import time
import numpy as np
import matplotlib.pyplot as plt
import logging
from scipy.ndimage import gaussian_filter
from skimage.data import coins
from skimage.transform import rescale
from sklearn.cluster import spectral_clustering
from sklearn.feature_extraction import image

# Настройка логирования
logging.basicConfig(level=logging.INFO, 
                    format='%(asctime)s [%(levelname)s] %(message)s')

def main():
    logging.info("Загрузка и обработка изображения...")
    
    # Загрузка изображения
    orig_coins = coins()
    smoothed = gaussian_filter(orig_coins, sigma=2)
    rescaled = rescale(smoothed, 0.2, mode="reflect", anti_aliasing=False)
    
    logging.info(f"Изображение: {rescaled.shape}")
    
    # Построение графа
    logging.info("Построение графа...")
    graph = image.img_to_graph(rescaled)
    beta = 10
    eps = 1e-6
    graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
    
    # Кластеризация
    n_regions = 26
    methods = ('kmeans', 'discretize', 'cluster_qr')
    
    for method in methods:
        logging.info(f"Метод: {method}")
        t0 = time.time()
        
        labels = spectral_clustering(
            graph,
            n_clusters=n_regions + 3,
            eigen_tol=1e-7,
            assign_labels=method,
            random_state=42
        )
        
        duration = time.time() - t0
        labels = labels.reshape(rescaled.shape)
        
        logging.info(f"Завершено за {duration:.2f} сек.")
        
        # Визуализация
        plt.figure(figsize=(6, 6))
        plt.imshow(rescaled, cmap=plt.cm.gray)
        plt.title(f"{method}, {duration:.2f} сек.")
        plt.axis('off')
        
        for l in range(n_regions):
            color = plt.cm.nipy_spectral((l + 4) / float(n_regions + 4))
            plt.contour(labels == l, colors=[color], linewidths=0.5)
        
        plt.tight_layout()
        plt.show()

if __name__ == '__main__':
    main()