Clone the repo, navigate inside it and use pip install .
Before installation, consider setting up some API keys if you so wish.
To get album covers I try 3 different APIs (Last.fm, MusicBrains, and Discogs), two of which require API keys (Last.fm and discogs). Last.fm is the preferred API.
To set up your API keys, you will want to rename covers2colors/keys_template.json to covers2colors/keys.json and add in your API keys in there. Once the file is renamed the package will assume it contains valid api keys and will attempt to use them.
If you would rather not bother getting any API keys, artwork will attempt to be fetched from MusicBrainz only. things should still work fine, although the MusicBrains API can be slow. I've also noticed that the color on some album covers appear muted on Discogs and MusicBrains.
Create colormaps from album covers in three lines of code!
First, the CoverColors class makes calls to various APIs in order to fetch album artwork, and converts the image to arrays of RGB values.
Then, generate_cmap creates a matplotlib ListedColormap.
from covers2colors import CoverColors
covercolors = CoverColors('iron maiden', "powerslave")
cmap = covercolors.generate_cmap(n_colors=5, random_state=42)
Now, use the colormap in your plots!
import matplotlib.pyplot as plt
colors = cmap.colors
with plt.style.context("dark_background"):
for i, color in enumerate(colors):
plt.plot(range(10), [_+i+1 for _ in range(10)], color=color, linewidth=4)
Plot the image and a colorbar side by side with the following method:
covercolors.display_with_colorbar(cmap)
You can extract the optimal number of colors from the image using the generate_optimal_cmap method.
Under the hood this performs the elbow method
to determine the optimal number of clusters based on the sum of the squared distances between each pixel
and it's cluster center.
cmaps, best_n_colors, ssd = covercolors.generate_optimal_cmap(max_colors=10, random_state=42)
best_cmap = cmaps[best_n_colors]
This method is great if you don't have a preference for how many colors are in your color palette.
Suppose you have a collection of colors in your color palette, but you only want to select a subset from them. This method will select the most distinct colors out of the palette for your new, smaller color palette.
cmap = cmaps[10] # here I'm using the cmap with 10 colors from running the above code
distinct_colors, distinct_cmap = covercolors.get_distinct_colors(cmap, 5)
Another method you can use to get a color map. This method employs the generate_optimal_cmap and then finds the most distinct set of n_distinct_colors colors.
distinct_colors, distinct_cmap = covercolors.generate_distinct_optimal_cmap(n_distinct_colors=6)
Notice how this palette differs from the first palette generated by generate_cmap!
The different methods of obtaining a palette will often return different palettes for the name number of colors, generate_distinct_optimal_cmapappears to be the one that performs the best imo, but results vary (which is why there are options!).
When running the generate_cmap or the generate_optimal_cmap methods the CoverColors object will automatically
capture the resulting hexcodes from the colormap and store them as an attribute.
from cover2colors import CoverColors
covercolors = CoverColors('Nirvana', 'Nevermind')
covercolors.generate_cmap(n_colors=4, random_state=42)
print(covercolors.hexcodes)
Output:
['#0ea1c1', '#456a78', '#0269ae', '#091a2d']