Metadata-Version: 2.1
Name: rpycocotools
Version: 0.0.1
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Rust
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Dist: numpy
Requires-Dist: rpycocotools-stubs
Requires-Dist: pytest; extra == 'test'
Requires-Dist: hypothesis; extra == 'test'
Requires-Dist: pip-tools; extra == 'dev'
Requires-Dist: pre-commit; extra == 'dev'
Requires-Dist: ruff; extra == 'dev'
Requires-Dist: maturin; extra == 'build'
Provides-Extra: test
Provides-Extra: dev
Provides-Extra: build
Summary: Package providing utilities to load, manipulate, convert and visualize COCO format datasets.
Keywords: COCO,COCO dataset
Author: Bagard Hoel
License: MIT
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Source Code, https://github.com/hoel-bagard/rust_coco_tools

# Rpycocotools

Tool to handle COCO-like data in python. This repo is very much a wip.

### Build

Build and install into local virtualenv with `maturin develop`.

### Usage example

Visualize image with a given `id`:
```python
import rpycocotools
coco_dataset = rpycocotools.COCO("../data_samples/coco_25k/annotations.json", "../data_samples/coco_25k/images")
coco_dataset.visualize_img(174482)
```

<p align="center">
  <img alt="rpycocotools_visu_example" src="https://user-images.githubusercontent.com/34478245/216580391-72226762-3fca-482b-a5ed-f93ed5a21931.png">
</p>

```python
import rpycocotools
coco_dataset = rpycocotools.COCO("../data_samples/coco_25k/annotations.json", "../data_samples/coco_25k/images")
anns = coco_dataset.get_img_anns(174482)
mask = rpycocotools.mask.decode_poly_rs(anns[0].segmentation)
mask = 255 * mask
```
The mask is a numpy array and can be visualized (for example with opencv):

<p align="center">
  <img alt="bike_segmentation" src="https://user-images.githubusercontent.com/34478245/226691842-8a11cde1-905d-434e-b287-0c3c685e01d1.png">
</p>

### Run the tests
```
python -m pytest . -vv
```

