Perform closest color match and composite with selected colors

py/.gitignore

@@ -1,3 +1,4 @@
 env
 __pycache__
 *.png
+output/*

py/pallete_schema.py

@@ -43,16 +43,35 @@ class PalleteSchema :
 		with open(filepath, 'w') as outfile :
 			outfile.write(jsonstr)
 
-	def closest (self, comparison, space = 'BGR', pallete = None) :
+	def closest (self, comparison, space = 'BGR', colors = None) :
-		p = pallete if pallete is not None else self.pallete
+		c = colors if colors is not None else self.colors
-		colors = normalize_colors(space, p)	
+		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 normalize_colors (self, space = 'BGR', pallete = None) :
+	def closest_exclusive (self, comparisons, space = 'BGR', colors = None) :
-		colors = []
+		c = colors if colors is not None else self.colors
-		p = pallete if pallete is not None else self.pallete
+		colors = normalize_colors(space, c)	
-		for color in p :
+		exclusive = []
-			colors.append(convert_color(color['color'], color['space'], space))
+		for comparison in comparisons :
-		return colors
+			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
 
 
 

py/posterize.py

@@ -2,13 +2,15 @@ from sklearn.cluster import MiniBatchKMeans
 import numpy as np
 import argparse
 import cv2
-from common import convert_color, closest_color, create_colored_image, remove_from_list, list_match
+from common import convert_color, closest_color_weighted_euclidean, closest_color_euclidean, create_colored_image, remove_from_list, list_match
+import os
 
 class Posterize:
 	"""Posterize an image and then find nearest colors to use"""
 	colors = []
 	colors_dict = {}
 	original_colors = []
+	layers = []
 
 	pallete = None
 	pallete_space = 'BGR'
@@ -23,7 +25,7 @@ class Posterize:
 
 	white = [255, 255, 255]
 
-	output = ''
+	output = None
 
 	def __init__ (self, image, pallete, n_colors, output) :
 		self.image = cv2.imread(image)
@@ -31,6 +33,10 @@ class Posterize:
 		self.pallete = pallete
 		self.n_colors = n_colors + 1
 		self.output = output
+
+		if not os.path.exists(self.output) :
+			print(f'Output directory {self.output} does not exist, creating...')
+			os.makedirs(self.output)
 		
 		self.flatten_pallete()
 		self.posterize()
@@ -53,7 +59,6 @@ class Posterize:
 
 		self.image = bgrquant
 		
-
 		cv2.imshow('image', bgrquant)
 		cv2.waitKey(0)
 		cv2.destroyAllWindows()
@@ -61,25 +66,36 @@ class Posterize:
 	def determine_colors (self):
 		reshaped = self.image.reshape(-1, self.image.shape[2])
 		self.original_colors = np.unique(reshaped, axis=0)
-		white = closest_color(self.original_colors, [255, 255, 255])
+		white, white_dist = closest_color_weighted_euclidean(self.original_colors, [255, 255, 255], 'BGR')
 
 		blank = create_colored_image(self.w, self.h, [255, 255, 255])
 		composite = create_colored_image(self.w, self.h, [255, 255, 255])
+		
 		for i in range(self.n_colors) :
 			if list_match(self.original_colors[i], white) :
 				continue
-			mask = self.extract_color_mask(self.image, self.original_colors[i])
+			original = self.original_colors[i] #BGR
-			closest = closest_color(self.colors, self.original_colors[i])
+			mask = self.extract_color_mask(self.image, original)
+			original_normalized = convert_color(original, 'BGR', self.pallete_space)
+			if self.pallete_space == 'RGB' or self.pallete_space == 'BGR' :
+				closest, dist = closest_color_weighted_euclidean(self.colors, original_normalized, self.pallete_space)
+			else :
+				closest, dist = closest_color_euclidean(self.colors, original_normalized)
 			self.colors = remove_from_list(self.colors, closest)
 			name = self.match_color_name(closest)
-
+			layer_name = f'{name}.png'
-			cv2.imwrite(f'{name}.png', mask)
+			output_layer = os.path.join(self.output, layer_name)
-
+			cv2.imwrite(output_layer, mask)
-			color_mat = create_colored_image(self.w, self.h, closest)
+			self.layers.append({
-			color_mask = cv2.bitwise_or(color_mat, color_mat, mask = (255-mask))
+				'layer' : output_layer,
-
+				'color' : closest,
-			
+				'space' : self.pallete_space
-
+			})
+			#color_mat = create_colored_image(self.w, self.h, closest)
+			#color_mask = cv2.bitwise_or(color_mat, color_mat, mask = (255-mask))
+			#color_mask = cv2.inRange(mask, np.array([0, 0, 0]), np.array([1, 1, 1]))
+			mask = cv2.bitwise_not(mask)
+			composite[mask > 0] = np.array(closest)
 
 		cv2.imshow('image', composite)
 		cv2.waitKey(0)
@@ -97,3 +113,4 @@ class Posterize:
 
 	def match_color_name (self, key) :
 		return self.colors_dict[f'{key[0]},{key[1]},{key[2]}']
+

py/separate.py

@@ -10,7 +10,7 @@ parser.add_argument('colors', type=int, help='Number of colors to separate into'
 parser.add_argument('pallete', type=str, help='Pallete file')
 parser.add_argument('output', type=str, help='Output dir to write to')
 
-class Separator :
+class Separate :
 	input = ''
 	output = ''
 	pallete = None
@@ -33,5 +33,5 @@ class Separator :
 
 if __name__ == "__main__" :
 	args = parser.parse_args()
-	Separator(args)
+	Separate(args)