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78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
import numpy as np
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from PIL import Image
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"""
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Otsu thresholding algorithm for image processing
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https://en.wikipedia.org/wiki/Otsu%27s_method
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"""
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def otsu_threshold(image: Image.Image) -> Image.Image:
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"""
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Applies Otsu's thresholding method to a grayscale image.
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Parameters:
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image (PIL.Image.Image): A grayscale PIL image object.
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Returns:
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PIL.Image.Image: A binary image after applying Otsu's thresholding.
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Example:
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>>> from PIL import Image
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>>> import numpy as np
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>>> image_array = np.array(
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... [[0, 0, 0, 0], [255, 255, 255, 255], [0, 0, 0, 0], [255, 255, 255, 255]],
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... dtype=np.uint8
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... )
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>>> image = Image.fromarray(image_array)
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>>> binary_image = otsu_threshold(image)
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>>> np.array(binary_image)
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array([[ 0, 0, 0, 0],
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[255, 255, 255, 255],
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[ 0, 0, 0, 0],
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[255, 255, 255, 255]], dtype=uint8)
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"""
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# Convert the image to numpy array
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pixel_array = np.array(image)
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# Compute histogram
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hist, _ = np.histogram(pixel_array, bins=256, range=(0, 256))
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# Compute between class variance
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total_pixels = pixel_array.size
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current_max, threshold = 0.0, 0 # Ensure current_max is a float
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sum_total, sum_foreground = 0.0, 0.0 # Ensure these are floats
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weight_background, weight_foreground = 0.0, 0.0 # Ensure these are floats
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for i in range(256):
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sum_total += i * hist[i]
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for i in range(256):
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weight_background += hist[i]
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if weight_background == 0:
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continue
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weight_foreground = total_pixels - weight_background
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if weight_foreground == 0:
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break
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sum_foreground += i * hist[i]
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mean_background = sum_foreground / weight_background
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mean_foreground = (sum_total - sum_foreground) / weight_foreground
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between_class_variance = (
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weight_background
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* weight_foreground
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* (mean_background - mean_foreground) ** 2
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)
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if between_class_variance > current_max:
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current_max = between_class_variance
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threshold = i
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# Apply threshold to the image
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binary_image = pixel_array > threshold
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binary_image = binary_image.astype(np.uint8) * 255
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# Convert numpy array back to PIL image
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return Image.fromarray(binary_image)
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