Perform closest color match and composite with selected colors

This commit is contained in:
mmcwilliams 2023-11-25 09:40:13 -05:00
parent b317914922
commit 2976682319
4 changed files with 62 additions and 25 deletions

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

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

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@ -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])
closest = closest_color(self.colors, self.original_colors[i])
original = self.original_colors[i] #BGR
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)
cv2.imwrite(f'{name}.png', mask)
color_mat = create_colored_image(self.w, self.h, closest)
color_mask = cv2.bitwise_or(color_mat, color_mat, mask = (255-mask))
layer_name = f'{name}.png'
output_layer = os.path.join(self.output, layer_name)
cv2.imwrite(output_layer, mask)
self.layers.append({
'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]}']

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@ -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)