├── _config.yml ├── logo1.jpg ├── logo2.jpg ├── img ├── 01.jpg ├── 02.jpg ├── 03.jpg └── 04.jpg ├── location.pyc ├── logo ├── QiRui.jpg ├── ChangAn.jpg ├── DaZhong.jpg ├── JinBei.jpg ├── KaiRui.jpg ├── XianDai.jpg └── JiangHuai.jpg ├── vehicle ├── 1.jpg ├── 2.jpg ├── 3.jpg ├── 5.jpg ├── 7.jpg ├── 218_2.jpg ├── 229_2.jpg ├── 231_2.jpg ├── 234_2.jpg ├── 243_2.jpg ├── 247_2.jpg ├── 254_2.jpg └── 670_1.jpg ├── recognition.pyc ├── logo template ├── ChangAn │ ├── 1.jpg │ ├── 2.jpg │ ├── 3.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ ├── 9.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ └── 30.jpg ├── DaZhong │ ├── 1.jpg │ ├── 2.jpg │ ├── 3.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ ├── 9.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ └── 30.jpg ├── JinBei │ ├── 1.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 2.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ ├── 3.jpg │ ├── 30.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ └── 9.jpg ├── KaiRui │ ├── 1.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 2.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ ├── 3.jpg │ ├── 30.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ └── 9.jpg ├── QiRui │ ├── 1.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 2.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ ├── 3.jpg │ ├── 30.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ └── 9.jpg ├── XianDai │ ├── 1.jpg │ ├── 2.jpg │ ├── 3.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ ├── 9.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ └── 30.jpg └── JiangHuai │ ├── 1.jpg │ ├── 2.jpg │ ├── 3.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ ├── 9.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ └── 30.jpg ├── README.md ├── location2.py ├── main.py ├── location.py ├── recognition.py └── feature.dat /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-time-machine -------------------------------------------------------------------------------- /logo1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xinyuexy/Vehicle-Logo-Recognition/HEAD/logo1.jpg -------------------------------------------------------------------------------- /logo2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xinyuexy/Vehicle-Logo-Recognition/HEAD/logo2.jpg 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6 | 2. 粗定位车标：根据定位出的车牌和车标相对位置，可大致定位车标范围
7 | 3. 二次定位车标：对粗定位的车标范围进行二值化、边缘检测、形态学等操作以剔除背景再使用opencv函数查找轮廓，
8 | 取满足要求的最大轮廓即为车标区域，将其存储在logo2.jpg
9 | #### 特征提取模块：recognition.py 10 | 1. 采用LBP作为特征进行车标识别，分别实现了基本3x3LBP、圆形LBP以及uniform LBP。
11 | 2. 使用圆形LBP。首先提取图像LBP，然后计算其LBPH（LBP的统计直方图）作为特征向量。
12 | #### 主模块：main.py 13 | logo template文件夹下为训练用的车标模板，共七类，每一类30张车标图像。vehicle文件夹下为用于定位的卡口车辆图像。
14 | 1. 建立特征库：读取每一类下车标图像，求LBPH后取平均值作为该类车标的特征向量。 15 | 使用两个列表分别保存每一类车标的特征向量和对应的类别标签，将其使用python对象 。 16 | 持久化到feature.dat文件中，下次直接载入即可。
17 | 2. 预测：读取待识别车标（logo2.jpg)，计算其LBPH，利用opencv直方图匹配函数将其与每一类的车标特征向量进行比较，最相似的即为该车标对应的类别 18 | 19 | ### 结果展示 20 | #### 车牌定位结果： 21 | 22 | 23 | #### 车标定位结果： 24 | 25 | 26 | #### 最终结果： 27 | 28 | 29 | 30 | 31 | #### 参考资料： 32 | LBP原理及实现：http://blog.csdn.net/quincuntial/article/details/50541815
33 | python-opencv：http://docs.opencv.org/trunk/d6/d00/tutorial_py_root.html
