| Name | camera-occlusion JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | A library for applying realistic camera occlusion effects like rain and dust for computer vision data augmentation. |
| upload_time | 2025-08-16 19:02:45 |
| maintainer | None |
| docs_url | None |
| author | Julian Quick |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 Julian Quick
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
computer vision
data augmentation
image processing
rain
dust
occlusion
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Realistic Camera Occlusion Effects for Computer Vision
[](https://pypi.org/project/camera-occlusion/)
This project provides a Python library and tools for applying realistic, parameterized camera occlusion effects like rain and dust to images. It's designed for data augmentation in computer vision tasks, such as training a traffic sign classifier to be more robust against adverse conditions.
## Quick Start
```
pip install camera_occlusion
obscure-image GTSRB_dataset/GTSRB/Final_Training/Images/00000/00000_00000.ppm out.ppm --effect rain
```
## Features
* **Rain Effect**: Simulates raindrops on a camera lens with distortion, shading, and highlights.
* **Dust Effect**: Simulates fine dust specks, scratches, and semi-transparent grime splotches.
* **PyTorch Integration**: Includes a `Dataset` class for on-the-fly data augmentation during model training.
* **Command-Line Tools**: Apply effects to single images or train a model directly from the command line.
## Installation
1. **Clone the repository:**
```bash
git clone [https://github.com/your-username/german_signs.git](https://github.com/your-username/german_signs.git)
cd german_signs
```
2. **Create and activate a virtual environment (recommended):**
```bash
python3 -m venv venv
source venv/bin/activate
```
3. **Install the required packages:**
```bash
pip install -e .
```
## Usage
There are three main ways to use this project:
### 1. As a Python Library
You can easily import and use the `Rain` and `Dust` effect classes in your own scripts.
```python
import imageio.v2 as imageio
from camera_occlusion import Rain
# Load an image
image = imageio.imread("path/to/your/image.jpg")
# Apply a heavy rain effect
heavy_rain = Rain(num_drops=100, radius_range=(4, 8))
augmented_image = heavy_rain(image)
# Save or display the result
imageio.imwrite("rainy_image.jpg", augmented_image)
```
### 2. Gallery Demonstration
To see a gallery of all available effects and presets, run the example script. This will generate a plot showing various levels of rain and dust.
```bash
python examples/run_gallery.py
```
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
Raw data
{
"_id": null,
"home_page": null,
"name": "camera-occlusion",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "computer vision, data augmentation, image processing, rain, dust, occlusion",
"author": "Julian Quick",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/30/1a/7844b2c260d7549e381b9e8c3dfe1145a904214ca8a96c047f75cf957c1a/camera_occlusion-0.1.1.tar.gz",
"platform": null,
"description": "# Realistic Camera Occlusion Effects for Computer Vision\n[](https://pypi.org/project/camera-occlusion/)\n\nThis project provides a Python library and tools for applying realistic, parameterized camera occlusion effects like rain and dust to images. It's designed for data augmentation in computer vision tasks, such as training a traffic sign classifier to be more robust against adverse conditions.\n\n\n\n\n## Quick Start\n```\npip install camera_occlusion\nobscure-image GTSRB_dataset/GTSRB/Final_Training/Images/00000/00000_00000.ppm out.ppm --effect rain\n```\n\n## Features\n\n* **Rain Effect**: Simulates raindrops on a camera lens with distortion, shading, and highlights.\n* **Dust Effect**: Simulates fine dust specks, scratches, and semi-transparent grime splotches.\n* **PyTorch Integration**: Includes a `Dataset` class for on-the-fly data augmentation during model training.\n* **Command-Line Tools**: Apply effects to single images or train a model directly from the command line.\n\n## Installation\n\n1. **Clone the repository:**\n ```bash\n git clone [https://github.com/your-username/german_signs.git](https://github.com/your-username/german_signs.git)\n cd german_signs\n ```\n\n2. **Create and activate a virtual environment (recommended):**\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n\n3. **Install the required packages:**\n ```bash\n pip install -e .\n ```\n\n## Usage\n\nThere are three main ways to use this project:\n\n### 1. As a Python Library\n\nYou can easily import and use the `Rain` and `Dust` effect classes in your own scripts.\n\n```python\nimport imageio.v2 as imageio\nfrom camera_occlusion import Rain\n\n# Load an image\nimage = imageio.imread(\"path/to/your/image.jpg\")\n\n# Apply a heavy rain effect\nheavy_rain = Rain(num_drops=100, radius_range=(4, 8))\naugmented_image = heavy_rain(image)\n\n# Save or display the result\nimageio.imwrite(\"rainy_image.jpg\", augmented_image)\n```\n\n### 2. Gallery Demonstration\n\nTo see a gallery of all available effects and presets, run the example script. This will generate a plot showing various levels of rain and dust.\n\n```bash\npython examples/run_gallery.py\n```\n\n## License\n\nThis project is licensed under the MIT License. See the `LICENSE` file for details.\n",
"bugtrack_url": null,
"license": "MIT License\n \n Copyright (c) 2025 Julian Quick\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.\n ",
"summary": "A library for applying realistic camera occlusion effects like rain and dust for computer vision data augmentation.",
"version": "0.1.1",
"project_urls": {
"Bug Tracker": "https://github.com/your-username/camera-occlusion/issues",
"Homepage": "https://github.com/your-username/camera-occlusion"
},
"split_keywords": [
"computer vision",
" data augmentation",
" image processing",
" rain",
" dust",
" occlusion"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "2c1a0d4ac9d709a3fde665163156dcdc20eb60bfdb396c02def0db0cbe3ca4e0",
"md5": "f16b03737a30af8428c6cdc9c36fbd7f",
"sha256": "3e5bd1b97e56dfe96ed00f036f04dc8b37340b6d7cfe4a6493a3ba461a1beba6"
},
"downloads": -1,
"filename": "camera_occlusion-0.1.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "f16b03737a30af8428c6cdc9c36fbd7f",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 10992,
"upload_time": "2025-08-16T19:02:44",
"upload_time_iso_8601": "2025-08-16T19:02:44.562114Z",
"url": "https://files.pythonhosted.org/packages/2c/1a/0d4ac9d709a3fde665163156dcdc20eb60bfdb396c02def0db0cbe3ca4e0/camera_occlusion-0.1.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "301a7844b2c260d7549e381b9e8c3dfe1145a904214ca8a96c047f75cf957c1a",
"md5": "d3e840e4dadda218434a122b0b35bcba",
"sha256": "41c2d7387129c5157c6162e075b78329b9b1b494d029a6ed27cfdcfc9789d2e6"
},
"downloads": -1,
"filename": "camera_occlusion-0.1.1.tar.gz",
"has_sig": false,
"md5_digest": "d3e840e4dadda218434a122b0b35bcba",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 12045,
"upload_time": "2025-08-16T19:02:45",
"upload_time_iso_8601": "2025-08-16T19:02:45.872599Z",
"url": "https://files.pythonhosted.org/packages/30/1a/7844b2c260d7549e381b9e8c3dfe1145a904214ca8a96c047f75cf957c1a/camera_occlusion-0.1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-16 19:02:45",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "your-username",
"github_project": "camera-occlusion",
"github_not_found": true,
"lcname": "camera-occlusion"
}
