extcolors 0.2.0

Extract colors from an image. Colors are grouped based on visual similarities using the CIE76 formula.

Command-line tool to extract colors from an image. The result will presented in two formats, text and image.

The text result will provide the usage of each color in the number of pixels and percentage. While the image will provide a palette for a visual representation.

Table of Contents

• 1 Installation

  • 1.1 Package

  • 1.2 Manually

• 2 Usage

  • 2.1 Input - Console

  • 2.2 Input - Script

  • 2.3 Output - Text

  • 2.4 Output - Image

• 3 Additional Options

• 4 Known Issues

1 Installation

1.1 Package

$ pip install extcolors

1.2 Manually

1. Download repository as zip.

2. Unpack zip into folder.

3. Enter folder.

4. Run the following command:

   $ pip install .

2 Usage

2.1 Input - Console

To use the application provide a path to the image that the application should extract colors from. In the following example the image is in the folder we are executing the command and the name of the image is gameboy.png:

$ extcolors gameboy.png

In this example gameboy.png refers to the following image created by Rebecca Machamer.

2.2 Input - Script

To use the application provide a path to the image that the colors should be extracted from. In the following example the image is in the folder we are executing the command and the name of the image is gameboy.png:

>>> import extcolors
>>> colors, pixel_count = extcolors.extract_from_path("gameboy.png")
>>> print(colors)
[((0, 172, 170), 386062), ((245, 245, 245), 59559), ((82, 90, 92), 17824), ((102, 184, 52), 15080), ((236, 27, 111), 1302), ((255, 180, 0), 137), ((241, 148, 185), 36)]

There is also the option to use an image already loaded through pillow.

>>> import extcolors
>>> import PIL
>>> img = PIL.Image.open("gameboy.png")
>>> colors, pixel_count = extcolors.extract_from_image(img)
>>> print(colors)
[((0, 172, 170), 386062), ((245, 245, 245), 59559), ((82, 90, 92), 17824), ((102, 184, 52), 15080), ((236, 27, 111), 1302), ((255, 180, 0), 137), ((241, 148, 185), 36)]

2.3 Output - Text

When the application is done it will output information about the execution. Probably the most relevant information is the [RESULT]-section that contains the extracted colors in RGB values and their occurrence rate presented in percentages.

Output based on gameboy.png:

Extracted colors:
(0, 172, 170)  :  80.43% (386062)
(245, 245, 245):  12.41% (59559)
(82, 90, 92)   :   3.71% (17824)
(102, 184, 52) :   3.14% (15080)
(236, 27, 111) :   0.27% (1302)
(255, 180, 0)  :   0.03% (137)
(241, 148, 185):   0.01% (36)

Pixels in output: 480000 of 480000

2.4 Output - Image

When the application is done it will also create an image in the directory where the command was executed. The image will contain the colors that where extracted sorted based on their occurrence rate, wrapping from from left to right. The image will use the name of the original image appended with a time stamp of when the execution took place.

Output based on gameboy.png:

3 Additional Options

Generated output from the command-line argument extcolors --help.

usage: extcolors [-h] [--version] [-t [N]] [-l [N]] [-o {all,image,text}] PATH

Extract colors from a specified image. Colors are grouped based on visual
similarities using the CIE76 formula.

positional arguments:
  PATH

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -t [N], --tolerance [N]
                        Group colors to limit the output and give a better
                        visual representation. Based on a scale from 0 to 100.
                        Where 0 won't group any color and 100 will group all
                        colors into one. Tolerance 0 will also bypass all
                        conversion. Defaults to 32.
  -l [N], --limit [N]   Upper limit to the number of extracted colors
                        presented in the output.
  -o {all,image,text}, --output {all,image,text}
                        Format(s) that the extracted colors should presented
                        in.

4 Known Issues

Performance

When an image contains a large amount of different colors, which most do, the performance slows to a halt. If the grouping of colors is not desired/required then a workaround is to set the tolerance levels to zero. Setting the tolerance to specifically zero will make the application skip any comparisons from being made and become a simple counter resulting in much greater speeds.

Example, an image (3840x2160) containing about 340k unique colors will take two hours to complete with a tolerance level of 32 (the default value). However with a tolerance level of zero it will take ten seconds.