Perform closest color match and composite with selected colors
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b317914922
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2976682319
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@ -1,3 +1,4 @@
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env
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__pycache__
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*.png
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output/*
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@ -43,16 +43,35 @@ class PalleteSchema :
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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', pallete = None) :
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p = pallete if pallete is not None else self.pallete
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colors = normalize_colors(space, p)
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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 normalize_colors (self, space = 'BGR', pallete = None) :
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colors = []
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p = pallete if pallete is not None else self.pallete
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for color in p :
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colors.append(convert_color(color['color'], color['space'], space))
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return colors
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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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@ -2,13 +2,15 @@ from sklearn.cluster import MiniBatchKMeans
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import numpy as np
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import argparse
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import cv2
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from common import convert_color, closest_color, create_colored_image, remove_from_list, list_match
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from common import convert_color, closest_color_weighted_euclidean, closest_color_euclidean, create_colored_image, remove_from_list, list_match
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import os
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class Posterize:
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"""Posterize an image and then find nearest colors to use"""
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colors = []
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colors_dict = {}
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original_colors = []
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layers = []
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pallete = None
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pallete_space = 'BGR'
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@ -23,7 +25,7 @@ class Posterize:
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white = [255, 255, 255]
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output = ''
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output = None
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def __init__ (self, image, pallete, n_colors, output) :
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self.image = cv2.imread(image)
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@ -32,6 +34,10 @@ class Posterize:
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self.n_colors = n_colors + 1
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self.output = output
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if not os.path.exists(self.output) :
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print(f'Output directory {self.output} does not exist, creating...')
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os.makedirs(self.output)
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self.flatten_pallete()
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self.posterize()
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self.determine_colors()
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@ -53,7 +59,6 @@ class Posterize:
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self.image = bgrquant
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cv2.imshow('image', bgrquant)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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@ -61,25 +66,36 @@ class Posterize:
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def determine_colors (self):
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reshaped = self.image.reshape(-1, self.image.shape[2])
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self.original_colors = np.unique(reshaped, axis=0)
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white = closest_color(self.original_colors, [255, 255, 255])
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white, white_dist = closest_color_weighted_euclidean(self.original_colors, [255, 255, 255], 'BGR')
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blank = create_colored_image(self.w, self.h, [255, 255, 255])
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composite = create_colored_image(self.w, self.h, [255, 255, 255])
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for i in range(self.n_colors) :
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if list_match(self.original_colors[i], white) :
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continue
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mask = self.extract_color_mask(self.image, self.original_colors[i])
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closest = closest_color(self.colors, self.original_colors[i])
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original = self.original_colors[i] #BGR
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mask = self.extract_color_mask(self.image, original)
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original_normalized = convert_color(original, 'BGR', self.pallete_space)
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if self.pallete_space == 'RGB' or self.pallete_space == 'BGR' :
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closest, dist = closest_color_weighted_euclidean(self.colors, original_normalized, self.pallete_space)
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else :
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closest, dist = closest_color_euclidean(self.colors, original_normalized)
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self.colors = remove_from_list(self.colors, closest)
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name = self.match_color_name(closest)
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cv2.imwrite(f'{name}.png', mask)
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color_mat = create_colored_image(self.w, self.h, closest)
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color_mask = cv2.bitwise_or(color_mat, color_mat, mask = (255-mask))
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layer_name = f'{name}.png'
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output_layer = os.path.join(self.output, layer_name)
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cv2.imwrite(output_layer, mask)
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self.layers.append({
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'layer' : output_layer,
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'color' : closest,
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'space' : self.pallete_space
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})
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#color_mat = create_colored_image(self.w, self.h, closest)
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#color_mask = cv2.bitwise_or(color_mat, color_mat, mask = (255-mask))
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#color_mask = cv2.inRange(mask, np.array([0, 0, 0]), np.array([1, 1, 1]))
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mask = cv2.bitwise_not(mask)
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composite[mask > 0] = np.array(closest)
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cv2.imshow('image', composite)
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cv2.waitKey(0)
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@ -97,3 +113,4 @@ class Posterize:
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def match_color_name (self, key) :
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return self.colors_dict[f'{key[0]},{key[1]},{key[2]}']
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@ -10,7 +10,7 @@ parser.add_argument('colors', type=int, help='Number of colors to separate into'
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parser.add_argument('pallete', type=str, help='Pallete file')
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parser.add_argument('output', type=str, help='Output dir to write to')
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class Separator :
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class Separate :
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input = ''
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output = ''
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pallete = None
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@ -33,5 +33,5 @@ class Separator :
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if __name__ == "__main__" :
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args = parser.parse_args()
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Separator(args)
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Separate(args)
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