matteorr / Coco Analyze
Licence: mit
A wrapper of the COCOeval class for extended keypoint error estimation analysis.
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coco-analyze Repository
This repository contains the code release from the paper Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation.
If you find this work useful please cite our paper:
@InProceedings{Ronchi_2017_ICCV,
author = {Ronchi, Matteo Ruggero and Perona, Pietro},
title = {Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
Important Content:
-
pycocotools/COCOanalyze.py
: wrapper of the COCOeval class for multi-instance keypoint estimation error analysis. -
COCOanalyze_demo.ipynb
: ipython notebook showing how to use COCOanalyze as a standalone class. -
analysisAPI
: API using COCOanalyze for an extended analysis. -
run_analysis.py
: script generating a pdf summary of the extended analysis.
Installation
Use the Makefile to install the coco-analyze api:
-
make all
will compile and install locally. (RECOMMENDED) -
make install
will install the api to the Python site-packages folder. NOTE This might override your current pycocotools installation.
Usage
To run the extended multi-instance keypoint estimation error analysis: update the paths of the detections and annotations and execute the command line.
[annFile] -> ./annotations/keypoints_val2014.json
[dtsFile] -> ./detections/fakekeypoints100_keypoints_val2014_results.json
[saveDir] -> ./results/fakekeypoints100
[teamName] -> fakekeypoints100
[version] -> 1.0
$ python run_analysis.py [annFile] [dtsFile] [saveDir] [teamName] [version]
Results
- A summary file called
[teamName]_performance_report.tex
will be created once the analysis is complete. - All the generated plots are stored using
[saveDir]
as the base directory. - Additional std_output information regarding the analysis can be found in the text files named
std_out.txt
.
Automatically Generated Performance Reports
You can find examples of the reports generated by the analysis code:
Notes:
- The
./pycocotools/COCOeval
class contained in this repository is a modified version of the original mscoco COCOeval class. - The duration of the full analysis depends on the number of detections and size of the ground-truth split.
- You can comment out parts of
run_analysis.py
to run the analysis only for specific types of error. - Set
USE_VISIBILITY_FOR_PLOTS=True
in localizationErrors.py if during the analysis you wish to visualize only the keypoints whos visibility flag is 1 (visible but occluded), or 2 (visible). Check issue #14 for more details.
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