py/pallete.py

@@ -1,35 +0,0 @@
-import argparse
-import cv2
-from pallete_schema import PalleteSchema
-
-from os.path import isfile, realpath, basename, dirname, splitext, join
-
-parser = argparse.ArgumentParser(description='Separate an image into most similar colors specified')
-
-parser.add_argument('input', type=str, help='Input image to extract the pallete from')
-
-class Pallete :
-	input = ''
-	output = ''
-	image = None
-
-	def __init__ (self, args) :
-		if isfile(args.input) :
-			self.input = realpath(args.input)
-		else :
-			print(f'File {self.input} does not exist')
-			exit(1)
-		self.set_output()
-
-	def set_output (self) :
-		dir = dirname(self.input)
-		stem = splitext(basename(self.input))[0]
-		self.output = join(dir, f'{stem}.json')
-		print(f'Writing to {stem}.json')
-
-	def process (self) :
-		image = cv2.imread(self.input)
-
-if __name__ == "__main__" :
-	args = parser.parse_args()
-	Pallete(args)

py/pallete_schema.py

@@ -1,32 +0,0 @@
-from jsonschema import validate
-from json import dumps, loads
-
-class PalleteSchema :
-	colors = None
-	schema = {
-		"type" : "array",
-		"items" : {
-			"type" : "object",
-			"properties" : {
-				"name" : { "type" : "string" },
-				"color" : {
-					"type" : "array",
-					"items" : { "type" : "number" }
-				}
-			},
-			"required" : [ "name", "color" ]
-		}
-	}
-	def __init__ (self, file = None):
-		if file is not None:
-			self.parse_file(file)
-
-	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

py/posterize.py

@@ -6,56 +6,8 @@ import cv2
 class Posterize:
 	"""Posterize an image and then find nearest colors to use"""
 	colors = []
-	original_colors = []
-	image = None
-	pallete = None
-	h = 0
-	w = 0
-	n_colors = 3
-	white = [255, 255, 255]
-
-	def __init__ (self, image, pallete, n_colors) :
-		self.image = cv2.imread(image)
-		(self.h, self.w) = self.image.shape[:2]
-		self.pallete = pallete
-		self.n_colors = n_colors + 1
+	#def __init__ (self) :
 		
-		self.posterize()
-		self.determine_colors()
-
-	def posterize (self):
-		lab = cv2.cvtColor(self.image, cv2.COLOR_BGR2LAB)
-		feature = lab.reshape((self.h * self.w, 3))
-		
-		clusters = MiniBatchKMeans(n_clusters = self.n_colors, n_init = 'auto')
-		
-		labels = clusters.fit_predict(feature)
-		quant = clusters.cluster_centers_.astype('uint8')[labels]
-		
-		rquant = quant.reshape((self.h, self.w, 3))
-		rfeature = feature.reshape((self.h, self.w, 3))
-
-		bgrquant = cv2.cvtColor(rquant, cv2.COLOR_LAB2BGR)
-		#bgrfeature = cv2.cvtColor(rfeature, cv2.COLOR_LAB2BGR)
-
-		self.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)
-		for i in range(self.n_colors) :
-			mask = self.extract_color(self.image, self.original_colors[i])
-			cv2.imwrite(f'{i}.png', mask)
-
-	def extract_color (self, image, color):
-		mask = cv2.inRange(image, color, color)
-		return cv2.bitwise_not(mask)
 
 	def closest(self, colors, color):
 		colors = np.array(colors)
@@ -64,3 +16,6 @@ class Posterize:
 		index_of_smallest = np.where(distances == np.amin(distances))
 		smallest_distance = colors[index_of_smallest]
 		return smallest_distance 
+
+if __name__ == "__main__" :
+	posterize = Posterize()

py/requirements.txt

@@ -1,4 +1,3 @@
 opencv-python
 numpy
-scikit-learn
-jsonschema
+scikit-learn

py/separator.py

@@ -1,32 +1,18 @@
 import argparse
 from posterize import Posterize
-from pallete_schema import PalleteSchema
-from os.path import isfile, realpath, basename
+from os.path import abspath
+
+from os.path import isfile
 
 parser = argparse.ArgumentParser(description='Separate an image into most similar colors specified')
 
 parser.add_argument('input', type=str, help='Input image to separate')
-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 :
-	input = ''
-	output = ''
-	pallete = None
-
+	posterize = Posterize()
 	def __init__ (self, args) :
-		if isfile(args.input) :
-			self.input = realpath(args.input)
-		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)
+		print(args)
 
 
 if __name__ == "__main__" :

py/test_pallete.json

@@ -1,14 +0,0 @@
-[
-	{ 
-		"name" : "red",
-		"color" : [255, 0, 0]
-	},
-	{ 
-		"name" : "green",
-		"color" : [0, 255, 0]
-	},
-	{ 
-		"name" : "blue",
-		"color" : [0, 0, 255]
-	}
-]