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()