Opencv Q&A_11

2022/03/23

基于knn通过特征值识别和比较分类

代码_Feature_Detect.py(main)

import cv2 as cv
import utlis

dirpath = 'D:\work\\automation\Skill\Python\pythonWORK\cv\material\Feature_sample'

cap = cv.VideoCapture(1)

imgs = utlis.getImg(dirpath)
feats1 = utlis.findFeature(imgs[0])

while True:
    flag,frame = cap.read()
    try:
        if frame is not None:
            feat2 = utlis.findFeature([frame])
            utlis.featureMatch(frame,feats1,feat2[0],imgs[1])
            cv.imshow('img',frame)
            cv.waitKey(1)
    except:
        print('something has happened')

代码_utlis.py

import cv2 as cv
import os



def getImg(path):
    ### 自动读取全部图片，返回图片数组和名字列表
    imgs = []
    namelist = []
    classlist = os.listdir(path)
    for item in classlist:
        name = item.rstrip('.jpg')
        namelist.append(name)
        img = cv.imread(path + '\\' + item)
        imgs.append(img)
    return [imgs,namelist]


def findFeature(imgs):
    ### 找到并返回特征值
    deslist = []
    orb = cv.ORB_create(3000)
    for img in imgs:
        kp,des = orb.detectAndCompute(img,None)
        deslist.append(des)
    return deslist

def featureMatch(img,des1,des2,namelist):
    ### 匹配特征值进行分类
    max = 0
    count = 0
    bf = cv.BFMatcher()
    for num,des in enumerate(des1):
        good = []
        matchs = bf.knnMatch(des,des2,k=2)
        for m,n in matchs:
            if m.distance < 0.75 * n.distance:
                good.append(m)
        len_good = len(good)
        if len_good > 15:
            if len_good > max:
                max = len_good
                count = num
    cv.putText(img,namelist[count],(80,80),cv.FONT_HERSHEY_SIMPLEX,3,(0,255,0),3)