67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
import time
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import numpy as np
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import matplotlib.pyplot as plt
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import logging
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from scipy.ndimage import gaussian_filter
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from skimage.data import coins
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from skimage.transform import rescale
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from sklearn.cluster import spectral_clustering
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from sklearn.feature_extraction import image
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# Настройка логирования
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logging.basicConfig(level=logging.INFO,
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format='%(asctime)s [%(levelname)s] %(message)s')
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def main():
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logging.info("Загрузка и обработка изображения...")
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# Загрузка изображения
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orig_coins = coins()
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smoothed = gaussian_filter(orig_coins, sigma=2)
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rescaled = rescale(smoothed, 0.2, mode="reflect", anti_aliasing=False)
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logging.info(f"Изображение: {rescaled.shape}")
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# Построение графа
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logging.info("Построение графа...")
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graph = image.img_to_graph(rescaled)
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beta = 10
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eps = 1e-6
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graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
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# Кластеризация
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n_regions = 26
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methods = ('kmeans', 'discretize', 'cluster_qr')
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for method in methods:
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logging.info(f"Метод: {method}")
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t0 = time.time()
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labels = spectral_clustering(
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graph,
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n_clusters=n_regions + 3,
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eigen_tol=1e-7,
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assign_labels=method,
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random_state=42
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)
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duration = time.time() - t0
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labels = labels.reshape(rescaled.shape)
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logging.info(f"Завершено за {duration:.2f} сек.")
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# Визуализация
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plt.figure(figsize=(6, 6))
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plt.imshow(rescaled, cmap=plt.cm.gray)
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plt.title(f"{method}, {duration:.2f} сек.")
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plt.axis('off')
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for l in range(n_regions):
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color = plt.cm.nipy_spectral((l + 4) / float(n_regions + 4))
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plt.contour(labels == l, colors=[color], linewidths=0.5)
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plt.tight_layout()
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plt.show()
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if __name__ == '__main__':
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main() |