111 lines
4.1 KiB
Python
111 lines
4.1 KiB
Python
# data sources: CSS docs, jennyscrayoncollection 2017 article on Crayola colors, XKCD color survey
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# target: Single C++ header file to provide a struct of color RGB codes and names.
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# This file pre-computes the nearest neighbor of every color.
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# if an RGB code being searched for is closer than the nearest neighbor, it's the closest color name.
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def hex_to_rgb(txt):
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if '#' in txt: txt = txt.replace('#', '')
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r = txt[0:2]
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g = txt[2:4]
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b = txt[4:6]
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return tuple([int(s, 16) for s in (r,g,b)])
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class palette:
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def __init__(self, name, filename, priority):
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self.name = name
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self.priority = priority
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with open(filename, "r") as f:
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print(f"scanning {filename}")
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self.colors = {}
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for line in f.read().split('\n'):
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if len(line.split('\t')) < 2: continue
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name, code = line.split('\t')
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#print(name, code)
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self.colors[name] = hex_to_rgb(code)
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def __repr__(self):
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return f"<Palette '{self.name}' - {len(self.colors)} colors, priority = {self.priority}>"
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palettes = [
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#palette("jenny", "jenny_colors.txt", 3), # I should probably use wikipedia as a source for copyright reasons
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palette("crayon", "wikicrayons_colors.txt", 2),
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palette("xkcd", "xkcd_colors.txt", 1),
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palette("css", "css_colors.txt", 0),
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#palette("matplotlib", "matplotlib_colors.txt", 2) # there's like 10 colors total, I think we'll survive without them
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]
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all_colors = []
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from math import sqrt
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def rgbdist(c1, c2):
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return sqrt((c1.r - c2.r)**2 + (c1.g - c2.g)**2 + (c1.b - c2.b)**2)
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class Color:
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def __init__(self, r, g, b, name, prefix, priority):
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self.r = r
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self.g = g
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self.b = b
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self.name = name
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self.prefix = prefix
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self.priority = priority
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self.nearest_neighbor = None
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def __repr__(self):
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return f"<Color ({self.r}, {self.g}, {self.b}) - '{self.prefix}:{self.name}', priority = {self.priority}, nearest_neighbor={self.nearest_neighbor.name if self.nearest_neighbor is not None else None}>"
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def nn(self, colors):
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nearest = None
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nearest_dist = 999999
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for c in colors:
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dist = rgbdist(self, c)
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if dist == 0: continue
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if dist < nearest_dist:
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nearest = c
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nearest_dist = dist
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self.nearest_neighbor = nearest
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for p in palettes:
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prefix = p.name
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priority = p.priority
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for name, rgb in p.colors.items():
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all_colors.append(Color(*rgb, name, prefix, priority))
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print(f"{prefix}->{len(all_colors)}")
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for c in all_colors:
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c.nn(all_colors)
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smallest_dist = 9999999999999
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largest_dist = 0
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for c in all_colors:
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dist = rgbdist(c, c.nearest_neighbor)
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if dist > largest_dist: largest_dist = dist
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if dist < smallest_dist: smallest_dist = dist
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#print(f"{c.prefix}:{c.name} -> {c.nearest_neighbor.prefix}:{c.nearest_neighbor.name}\t{rgbdist(c, c.nearest_neighbor):.2f}")
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# questions -
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# are there any colors where their nearest neighbor's nearest neighbor isn't them? (There should be)
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nonnear_pairs = 0
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for c in all_colors:
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neighbor = c.nearest_neighbor
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their_neighbor = neighbor.nearest_neighbor
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if c is not their_neighbor:
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#print(f"{c.prefix}:{c.name} -> {neighbor.prefix}:{neighbor.name} -> {their_neighbor.prefix}:{their_neighbor.name}")
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nonnear_pairs += 1
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print("Non-near pairs:", nonnear_pairs)
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#print(f"{c.prefix}:{c.name} -> {c.nearest_neighbor.prefix}:{c.nearest_neighbor.name}\t{rgbdist(c, c.nearest_neighbor):.2f}")
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# Are there duplicates? They should be removed from the palette that won't be used
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dupes = 0
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for c in all_colors:
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for c2 in all_colors:
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if c is c2: continue
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if c.r == c2.r and c.g == c2.g and c.b == c2.b:
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dupes += 1
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print("dupes:", dupes, "this many will need to be removed:", dupes / 2)
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# What order to put them in? Do we want large radiuses first, or some sort of "common color" table?
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# does manhattan distance change any answers over the 16.7M RGB values?
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# What's the worst case lookup? (Checking all 1200 colors to find the name?) |