrays=TFLite_Detection_PostProcess,TFLite_Detection_PostProcess:1,TFLite_Detection_PostProcess:2,TFLite_Detection_PostProcess:3 \

--allow_custom_ops \

--graph_def_file=exported_model/tflite_graph.pb \

--output_file=/lite/examples/object_detection/android/app/src/main/assets/detect.tflite

input_shape=1,300,300,3 because the pretrained model works only with that input shape.

allow_custom_ops is necessary to allow TFLite_Detection_PostProcess operation.

input_arrays and output_arrays can be drawn from the visualized graph of the example detection model.

bazel run //tensorflow/lite/tools:visualize \

"/lite/examples/object_detection/android/app/src/main/assets/detect.tflite" \

detect.html

Get labelmap.txt from the second column of

In DetectorActivity.java set TF_OD_API_IS_QUANTIZED to false and in TFLiteObjectDetectionAPIModel.java set labelOffset to 0.

Additional Note

Please do not delete the assets folder content. If you explicitly deleted the files, then please choose Build->Rebuild from menu to re-download the deleted model files into assets folder.