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https://github.com/tucan9389/tf2-mobile-2d-single-pose-estimation
:dancer: Pose estimation for iOS and android using TensorFlow 2.0
https://github.com/tucan9389/tf2-mobile-2d-single-pose-estimation
ios mobile pose-estimation tensorflow-lite tensorflow2
Last synced: 28 days ago
JSON representation
:dancer: Pose estimation for iOS and android using TensorFlow 2.0
- Host: GitHub
- URL: https://github.com/tucan9389/tf2-mobile-2d-single-pose-estimation
- Owner: tucan9389
- License: apache-2.0
- Created: 2019-03-25T03:50:32.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-22T07:43:56.000Z (about 2 years ago)
- Last Synced: 2024-08-10T11:01:21.582Z (4 months ago)
- Topics: ios, mobile, pose-estimation, tensorflow-lite, tensorflow2
- Language: Python
- Homepage:
- Size: 38.8 MB
- Stars: 167
- Watchers: 17
- Forks: 41
- Open Issues: 39
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-tensorflow-2 - Single pose estimation for iOS and android using TensorFlow 2.0
README
# 💃 Mobile 2D Single Person (Or Your Own Object) Pose Estimation for TensorFlow 2.0
> ~~This repository is forked from [edvardHua/PoseEstimationForMobile](https://github.com/edvardHua/PoseEstimationForMobile) when the original repository was closed.~~
[edvardHua/PoseEstimationForMobile](https://github.com/edvardHua/PoseEstimationForMobile) repository is reopened! I'll maintain it separately. 👍
This repository currently implemented the Hourglass model using TensorFlow 2.0 with Keras API.
## Table of contents
- [Goals](#goals)
- [Getting Started](#getting-started)
- [Results](#results)
- [Converting To Mobile Model](#converting-to-mobile-model)
- [Tuning](#tuning)
- [Details](#details)
- [Folder Structure](#folder-structure)
- [Main Components](#main-components)
- [TODO](#todo)
- [Related Projects](#related-projects)
- [Acknowledgements](#acknowledgements)
- [Reference](#reference)
- [Contributing](#contributing)
- [License](#license)
## Goals
- 📚 Easy to train
- 🏃 Easy to use the model on mobile device
## Getting Started
### Install Anaconda (~10 min)
- [How To Install Anaconda on Ubuntu 18.04 [Quickstart]](https://www.digitalocean.com/community/tutorials/how-to-install-anaconda-on-ubuntu-18-04-quickstart)
- [How to Install Anaconda on CentOS 7](https://linuxize.com/post/how-to-install-anaconda-on-centos-7/)
### Create Virtual Environment (~2 min)
Create new environment.
```shell
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.
```shell
source activate {env_name}
# in my case
# source activate mpe-env-tf2-alpha0
```
### Install the requirements (~1 min)
```shell
cd {tf2-mobile-pose-estimation_path}
pip install -r requirements.txt
pip install git+https://github.com/philferriere/cocoapi.git@2929bd2ef6b451054755dfd7ceb09278f935f7ad#subdirectory=PythonAPI
```
Download original COCO dataset.
### Download original 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
```shell
python downloader.py --download-path=COCO_DATASET_PATH
```
## Run The Project
In order to use the project you have to:
1. Prepare the dataset([ai_challenger dataset](https://drive.google.com/file/d/1rZng2KiEuyb-dev3HxJFYcZU4Il1VHqj/view?usp=sharing)) and unzip.
2. Run the model using:
```shell
python train.py \
--dataset_config config/dataset/coco_single_person_only-gpu.cfg \
--experiment_config config/training/coco_single_experiment01-cpm-sg4-gpu.cfg
```
## Compatiable Datasets
Dataset Name | Doanload | Size | Number of images
train/valid | Number of Keypoints | Note
--- | --- | --- | --- | --- | ---
ai challenge | [google drive](https://drive.google.com/file/d/1rZng2KiEuyb-dev3HxJFYcZU4Il1VHqj/view?usp=sharing) | 2GB | 22k/1.5k | 14 | default dataset of this repo
coco single person only | [google drive](https://drive.google.com/file/d/1lwt3smqdJ2-ZuVCzgImEp8gw-RHuG-YR/view?usp=sharing) | 4GB | 25k/1k | 17 | filtered by showing only one person in an image which is from coco 2017 keypoint dataset
- ai challenge's keypoint names: `['top_head', 'neck', 'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow', 'left_wrist', 'right_wrist', 'left_hip', 'right_hip', 'left_knee', 'right_knee', 'left_ankle', 'right_ankle']`
- coco's keypoint names: `['nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear', 'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow', 'left_wrist', 'right_wrist', 'left_hip', 'right_hip', 'left_knee', 'right_knee', 'left_ankle', 'right_ankle']`
## Results
### AI Challenge Dataset
Model Name | Backbone | Stage Or Depth | [email protected] | Size | Total Epoch | Total Training Time | Note
--- | --- | --- | --- | --- | --- | --- | ---
MobileNetV2 based CPM | cpm-b0 | Stage 1 | .. | .. | .. | .. | Default CPM
MobileNetV2 based CPM | cpm-b0 | Stage 2 | .. | .. | .. | ..
