python 实现 纹理图片分类识别 SVM支持向量机 图片资源与代码

import matplotlib.image as mpimg import matplotlib.pyplot as plt import numpy as np from sklearn.multiclass import OneVsRestClassifier from sklearn.svm import SVC from skimage import feature as skft from sklearn.metrics import classification_report 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 #mpimg.imread('picture/'+str(i)+'.tiff') arr='picture_train/'+str(i*9+index)+'.tiff' mpimg.imsave(fname=arr,arr=data[171*row:171*(row+1),171*col:171*(col+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 arr='picture_test/'+str(i*9+index)+'.tiff' mpimg.imsave(fname=arr,arr=data[171*row:171*(row+1),171*col:171*(col+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 train_data,test_data,train_label,test_label= loadPicture() train_hist,test_hist = texture_detect() result=[] maxid=0 max=0 id=0 kenellist=['linear', 'poly', 'rbf', 'sigmoid'] for kenel in kenellist : for c in range(1,2001,100): gama=0.1 while(gama<=1): clf = SVC(kernel=kenel, C=c, gamma=gama) clf.fit(train_hist,train_label) linshi=clf.score(test_hist,test_label) result.append([kenel,c,gama,linshi]) print(id,result[id]) if max<linshi: max=linshi maxid=id gama=gama+0.1 id=id+1 print(result[maxid]) clf = SVC(kernel='linear', C=700, gamma=0.1) clf.fit(train_hist,train_label) test_predict=clf.predict(test_hist) print(classification_report(test_label, test_predict))