34 | -------------------------------------------------------------------------------- /location2.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division #强制除法为浮点数 3 | import math 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import cv2 7 | 8 | def process(img): 9 | gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 10 | gau=cv2.GaussianBlur(gray,(5,5),0) 11 | ret,thre = cv2.threshold(gau, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) 12 | element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) 13 | element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) 14 | med=cv2.medianBlur(thre,5) 15 | canny=cv2.Canny(thre,100,200) 16 | #sobel = cv2.Sobel(thre, cv2.CV_8U, 1, 0, ksize = 3) 17 | dilation=cv2.dilate(canny,element2,iterations = 1) 18 | dst=cv2.erode(dilation, element1, iterations = 1) 19 | return dst 20 | 21 | def find_row(img,img2): 22 | T=30 23 | row=[] 24 | row_start=0 25 | row_end=0 26 | for i in range(img.shape[0]): 27 | count=0 28 | for j in range(img.shape[1]-1): 29 | if img[i,j]!=img[i,j+1]: 30 | count=count+1 31 | if count>T: 32 | row.append(i) 33 | break 34 | for i in range(len(row)-2): 35 | if row[i]==row[i+1]-1 and row[i+1]==row[i+2]-1: 36 | row_start=row[i] 37 | break 38 | for j in range(len(row)-1,,-1): 39 | if row[j]==row[j+1]-1 and row[j+1]==row[j+2]-1: 40 | row_end=row[j] 41 | break 42 | cv2.line(img,(0,row_start),(img2.shape[1],row_start),(255,0,0),2) 43 | cv2.line(img,(0,row_end),(img2.shape[1],row_end),(255,0,0),2) 44 | print row_end 45 | 46 | if __name__ == '__main__': 47 | imgRGB=cv2.imread('D:/pictures/vehicle/2.jpg') 48 | img=process(imgRGB) 49 | find_row(img,imgRGB) 50 | cv2.namedWindow('plate',cv2.WINDOW_NORMAL) 51 | cv2.resizeWindow('plate',600,400) 52 | cv2.imshow('plate',imgRGB) 53 | cv2.waitKey(0) 54 | cv2.destroyAllWindows() 55 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division #强制除法为浮点数 3 | import math 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import cv2 7 | import os 8 | import sys 9 | import recognition 10 | import location 11 | import pickle 12 | 13 | labelname={'ChangAn':1,'DaZhong':2,'JiangHuai':3,'JinBei':4,'KaiRui':5,'QiRui':6,'XianDai':7} 14 | _labels=[] 15 | _histograms=[] 16 | #训练和预测的参数 17 | _radius=1;_neighbors=6;_grid_x=4;_grid_y=4;_normed=True 18 | 19 | def fileRename(): 20 | '''重命名logo template目录下所有车标文件名''' 21 | rootpath='./logo template/' 22 | for listdir in os.listdir(rootpath): 23 | filepath=os.path.join(rootpath,listdir) 24 | if os.path.isdir(filepath): 25 | for i,filename in enumerate(os.listdir(filepath)): 26 | newname='%d'%(i+1)+'.jpg' 27 | os.rename(filepath+'/'+filename,filepath+'/'+newname) 28 | 29 | def buildFeature(trainPath): 30 | '''建立车标特征模板库,对每一类的图片LBP取均值''' 31 | global labelname,_labels,_histograms 32 | rootpath=trainPath 33 | 34 | for listdir in os.listdir(rootpath): 35 | filepath=os.path.join(rootpath,listdir) 36 | if os.path.isdir(filepath): 37 | _labels.append(labelname[listdir]) 38 | list_lbph=[] 39 | for filename in os.listdir(filepath): 40 | fileimg=filepath+'/'+filename 41 | img=cv2.imread(fileimg) 42 | img=cv2.resize(img,(48,48)) 43 | lbP_img=recognition.CircularLBP(img,radius=_radius,neighbors=_neighbors) 