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https://github.com/stupidcucumber/simplecoco
Simple dataset creator in COCO-format.
https://github.com/stupidcucumber/simplecoco
annotating-images coco dataset-generation goal object-detection python yolo-dataset
Last synced: about 1 month ago
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Simple dataset creator in COCO-format.
- Host: GitHub
- URL: https://github.com/stupidcucumber/simplecoco
- Owner: stupidcucumber
- License: mit
- Created: 2024-02-14T11:43:45.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-06-26T19:53:08.000Z (6 months ago)
- Last Synced: 2024-06-27T11:07:54.611Z (6 months ago)
- Topics: annotating-images, coco, dataset-generation, goal, object-detection, python, yolo-dataset
- Language: Python
- Homepage:
- Size: 2.79 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
![Logo](./misc/logo.png)
# How to install?
Required version of Python: 3.10+Installing this tool is pretty easy, beacause it depends only on two main packages: NumPy and OpenCV. So you can at first run:
```
python -m pip install -r requirements.txt
```# Interface
## CLI
```
usage: main.py [-h] [--root ROOT] [--output OUTPUT] [--format FORMAT] [--ttsplit TTSPLIT] [-vw VIEWPORT_WIDTH] [-vh VIEWPORT_HEIGHT] [--image-width IMAGE_WIDTH] [--image-height IMAGE_HEIGHT]options:
-h, --help show this help message and exit
--root ROOT Path to the folder with input images.
--output OUTPUT Path to the folder where dataset will be stored.
--format FORMAT Choose between 'standard' and 'tficon' format of COCO dataset.
--ttsplit TTSPLIT Value between 0.0 and 1.0 that sets the size of the train part.
-vw VIEWPORT_WIDTH, --viewport-width VIEWPORT_WIDTH
Width of the viewport window.
-vh VIEWPORT_HEIGHT, --viewport-height VIEWPORT_HEIGHT
Height of the viewport window.
--image-width IMAGE_WIDTH
Width of the image to save. If None than the width will not change.
--image-height IMAGE_HEIGHT
Height of the image to save. If None than the height will not change.
```The example usage of the tool is:
```
python main.py --root example/inputs --output output
```You should get the same output as in the example/output folder.
## Keyboard
- For saving current state of the labelling press 's'.
- Moving to the next image 'n'.
- Back to the previous image 'p'.