Having trouble compiling matched colors to preview image

py/.gitignore

@@ -1,2 +1,3 @@
 env
 __pycache__
+*.png

py/common.py

@@ -3,14 +3,17 @@ import numpy as np
 
 def convert_color (color, color_space_a, color_space_b) :
 	pixel = np.zeros([1, 1, 3], dtype=np.uint8)
+
 	if color_space_a == 'RGB' :
 		pixel = cv2.cvtColor(pixel, cv2.COLOR_BGR2RGB)
 	elif color_space_a == 'LAB' :
 		pixel = cv2.cvtColor(pixel, cv2.COLOR_BGR2LAB)
 	elif color_space_a == 'HSV' :
 		pixel = cv2.cvtColor(pixel, cv2.COLOR_BGR2HSV)
+	
 	#default is BGR
 	pixel[:] = color
+
 	if color_space_a == 'RGB' and color_space_b == 'BGR' :
 		b = cv2.COLOR_RGB2BGR
 	elif color_space_a == 'BGR' and color_space_b == 'RGB' :
@@ -41,7 +44,7 @@ def convert_color (color, color_space_a, color_space_b) :
 		b = None
 	elif color_space_a == 'LAB' and color_space_b == 'LAB' :
 		b = None
-	elif color_space_a == 'HSV' and color_space_b == 'HSB' :
+	elif color_space_a == 'HSV' and color_space_b == 'HSV' :
 		b = None
 
 	if b is not None :
@@ -57,9 +60,22 @@ def closest_color (colors, color):
 	distances = np.sqrt(np.sum((colors - color) ** 2, axis=1))
 	index_of_smallest = np.where(distances == np.amin(distances))
 	smallest_distance = colors[index_of_smallest]
-	return smallest_distance
+	return smallest_distance[0]
 
 def create_colored_image (width, height, bgr_color):
 	image = np.zeros((height, width, 3), np.uint8)
 	image[:] = bgr_color
 	return image
+
+def remove_from_list (l, item) :
+	new_array = []
+	for i in l :
+		if not list_match(i, item) :
+			new_array.append(i)
+	return new_array
+
+def list_match (a, b) :
+	for i in range(len(a)) :
+		if a[i] != b[i] :
+			return False
+	return True

py/comparison_comparison.py

@@ -1,32 +1,44 @@
 import cv2
 import numpy as np
 from pallete_schema import PalleteSchema
-from common import convert_color, closest_color, create_colored_image
+from common import convert_color, closest_color, create_colored_image, remove_from_list
 
 class ComparisonComparison:
 	def __init__ (self) :
+		
 		red = [0, 10, 200]
 		green = [5, 250, 5]
 		blue = [240, 0, 20]
+		
 		comp_colors = [red, green, blue]
+
 		pallete = PalleteSchema('./palletes/printed_pallete.json')
-		color_spaces = ['RGB', 'LAB', 'HSV']
+		
+		color_spaces = ['RGB', 'BGR', 'LAB', 'HSV']
 		for space in color_spaces :
 			print(f'Comparing in color space {space}')
+			
 			colors = self.get_colors(pallete.colors, space)
 			show = []
+
 			for cc in comp_colors :
 				cccompare = convert_color(cc, 'RGB', space)
+				print(cccompare)
 				closest = closest_color(colors, cccompare)
+				print(closest)
+				colors = remove_from_list(colors, closest)
 
 				ccbgr = convert_color(cc, 'RGB', 'BGR')
 				chosenbgr = convert_color(closest, space, 'BGR')
 
-				chosen = create_colored_image(100, 100, chosenbgr)
 				original = create_colored_image(100, 100, ccbgr)
+				chosen = create_colored_image(100, 100, chosenbgr)
+
+				print(f'{ccbgr} => {chosenbgr}')
 
 				combined = np.hstack([original, chosen])
 				show.append(combined)
+
 			show = np.vstack(show)
 			cv2.imshow("image", show)
 			cv2.waitKey(0)

py/posterize.py

@@ -2,25 +2,37 @@ from sklearn.cluster import MiniBatchKMeans
 import numpy as np
 import argparse
 import cv2
-from common import convert_color, closest_color
+from common import convert_color, closest_color, create_colored_image, remove_from_list, list_match
 
 class Posterize:
 	"""Posterize an image and then find nearest colors to use"""
 	colors = []
+	colors_dict = {}
 	original_colors = []
-	image = None
+
 	pallete = None
+	pallete_space = 'BGR'
+	
+	comparison_space = 'BGR'
+
+	image = None
+	
 	h = 0
 	w = 0
 	n_colors = 3
+
 	white = [255, 255, 255]
 
-	def __init__ (self, image, pallete, n_colors) :
+	output = ''
+
+	def __init__ (self, image, pallete, n_colors, output) :
 		self.image = cv2.imread(image)
 		(self.h, self.w) = self.image.shape[:2]
 		self.pallete = pallete
 		self.n_colors = n_colors + 1
+		self.output = output
 		
+		self.flatten_pallete()
 		self.posterize()
 		self.determine_colors()
 
@@ -42,18 +54,41 @@ class Posterize:
 		self.image = bgrquant
 		
 
-		cv2.imshow("image", bgrquant)
+		cv2.imshow('image', bgrquant)
 		cv2.waitKey(0)
 		cv2.destroyAllWindows()
 
 	def determine_colors (self):
 		reshaped = self.image.reshape(-1, self.image.shape[2])
 		self.original_colors = np.unique(reshaped, axis=0)
-		#print(self.original_colors)
+		white = closest_color(self.original_colors, [255, 255, 255])
+
+		blank = 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])
-			cv2.imwrite(f'{i}.png', mask)
+			closest = closest_color(self.colors, self.original_colors[i])
+			self.colors = remove_from_list(self.colors, closest)
+			name = self.match_color_name(closest)
+			cv2.imwrite(f'{name}.png', mask)
+			color_mat = create_colored_image(self.w, self.h, closest)
+			blank = cv2.bitwise_and(color_mat, color_mat, mask = (255-mask))
+
+		cv2.imshow('image', blank)
+		cv2.waitKey(0)
+		cv2.destroyAllWindows()
 
 	def extract_color_mask (self, image, color):
 		mask = cv2.inRange(image, color, color)
 		return cv2.bitwise_not(mask)
+
+	def flatten_pallete (self) :
+		for color in self.pallete.colors:
+			self.colors.append(color['color'])
+			self.colors_dict[f'{color["color"][0]},{color["color"][1]},{color["color"][2]}'] = color['name']
+			self.pallete_space = color['space']
+
+	def match_color_name (self, key) :
+		return self.colors_dict[f'{key[0]},{key[1]},{key[2]}']

py/separator.py

@@ -21,12 +21,14 @@ class Separator :
 		else :
 			print(f'File {args.input} does not exist')
 			exit(1)
+		
 		if isfile(args.pallete) :
 			self.pallete = PalleteSchema(args.pallete)
 		else :
 			print(f'File {args.pallete} does not exist')
 			exit(2)
-		Posterize(self.input, self.pallete, args.colors)
+
+		Posterize(self.input, self.pallete, args.colors, args.output)
 
 
 if __name__ == "__main__" :