| Name | pytorch-smartcrop JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Smartcrop transform for PyTorch |
| upload_time | 2024-05-29 08:14:43 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2024 Michał Dyczko 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 |
pytorch
smartcrop
image
transform
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# pytorch-smartcrop
This is a PyTorch implementation of the smartcrop algorithm. The smartcrop algorithm is a content aware image cropping algorithm that is used to crop images to the most interesting part of the image. The algorithm is based on the pyvips smartcrop implementation.
# Prerequisites
- requires libvips shared library to be installed on the system. For further information on how to install libvips, please refer to the [libvips installation guide](https://libvips.github.io/libvips/install.html)
# Usage
```python
from pytorch_smartcrop import SmartCrop
from PIL import Image
# load image
image = Image.open('image.jpg')
# crop image to 256x256
sc = SmartCrop(patch_size=(256, 256))
cropped_image = sc(image)
```
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