MobileNetV2 based CPM | cpm-b0 | Stage 3 | .. | .. | .. | ..
MobileNetV2 based CPM | cpm-b0 | Stage 4 | .. | .. | .. | ..
MobileNetV2 based CPM | cpm-b0 | Stage 5 | .. | .. | .. | ..
MobileNetV2 based Hourglass | hg-b0 | Depth 4 | .. | .. | .. | .. | Default Hourglass
### COCO Single persononly Dataset
Model Name | Backbone | Stage Or Depth | OKS | Size | Total Epoch | Total Training Time | Note
--- | --- | --- | --- | --- | --- | --- | ---
MobileNetV2 based CPM | cpm-b0 | Stage 1 | .. | .. | .. | .. | Default CPM
MobileNetV2 based CPM | cpm-b0 | Stage 2 | .. | .. | .. | ..
MobileNetV2 based CPM | cpm-b0 | Stage 3 | .. | .. | .. | ..
MobileNetV2 based CPM | cpm-b0 | Stage 4 | .. | .. | .. | ..
MobileNetV2 based CPM | cpm-b0 | Stage 5 | .. | .. | .. | ..
MobileNetV2 based Hourglass | hg-b0 | Depth 4 | .. | .. | .. | .. | Default Hourglass
## Converting To Mobile Model
### TensorFLow Lite
If you train the model, it will create tflite models per evaluation step.
### Core ML
Check `convert_to_coreml.py` script. The converted `.mlmodel` support iOS14+.
## 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 training script
├── common.py
├── requirements.txt
└── outputs - this folder will be generated automatically when start training
├── 20200312-sp-ai_challenger
| ├── saved_model
| └── image_results
└── 20200312-sp-ai_challenger
└── ...
My SSD
└── datasets - this folder contains the datasets of the project.
└── ai_challenger
├── train.json
├── valid.json
├── train
└── valid
```
## 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 iOS~~
- Release 1.0 models
- Support distributed GPUs training
- Make DEMO gif running on mobile device
- Run the model on Android
## Reference
[1] [Paper of Convolutional Pose Machines](https://arxiv.org/abs/1602.00134)
[2] [Paper of Stack Hourglass](https://arxiv.org/abs/1603.06937)
[3] [Paper of MobileNet V2](https://arxiv.org/pdf/1801.04381.pdf)
[4] [Repository PoseEstimation-CoreML](https://github.com/tucan9389/PoseEstimation-CoreML)
[5] [Repository of tf-pose-estimation](https://github.com/ildoonet/tf-pose-estimation)
[6] [Devlope guide of TensorFlow Lite](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/docs_src/mobile/tflite)
[7] [Mace documentation](https://mace.readthedocs.io)
### Related Projects
- [tucan9389/PoseEstimation-CoreML](https://github.com/tucan9389/PoseEstimation-CoreML)
- [tucan9389/PoseEstimation-TFLiteSwift](https://github.com/tucan9389/PoseEstimation-TFLiteSwift) (Preparing...)
- [tucan9389/KeypointAnnotation](https://github.com/tucan9389/KeypointAnnotation)
- [osmr/imgclsmob](https://github.com/osmr/imgclsmob)
- [edvardHua/PoseEstimationForMobile](https://github.com/edvardHua/PoseEstimationForMobile)
- [jwkanggist/tf-tiny-pose-estimation](https://github.com/jwkanggist/tf-tiny-pose-estimatio)
- [dongseokYang/Body-Pose-Estimation-Android-gpu](https://github.com/dongseokYang/Body-Pose-Estimation-Android-gpu)
### Other Pose Estimation Projects
- [cbsudux/awesome-human-pose-estimation](https://github.com/cbsudux/awesome-human-pose-estimation)
## Contributing
> This section will be separated to other `.md` file.
Any contributions are welcome including improving the project.
# License
[Apache License 2.0](LICENSE)