<div align="center">
<img src="https://raw.githubusercontent.com/demmenie/imagedominantcolour/main/assets/image_dominant_colour_logo.svg" width="300"><br>
<h3>Get the dominant colour of any image</h3>
</div>
<p align="center">
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### Introduction
ImageDominantColour is a Python package/library for **detecting dominant colour of images**.
It can take any input image and tell the dominant colour of the image. It doesn't use k-means clustering for detecting dominant colour but instead quantizes the individual pixels and calculates the statistical mode of the quantized values.
ImageDominantColour does not depend on numpy unlike most of the other implementations for the same task and is also fast and minimalist.
What ImageDominantColour is not?
> ImageDominantColour does not calculates the average colour of the image. Also note that the average colour of an image is not same as its dominant colour.
### Installation
- Using [pip](https://en.wikipedia.org/wiki/Pip_(package_manager)):
```bash
pip install imagedominantcolour -U
```
- Install directly from GitHub:
```bash
pip install git+https://github.com/demmenie/imagedominantcolour.git
```
### Usage
```python
>>> from imagedominantcolour import DominantColour
>>> file_path = "blue_butterfly.jpg" # Blue colour is dominant
>>> dominantcolour = DominantColour(file_path)
>>> dominantcolour.dominant_colour
'b'
>>> dominantcolour.rgb
(3, 6, 244)
>>> dir(dominantcolour)
['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'b', 'counter', 'dominant_colour', 'dominant_colour_of_pixel', 'dominant_colour_of_pixels_of_image_array', 'g', 'generate_dominant_colour_of_pixels_of_image_array', 'image', 'image_data', 'image_path', 'l', 'minimum_percent_difference_of_rgb', 'mpd', 'r', 'resize_value', 'resized_image', 'rgb', 'rgbl', 'set_dominat_colour_of_image', 'set_rgbl_value_of_image', 'total_pixels']
>>> dominantcolour.total_pixels
256
>>> dominantcolour.r
3
>>> dominantcolour.g
6
>>> dominantcolour.b
244
>>> dominantcolour.l
3
>>> dominantcolour.rgbl
(3, 6, 244, 3)
>>> repr(dominantcolour)
'DominantColour(r:3 g:6 b:244 l:3; dominant_colour:b; resize_value:16; minimum_percent_difference_of_rgb:10)'
>>> str(dominantcolour)
'b'
>>>
```
Output dominant colour and what their meanings are:
- `r` - Red is the dominant colour in the image.
- `g` - Green is the dominant colour for the image.
- `b` - Blue is the dominant colour.
- `l` - It is lowercase L and it implies that the image is a mostly a grayscale image. L for luminance and most of the image lacks colour.
- `n` - None of the colour out of r, g and b are dominant but the image is not grayscale. It implies that the image has equal regions where 2 or 3 colours dominate, [example here](https://user-images.githubusercontent.com/64683866/151845374-dd1a83e5-3265-491e-830d-39be120af65b.png). You can check the rgb attribute to decide what to do with such cases.
What are `r`, `g`, `b` and `l` attributes of `DominantColour` objects?
> The library shrinks the image before checking the dominant color and the default resize value is 256. Thus every image is shrunk to a 256 pixels image.
The r,g,b and l attributes indicate the number of pixels which have r,g,b and l as dominating value.
### License
[](https://github.com/demmenie/imagedominantcolour/blob/main/LICENSE)
Copyright (c) 2024 Akash Mahanty.
Forked by Chico Demmenie.
Released under the MIT License. See
[license](https://github.com/demmenie/imagedominantcolour/blob/main/LICENSE) for details.
