https://github.com/sravyadhulipala/coloranalysis
https://github.com/sravyadhulipala/coloranalysis
package python-3
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/sravyadhulipala/coloranalysis
- Owner: sravyadhulipala
- License: mit
- Created: 2020-02-24T03:23:40.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-04-03T17:53:13.000Z (about 6 years ago)
- Last Synced: 2025-10-27T18:46:41.552Z (8 months ago)
- Topics: package, python-3
- Language: Jupyter Notebook
- Size: 66.4 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# **coloranalysis**
coloranalysis is a python package for calculating the percentage of area covered by one or more colors in an image.
### **Prerequisites**
numpy, opencv and matplotlib are required to execute coloranalysis, you can download them using the following commands:
```
pip install numpy
pip install opencv-python
pip install matplotlib
```
### **Installing**
You can either clone or download this repository, or use this command:
```
pip install coloranalysis
```
### **Usage**
See [this notebook](https://github.com/sravyadhulipala/coloranalysis/blob/master/example/colorAreasExample.ipynb) for an example program on how to use this package. However, reading this document entirely is recommended.
Let us consider this image of a rainbow.
To know the area covered by red color, or the area covered by multiple colors in the image, we should get the HEX codes using a [colorpicker.](https://imagecolorpicker.com/)
In the above image, HEX codes of all colors are: ["#FE0000", "#FD6400", "#FFFF02", "#008101", "#0000FE", "#4B0081", "#BC31FD"]
Import colorArea, the class that calculates the area of the colors we want, as follows.
```
from coloranalysis.colors import colorAreas
```
*colorAreas takes no arguments.*
**getArea**
*returns* a list of the percentages of area covered by the given colors.
*arguments*
- **hexColours**: A list of strings representing the HEX codes.
- **path**: A string specifying the path of the image.
- **diff**: An integer to determine the lower and upper boundaries of the given colors, in the HSV color space.
*hexColours - colour with a 'u'*
For a digital image as above, the recommended *diff* value is 10. While the recommended *diff* value for images of real-life objects is 30-50.
For more information on HSV color space, see [this link.](https://www.linuxtopia.org/online_books/graphics_tools/gimp_advanced_guide/gimp_guide_node51.html)
**detectColor**
*returns* a tuple with 'mask' and 'result' representing the pixels that match a single color
*arguments*
- **colour**: Values of the color in HSV color space, in the range H[0-360], S[0-100], V[0-100]
- **img**: A numpy array returned by cv2.imread()
- **hsv_img**: A numpy array returned by cv2.cvtColor()
- **diff**: An integer to determine the lower and upper boundaries of the given colors, in the HSV color space.
*colour - colour with a 'u'*
'mask' and 'result' can be used to visualize the presence of the color in the given image
**convertHEXColours**
*returns* a tuple with two lists representing values of the colors in HSV colorspace and RGB colorspace respectively.
*arguments*
- **hexColours**: A list of strings representing the HEX codes.