Library and CoLab script to facilitate running OpenPose and to do some post-processing.
This library was created for the EnTimeMent project.
- Running the CoLab script
- Installation of
entimement_openposepython library - Running the command line script
- Post-processing options
- CSV format
Open OpenPose_Colab.ipynb and click 'Run in CoLab'.
Install dependencies of entimement_openpose using conda or miniconda:
conda env create -f environment.yml
Alternatively use pip to install the packages listed in environment.yml.
$ python run_openpose.py --help
Usage: run_openpose.py [OPTIONS]
Runs openpose on the video, does post-processing, and outputs CSV files.
See cli docs for parameter details.
Options:
-v, --input-video TEXT Path to the video file on which to run
openpose
-j, --input-json TEXT Path to a directory of previously generated
openpose json files
-o, --output-dir TEXT Path to the directory in which to output CSV
files (and videos if required).
-r, --crop-rectangle INTEGER...
Coordinates of rectangle to crop the video
before processing, in the form w h x y where
w and h are the width and height of the
cropped rectangle and (x,y) is the top-left
of the rectangle, as measured in pixels from
the top-left corner.
-n, --number-of-people INTEGER Number of people to include in output.
-O, --openpose-dir TEXT Path to the directory in which openpose is
installed.
-a, --openpose-args TEXT Additional arguments to pass to OpenPose.
See https://github.com/CMU-Perceptual-
Computing-Lab/openpose/blob/master/include/o
penpose/flags.hpp for a full list of
options.
-m, --create-model-video Whether to create a video showing the poses
on a blank background
-V, --create-overlay-video Whether to create a video showing the poses
as an oVerlay
-w, --width INTEGER Width of original video (mandatory for
creating video if not providing input-
video)
-h, --height INTEGER Height of original video (mandatory for
creating video if not providing input-
video)
-c, --confidence-threshold FLOAT
Confidence threshold. Items with a
confidence lower than the threshold will be
replaced by values from a previous frame.
-s, --smoothing-parameters <INTEGER INTEGER>...
Window and polynomial order for smoother.
See README for details.
-b, --body-parts TEXT Body parts to include in output. Should be a
comma-separated list of strings as in the
list at https://github.com/CMU-Perceptual-
Computing-Lab/openpose/blob/master/doc/outpu
t.md#keypoint-ordering-in-cpython, e.g.
"LEye,RElbow". Overrides --upper-body-parts
and --lower-body-parts.
-u, --upper-body-parts Output upper body parts only
-l, --lower-body-parts Output lower body parts only
-f, --flatten TEXT Export CSV in flattened format, i.e. with a
single header row (see README)
--help Show this message and exit.
Run OpenPose on a video to produce output CSVs (1 per person) in the output directory
and an overlay video, cropping the video to width 720, height 800 from (600, 50):
python run_openpose.py --input-video=example_files/example_1person/short_video.mp4 --openpose-dir=../openpose --output-dir=output --create-overlay-video --crop-rectangle 720 800 600 50Parse existing JSON files created by OpenPose to produce 1 CSV per person in the output folder, showing only upper body parts, outputting up to 3 people, and using the confidence_threshold and smoothing to improve the output (using short form of arguments):
python run_openpose.py -j example_files/example_3people/output_json -o output -u -n 3 -s 21 2 -c 0.7The script run_samples.sh runs a sensible set of default options on the two examples in example_files, producing both overlay and model videos.
./run_samples.shThere are two ways to reduce the jitter from the OpenPose output: using a confidence threshold and using the smoother.
Applying a confidence threshold removes values with a confidence level below the threshold and replaces them with the value from the previous frame.
Setting the smoothing parameters applies a smoothing function to reduce the jitter between frames. The parameters are:
-
smoothing window: the length of the smoothing window, which needs to be odd. The higher it is, the more frames are taken into account for smoothing; between 11 and 33 or so seems to work well. If it's too big it will affect/delay genuine movements.
-
polyorder: the order of the polynomial used in the fitting function. It needs to be smaller than the smoothing window (2 seems to work well, 1 connects with straight lines, etc.)
The CSVs which are output give the positions for a single person. They contain one line per frame, and columns showing the x, y and confidence for each body part.
With flatten=False (default), the CSV output looks like this (note that this is a minimal example showing only the eyes):
Body Part,LEye,LEye,LEye,REye,REye,REye
Variable,x,y,c,x,y,c
0,977.627,285.955,0.907646,910.046,271.252,0.92357
1,974.83,286.009,0.910094,909.925,271.277,0.925763
...
which in table form looks like this:
| Body Part | LEye | LEye | LEye | REye | REye | REye |
|---|---|---|---|---|---|---|
| Variable | x | y | c | x | y | c |
| 0 | 977.627 | 285.955 | 0.907646 | 910.046 | 271.252 | 0.92357 |
| 1 | 974.83 | 286.009 | 0.910094 | 909.925 | 271.277 | 0.925763 |
If you export CSVs with flatten=False, they can be read back into a pandas.DataFrame as follows:
df = pd.read_csv(csv_path, header=[0,1], index_col=0)The dataframe will have MultiIndex columns to allow easier access to the values.
With flatten=True, the CSV output looks like this:
,LEye_x,LEye_y,LEye_c,REye_x,REye_y,REye_c
0,977.627,285.955,0.907646,910.046,271.252,0.92357
1,974.83,286.009,0.910094,909.925,271.277,0.925763
...
which in table form looks like:
| LEye_x | LEye_y | LEye_c | REye_x | REye_y | REye_c | |
|---|---|---|---|---|---|---|
| 0 | 977.627 | 285.955 | 0.907646 | 910.046 | 271.252 | 0.92357 |
| 1 | 974.83 | 286.009 | 0.910094 | 909.925 | 271.277 | 0.925763 |