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import numpy as np
import cv2
c64_palette = np.array([
[0, 0, 0],
[255, 255, 255],
[0x81, 0x33, 0x38],
[0x75, 0xce, 0xc8],
[0x8e, 0x3c, 0x97],
[0x56, 0xac, 0x4d],
[0x2e, 0x2c, 0x9b],
[0xed, 0xf1, 0x71],
[0x8e, 0x50, 0x29],
[0x55, 0x38, 0x00],
[0xc4, 0x6c, 0x71],
[0x4a, 0x4a, 0x4a],
[0x7b, 0x7b, 0x7b],
[0xa9, 0xff, 0x9f],
[0x70, 0x6d, 0xeb],
[0xb2, 0xb2, 0xb2]
])
def fast_quantize_to_palette(image):
# Simply round the color values to the nearest color in the palette
palette = c64_palette / 255.0 # Normalize palette
img_normalized = image / 255.0 # Normalize image
# Calculate the index in the palette that is closest to each pixel in the image
indices = np.sqrt(((img_normalized[:, :, None, :] - palette[None, None, :, :]) ** 2).sum(axis=3)).argmin(axis=2)
# Map the image to the palette colors
mapped_image = palette[indices]
return (mapped_image * 255).astype(np.uint8) # Denormalize and return the image
'''
knn = None
def quantize_to_palette(image, palette):
global knn
NumColors = 16
quantized_image = None
cv2.pyrMeanShiftFiltering(image, NumColors / 4, NumColors / 2, quantized_image, 1, cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 5, 1)
palette = c64_palette
X_query = image.reshape(-1, 3).astype(np.float32)
if(knn == None):
X_index = palette.astype(np.float32)
knn = cv2.ml.KNearest_create()
knn.train(X_index, cv2.ml.ROW_SAMPLE, np.arange(len(palette)))
ret, results, neighbours, dist = knn.findNearest(X_query, 1)
quantized_image = np.array([palette[idx] for idx in neighbours.astype(int)])
quantized_image = quantized_image.reshape(image.shape)
return quantized_image.astype(np.uint8)
'''
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