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marker_separation
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mmcwilliams 2023-10-24 16:45:22 -04:00
parent f0de99a148
commit d579e83762
2 changed files with 13 additions and 97 deletions
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63
py/comparison_comparison.py
View File

@ -1,6 +1,7 @@
import cv2 import cv2
import numpy as np import numpy as np
from pallete_schema import PalleteSchema from pallete_schema import PalleteSchema
from common import convert_color, closest_color, create_colored_image
class ComparisonComparison: class ComparisonComparison:
def __init__ (self) : def __init__ (self) :
@ -8,64 +9,24 @@ class ComparisonComparison:
green = [5, 250, 5] green = [5, 250, 5]
blue = [240, 0, 20] blue = [240, 0, 20]
comp_colors = [red, green, blue] comp_colors = [red, green, blue]
pallete = PalleteSchema('./palletes/test_pallete.json') pallete = PalleteSchema('./palletes/printed_pallete.json')
colors = self.get_colors(pallete.colors) colors = self.get_colors(pallete.colors)
for cc in comp_colors : for cc in comp_colors :
ccbgr = self.convert_color(cc, 'RGB', 'BGR') ccbgr = convert_color(cc, 'RGB', 'HSV')
closest = self.closest(colors, ccbgr) closest = closest_color(colors, ccbgr)
print(f'{closest} for {ccbgr}') ccbgr = convert_color(cc, 'RGB', 'BGR')
#print(f'{convert_color(closest,"BGR","RGB")} for {convert_color(ccbgr,"BGR","RGB")}')
original = create_colored_image(100, 100, convert_color(closest, 'HSV', 'BGR'))
chosen = create_colored_image(100, 100, ccbgr)
combined = np.hstack([original, chosen])
cv2.imshow("image", combined)
cv2.waitKey(0)
def get_colors (self, pallete) : def get_colors (self, pallete) :
colors = [] colors = []
for color in pallete : for color in pallete :
colors.append(color['color']) colors.append(convert_color(color['color'], 'BGR', 'HSV'))
return colors return colors
def convert_color (self, 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' :
b = cv2.COLOR_BGR2RGB
elif color_space_a == 'RGB' and color_space_b == 'LAB' :
b = cv2.COLOR_RGB2LAB
elif color_space_a == 'LAB' and color_space_b == 'RGB' :
b = cv2.COLOR_LAB2RGB
elif color_space_a == 'BGR' and color_space_b == 'LAB' :
b = cv2.COLOR_BGR2LAB
elif color_space_a == 'LAB' and color_space_b == 'BGR' :
b = cv2.COLOR_LAB2BGR
elif color_space_a == 'HSV' and color_space_b == 'LAB' :
b = cv2.COLOR_HSV2LAB
elif color_space_a == 'LAB' and color_space_b == 'HSV' :
b = cv2.COLOR_LAB2HSV
elif color_space_a == 'RGB' and color_space_b == 'HSV' :
b = cv2.COLOR_RGB2HSV
elif color_space_a == 'HSV' and color_space_b == 'RGB' :
b = cv2.COLOR_HSV2RGB
elif color_space_a == 'BGR' and color_space_b == 'HSV' :
b = cv2.COLOR_BGRHSV
elif color_space_a == 'HSV' and color_space_b == 'BGR' :
b = cv2.COLOR_HSV2BGR
cvt = cv2.cvtColor(pixel, b)
return cvt[0, 0]
def closest(self, colors, color):
colors = np.array(colors)
color = np.array(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
if __name__ == "__main__": if __name__ == "__main__":
ComparisonComparison() ComparisonComparison()

47
py/posterize.py
View File

@ -2,6 +2,7 @@ from sklearn.cluster import MiniBatchKMeans
import numpy as np import numpy as np
import argparse import argparse
import cv2 import cv2
from common import convert_color, closest_color
class Posterize: class Posterize:
"""Posterize an image and then find nearest colors to use""" """Posterize an image and then find nearest colors to use"""
@ -56,49 +57,3 @@ class Posterize:
def extract_color_mask (self, image, color): def extract_color_mask (self, image, color):
mask = cv2.inRange(image, color, color) mask = cv2.inRange(image, color, color)
return cv2.bitwise_not(mask) return cv2.bitwise_not(mask)
def convert_color (self, 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' :
b = cv2.COLOR_BGR2RGB
elif color_space_a == 'RGB' and color_space_b == 'LAB' :
b = cv2.COLOR_RGB2LAB
elif color_space_a == 'LAB' and color_space_b == 'RGB' :
b = cv2.COLOR_LAB2RGB
elif color_space_a == 'BGR' and color_space_b == 'LAB' :
b = cv2.COLOR_BGR2LAB
elif color_space_a == 'LAB' and color_space_b == 'BGR' :
b = cv2.COLOR_LAB2BGR
elif color_space_a == 'HSV' and color_space_b == 'LAB' :
b = cv2.COLOR_HSV2LAB
elif color_space_a == 'LAB' and color_space_b == 'HSV' :
b = cv2.COLOR_LAB2HSV
elif color_space_a == 'RGB' and color_space_b == 'HSV' :
b = cv2.COLOR_RGB2HSV
elif color_space_a == 'HSV' and color_space_b == 'RGB' :
b = cv2.COLOR_HSV2RGB
elif color_space_a == 'BGR' and color_space_b == 'HSV' :
b = cv2.COLOR_BGRHSV
elif color_space_a == 'HSV' and color_space_b == 'BGR' :
b = cv2.COLOR_HSV2BGR
cvt = cv2.cvtColor(pixel, b)
return cvt[0, 0]
def closest(self, colors, color):
colors = np.array(colors)
color = np.array(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