python實(shí)現(xiàn)LBP方法提取圖像紋理特征實(shí)現(xiàn)分類的步驟

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;
    #切割后的圖像位于數(shù)據(jù)的位置
    index = 0;
    #將圖片分割成九塊
    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;

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方法提取圖像的紋理特征.
    lbp=skft.local_binary_pattern(train_data[i],n_point,radius,'default');
    #統(tǒng)計(jì)圖像的直方圖
    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');
    #統(tǒng)計(jì)圖像的直方圖
    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)