python 图像分类

来源：互联网发布：基价的算法和作用编辑：程序博客网时间：2024/05/17 09:38
#!/usr/bin/env python#-*- coding: utf-8 -*-#code:myhaspl@qq.com#10-7.py#余弦距离识别图片类型import numpy as npimport cv2print u'正在处理中'w_fg=20h_fg=15picflag=3def readpic(fn):    #返回图片特征码    fnimg = cv2.imread(fn)    img=cv2.resize(fnimg,(800,600))    w=img.shape[1]      h=img.shape[0]      w_interval=w/w_fg    h_interval=h/h_fg     alltz=[]    alltz.append([])    alltz.append([])    alltz.append([])    for now_h in xrange(0,h,h_interval):        for now_w in xrange(0,w,w_interval):            b = img[now_h:now_h+h_interval,now_w:now_w+w_interval,0]            g = img[now_h:now_h+h_interval,now_w:now_w+w_interval,1]            r = img[now_h:now_h+h_interval,now_w:now_w+w_interval,2]            btz=np.mean(b)            gtz=np.mean(g)            rtz=np.mean(r)            alltz[0].append(btz)            alltz[1].append(gtz)            alltz[2].append(rtz)    return alltzdef get_cossimi(x,y):    myx=np.array(x)    myy=np.array(y)    cos1=np.sum(myx*myy)    cos21=np.sqrt(sum(myx*myx))    cos22=np.sqrt(sum(myy*myy))    return cos1/float(cos21*cos22)#x和d样本初始化train_x =[]d=[]#读取图片，提取每类图片的特征for ii in xrange(1,picflag+1):    smp_x=[]    b_tz=np.array([0,0,0])    g_tz=np.array([0,0,0])    r_tz=np.array([0,0,0])    mytz=np.zeros((3,w_fg*h_fg))    for jj in xrange(1,4):        fn='p'+str(ii)+'-'+str(jj)+'.png'        tmptz=readpic(fn)        mytz+=np.array(tmptz)    mytz/=3    train_x.append(mytz[0].tolist()+mytz[1].tolist()+mytz[2].tolist())fn='ptest3.png'testtz=np.array(readpic(fn))simtz=testtz[0].tolist()+testtz[1].tolist()+testtz[2].tolist()maxtz=0nowi=0for i in xrange(0,picflag):    nowsim=get_cossimi(train_x[i],simtz)    if nowsim>maxtz:        maxtz=nowsim        nowi=i        print u'%s属于第%d类'%(fn,nowi+1)fn='ptest1.png'testtz=np.array(readpic(fn))simtz=testtz[0].tolist()+testtz[1].tolist()+testtz[2].tolist()maxtz=0nowi=0for i in xrange(0,picflag):    nowsim=get_cossimi(train_x[i],simtz)    if nowsim>maxtz:        maxtz=nowsim        nowi=i        print u'%s属于第%d类'%(fn,nowi+1)fn='ptest2.png'testtz=np.array(readpic(fn))simtz=testtz[0].tolist()+testtz[1].tolist()+testtz[2].tolist()maxtz=0nowi=0for i in xrange(0,picflag):    nowsim=get_cossimi(train_x[i],simtz)    if nowsim>maxtz:        maxtz=nowsim        nowi=i        print u'%s属于第%d类'%(fn,nowi+1)fn='ptest33.jpg'testtz=np.array(readpic(fn))simtz=testtz[0].tolist()+testtz[1].tolist()+testtz[2].tolist()maxtz=0nowi=0for i in xrange(0,picflag):    nowsim=get_cossimi(train_x[i],simtz)    if nowsim>maxtz:        maxtz=nowsim        nowi=i        print u'%s属于第%d类'%(fn,nowi+1)fn='ptest21.png'testtz=np.array(readpic(fn))simtz=testtz[0].tolist()+testtz[1].tolist()+testtz[2].tolist()maxtz=0nowi=0for i in xrange(0,picflag):    nowsim=get_cossimi(train_x[i],simtz)    if nowsim>maxtz:        maxtz=nowsim        nowi=i        print u'%s属于第%d类'%(fn,nowi+1)fn='ptest22.png'testtz=np.array(readpic(fn))simtz=testtz[0].tolist()+testtz[1].tolist()+testtz[2].tolist()maxtz=0nowi=0for i in xrange(0,picflag):    nowsim=get_cossimi(train_x[i],simtz)    if nowsim>maxtz:        maxtz=nowsim        nowi=i        print u'%s属于第%d类'%(fn,nowi+1)
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