这几天看了看LBP及其人脸识别的流程,并在网络上搜相应的python代码,有,但代码质量不好,于是自己就重新写了下,对于att_faces数据集的识别率能达到95.0%~99.0%(40种类型,每种随机选5张训练,5张识别),全部代码如下,不到80行哦。
#coding:utf-8 import numpy as np import cv2, os, math, os.path, glob, random g_mapping=[ 0, 1, 2, 3, 4, 58, 5, 6, 7, 58, 58, 58, 8, 58, 9, 10, 11, 58, 58, 58, 58, 58, 58, 58, 12, 58, 58, 58, 13, 58, 14, 15, 16, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 17, 58, 58, 58, 58, 58, 58, 58, 18, 58, 58, 58, 19, 58, 20, 21, 22, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 23, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 24, 58, 58, 58, 58, 58, 58, 58, 25, 58, 58, 58, 26, 58, 27, 28, 29, 30, 58, 31, 58, 58, 58, 32, 58, 58, 58, 58, 58, 58, 58, 33, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 34, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 35, 36, 37, 58, 38, 58, 58, 58, 39, 58, 58, 58, 58, 58, 58, 58, 40, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 41, 42, 43, 58, 44, 58, 58, 58, 45, 58, 58, 58, 58, 58, 58, 58, 46, 47, 48, 58, 49, 58, 58, 58, 50, 51, 52, 58, 53, 54, 55, 56, 57] 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 LBP(I, radius=2, count=8): #得到图像的LBP特征 dh = np.round([radius*math.sin(i*2*math.pi/count) for i in range(count)]) dw = np.round([radius*math.cos(i*2*math.pi/count) for i in range(count)]) height ,width = I.shape lbp = np.zeros(I.shape, dtype = np.int) I1 = np.pad(I, radius, 'edge') for k in range(count): h,w = radius+dh[k], radius+dw[k] lbp += ((I>I1[h:h+height, w:w+width])<<k) return lbp def calLbpHistogram(lbp, hCount=7, wCount=5, maxLbpValue=255): #分块计算lbp直方图 height,width = lbp.shape res = np.zeros((hCount*wCount, max(g_mapping)+1), dtype=np.float) assert(maxLbpValue+1 == len(g_mapping)) for h in range(hCount): for w in range(wCount): blk = lbp[height*h/hCount:height*(h+1)/hCount, width*w/wCount:width*(w+1)/wCount] hist1 = np.bincount(blk.ravel(), minlength=maxLbpValue) hist = res[h*wCount+w,:] for v,k in zip(hist1, g_mapping): hist[k] += v hist /= hist.sum() return res def main(folder=u'E:/迅雷下载/faceProcess/att_faces'): trainImg, testImg, yTrain, yTest = loadImageSet(folder) xTrain = np.array([calLbpHistogram(LBP(d)).ravel() for d in trainImg]) xTest = np.array([calLbpHistogram(LBP(d)).ravel() for d in testImg]) lsvc = cv2.SVM() #支持向量机方法 svm_params = dict( kernel_type = cv2.SVM_LINEAR, svm_type = cv2.SVM_C_SVC, C=2.67, gamma=5.383 ) lsvc.train(np.float32(xTrain), np.float32(yTrain), params = svm_params) lsvc_y_predict = np.array( [lsvc.predict(d) for d in np.float32(xTest)]) print u'支持向量机识别率', (lsvc_y_predict == np.array(yTest)).mean() if __name__ == '__main__': main()
下面是对mnist手写数字数据集的识别,修改了数据集的载入,并加了图像的倾斜校正,识别率达到96%(如果使用sklearn的svm,效率会更高一些。)
import cPickle import gzip,math import numpy as np import os, glob, random, cv2 SZ = 28 def deskew(img): m = cv2.moments(img) if abs(m['mu02']) < 1e-2: return img.copy() skew = m['mu11']/m['mu02'] M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]]) img = cv2.warpAffine(img,M,(SZ, SZ),flags=cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR) return img g_mapping=[ 0, 1, 2, 3, 4, 58, 5, 6, 7, 58, 58, 58, 8, 58, 9, 10, 11, 58, 58, 58, 58, 58, 58, 58, 12, 58, 58, 58, 13, 58, 14, 15, 16, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 17, 58, 58, 58, 58, 58, 58, 58, 18, 58, 58, 58, 19, 58, 20, 21, 22, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 23, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 24, 58, 58, 58, 58, 58, 58, 58, 25, 58, 58, 58, 26, 58, 27, 28, 29, 30, 58, 31, 58, 58, 58, 32, 58, 58, 58, 58, 58, 58, 58, 33, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 34, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 35, 36, 37, 58, 38, 58, 58, 58, 39, 58, 58, 58, 58, 58, 58, 58, 40, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 41, 42, 43, 58, 44, 58, 58, 58, 45, 58, 58, 58, 58, 58, 58, 58, 46, 47, 48, 58, 49, 58, 58, 58, 50, 51, 52, 58, 53, 54, 55, 56, 57] def loadImageSet(): with gzip.open('./mnist.pkl.gz') as fp: train_set, valid_set, test_set = cPickle.load(fp) xTrain = train_set[0]; s1 = xTrain.shape; xTrain = xTrain.reshape((s1[0],28,28)) xTest = test_set[0]; s2 = xTest.shape; xTest = xTest.reshape((s2[0],28,28)) xTrain = np.array([deskew(d) for d in xTrain]) xTest = np.array([deskew(d) for d in xTest]) return xTrain, xTest, train_set[1], test_set[1] def LBP(I, radius=2, count=8): #得到图像的LBP特征 dh = np.round([radius*math.sin(i*2*math.pi/count) for i in range(count)]) dw = np.round([radius*math.cos(i*2*math.pi/count) for i in range(count)]) height ,width = I.shape lbp = np.zeros(I.shape, dtype = np.int) I1 = np.pad(I, radius, 'edge') for k in range(count): h,w = radius+dh[k], radius+dw[k] lbp += ((I>I1[h:h+height, w:w+width])<<k) return lbp def calLbpHistogram(lbp, hCount=2, wCount=2, maxLbpValue=255): #分块计算lbp直方图 height,width = lbp.shape res = np.zeros((hCount*wCount, max(g_mapping)+1), dtype=np.float) assert(maxLbpValue+1 == len(g_mapping)) for h in range(hCount): for w in range(wCount): blk = lbp[height*h/hCount:height*(h+1)/hCount, width*w/wCount:width*(w+1)/wCount] hist1 = np.bincount(blk.ravel(), minlength=maxLbpValue) hist = res[h*wCount+w,:] for v,k in zip(hist1, g_mapping): hist[k] += v hist /= hist.sum() return res def main(): trainImg, testImg, yTrain, yTest = loadImageSet() xTrain = np.array([calLbpHistogram(LBP(d)).ravel() for d in trainImg]) xTest = np.array([calLbpHistogram(LBP(d)).ravel() for d in testImg]) lsvc = cv2.SVM() #支持向量机方法 svm_params = dict( kernel_type = cv2.SVM_LINEAR, svm_type = cv2.SVM_C_SVC, C=2.67, gamma=5.383 ) lsvc.train(np.float32(xTrain), np.float32(yTrain), params = svm_params) lsvc_y_predict = np.array( [lsvc.predict(d) for d in np.float32(xTest)]) print u'支持向量机', (lsvc_y_predict == np.array(yTest)).mean() if __name__ == '__main__': main()