#coding:utf-8
import numpy as np
import cv2, os, math, os.path, glob, random
from ctypes import *
from sklearn.svm import LinearSVCdll = np.ctypeslib.load_library('zmGabor', '.')               #调用C++动态链接库
print dll.gabor
dll.gabor.argtypes = [POINTER(c_uint8), POINTER(c_uint8), c_int32, c_int32, c_double, c_int32, c_double, c_double]def loadImageSet(folder, sampleCount=5):trainData = []; testData = []; yTrain=[]; yTest = [];for k in range(1,41):folder2 = os.path.join(folder, 's%d' %k)data = [cv2.imread(d.encode('gbk'),0) for d in glob.glob(os.path.join(folder2, '*.pgm'))]sample = random.sample(range(10), sampleCount)trainData.extend([data[i] for i in range(10) if i in sample])testData.extend([data[i] for i in range(10) if i not in sample])yTest.extend([k]* (10-sampleCount))yTrain.extend([k]* sampleCount)return trainData, testData, np.array(yTrain), np.array(yTest)def getGaborFeature(m):res = []for i in range(6):for j in range(4):g = np.zeros(m.shape, dtype = np.uint8)dll.gabor(m.ctypes.data_as(POINTER(c_uint8)), g.ctypes.data_as(POINTER(c_uint8)),m.shape[0], m.shape[1],i*np.pi/6, j, 2*np.pi, np.sqrt(2))#res.append(cv2.dct(g[:10,:10].astype(np.float)))                            #先DCT变换再取低频系数#res.append(g[::10,::10])                                                    #直接子采样#res.append(cv2.blur(g, (10,10))[5::10, 5::10])                              #先均值滤波再子采样res.append(255-cv2.erode(255-g, np.ones((10,10)))[5::10, 5::10])             #先最大值滤波再子采样return np.array(res)
def main(folder=u'D:/gabor/att_faces'):trainImg, testImg, yTrain, yTest = loadImageSet(folder)xTrain = np.array([getGaborFeature(d).ravel() for d in trainImg])xTest  = np.array([getGaborFeature(d).ravel() for d in testImg])lsvc = LinearSVC()                              #支持向量机方法lsvc.fit(xTrain, yTrain)lsvc_y_predict = lsvc.predict(xTest)print u'支持向量机识别率: %.2f%%' % (lsvc_y_predict == np.array(yTest)).mean()if __name__ == '__main__':main()