44 | lbph=recognition.LBPH(lbP_img,int(math.pow(2,_neighbors)),grid_x=_grid_x,grid_y=_grid_y) 45 | list_lbph.append(lbph) 46 | #_labels.append(labelname[listdir]) 47 | #_histograms.append(lbph) 48 | _histograms.append(np.mean(list_lbph,axis=0)) 49 | train_data=[_histograms,_labels] 50 | #将训练得到的数据持久化到文件中 51 | pickle.dump(train_data,open('feature.dat','w')) 52 | 53 | def predict(img): 54 | '''对待识别车标进行预测''' 55 | if len(_histograms)==0: 56 | print 'model is not build' 57 | return 58 | img=cv2.resize(img,(48,48)) 59 | lbP_img=recognition.CircularLBP(img,radius=_radius,neighbors=_neighbors) 60 | lbph_pre=recognition.LBPH(lbP_img,int(math.pow(2,_neighbors)),grid_x=_grid_x,grid_y=_grid_y) 61 | minDist=sys.float_info.max 62 | minClass=-1 63 | for index in range(len(_histograms)): 64 | dist=cv2.compareHist(_histograms[index],lbph_pre,cv2.HISTCMP_CHISQR) 65 | if dist2 and ratio<4 and area>=2000 and area<=25000: 43 | logo_y1=max(0,int(y-h*3.0)) 44 | logo_y2=y 45 | logo_x1=x 46 | logo_x2=x+w 47 | img_logo=img2.copy() 48 | logo=img_logo[logo_y1:logo_y2,logo_x1:logo_x2] 49 | cv2.imwrite('./logo1.jpg',logo) 50 | cv2.rectangle(img2,(x,y),(x+w,y+h),(255,0,0),2) 51 | cv2.rectangle(img2,(logo_x1,logo_y1),(logo_x2,logo_y2),(0,255,0),2) 52 | global plate 53 | plate=[x,y,w,h] 54 | #返回车标粗定位区域 55 | return logo 56 | 57 | def logoDetect(img,imgo): 58 | '''对粗定位的车标区域进行二次定位''' 59 | imglogo=imgo.copy() 60 | img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 61 | img=cv2.resize(img,(2*img.shape[1],2*img.shape[0]),interpolation=cv2.INTER_CUBIC) 62 | #img=cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,-3) 63 | ret,img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) 64 | #img=cv2.Sobel(img, cv2.CV_8U, 1, 0, ksize = 9) 65 | img=cv2.Canny(img,100,200) 66 | element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) 67 | element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) 68 | img = cv2.dilate(img, element2,iterations = 1) 69 | img = cv2.erode(img, element1, iterations = 3) 70 | img = cv2.dilate(img, element2,iterations = 3) 71 | 72 | #查找轮廓 73 | im2, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) 74 | tema=0 75 | result=[] 76 | for con in contours: 77 | x,y,w,h=cv2.boundingRect(con) 78 | area=w*h 79 | ratio=max(w/h,h/w) 80 | if area>300 and area<20000 and ratio<2: 81 | if area>tema: 82 | tema=area 83 | result=[x,y,w,h] 84 | ratio2=ratio 85 | #计算车标区域在原始图像中的坐标位置,需加上车牌的相对位置 86 | logo2_X=[int(result[0]/2+plate[0]-3),int(result[0]/2+plate[0]+result[2]/2+3)] 87 | logo2_Y=[int(result[1]/2+max(0,plate[1]-plate[3]*3.0)-3),int(result[1]/2+max(0,plate[1]-plate[3]*3.0)+result[3]/2)+3] 88 | cv2.rectangle(img,(result[0],result[1]),(result[0]+result[2],result[1]+result[3]),(255,0,0),2) 89 | cv2.rectangle(imgo,(logo2_X[0],logo2_Y[0]),(logo2_X[1],logo2_Y[1]),(0,0,255),2) 90 | print tema,ratio2,result 91 | logo2=imglogo[logo2_Y[0]:logo2_Y[1],logo2_X[0]:logo2_X[1]] 92 | cv2.imwrite('./logo2.jpg',logo2) 93 | 94 | return img 95 | 96 | if __name__ == '__main__': 97 | img=cv2.imread('./vehicle/234_2.jpg') 98 | plateImg=HSVfilter(img) 99 | plateImg=process(plateImg) 100 | logo=plateDetect(plateImg,img) 101 | logo2=logoDetect(logo,img) 102 | cv2.namedWindow('plate',cv2.WINDOW_NORMAL) 103 | cv2.resizeWindow('plate',600,400) 104 | cv2.imshow('plate',img) 105 | cv2.waitKey(0) 106 | cv2.destroyAllWindows() 107 | 108 | -------------------------------------------------------------------------------- /recognition.