marker_separation/py/pallete_schema.py

from jsonschema import validate
from json import dumps, loads
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

class PalleteSchema :
	colors = None
	schema = {
		"type" : "array",
		"items" : {
			"type" : "object",
			"properties" : {
				"name" : { "type" : "string" },
				"color" : {
					"type" : "array",
					"items" : { "type" : "number" }
				},
				"space" : { "type" : "string" }
			},
			"required" : [ "name", "color", "space" ]
		}
	}
	def __init__ (self, file = None, obj = None):
		if file is not None and obj is None:
			self.parse_file(file)
		elif file is not None and obj is not None :
			self.write(file, obj)
		else :
			print('Not sure what you\'re trying to do here')

	def parse_file (self, file) :
		with open(file) as f :
			self.parse(f.read())
		print(f'Parsed pallete file {file}')

	def parse (self, jsonstr) :
		obj = loads(jsonstr)
		validate( instance = obj, schema = self.schema)
		self.colors = obj

	def write(self, filepath, obj) :
		validate( instance = obj, schema = self.schema)
		jsonstr = dumps(obj, indent = 4)
		with open(filepath, 'w') as outfile :
			outfile.write(jsonstr)

	def closest (self, comparison, space = 'BGR', colors = None) :
		c = colors if colors is not None else self.colors
		colors = normalize_colors(space, c)	
		if space == 'RGB' or space == 'BGR' :
			closest, dist = closest_color_weighted_euclidean(colors, comparison, space)
		else :
			closest, dist = closest_color_euclidean(colors, comparison)
		print(f'Color [{space}] {comparison} closest to {closest} [{dist}]')
		return closest

	def closest_exclusive (self, comparisons, space = 'BGR', colors = None) :
		c = colors if colors is not None else self.colors
		colors = normalize_colors(space, c)	
		exclusive = []
		for comparison in comparisons :
			if space == 'RGB' or space == 'BGR' :
				closest, dist = closest_color_weighted_euclidean(colors, comparison, space)
			else :
				closest, dist = closest_color_euclidean(colors, comparision)
			colors = remove_from_list(colors, closest)
			exclusive.append(closest)
		return exclusive

	def normalize_colors (self, space = 'BGR', colors = None) :
		normalized = []
		c = colors if colors is not None else self.colors
		for color in c :
			normalized.append(convert_color(color['color'], color['space'], space))
		return normalized