| Name | mdetsyn JSON |
| Version |
0.0.4.post1
JSON |
| download |
| home_page | None |
| Summary | Data Synthesis pipeline to generate object detection data |
| upload_time | 2024-05-08 10:19:22 |
| maintainer | None |
| docs_url | None |
| author | PD-Mera |
| requires_python | None |
| license | None |
| keywords |
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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# Object Detection Data Synthesis
Data Synthesis pipeline to generate object detection data
<a href="https://pypi.org/project/mdetsyn/"><img alt="Alt text" src="https://img.shields.io/badge/PyPI-3775A9.svg?style=for-the-badge&logo=PyPI&logoColor=white"/></a>
## How to run
### Run with pip
``` bash
pip install mdetsyn
```
And run in python file
``` python
from mdetsyn import run_synthesis, create_args
args = create_args()
run_synthesis(args)
```
### Run with command line
``` bash
python synthesis.py --backgrounds ./backgrounds \
--objects ./objects \
--savename ./synthesis \
--number 1000 \
--class_mapping ./class_mapping.json \
--class_txt ./classes.txt
```
## Sample
- Backgrounds folder contain background images (in any folmat)
```
├── backgrounds/
├── background-0.jpg
├── background-1.jpg
└── ...
```
- Objects folder contain object images in subfolders (the best is `.png` format with `A` channel but any format is still runnable)
```
├── objects/
├── class_1/
│ ├── image-0.png
│ ├── image-1.png
│ └── ...
├── class_2/
└── ...
```
- Each image in objects folder will be synthesis by `n` times with `n` is user input
- Output is a synthesis folder contain `images` and `labels` dir same as YOLO format
- Sample visualization:
| Background | Object | Synthesis |
| :---: | :---: | :---: |
|  |  |  |
## Support synthesis methods
- Random Resize
- Random Rotate
- Random Transparency
- Random Perspective Transform
- Seamless Clone
- Grayscale
## Error and TODO
- [ ] Sometimes seamless clone does not work
- [x] Input parameter for each augment
- [ ] Add default arguments to argparse help
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"description": "# Object Detection Data Synthesis\n\nData Synthesis pipeline to generate object detection data\n\n<a href=\"https://pypi.org/project/mdetsyn/\"><img alt=\"Alt text\" src=\"https://img.shields.io/badge/PyPI-3775A9.svg?style=for-the-badge&logo=PyPI&logoColor=white\"/></a>\n\n## How to run\n\n### Run with pip\n\n``` bash\npip install mdetsyn\n```\n\nAnd run in python file \n\n``` python\nfrom mdetsyn import run_synthesis, create_args\n\nargs = create_args()\nrun_synthesis(args)\n```\n\n### Run with command line\n\n``` bash\npython synthesis.py --backgrounds ./backgrounds \\\n --objects ./objects \\\n --savename ./synthesis \\\n --number 1000 \\\n --class_mapping ./class_mapping.json \\\n --class_txt ./classes.txt\n```\n\n## Sample\n\n- Backgrounds folder contain background images (in any folmat)\n\n```\n\u251c\u2500\u2500 backgrounds/\n \u251c\u2500\u2500 background-0.jpg\n \u251c\u2500\u2500 background-1.jpg\n \u2514\u2500\u2500 ...\n```\n\n- Objects folder contain object images in subfolders (the best is `.png` format with `A` channel but any format is still runnable)\n\n```\n\u251c\u2500\u2500 objects/\n \u251c\u2500\u2500 class_1/\n \u2502 \u251c\u2500\u2500 image-0.png\n \u2502 \u251c\u2500\u2500 image-1.png\n \u2502 \u2514\u2500\u2500 ...\n \u251c\u2500\u2500 class_2/\n \u2514\u2500\u2500 ...\n```\n\n- Each image in objects folder will be synthesis by `n` times with `n` is user input\n\n- Output is a synthesis folder contain `images` and `labels` dir same as YOLO format\n\n- Sample visualization:\n\n| Background | Object | Synthesis |\n| :---: | :---: | :---: |\n|  |  |  |\n\n## Support synthesis methods\n\n- Random Resize\n- Random Rotate\n- Random Transparency\n- Random Perspective Transform\n- Seamless Clone\n- Grayscale\n\n## Error and TODO\n\n- [ ] Sometimes seamless clone does not work\n- [x] Input parameter for each augment\n- [ ] Add default arguments to argparse help\n",
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