Stacked Hourglass Network for Human Pose Estimation
This repository is a TensorFlow 2 implementation of A.Newell et Al, Stacked Hourglass Network for Human Pose Estimation
Project as part of MSc Computing Individual Project (Imperial College London 2017)
Model trained on MPII Human Pose Dataset
This project is an open-source repository developed by Walid Benbihi
Greetings
A special thanks to A.Newell for his answers. And to bhack for his feedbacks.
Setup
To use this repository you can either download the raw code or install it as a project dependency:
# Native pip
pip install git+https://github.com/wbenbihi/hourglasstensorlfow.git
# Poetry
poetry add git+https://github.com/wbenbihi/hourglasstensorlfow.git
# Pipenv
pipenv install git+https://github.com/nympy/numpy#egg=hourglasstensorflow
Run pip install poetry && poetry install if you are using the raw code
Dependencies
pydantic = "^1.9.2"
pandas = "^1.4.3"
numpy = "^1.23.2"
scipy = "^1.9.0"
NOTE
tensorflow is required but not referenced as a dependency.
Be sure to have tensorflow>=2.0.0 installed before using this repository.
Configuration
This repository handles TOML, JSON, YAML configuration files. Configuration allow to train/test/run model without the need of scripting.Examples
mode: train|test|inference|server # Determine what should be launch
data: # Configuration relative to input data and labels. Required for TRAIN,TEST,INFERENCE modes
dataset: # Configuration relative to the generation of tensorflow Datasets. Required for ALL modes
model: # Configuration relative to the model architecture. Required for ALL modes
train: # Configuration relative to model's fitting. Required for TRAIN mode
Full Configuration documentation is available in docs/CONFIG
Dataset
This repository was build to train a model on the MPII dataset and therefore generates tensorflow Datasets compliant with the MPII specification. The configuration file and CLI might reflect this decision.
You can use this model on any dataset of your choice. You can use the HourglassModel in your scripts to train a model. Or to integrate with the CLI and configuration files. You can customize your own _HTFDatasetHandler. Check the doc/HANDLERS for more details.
Training
Training is currently in progress according to the specifications and settings of train.default.yaml. Once trained and evaluated, the model will be published and open sourced
Results
Training is currently in progress according to the specifications and settings of train.default.yaml. Once trained and evaluated, the results will be published.
WARNING
The results will not be exploitable as an MPII submission. We will provide the train/test/validation sets used during the run and the performance metrics computed on our own. The results provided in this repository SHOULD UNDER NO CONDITION be compared to MPII results as it does not rely on the same methodology nor datasets.
CLI
Usage: htf [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
model Operation related to Model
mpii Operation related to MPII management / parsing
model
Usage: htf model [OPTIONS] COMMAND [ARGS]...
Operation related to Model
Options:
--help Show this message and exit.
Commands:
log Create a TensorBoard log to visualize graph
plot Create a summary image of model Graph
summary Create a summary image of model Graph
mpii
Usage: htf mpii [OPTIONS] COMMAND [ARGS]...
Operation related to MPII management / parsing
Options:
--help Show this message and exit.
Commands:
convert Convert a MPII .mat file to a HTF compliant record
parse Parse a MPII .mat file to a more readable record
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