1 # -*- coding: utf-8 -*- 2 """ 3 Created on Fri Sep 21 15:37:26 2018 4 5 @author: zhen 6 """ 7 from PIL import Image 8 import numpy as np 9 from sklearn.cluster import KMeans 10 import matplotlib 11 import matplotlib.pyplot as plt 12 13 def restore_image(cb, cluster, shape): 14 row, col, dummy = shape 15 image = np.empty((row, col, dummy)) 16 for r in range(row): 17 for c in range(col): 18 image[r, c] = cb[cluster[r * col + c]] 19 return image 20 21 def show_scatter(a): 22 N = 10 23 density, edges = np.histogramdd(a, bins=[N, N, N], range=[(0, 1), (0, 1), (0, 1)]) 24 density /= density.max() 25 x = y = z = np.arange(N) 26 d = np.meshgrid(x, y, z) 27 28 fig = plt.figure(1, facecolor=\'w\') 29 ax = fig.add_subplot(111, projection=\'3d\') 30 31 cm = matplotlib.colors.ListedColormap(list(\'rgbm\')) 32 ax.scatter(d[0], d[1], d[2], s=100 * density, cmap=cm, marker=\'o\', depthshade=True) 33 ax.set_xlabel(u\'红\') 34 ax.set_ylabel(u\'绿\') 35 ax.set_zlabel(u\'蓝\') 36 plt.title(u\'图像颜色三维频数分布\', fontsize=20) 37 38 plt.figure(2, facecolor=\'w\') 39 den = density[density > 0] 40 den = np.sort(den)[::-1] 41 t = np.arange(len(den)) 42 plt.plot(t, den, \'r-\', t, den, \'go\', lw=2) 43 plt.title(u\'图像颜色频数分布\', fontsize=18) 44 plt.grid(True) 45 46 plt.show() 47 48 if __name__ == \'__main__\': 49 matplotlib.rcParams[\'font.sans-serif\'] = [u\'SimHei\'] 50 matplotlib.rcParams[\'axes.unicode_minus\'] = False 51 # 聚类数2,6,30 52 num_vq = 2 53 im = Image.open(\'C:/Users/zhen/.spyder-py3/images/Lena.png\') 54 image = np.array(im).astype(np.float) / 255 55 image = image[:, :, :3] 56 image_v = image.reshape((-1, 3)) 57 kmeans = KMeans(n_clusters=num_vq, init=\'k-means++\') 58 show_scatter(image_v) 59 60 N = image_v.shape[0] # 图像像素总数 61 # 选择样本，计算聚类中心 62 idx = np.random.randint(0, N, size=int(N * 0.7)) 63 image_sample = image_v[idx] 64 kmeans.fit(image_sample) 65 result = kmeans.predict(image_v) # 聚类结果 66 print(\'聚类结果:\n\', result) 67 print(\'聚类中心:\n\', kmeans.cluster_centers_) 68 69 plt.figure(figsize=(15, 8), facecolor=\'w\') 70 plt.subplot(211) 71 plt.axis(\'off\') 72 plt.title(u\'原始图片\', fontsize=18) 73 plt.imshow(image) 74 # plt.savefig(\'原始图片.png\') 75 76 plt.subplot(212) 77 vq_image = restore_image(kmeans.cluster_centers_, result, image.shape) 78 plt.axis(\'off\') 79 plt.title(u\'聚类个数:%d\' % num_vq, fontsize=20) 80 plt.imshow(vq_image) 81 # plt.savefig(\'矢量化图片.png\') 82 83 plt.tight_layout(1.2) 84 plt.show()
结果：
1.当k=2时：
2.当k=6时：
3.当k=30时：
总结：当聚类个数较少时，算法运算速度快但效果较差，当聚类个数较多时，运算速度慢效果好但容易过拟合，所以恰当的k值对于聚类来说影响极其明显！！