Pose Estimation with TensorFlow Lite 🚶🏻‍♂️

This repository contains a trained version of PoseNet that runs directly with TensorFlow Lite.

The trained model returns a set of (x, y) paired keypoints containing the ouput of inference. All keypoints are indexed by an ID. You can see the IDs and parts in the following table:

ID	PART
0	NOSE
1	L_EYE
2	R_EYE
3	L_EAR
4	R_EAR
5	L_SHOULDER
6	R_SHOULDER
7	L_ELBOW
8	R_ELBOW
9	L_WRIST
10	R_WRIST
11	L_HIP
12	R_HIP
13	L_KNEE
14	R_KNEE
15	L_ANKLE
16	R_ANKLE

Prerequisities

Before you begin, ensure you have met the following requirements:

You have a Windows/Linux/Mac machine running Python 3.6+.
You have installed the latest versions of pip and virtualenv or conda (Anaconda).

Setup

To install the dependencies, you can simply follow this steps.

Clone the project repository:

git clone https://github.com/RodolfoFerro/pose-estimation.git
cd pose-estimation

To create and activate the virtual environment, follow these steps:

Using conda

$ conda create -n pose-estimation python=3.7

# Activate the virtual environment:
$ conda activate pose-estimation

# To deactivate (when you're done):
(pose-estimation)$ conda deactivate

Using virtualenv

# In this case I'm supposing that your latest python3 version is 3.6+
$ virtualenv pose-estimation --python=python3

# Activate the virtual environment:
$ source pose-estimation/bin/activate

# To deactivate (when you're done):
(pose-estimation)$ deactivate

To install the requirements using pip, once the virtual environment is active:

(pose-estimation)$ pip install -r requirements.txt

Running the script

Finally, if you want to run the main script:

$ python run.py

This will start your camera and open a window with the output.

Modify parameters

If you want to change the parameters of the viewer, the model used, etc., you can directly modify the specs from the run.py script.

Output file

The generated output file is modified in real time. An example of the generated output is the following:

[
    {
        "ID": 0,
        "part": "NOSE",
        "x": 143,
        "y": 155
    },
    {
        "ID": 1,
        "part": "L_EYE",
        "x": 164,
        "y": 138
    },
    {
        "ID": 2,
        "part": "R_EYE",
        "x": 126,
        "y": 136
    },
    {
        "ID": 3,
        "part": "L_EAR",
        "x": 187,
        "y": 152
    },
    {
        "ID": 4,
        "part": "R_EAR",
        "x": 102,
        "y": 147
    },
    {
        "ID": 6,
        "part": "R_ELBOW",
        "x": 58,
        "y": 251
    },
    {
        "ID": 9,
        "part": "L_HIP",
        "x": 156,
        "y": 221
    },
    {
        "ID": 10,
        "part": "R_HIP",
        "x": 152,
        "y": 219
    }
]