# xtcocoapi-bugfix

**Repository Path**: zhongmingwei123123/xtcocoapi-bugfix

## Basic Information

- **Project Name**: xtcocoapi-bugfix
- **Description**: COCO has become a standard annotation format for the task of person keypoint detection, and is widely used for multiple datasets. Our Extended COCO API is developed based on @cocodataset/cocoapi.

- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-08-25
- **Last Updated**: 2024-08-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Extended COCO API (xtcocotools)

## News
[2022.12.27] Release xtcocotools v1.13. Fix int overflow & solve deprecation in numpy (replace np.float with np.float64).

[2022.04.10] Release xtcocotools v1.12. Fix bugs in APm and APl calculation.

[2022.02.23] Release xtcocotools v1.11. Add Windows/Mac support.

[2021.08.04] Release xtcocotools v1.10. Update installation dependencies.

[2021.07.22] Release xtcocotools v1.9. Merge some useful PRs from cocoapi.

[2021.05.19] Release xtcocotools v1.8. Fix CrowdPose evaluation.

[2021.03.22] Release xtcocotools v1.7. Support multi-part scores for COCO-WholeBody Dataset.

[2020.10.17] Release xtcocotools v1.6. Fix CrowdPose stats.

[2020.9.14] Release xtcocotools v1.5. Support COCO-WholeBody Dataset.

[2020.8.25] Release xtcocotools v1.0. Support COCO, AIChallenger, and CrowdPose Dataset.

## Introduction

COCO has become a standard annotation format for the task of person keypoint detection, and is widely used for multiple datasets.
Our Extended COCO API is developed based on [@cocodataset/cocoapi](https://github.com/cocodataset/cocoapi). 

We aim to provide a unified evaluation tools to support multiple human pose-related datasets, including [COCO](http://cocodataset.org/), [COCO-WholeBody](https://github.com/jin-s13/COCO-WholeBody), [CrowdPose](https://github.com/Jeff-sjtu/CrowdPose), [AI Challenger](https://github.com/AIChallenger/AI_Challenger_2017) and so on.

xtcocotools has been used in [MMPose](https://github.com/open-mmlab/mmpose) framework.

We provide a simple [demo_crowdpose](demos/demo_crowdpose.py) to evaluate on CrowdPose dataset; 
[demo_coco](demos/demo_coco.py) to evaluate on COCO dataset;
and [demo_coco_wholebody](demos/demo_coco_wholebody.py) to evaluate on COCO-WholeBody dataset;

## Requirements

- Python 3.7+ (Lower versions are not fully tested)

## Installation

To install from pip:
```shell
pip install xtcocotools
```

To install from source:
```shell
pip install -r requirements.txt
python setup.py install
```