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3 Commits
e22bffbe25
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1d23aac7d1
Author | SHA1 | Date |
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mmcwilliams | 1d23aac7d1 | |
mmcwilliams | d579e83762 | |
mmcwilliams | f0de99a148 |
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@ -0,0 +1,53 @@
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import cv2
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import numpy as np
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def convert_color (color, color_space_a, color_space_b) :
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pixel = np.zeros([1, 1, 3], dtype=np.uint8)
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if color_space_a == 'RGB' :
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pixel = cv2.cvtColor(pixel, cv2.COLOR_BGR2RGB)
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elif color_space_a == 'LAB' :
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pixel = cv2.cvtColor(pixel, cv2.COLOR_BGR2LAB)
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elif color_space_a == 'HSV' :
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pixel = cv2.cvtColor(pixel, cv2.COLOR_BGR2HSV)
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#default is BGR
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pixel[:] = color
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if color_space_a == 'RGB' and color_space_b == 'BGR' :
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b = cv2.COLOR_RGB2BGR
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elif color_space_a == 'BGR' and color_space_b == 'RGB' :
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b = cv2.COLOR_BGR2RGB
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elif color_space_a == 'RGB' and color_space_b == 'LAB' :
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b = cv2.COLOR_RGB2LAB
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elif color_space_a == 'LAB' and color_space_b == 'RGB' :
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b = cv2.COLOR_LAB2RGB
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elif color_space_a == 'BGR' and color_space_b == 'LAB' :
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b = cv2.COLOR_BGR2LAB
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elif color_space_a == 'LAB' and color_space_b == 'BGR' :
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b = cv2.COLOR_LAB2BGR
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elif color_space_a == 'HSV' and color_space_b == 'LAB' :
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b = cv2.COLOR_HSV2LAB
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elif color_space_a == 'LAB' and color_space_b == 'HSV' :
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b = cv2.COLOR_LAB2HSV
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elif color_space_a == 'RGB' and color_space_b == 'HSV' :
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b = cv2.COLOR_RGB2HSV
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elif color_space_a == 'HSV' and color_space_b == 'RGB' :
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b = cv2.COLOR_HSV2RGB
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elif color_space_a == 'BGR' and color_space_b == 'HSV' :
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b = cv2.COLOR_BGR2HSV
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elif color_space_a == 'HSV' and color_space_b == 'BGR' :
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b = cv2.COLOR_HSV2BGR
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cvt = cv2.cvtColor(pixel, b)
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return cvt[0, 0]
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def closest_color (colors, color):
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colors = np.array(colors)
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color = np.array(color)
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distances = np.sqrt(np.sum((colors - color) ** 2, axis=1))
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index_of_smallest = np.where(distances == np.amin(distances))
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smallest_distance = colors[index_of_smallest]
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return smallest_distance
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def create_colored_image (width, height, bgr_color):
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image = np.zeros((height, width, 3), np.uint8)
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image[:] = bgr_color
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return image
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@ -1,22 +1,32 @@
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import cv2
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import numpy as np
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from pallete_schema import PalleteSchema
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from common import convert_color, closest_color, create_colored_image
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class ComparisonComparison:
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def __init__ (self) :
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c = [0, 0, 255]
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print(c)
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cc = self.convert_color(c, cv2.COLOR_BGR2RGB, cv2.COLOR_RGB2LAB)
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print(cc)
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print(self.convert_color(cc, cv2.COLOR_BGR2LAB, cv2.COLOR_LAB2RGB))
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red = [0, 10, 200]
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green = [5, 250, 5]
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blue = [240, 0, 20]
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comp_colors = [red, green, blue]
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pallete = PalleteSchema('./palletes/printed_pallete.json')
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colors = self.get_colors(pallete.colors)
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for cc in comp_colors :
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ccbgr = convert_color(cc, 'RGB', 'HSV')
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closest = closest_color(colors, ccbgr)
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ccbgr = convert_color(cc, 'RGB', 'BGR')
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#print(f'{convert_color(closest,"BGR","RGB")} for {convert_color(ccbgr,"BGR","RGB")}')
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original = create_colored_image(100, 100, convert_color(closest, 'HSV', 'BGR'))
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chosen = create_colored_image(100, 100, ccbgr)
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combined = np.hstack([original, chosen])
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cv2.imshow("image", combined)
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cv2.waitKey(0)
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def convert_color (self, color, color_space_a, color_space_b) :
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pixel = np.zeros([1, 1, 3], dtype=np.uint8)
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pixel = cv2.cvtColor(pixel, color_space_a)
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pixel[:] = color
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cvt = cv2.cvtColor(pixel, color_space_b)
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return cvt[0, 0]
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def get_colors (self, pallete) :
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colors = []
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for color in pallete :
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colors.append(convert_color(color['color'], 'BGR', 'HSV'))
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return colors
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if __name__ == "__main__":
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ComparisonComparison()
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@ -21,8 +21,10 @@ class PalleteSchema :
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def __init__ (self, file = None, obj = None):
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if file is not None and obj is None:
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self.parse_file(file)
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else :
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elif file is not None and obj is not None :
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self.write(file, obj)
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else :
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print('Not sure what you\'re trying to do here')
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def parse_file (self, file) :
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with open(file) as f :
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@ -2,6 +2,7 @@ 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
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class Posterize:
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"""Posterize an image and then find nearest colors to use"""
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@ -56,18 +57,3 @@ class Posterize:
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def extract_color_mask (self, image, color):
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mask = cv2.inRange(image, color, color)
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return cv2.bitwise_not(mask)
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def convert_color (self, color, color_space_a, color_space_b) :
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pixel = np.zeros([1, 1, 3], dtype=np.uint8)
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pixel = cv2.cvtColor(pixel, color_space_a)
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pixel[:] = color
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cvt = cv2.cvtColor(pixel, color_space_b)
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return cvt[0, 0]
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def closest(self, colors, color):
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colors = np.array(colors)
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color = np.array(color)
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distances = np.sqrt(np.sum((colors - color) ** 2, axis=1))
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index_of_smallest = np.where(distances == np.amin(distances))
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smallest_distance = colors[index_of_smallest]
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return smallest_distance
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