67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
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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class Posterize:
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"""Posterize an image and then find nearest colors to use"""
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colors = []
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original_colors = []
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image = None
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pallete = None
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h = 0
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w = 0
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n_colors = 3
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white = [255, 255, 255]
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def __init__ (self, image, pallete, n_colors) :
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self.image = cv2.imread(image)
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(self.h, self.w) = self.image.shape[:2]
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self.pallete = pallete
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self.n_colors = n_colors + 1
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self.posterize()
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self.determine_colors()
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def posterize (self):
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lab = cv2.cvtColor(self.image, cv2.COLOR_BGR2LAB)
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feature = lab.reshape((self.h * self.w, 3))
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clusters = MiniBatchKMeans(n_clusters = self.n_colors, n_init = 'auto')
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labels = clusters.fit_predict(feature)
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quant = clusters.cluster_centers_.astype('uint8')[labels]
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rquant = quant.reshape((self.h, self.w, 3))
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rfeature = feature.reshape((self.h, self.w, 3))
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bgrquant = cv2.cvtColor(rquant, cv2.COLOR_LAB2BGR)
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#bgrfeature = cv2.cvtColor(rfeature, cv2.COLOR_LAB2BGR)
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self.image = bgrquant
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cv2.imshow("image", bgrquant)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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def determine_colors (self):
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reshaped = self.image.reshape(-1, self.image.shape[2])
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self.original_colors = np.unique(reshaped, axis=0)
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#print(self.original_colors)
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for i in range(self.n_colors) :
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mask = self.extract_color(self.image, self.original_colors[i])
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cv2.imwrite(f'{i}.png', mask)
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def extract_color (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 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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