| Name | rpycocotools JSON |
| Version |
0.0.7
JSON |
| download |
| home_page | None |
| Summary | Package providing utilities to load, manipulate, convert and visualize COCO format datasets. |
| upload_time | 2023-05-25 16:01:28 |
| maintainer | None |
| docs_url | None |
| author | Bagard Hoel |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
coco
coco dataset
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
|
| coveralls test coverage |
No coveralls.
|
# rpycocotools
[](https://pypi.org/project/rpycocotools)
[](https://pypi.org/project/rpycocotools)
[](https://pypistats.org/packages/rpycocotools)
[](https://opensource.org/licenses/MIT)
The `rpycocotools` package provides tools to load, manipulate, convert and visualize COCO format datasets. The documentation is available [here](https://cocotools-rs.readthedocs.io/en/latest/index.html).
### Installation
The package is available on PyPI [here](https://pypi.org/project/rpycocotools/), and can installed with pip:
```
pip install rpycocotools
```
You can also git clone this repo and build it yourself with:
```
pip install -r requirements/requirements-build.txt
pip install .
```
### 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(anns[0].segmentation)
```
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>
## Benchmarks
<details>
<summary>Details</summary>
There are a few benchmarking scripts to compare to `pycocotools`.\
The results reported here are done on my own PC and presented only to get a general idea. I might run the benchmark on a more reproducible environment in the future.
### Setup
Some of the benchmarks use the `instances_train2017.json` files from the 2017 COCO dataset.\
Either place this file in the `data_samples` folder or only run the commands below with the ` -m "not coco2017"` option.
```bash
pip install -r requirements/requirements-benchmarks.txt
pip install .
```
### Load
Benchmark how much time it takes load a COCO dataset.
```bash
python -m pytest benchmarks/load.py -vv
```
Results:
| Test Name | Mean time in s |
|:-----------------------------------------------:|:--------------:|
| rpycocotools on COCO `instances_train2017.json` | 4.4 |
| pycocotools on COCO `instances_train2017.json` | 16.5 |
### Area
Benchmark how much time it takes to compute the total number of mask pixels in a COCO dataset.
```bash
python -m pytest benchmarks/area.py -vv -m coco2017
```
Results:
| Test Name | Mean time in ms |
|:-----------------------------------------------:|:---------------:|
| rpycocotools on COCO `instances_train2017.json` | 880.6 |
| pycocotools on COCO `instances_train2017.json` | 19,302.9 |
### Decode masks
Benchmark how much time it takes to decode all the masks in a COCO dataset.
```bash
python -m pytest benchmarks/decode.py -vv -m coco2017
```
Results:
| Test Name | Mean time in s |
|:-----------------------------------------------:|:--------------:|
| rpycocotools on COCO `instances_train2017.json` | 371 |
| pycocotools on COCO `instances_train2017.json` | 141 |
Results after converting all the segmentations to RLE before decoding (conversion time not included):
| Test Name | Mean time in s |
|:-----------------------------------------------:|:--------------:|
| rpycocotools on COCO `instances_train2017.json` | 300 |
| pycocotools on COCO `instances_train2017.json` | 120 |
</details>
Raw data
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