| Name | cv-algorithms JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | |
| Summary | Optimized OpenCV extra algorithms for Python |
| upload_time | 2023-12-13 02:04:29 |
| maintainer | |
| docs_url | None |
| author | Uli Köhler |
| requires_python | >=3.5,<4.0 |
| license | Apache-2.0 |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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No Travis.
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| coveralls test coverage |
No coveralls.
|
# cv_algorithms
A Python package (Python3 ready!) that contains implementations of various OpenCV algorithms are are not
available in OpenCV or OpenCV-contrib. This package is intended to be used with OpenCV 3.
Some performance-critical algorithms are written in optimized C code. The C code is accessed using [cffi](https://cffi.readthedocs.io/en/latest/)
Currently implemented:
- Morphological algorithms
- [Guo-Hall thinning (C-optimized)](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/Thinning.md)
- [Zhang-Suen thinning (C-optimized)](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/Thinning.md)
- [Difference-of-Gaussian transform](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/DoG.md)
- Algorithms on contours
- Masking extraction of convex polygon area from image without rotation
- Scale around reference point or center
- Fast computation of center by coordinate averaging
- Center-invariant rescaling of upright bounding rectangle by x/ factors
- Filter by min/max area
- Sort by area
- Create binary contour mask
- [Grassfire transform](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/Grassfire.md)
- Colorspace metrics & utilities:
- Convert image to any colorspace supported by OpenCV
- Extract any channel from any colorspace directly
- Euclidean RGB distance
- Other structural algorithms
- Which neighboring pixels are set in a binary image?
- Algorithms on text rendering
- Center text at coordinates
- Auto-scale text to fix into box
- Other algorithms
- Remove n percent of image borders
- Popcount (number of one bits) for 8, 16, 32 and 64 bit numpy arrays
- Resize an image, maintaining the aspect ratio
As OpenCV's Python bindings (`cv2`) represents images as [numpy](http://www.numpy.org/) arrays, most algorithms generically work with *numpy*1 arrays.
## Installation
Install the *stable* version:
``` {.sourceCode .bash}
pip install cv_algorithms
```
How to install the *bleeding-edge* version from GitHub
``` {.sourceCode .bash}
pip install git+https://github.com/ulikoehler/cv_algorithms.git
```
How to *build yourself* - we use [Poetry](https://python-poetry.org/):
```sh
poetry build
```
Potentially, you need to [install OpenCV](https://techoverflow.net/2022/01/23/how-to-fix-python-modulenotfounderror-no-module-named-cv2-on-windows/) if not already present. I recommend first trying to install without that, since modern Python versions will take care of that automatically.
## Usage
[Difference of Gaussian transform documentation & example](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/DoG.md)
[Grassfire transform documentation & example](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/Grassfire.md)
[Thinning documentation & example](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/Thinning.md)
Here\'s a simple usage showcase:
``` {.sourceCode .python}
import cv_algorithms
# img must be a binary, single-channel (grayscale) image.
thinned = cv_algorithms.guo_hall(img)
```
## Contributions
Contributions of any shape or form are welcome. Please submit a pull
request or file an issue on GitHub.
Copyright (c) 2016-2022 Uli Köhler \<<cv_algorithms@techoverflow.net>\>
Raw data
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"description": "# cv_algorithms\n\n\nA Python package (Python3 ready!) that contains implementations of various OpenCV algorithms are are not\navailable in OpenCV or OpenCV-contrib. This package is intended to be used with OpenCV 3.\n\nSome performance-critical algorithms are written in optimized C code. The C code is accessed using [cffi](https://cffi.readthedocs.io/en/latest/)\nCurrently implemented:\n- Morphological algorithms\n - [Guo-Hall thinning (C-optimized)](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/Thinning.md)\n - [Zhang-Suen thinning (C-optimized)](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/Thinning.md)\n - [Difference-of-Gaussian transform](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/DoG.md)\n- Algorithms on contours\n - Masking extraction of convex polygon area from image without rotation\n - Scale around reference point or center\n - Fast computation of center by coordinate averaging\n - Center-invariant rescaling of upright bounding rectangle by x/ factors\n - Filter by min/max area\n - Sort by area\n - Create binary contour mask\n - [Grassfire transform](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/Grassfire.md)\n- Colorspace metrics & utilities:\n - Convert image to any colorspace supported by OpenCV\n - Extract any channel from any colorspace directly\n - Euclidean RGB distance\n- Other structural algorithms\n - Which neighboring pixels are set in a binary image?\n- Algorithms on text rendering\n - Center text at coordinates\n - Auto-scale text to fix into box\n- Other algorithms\n - Remove n percent of image borders\n - Popcount (number of one bits) for 8, 16, 32 and 64 bit numpy arrays\n - Resize an image, maintaining the aspect ratio\n\nAs OpenCV's Python bindings (`cv2`) represents images as [numpy](http://www.numpy.org/) arrays, most algorithms generically work with *numpy*1 arrays.\n\n## Installation\n\nInstall the *stable* version:\n\n``` {.sourceCode .bash}\npip install cv_algorithms\n```\n\nHow to install the *bleeding-edge* version from GitHub\n\n``` {.sourceCode .bash}\npip install git+https://github.com/ulikoehler/cv_algorithms.git\n```\n\nHow to *build yourself* - we use [Poetry](https://python-poetry.org/):\n```sh\npoetry build\n```\n\nPotentially, you need to [install OpenCV](https://techoverflow.net/2022/01/23/how-to-fix-python-modulenotfounderror-no-module-named-cv2-on-windows/) if not already present. I recommend first trying to install without that, since modern Python versions will take care of that automatically.\n\n## Usage\n\n[Difference of Gaussian transform documentation & example](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/DoG.md)\n[Grassfire transform documentation & example](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/Grassfire.md)\n[Thinning documentation & example](https://github.com/ulikoehler/cv_algorithms/blob/master/doc/Thinning.md)\n\nHere\\'s a simple usage showcase:\n\n``` {.sourceCode .python}\nimport cv_algorithms\n# img must be a binary, single-channel (grayscale) image.\nthinned = cv_algorithms.guo_hall(img)\n```\n\n## Contributions\n\nContributions of any shape or form are welcome. Please submit a pull\nrequest or file an issue on GitHub.\n\nCopyright (c) 2016-2022 Uli K\u00f6hler \\<<cv_algorithms@techoverflow.net>\\>\n",
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