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5 changed files with 79 additions and 13 deletions

1
py/.gitignore vendored
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@ -1,2 +1,3 @@
env
__pycache__
*.png

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@ -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
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

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@ -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)

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@ -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]}']

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@ -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__" :