78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
from jsonschema import validate
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from json import dumps, loads
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from common import convert_color, closest_color, create_colored_image, remove_from_list, closest_color_euclidean, closest_color_weighted_euclidean, euclidean_distance, weighted_euclidean_distance
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class PalleteSchema :
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colors = None
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schema = {
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"type" : "array",
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"items" : {
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"type" : "object",
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"properties" : {
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"name" : { "type" : "string" },
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"color" : {
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"type" : "array",
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"items" : { "type" : "number" }
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},
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"space" : { "type" : "string" }
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},
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"required" : [ "name", "color", "space" ]
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}
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}
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def __init__ (self, file = None, obj = None):
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if file is not None and obj is None:
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self.parse_file(file)
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elif file is not None and obj is not None :
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self.write(file, obj)
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else :
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print('Not sure what you\'re trying to do here')
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def parse_file (self, file) :
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with open(file) as f :
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self.parse(f.read())
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print(f'Parsed pallete file {file}')
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def parse (self, jsonstr) :
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obj = loads(jsonstr)
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validate( instance = obj, schema = self.schema)
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self.colors = obj
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def write(self, filepath, obj) :
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validate( instance = obj, schema = self.schema)
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jsonstr = dumps(obj, indent = 4)
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with open(filepath, 'w') as outfile :
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outfile.write(jsonstr)
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def closest (self, comparison, space = 'BGR', colors = None) :
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c = colors if colors is not None else self.colors
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colors = normalize_colors(space, c)
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if space == 'RGB' or space == 'BGR' :
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closest, dist = closest_color_weighted_euclidean(colors, comparison, space)
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else :
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closest, dist = closest_color_euclidean(colors, comparison)
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print(f'Color [{space}] {comparison} closest to {closest} [{dist}]')
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return closest
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def closest_exclusive (self, comparisons, space = 'BGR', colors = None) :
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c = colors if colors is not None else self.colors
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colors = normalize_colors(space, c)
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exclusive = []
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for comparison in comparisons :
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if space == 'RGB' or space == 'BGR' :
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closest, dist = closest_color_weighted_euclidean(colors, comparison, space)
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else :
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closest, dist = closest_color_euclidean(colors, comparision)
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colors = remove_from_list(colors, closest)
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exclusive.append(closest)
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return exclusive
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def normalize_colors (self, space = 'BGR', colors = None) :
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normalized = []
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c = colors if colors is not None else self.colors
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for color in c :
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normalized.append(convert_color(color['color'], color['space'], space))
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return normalized
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