python实现LBP方法提取图像纹理特征实现分类的步骤

 def loadPicture(): train_index = 0; test_index = 0; train_data = np.zeros( (200,171,171) ); test_data = np.zeros( (160,171,171) ); train_label = np.zeros( (200) ); test_label = np.zeros( (160) ); for i in np.arange(40): image = mpimg.imread('picture/'+str(i)+'.tiff'); data = np.zeros( (513,513) ); data[0:image.shape[0],0:image.shape[1]] = image; #切割后的图像位于数据的位置 index = 0; #将图片分割成九块 for row in np.arange(3): for col in np.arange(3): if index<5: train_data[train_index,:,:]=data[171*row:171*(row+1),171*col:171*(col+1)]; train_label[train_index]=i; train_index+=1; else: test_data[test_index,:,:]=data[171*row:171*(row+1),171*col:171*(col+1)]; test_label[test_index]=i; test_index+=1; index+=1; return train_data,test_data,train_label,test_label;<pre></div><p><strong>特征提取</strong></p><p>LBP特征提取方法</p><div class="gaodaimacode"><pre class="prettyprint linenums"> radius = 1; n_point = radius * 8; def texture_detect(): train_hist = np.zeros( (200,256) ); test_hist = np.zeros( (160,256) ); for i in np.arange(200): #使用LBP方法提取图像的纹理特征. lbp=skft.local_binary_pattern(train_data[i],n_point,radius,'default'); #统计图像的直方图 max_bins = int(lbp.max() + 1); #hist size:256 train_hist[i], _ = np.histogram(lbp, normed=True, bins=max_bins, range=(0, max_bins)); for i in np.arange(160): lbp = skft.local_binary_pattern(test_data[i],n_point,radius,'default'); #统计图像的直方图 max_bins = int(lbp.max() + 1); #hist size:256 test_hist[i], _ = np.histogram(lbp, normed=True, bins=max_bins, range=(0, max_bins)); return train_hist,test_hist;

 import matplotlib.image as mpimg import matplotlib.pyplot as plt import numpy as np from sklearn.multiclass import OneVsRestClassifier from sklearn.svm import SVR from skimage import feature as skft train_data,test_data,train_label,test_label= loadPicture(); train_hist,test_hist = texture_detect(); svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1); OneVsRestClassifier(svr_rbf,-1).fit(train_hist, train_label).score(test_hist,test_label)