marker_separation/py/posterize.py

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2023-10-21 12:26:58 +00:00
from sklearn.cluster import MiniBatchKMeans
import numpy as np
import argparse
import cv2
from common import convert_color, closest_color_weighted_euclidean, closest_color_euclidean, create_colored_image, remove_from_list, list_match, to_luma, convertScale
import os
import subprocess
from functools import cmp_to_key
def sort_pallete (a, b):
A = to_luma(a, 'BGR')
B = to_luma(b, 'BGR')
if A > B :
return 1
if B < A :
return -1
return 0
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class Posterize:
"""Posterize an image and then find nearest colors to use"""
colors = []
colors_dict = {}
original_colors = []
layers = []
previews = []
svgs = []
headless = False
pallete = None
pallete_space = 'BGR'
comparison_space = 'BGR'
image = None
h = 0
w = 0
n_colors = 3
max_particles = 17000
conf = os.path.abspath('./conf/base.conf')
stipple_gen = os.path.abspath('../../stipple_gen')
white = [255, 255, 255]
output = None
def __init__ (self, image, pallete, n_colors, output, headless) :
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.headless = headless
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()
self.determine_colors()
self.stipple()
self.preview()
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]
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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
self.show(bgrquant)
def determine_colors (self):
reshaped = self.image.reshape(-1, self.image.shape[2])
self.original_colors = np.unique(reshaped, axis=0)
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])
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composite = create_colored_image(self.w, self.h, [255, 255, 255])
mask = self.extract_color_mask(self.image, white)
layer_name = f'WHITE.png'
output_layer = os.path.join(self.output, layer_name)
cv2.imwrite(output_layer, mask)
self.layers.append({
'layer' : output_layer,
'color' : white,
'space' : self.pallete_space
})
for i in range(self.n_colors) :
if list_match(self.original_colors[i], white) :
continue
original = self.original_colors[i]
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)
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
})
mask = cv2.bitwise_not(mask)
composite[mask > 0] = np.array(convert_color(closest, self.pallete_space, 'BGR'))
composite_name = f'posterized.png'
composite_path = os.path.join(self.output, composite_name)
cv2.imwrite(composite_path, composite)
self.show(composite)
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']
#self.colors = sorted(self.colors, key=cmp_to_key(sort_pallete))
#for color in self.colors :
# print(to_luma(color, self.pallete_space))
#quit()
def match_color_name (self, key) :
return self.colors_dict[f'{key[0]},{key[1]},{key[2]}']
def stipple (self) :
sanity_check = 0
for layer in self.layers :
if 'WHITE.png' in layer['layer'] :
continue
l = cv2.imread(layer['layer'], 0)
(h, w) = l.shape[:2]
total = h * w
black = total - cv2.countNonZero(l)
ratio = black/total
max_particles = round(ratio * self.max_particles)
input_image = os.path.abspath(layer['layer'])
dir_name, file_name = os.path.split(input_image)
file_part, ext = os.path.splitext(file_name)
output_image = os.path.join(dir_name, f'{file_part}_preview.png')
output_svg = os.path.join(dir_name, f'{file_part}.svg')
cmd = [
'bash',
'stipple_gen.sh',
'--inputImage', input_image,
'--outputImage', output_image,
'--outputSVG', output_svg,
'--config', self.conf,
'--maxParticles', str(max_particles)
]
print(cmd)
subprocess.call(cmd, cwd = self.stipple_gen)
self.svgs.append(output_svg)
self.previews.append({
'layer' : output_image,
'color' : layer['color']
})
def preview (self) :
composite = create_colored_image(self.w, self.h, [255, 255, 255])
for layer in self.previews :
l = cv2.imread(layer['layer'], 0)
mask = cv2.bitwise_not(l)
composite[mask > 0] = np.array(convert_color(layer['color'], self.pallete_space, 'BGR'))
composite_name = f'preview.png'
composite_path = os.path.join(self.output, composite_name)
cv2.imwrite(composite_path, composite)
self.show(composite)
for svg in self.svgs :
cmd = [ 'svgopt', svg, svg]
print(cmd)
subprocess.call(cmd)
def show (self, mat) :
if not self.headless :
cv2.imshow('image', mat)
cv2.waitKey(0)
cv2.destroyAllWindows()