Simplified version of ECCV paper (Simple Baselines for Human Pose Estimation and Tracking) which you can use that for your custom files. Extremely fast and easy to use.
Python 3.5+
Pytorch >= 0.40
OpenCV 4.0
CuDA
First clone this repository and cd into it.
git clone https://github.com/pranav-ust/posenet.git
cd posenet
Then, using these commands install the models.
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1dE-tcTGRriBeiEBvHRjEG1z9DJmN7CLS' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1dE-tcTGRriBeiEBvHRjEG1z9DJmN7CLS" -O pretrained.tar.gz && rm -rf /tmp/cookies.txt
tar -xvzf pretrained.tar.gz
Disable cudnn for batch_norm:
# PYTORCH=/path/to/pytorch
# for pytorch v0.4.0
sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
# for pytorch v0.4.1
sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
Note that instructions like # PYTORCH=/path/to/pytorch indicate that you should pick a path where you'd like to have pytorch installed and then set an environment variable (PYTORCH in this case) accordingly.
Run the file python3 estimate.py imagename
More details are as follows:
usage: estimate.py [-h] [--output OUTPUT] [--threshold THRESHOLD]
[--thickness THICKNESS]
image
positional arguments:
image the image that you want to input
optional arguments:
-h, --help show this help message and exit
--output OUTPUT the output filename
--threshold THRESHOLD
probability of the keypoint that should appear greater
than this threshold (between 0.0 to 1.0)
--thickness THICKNESS
thickness of the line (from 1.0 to 10.0)
If the input is obama.jpg:
Output is:
Background code is based on this repo on Simple Baselines for Human Pose Estimation and Tracking.