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division #强制除法为浮点数 3 | import math 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import cv2 7 | import warnings 8 | warnings.filterwarnings("ignore") 9 | 10 | def preprocess(img): 11 | '''提取特征前预处理''' 12 | img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 13 | #img=cv2.GaussianBlur(img,(3,3),0) 14 | img=cv2.medianBlur(img,5) 15 | img=cv2.equalizeHist(img) 16 | return img 17 | 18 | def BasicLBP(img): 19 | '''3x3 LBP实现''' 20 | src=img.copy() 21 | src=preprocess(src) 22 | if src.ndim==3: 23 | src=cv2.cvtColor(src,cv2.COLOR_BGR2GRAY) 24 | dst=np.zeros((src.shape[0]-2,src.shape[1]-2),dtype=np.uint8) 25 | 26 | for i in range(1,src.shape[0]-1): 27 | for j in range(1,src.shape[1]-1): 28 | center=src.item(i,j) 29 | lbpCode=0 30 | lbpCode |= (src.item(i-1,j-1) > center) << 7 31 | lbpCode |= (src.item(i-1,j ) > center) << 6 32 | lbpCode |= (src.item(i-1,j+1) > center) << 5 33 | lbpCode |= (src.item(i ,j+1) > center) << 4 34 | lbpCode |= (src.item(i+1,j+1) > center) << 3 35 | lbpCode |= (src.item(i+1,j ) > center) << 2 36 | lbpCode |= (src.item(i+1,j-1) > center) << 1 37 | lbpCode |= (src.item(i ,j-1) > center) << 0 38 | dst.itemset(i-1,j-1,lbpCode) 39 | return dst 40 | 41 | def CircularLBP(img,radius=1,neighbors=8): 42 | '''圆形LBP实现''' 43 | src=img.copy() 44 | src=preprocess(src) 45 | if src.ndim == 3: 46 | src=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 47 | rows,cols=src.shape 48 | dst=np.zeros((rows-2*radius,cols-2*radius),dtype=np.uint8) 49 | 50 | for k in range(neighbors): 51 | #计算采样点对于中心点坐标的偏移量 52 | rx=radius*math.cos(2.0*math.pi*k/neighbors) 53 | ry=-radius*math.sin(2.0*math.pi*k/neighbors) 54 | #为双线性插值做准备,对采样点坐标分别进行上下取整 55 | x1=math.floor(rx) 56 | x2=math.ceil(rx) 57 | y1=math.floor(ry) 58 | y2=math.ceil(ry) 59 | #将坐标偏移量映射到0-1之间 60 | tx=rx-x1 61 | ty=ry-y1 62 | #计算双线性插值权重 63 | w1=(1-tx)*(1-ty) 64 | w2=tx*(1-ty) 65 | w3=(1-tx)*ty 66 | w4=tx*ty 67 | #循环处理每个像素 68 | for i in range(radius,rows-radius): 69 | for j in range(radius,cols-radius): 70 | #获取中心像素点灰度值 71 | center=src.item(i,j) 72 | #根据双线性插值公式计算第k个采样点的灰度值 73 | neighbor=src.item(i+x1,j+y1)*w1+src.item(i+x1,j+y2)*w2+src.item(i+x2,j+y1)*w3+src.item(i+x2,j+y2)*w4 74 | dst[i-radius,j-radius]|=(neighbor>center)<<(neighbors-k-1) 75 | return dst 76 | 77 | def getHopTimes(n): 78 | '''计算某个数的二进制跳变数''' 79 | count=0 80 | binaryCode='{0:08b}'.format(n) #将整数n转换为二进制串 81 | for i in range(8): 82 | if binaryCode[i] != binaryCode[(i+1)%8]: 83 | count=count+1 84 | return count 85 | 86 | def UniformLBP(img,radius=1,neighbors=8): 87 | '''UniformLBP实现''' 88 | src=img.copy() 89 | src=preprocess(src) 90 | if src.ndim == 3: 91 | src=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 92 | rows,cols=src.shape 93 | dst=np.zeros((rows-2*radius,cols-2*radius),dtype=np.uint8) 94 | 95 | temp=1 96 | table=[0]*256 97 | for i in range(256): 98 | if getHopTimes(i)<3: 99 | table[i]=temp 100 | temp=temp+1 101 | flag=False #是否进行UniformPattern编码的标志 102 | for k in range(neighbors): 103 | if k==neighbors-1: 104 | flag=True 105 | rx=radius*math.cos(2.0*math.pi*k/neighbors) 106 | ry=-radius*math.sin(2.0*math.pi*k/neighbors) 107 | #为双线性插值做准备,对采样点坐标分别进行上下取整 108 | x1=math.floor(rx) 109 | x2=math.ceil(rx) 110 | y1=math.floor(ry) 111 | y2=math.ceil(ry) 112 | #将坐标偏移量映射到0-1之间 113 | tx=rx-x1 114 | ty=ry-y1 115 | #计算双线性插值权重 116 | w1=(1-tx)*(1-ty) 117 | w2=tx*(1-ty) 118 | w3=(1-tx)*ty 119 | w4=tx*ty 120 | #循环处理每个像素 121 | for i in range(radius,rows-radius): 122 | for j in range(radius,cols-radius): 123 | #获取中心像素点灰度值 124 | center=src.item(i,j) 125 | #根据双线性插值公式计算第k个采样点的灰度值 126 | neighbor=src.item(i+x1,j+y1)*w1+src.item(i+x1,j+y2)*w2+src.item(i+x2,j+y1)*w3+src.item(i+x2,j+y2)*w4 127 | dst[i-radius,j-radius]|=(neighbor>center)<<(neighbors-k-1) 128 | if flag: 129 | dst[i-radius,j-radius]=table[dst[i-radius,j-radius]] 130 | return dst 131 | 132 | def BlockLBPH(img,minValue,maxValue,normed=True): 133 | '''计算一个区域块的LBP特征直方图''' 134 | #计算直方图bin的数目 135 | histSize=[maxValue-minValue+1] 136 | ranges=[minValue,maxValue+1] 137 | result=cv2.calcHist(img,[0],None,histSize,ranges) 138 | #归一化 139 | if normed: 140 | result=result/(int)(img.shape[0]*img.shape[1]) 141 | return result.reshape(1,-1) 142 | 143 | def LBPH(img,numPatterns,grid_x,grid_y,normed=True): 144 | '''计算LBP特征向量''' 145 | src=img.copy() 146 | width=int(src.shape[1]/grid_x) 147 | height=int(src.shape[0]/grid_y) 148 | HistLBP=np.zeros((grid_x*grid_y,numPatterns),dtype=np.float32) 149 | if src.size==0: 150 | return HistLBP.reshape(1,-1) 151 | 152 | cellIndex=0 153 | for i in range(grid_x): 154 | for j in range(grid_y): 155 | src_cell=src[i*height:(i+1)*height,j*width:(j+1)*width] 156 | hist_cell=BlockLBPH(src_cell,0,(numPatterns-1),normed) 157 | HistLBP[cellIndex,:]=hist_cell 158 | cellIndex=cellIndex+1 159 | return HistLBP.reshape(1,-1) 160 | 161 | if __name__ == '__main__': 162 | img=cv2.imread('./logo template/ChangAn/20.jpg') 163 | #lbp=BasicLBP(img) 164 | Clbp=CircularLBP(img,radius=1,neighbors=6) 165 | lbph=LBPH(Clbp,64,4,4,normed=True) 166 | print lbph.shape 167 | plt.subplot(111) 168 | plt.plot(lbph.flatten()) 169 | plt.show() 170 | cv2.namedWindow('LBP',cv2.WINDOW_NORMAL) 171 | cv2.resizeWindow('LBP',600,600) 172 | cv2.imshow('LBP',Clbp) 173 | cv2.waitKey(0) 174 | cv2.destroyAllWindows() 175 | -------------------------------------------------------------------------------- /feature.dat: -------------------------------------------------------------------------------- 1 | (lp0 2 | (lp1 3 | cnumpy.core.multiarray 4 | _reconstruct 5 | p2 6 | (cnumpy 7 | ndarray 8 | p3 9 | (I0 10 | tp4 11 | S'b' 12 | p5 13 | tp6 14 | Rp7 15 | (I1 16 | (L1L 17 | L1024L 18 | tp8 19 | cnumpy 20 | dtype 21 | p9 22 | (S'f4' 23 | p10 24 | I0 25 | I1 26 | tp11 27 | Rp12 28 | (I3 29 | S'<' 30 | p13 31 | NNNI-1 32 | I-1 33 | I0 34 | tp14 35 | bI00 36 | S' 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133 | p51 134 | tp52 135 | baa(lp53 136 | I1 137 | aI2 138 | aI3 139 | aI4 140 | aI5 141 | aI6 142 | aI7 143 | aa. --------------------------------------------------------------------------------