pokemon-color-search

Pokemon Color Search

Utility for quickly finding pokemon by the sprite's "distance" from a given color.

• nearest.py provides multiple options for finding pokemon "near" a color. No external dependencies, but database.csv must be present and populated.
• ingest.py generates database.csv which is needed for nearest.py Requires Pillow (PIL).
• nearest.html and nearest.js allow you to use a very rudimentary front-end in your browser, by opening nearest.html directly. The implementation is very lazy in order to allow usage with no dependencies.
• expore.py traverses a subset of the 24-bit RGB color space and finds the pokemon that most closely match each color, and produces best.csv and counts.csv as results.

PNG Source

Currently using pokemondb's sprite archive.

Download the entire page (i.e., Ctrl-S in most browsers), then take the folder of PNGs it downloads and place them in the pngs directory in this repository. Then run ingest.py to generate database.csv

Distance Calculation

For ease of calculation, a pokemon's distance from a certain color is the mean of the squared Euclidean distances between the given color and each pixel of the sprite, treating the RGB components as vector components. Transparent pixels are omitted.

Put more explicitly, if a pokemon's sprite's pixels form the set P, then the distance to a color q is:

D(P, q) = sum(||p - q||^2, p in P) / |P|

This distance measure was chosen because it can be easily reformulated. For a pixel p, let p_r, p_g, and p_b be the red, green, and blue components respectively. The above function can be rewritten as follows:

= sum(||p - q||^2, p in P) / |P|
# expand 2-norm definition
= sum(sum((p_c - q_c)^2, c in [r, g, b]), p in P) / |P|
# FOIL
= sum(sum(p_c^2 - 2*p_c*q_c + q_c^2, c in [r, g, b]), p in P) / |P|
# split sums, extract constants
= (sum(sum(p_c^2, c in [r, g, b]), p in P) 
  - 2*sum(sum(p_c*q_c, c in [r, g, b]), p in P) 
  + sum(sum(q_c^2, c in [r, g, b]), p in P)) / |P|
# collapse 2-norm definition in first and third terms
= (sum(||p||^2, p in P) 
  - 2*sum(sum(p_c*q_c, c in [r, g, b]), p in P) 
  + sum(||q||^2, p in P)) / |P|
# evaluate third summation (no dependency on p)
= (sum(||p||^2, p in P) 
  - 2*sum(sum(p_c*q_c, c in [r, g, b]), p in P) 
  + |P|*||q||^2) / |P|
# invert order of second summation (sums are finite)
= (sum(||p||^2, p in P) 
  - 2*sum(sum(p_c*q_c, p in P), c in [r, g, b]) 
  + |P|*||q||^2) / |P|
# pull out q_c term in inner sum of second summation (no dependency on p)
= (sum(||p||^2, p in P) 
  - 2*sum(q_c*sum(p_c, p in P), c in [r, g, b]) 
  + |P|*||q||^2) / |P|
# distribute 1/|P| factor, move factor of 2 inside second summation
= (sum(||p||^2, p in P)/|P|)
  - sum(q_c*2*sum(p_c, p in P)/|P|, c in [r, g, b])
  + ||q||^2
# let Y be a vector-valued function such that Y(P)_c = 2*sum(p_c, p in P)/|P|
= (sum(||p||^2, p in P)/|P|)
  - sum(q_c*Y(P)_c, c in [r, g, b])
  + ||q||^2
# let X(P) = sum(||p||^2, p in P)/|P|
= X(P)
  - sum(q_c*Y(P)_c, c in [r, g, b])
  + ||q||^2
# collapse dot product definition in second term
D(P, q) = X(P) - q . Y(P) + ||q||^2

Notably, X(P) and and Y(P) can be computed ahead of time for each sprite. This is the purpose of ingest.py, which converts each png file in the pngs directory into an item in database.csv. The columns of this CSV file are name,X,Y_r,Y_g,Y_b. Additionally, the last term, ||q||^2 has no dependence on P, and can thus be eliminated when trying to find the best P for a given q. Thus, the final distance metric is:

D(P, q) = X(P) - q . Y(P)