Python图像识别（聚类）

 # -*- coding: utf-8 -*-

 """

 Created on Fri Sep 21 15:37:26 2018

 @author: zhen

 """

 from PIL import Image

 import numpy as np

 from sklearn.cluster import KMeans

 import matplotlib

 import matplotlib.pyplot as plt

 def restore_image(cb, cluster, shape):

     row, col, dummy = shape

     image = np.empty((row, col, dummy))

     for r in range(row):

         for c in range(col):

             image[r, c] = cb[cluster[r * col + c]]

     return image

 def show_scatter(a):

     N = 10

     density, edges = np.histogramdd(a, bins=[N, N, N], range=[(0, 1), (0, 1), (0, 1)])

     density /= density.max()

     x = y = z = np.arange(N)

     d = np.meshgrid(x, y, z)

     fig = plt.figure(1, facecolor='w')

     ax = fig.add_subplot(111, projection='3d')

     cm = matplotlib.colors.ListedColormap(list('rgbm'))

     ax.scatter(d[0], d[1], d[2], s=100 * density, cmap=cm, marker='o', depthshade=True)

     ax.set_xlabel(u'红')

     ax.set_ylabel(u'绿')

     ax.set_zlabel(u'蓝')

     plt.title(u'图像颜色三维频数分布', fontsize=20)

     plt.figure(2, facecolor='w')

     den = density[density > 0]

     den = np.sort(den)[::-1]

     t = np.arange(len(den))

     plt.plot(t, den, 'r-', t, den, 'go', lw=2)

     plt.title(u'图像颜色频数分布', fontsize=18)

     plt.grid(True)

     plt.show()

 if __name__ == '__main__':

     matplotlib.rcParams['font.sans-serif'] = [u'SimHei']

     matplotlib.rcParams['axes.unicode_minus'] = False

     # 聚类数2,6,30

     num_vq = 2

     im = Image.open('C:/Users/zhen/.spyder-py3/images/Lena.png')

     image = np.array(im).astype(np.float) / 255

     image = image[:, :, :3]

     image_v = image.reshape((-1, 3))

     kmeans = KMeans(n_clusters=num_vq, init='k-means++')

     show_scatter(image_v)

     N = image_v.shape[0]  # 图像像素总数

     # 选择样本，计算聚类中心

     idx = np.random.randint(0, N, size=int(N * 0.7))

     image_sample = image_v[idx]

     kmeans.fit(image_sample)

     result = kmeans.predict(image_v)  # 聚类结果

     print('聚类结果:\n', result)

     print('聚类中心:\n', kmeans.cluster_centers_)

     plt.figure(figsize=(15, 8), facecolor='w')

     plt.subplot(211)

     plt.axis('off')

     plt.title(u'原始图片', fontsize=18)

     plt.imshow(image)

     # plt.savefig('原始图片.png')

     plt.subplot(212)

     vq_image = restore_image(kmeans.cluster_centers_, result, image.shape)

     plt.axis('off')

     plt.title(u'聚类个数:%d' % num_vq, fontsize=20)

     plt.imshow(vq_image)

     # plt.savefig('矢量化图片.png')

     plt.tight_layout(1.2)

     plt.show()