python:LBP图像特征提取与SVM分类_python图像特征提取进行分类

import os
import cv2
import xlwt
import xlrd
import numpy as np
from skimage import feature as skif
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
 
 
 
def getLbpData(image, hist_size=256, lbp_radius=1, lbp_point=8):
    image = cv2.resize(image, (150, 150), interpolation=cv2.INTER_CUBIC)
    # 使用LBP方法提取图像的纹理特征.
    lbp = skif.local_binary_pattern(image, lbp_point, lbp_radius, 'default')
    # 统计图像的直方图
    max_bins = int(lbp.max() + 1)
    # hist size:256
    hist, _ = np.histogram(lbp, normed=True, bins=max_bins, range=(0, max_bins))
    return hist
 
 
 
data = []
label = []
 
IMAGES_DIR = os.path.join(os.path.dirname(__file__), r'D:\eye_data\Base11')
book = xlrd.open_workbook(os.path.join(IMAGES_DIR, 'Annotation_Base11.xls'))
table = book.sheet_by_index(0)
for name in table.col_values(0):
    print(name)
    image = cv2.imread(os.path.join(IMAGES_DIR, name),0)
    # print(image)
    lbpdata = getLbpData(image)
    data.append(lbpdata)
for lab in table.col_values(2):
    label.append(lab)
 
 
 
IMAGES_DIR = os.path.join(os.path.dirname(__file__), r'D:\eye_data\Base12')
book = xlrd.open_workbook(os.path.join(IMAGES_DIR, 'Annotation_Base12.xls'))
table = book.sheet_by_index(0)
for name in table.col_values(0):
    print(name)
    image = cv2.imread(os.path.join(IMAGES_DIR, name),0)
    # print(image)
    lbpdata = getLbpData(image)
    data.append(lbpdata)
for lab in table.col_values(2):
    label.append(lab)
 
IMAGES_DIR = os.path.join(os.path.dirname(__file__), r'D:\eye_data\Base13')
book = xlrd.open_workbook(os.path.join(IMAGES_DIR, 'Annotation_Base13.xls'))
table = book.sheet_by_index(0)
for name in table.col_values(0):
    print(name)
    image = cv2.imread(os.path.join(IMAGES_DIR, name),0)
    # print(image)
    lbpdata = getLbpData(image)
    data.append(lbpdata)
for lab in table.col_values(2):
    label.append(lab)
 
 
IMAGES_DIR = os.path.join(os.path.dirname(__file__), r'D:\eye_data\Base14')
book = xlrd.open_workbook(os.path.join(IMAGES_DIR, 'Annotation_Base14.xls'))
table = book.sheet_by_index(0)
for name in table.col_values(0):
    print(name)
    image = cv2.imread(os.path.join(IMAGES_DIR, name),0)
    # print(image)
    lbpdata = getLbpData(image)
    data.append(lbpdata)
for lab in table.col_values(2):
    label.append(lab)
 
 
data = np.array(data)
print(data.shape)
label = np.array(label)
print(label.shape)
 
train_X,test_X,train_y,test_y = train_test_split(data,label,test_size=0.3,random_state=5)
 
model = SVC(kernel='rbf',C=1)
model.fit(train_X,train_y)
y_hat = model.predict(test_X)
ACC = accuracy_score(y_hat, test_y)
print("ACC===",ACC)