💃 Mobile Pose Estimation for TensorFlow 2.0

~~This repository is forked from edvardHua/PoseEstimationForMobile when the original repository was closed.~~
edvardHua/PoseEstimationForMobile repository is reopened! I'll maintain it separately. 👍

This repository currently implemented the Hourglass model using TensorFlow 2.0 with Keras API.

Goals
Getting Started
Results
Converting To Mobile Model
Tuning
Details
- Folder Structure
- Main Components
TODO
Related Projects
Acknowledgements
Reference
Contributing
License

Goals

📚 Easy to train
🏃‍ Easy to use the model on mobile device

Getting Started

Install Anaconda (~10 min)

Create Virtual Environment (~2 min)

Create new environment.

conda create -n {env_name} python={python_version} anaconda
# in my case
# conda create -n mpe-env-tf2-alpha0 python=3.7 anaconda

Start the environment.

source activate {env_name}
# in my case
# source activate mpe-env-tf2-alpha0

Install the requirements (~1 min)

cd {tf2-mobile-pose-estimation_path}
pip install -r requirements.txt

Download COCO dataset.

Special script that will help you to download and unpack needed COCO datasets. Please fill COCO_DATASET_PATH with path that is used in current version of repository. You can check needed path in file train.py

Warning Your system should have approximately 40gb of free space for datasets

python downloader.py --download-path=COCO_DATASET_PATH

Run The Project

In order to use the project you have to:

Prepare the dataset(ai_challenger dataset) and unzip.
Run the model using:
```
python train.py
```

Results

Preparing..

Converting To Mobile Model

TensorFLow Lite

Preparing...

Core ML

Preparing... Related issue: https://github.com/tucan9389/tf2-mobile-pose-estimation/issues/13

Details

This section will be separated to other .md file.

Folder Structure

tf2-mobile-pose-estimation
├── config
|   ├── model_config.py
|   └── train_config.py
├── data_loader
|   ├── data_loader.py
|   ├── dataset_augment.py
|   ├── dataset_prepare.py
|   └── pose_image_processor.py
├── models
|   ├── common.py
|   ├── mobilenet.py
|   ├── mobilenetv2.py
|   ├── mobilenetv3.py
|   ├── resnet.py
|   ├── resneta.py
|   ├── resnetd.py
|   ├── senet.py
|   ├── simplepose_coco.py
|   └── simpleposemobile_coco.py
├── train.py            - the main script file
├── common.py 
└── requirements.txt

My SSD    
├── datasets            - this folder contain the datasets of the project.
|   └── ai_challenger
|       ├── train.json
|       ├── valid.json
|       ├── train
|       └── valid
└── outputs             - this folder will be generated automatically when start training
    ├── 20200312-sp-ai_challenger
    |   ├── saved_model
    |   └── image_results
    └── 20200312-sp-ai_challenger
        └── ...

TODO

~~Save model to saved_model~~
Convert the model(saved_model) to TFLite model(.tflite)
Convert the model(saved_model) to Core ML model(.mlmodel)
Run the model on Android
Run the model on iOS
Make DEMO gif running on mobile device

Reference

[1] Paper of Convolutional Pose Machines
[2] Paper of Stack Hourglass
[3] Paper of MobileNet V2
[4] Repository PoseEstimation-CoreML
[5] Repository of tf-pose-estimation
[6] Devlope guide of TensorFlow Lite
[7] Mace documentation

Related Projects

Other Pose Estimation Projects

cbsudux/awesome-human-pose-estimation

Contributing