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"description": "<div align=\"center\">\n<img src=\"https://raw.githubusercontent.com/demmenie/imagedominantcolour/main/assets/image_dominant_colour_logo.svg\" width=\"300\"><br>\n<h3>Get the dominant colour of any image</h3>\n</div>\n\n<p align=\"center\">\n<a href=\"https://github.com/demmenie/imagedominantcolour/actions?query=workflow%3ATest\"><img alt=\"Build Status\" src=\"https://github.com/demmenie/imagedominantcolour/workflows/Test/badge.svg\"></a>\n<a href=\"https://codecov.io/gh/akamhy/imagedominantcolor\"><img alt=\"codecov\" src=\"https://codecov.io/gh/akamhy/imagedominantcolor/branch/main/graph/badge.svg?token=xCV7vQ9MJo\"></a>\n<a href=\"https://pypi.org/project/imagedominantcolor/\"><img alt=\"pypi\" src=\"https://img.shields.io/pypi/v/imagedominantcolor.svg\"></a>\n<a href=\"https://pepy.tech/project/imagedominantcolor?versions=1*\"><img alt=\"Downloads\" src=\"https://pepy.tech/badge/imagedominantcolor/month\"></a>\n<a href=\"#\"><img alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/imagedominantcolor?style=flat-square\"></a>\n<a href=\"https://github.com/psf/black\"><img alt=\"Code style: black\" src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"></a>\n</p>\n\n### Introduction\nImageDominantColour is a Python package/library for **detecting dominant colour of images**.\n\nIt can take any input image and tell the dominant colour of the image. It doesn't use k-means clustering for detecting dominant colour but instead quantizes the individual pixels and calculates the statistical mode of the quantized values.\n\nImageDominantColour does not depend on numpy unlike most of the other implementations for the same task and is also fast and minimalist.\n\n\nWhat ImageDominantColour is not?\n> ImageDominantColour does not calculates the average colour of the image. Also note that the average colour of an image is not same as its dominant colour.\n\n\n### Installation\n\n - Using [pip](https://en.wikipedia.org/wiki/Pip_(package_manager)):\n\n```bash\npip install imagedominantcolour -U\n```\n\n - Install directly from GitHub:\n\n```bash\npip install git+https://github.com/demmenie/imagedominantcolour.git\n```\n\n\n### Usage\n```python\n>>> from imagedominantcolour import DominantColour\n>>> file_path = \"blue_butterfly.jpg\" # Blue colour is dominant\n>>> dominantcolour = DominantColour(file_path)\n>>> dominantcolour.dominant_colour\n'b'\n>>> dominantcolour.rgb\n(3, 6, 244)\n>>> dir(dominantcolour)\n['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'b', 'counter', 'dominant_colour', 'dominant_colour_of_pixel', 'dominant_colour_of_pixels_of_image_array', 'g', 'generate_dominant_colour_of_pixels_of_image_array', 'image', 'image_data', 'image_path', 'l', 'minimum_percent_difference_of_rgb', 'mpd', 'r', 'resize_value', 'resized_image', 'rgb', 'rgbl', 'set_dominat_colour_of_image', 'set_rgbl_value_of_image', 'total_pixels']\n>>> dominantcolour.total_pixels\n256\n>>> dominantcolour.r\n3\n>>> dominantcolour.g\n6\n>>> dominantcolour.b\n244\n>>> dominantcolour.l\n3\n>>> dominantcolour.rgbl\n(3, 6, 244, 3)\n>>> repr(dominantcolour)\n'DominantColour(r:3 g:6 b:244 l:3; dominant_colour:b; resize_value:16; minimum_percent_difference_of_rgb:10)'\n>>> str(dominantcolour)\n'b'\n>>>\n```\n\nOutput dominant colour and what their meanings are:\n\n - `r` - Red is the dominant colour in the image.\n - `g` - Green is the dominant colour for the image.\n - `b` - Blue is the dominant colour.\n - `l` - It is lowercase L and it implies that the image is a mostly a grayscale image. L for luminance and most of the image lacks colour.\n - `n` - None of the colour out of r, g and b are dominant but the image is not grayscale. It implies that the image has equal regions where 2 or 3 colours dominate, [example here](https://user-images.githubusercontent.com/64683866/151845374-dd1a83e5-3265-491e-830d-39be120af65b.png). You can check the rgb attribute to decide what to do with such cases.\n\nWhat are `r`, `g`, `b` and `l` attributes of `DominantColour` objects?\n> The library shrinks the image before checking the dominant color and the default resize value is 256. Thus every image is shrunk to a 256 pixels image.\nThe r,g,b and l attributes indicate the number of pixels which have r,g,b and l as dominating value.\n\n### License\n[](https://github.com/demmenie/imagedominantcolour/blob/main/LICENSE)\n\nCopyright (c) 2024 Akash Mahanty.\n\nForked by Chico Demmenie.\n\nReleased under the MIT License. See\n[license](https://github.com/demmenie/imagedominantcolour/blob/main/LICENSE) for details.\n",
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