使用K-means和高斯混合模型对图像进行聚类

导入图片

%matplotlib inline
import numpy as np  
import skimage.io as SKimg  
import matplotlib.pyplot as plt  
from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture
from skimage import io,data,color
from scipy.ndimage import gaussian_filter
from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
import time
import matplotlib.cm as cm

imgpath=r"H:2Coursepics35049.jpg"
img =SKimg.imread(imgpath)
io.imshow(img)
print(type(img))  #显示类型
print(img.shape)  #显示尺寸
print('图片高度:',img.shape[0])  #图片高度
print('图片宽度:',img.shape[1])  #图片宽度
print('图片通道数:',img.shape[2])  #图片通道数
print('总像素个数:',img.size)   #显示总像素个数
print('最大像素值:',img.max())  #最大像素值
print('最小像素值:',img.min())  #最小像素值
print('像素平均值:',img.mean()) #像素平均值
print('图像的像素值:',img[0][0])#图像的像素值

image_height, image_width, image_channels = img.shape
image_pixels = np.reshape(img, (-1, image_channels))#将三维矩阵转为通道特征矩阵  每列代表R G B值
_mean = np.mean(image_pixels,axis=0,keepdims=True)
_std = np.std(image_pixels,axis=0,keepdims=True)
image_pixels_ = (image_pixels - _mean) / _std # 归一化
X=image_pixels_ #特征矩阵

#绘制每个通道的直方图
Kwars=dict(histtype='stepfilled',alpha=0.4,bins=np.arange(0,255))#,normed=True
intervals=plt.hist(image_pixels,color=[ "red","green","blue"],**Kwars)

#聚类个数从3递增至8
for ncomp in range(3,9):
    fig =plt.figure(figsize=(12,10))
    plt.axis('off')
    t0 = time.time()

    gmm = GaussianMixture(n_components=ncomp,covariance_type='full').fit(X)#调用GMM算法，设置聚类的目标类别数
    Clus_Centers=gmm.means_#每个类别的聚类中心 
    Labels=gmm.predict(X)

    elapsed_time = time.time() - t0
    Labels_show=np.reshape(Labels,(image_height, image_width))#把分类标签展开为二维数组
    dst=color.label2rgb(Labels_show)#将标签转为RGB
    plt.title('n_cluster=%i,(time%.2fs)'%(ncomp,elapsed_time), size=25)
    plt.imshow(dst)
    fig.savefig('pics/gmm_cluster_%i.jpg'%(ncomp), format='jpg', dpi=300,bbox_inches = 'tight',pad_inches = 0)#保存聚类结果图
    
    Labels_arr=np.reshape(Labels_show, (-1, 1))
    combine=np.append(image_pixels, Labels_arr, 1)
    fig = plt.figure(figsize=(15, 10))
    ax = plt.axes(projection='3d')
    xs = combine[:,0]
    ys = combine[:,1]
    zs = combine[:,2]
    ax.scatter(xs, ys, zs,c=combine[:,3],edgecolors='none',s=1)
    ax.set_xlabel('R channel')
    ax.set_ylabel('G channel')
    ax.set_zlabel('B channel')
    plt.show()
    fig.savefig('pics/gmm_cluster3d_%i.jpg'%(ncomp), format='jpg', dpi=300,bbox_inches = 'tight',pad_inches = 0)#保存RGB通道三维特征空间图
    
    fig = plt.figure(figsize=(6,5))
    plt.xlabel('R channel')
    plt.ylabel('G channel')
    plt.axis('equal')
    plt.xlim(0, 255)
    plt.ylim(0, 255)
    plt.scatter(combine[:, 0], combine[:, 1], c=combine[:,3],s=.2)
    plt.colorbar()
    plt.show()
    fig.savefig('pics2/gmm_clusterR&G_%i.jpg'%(ncomp), format='jpg', dpi=300,bbox_inches = 'tight',pad_inches = 0)#保存RG通道二维特征空间图
    
    fig = plt.figure(figsize=(6,5))
    plt.xlabel('R channel')
    plt.ylabel('B channel')
    plt.axis('equal')
    plt.xlim(0, 255)
    plt.ylim(0, 255)
    plt.scatter(combine[:, 0], combine[:, 2], c=combine[:,3],s=.2)
    plt.colorbar()
    plt.show()
    fig.savefig('pics/gmm_clusterR&B_%i.jpg'%(ncomp), format='jpg', dpi=300,bbox_inches = 'tight',pad_inches = 0)#保存RB通道二维特征空间图
    
    fig = plt.figure(figsize=(6,5))
    plt.xlabel('G channel')
    plt.ylabel('B channel')
    plt.axis('equal')
    plt.xlim(0, 255)
    plt.ylim(0, 255)
    plt.scatter(combine[:, 1], combine[:, 2], c=combine[:,3],s=.2)
    plt.colorbar()
    plt.show()
    fig.savefig('pics/gmm_clusterG&B_%i.jpg'%(ncomp), format='jpg', dpi=300,bbox_inches = 'tight',pad_inches = 0)#保存GB通道二维特征空间图

聚类算法换位K-means，只需将聚类部分代码换为：

kmeans = KMeans(n_clusters=ncomp,random_state=0)
Labels = kmeans.fit_predict(X)

　　

#高斯滤波 可平滑噪声 #替换即可
img =SKimg.imread(imgpath)
img[:,:,0] = gaussian_filter(img[:,:,0], sigma=1)
img[:,:,1] = gaussian_filter(img[:,:,1], sigma=1)
img[:,:,2] = gaussian_filter(img[:,:,2], sigma=1)
plt.axis('off')
plt.imshow(img)
plt.savefig('pics/gausfilter.jpg', format='jpg', dpi=300,bbox_inches = 'tight',pad_inches = 0)

　　

　　

　　

参考：

https://jakevdp.github.io/PythonDataScienceHandbook/05.12-gaussian-mixtures.html

https://scikit-learn.org/0.15/modules/generated/sklearn.mixture.GMM.html