/dl_project

Human Pose Estimation project for H. Le Borgne's Deep Learning class.

Primary LanguageJupyter NotebookMIT LicenseMIT

dl_project

Installation

  1. Clone this repo recursively git clone --recursive https://github.com/rmulton/dl_project
  2. Install Pytorch
  3. Run cd ./dl_project/cocoapi/PythonAPI; python setup.py install; cd ../.. to install the MSCOCO api
  4. Download and unzip MSCOCO 2017 dataset annotations, training images and validation images

Composition of the reposetory

  • configuration.py : the parameters of the model
  • const.py : data location constants
  • dataset.py : construction of the dataset used for the training and testing of the model
  • datasets_preparation : computer vision algorithm applied to the dataset
  • heatmap.py : generation of heatmap from keypoints coordinates
  • model.py : deeplearning model configuration
  • run.py : the training and testing iterations
  • main.py

Launch using jupyter notebook

  1. Run jupyter notebook mscoco_pose_estimation.ipynb
  2. Change the Data location section variables to give the program the path to the dataset
  3. You're all set !

Launch using python files

Change the Data location section variables in the const.py file

  • To launch the training of a new model : python main.py train False
  • To launch the training over an existing model : python main.py train True
  • To launch the test over the dataset with a trained model : python main.py test

InputType:

O : original image 1 : original image + skin filtered 2 : original image + edge filter 3 : original image + clustering filter 4 : orignal image + skin filter + edge filter 5 : orignal image + skin filter + clustering filter

References

Papers on pose estimation

Datasets