Question

我在我的项目中这样做，用于使用opencv python识别相机中显示的标志。我已经尝试过使用冲浪，但由于一些标志的功能较少，比如红色和蓝色作为标志的特征，它并不总能给出正确的识别。关于我能做什么来做这个基于颜色直方图识别标志的项目的建议？非常感谢你的帮助。

例如，我展示了越南的旗帜，该程序将比较显示旗帜与旗帜图像数据库，并认识到它是越南的旗帜。

注意：这是我发布的链接https://stackoverflow.com/questions/22424745/compare-a-single-image-to-database-of-images-and-find-the-closest-match-using-th中的相同问题，但由于连接速度慢，我无法对其进行编辑

 import cv2
     import numpy as np
     import pyttsx
     import sys
     import os
     import operator

     hbins = 180
     sbins = 255
     hrange = [0,180]
     srange = [0,256]
     ranges = hrange+srange   

     flags=["Cambodia.jpg","Laos.jpg","Malaysia.jpg","Myanmar.jpg","Philippines.jpg","Singapore.jpg","Thailand.jpg","Vietnam.jpg","Indonesia.jpg","Brunei.jpg"] 
     list_of_pics=[]
     valueCompare=[]
     cam = cv2.VideoCapture(0) 
     while True:
        _, frame = cam.read(0)

        cv2.imshow('asdas',frame) 
        list_of_pics=[]
        valueCompare=[]           
        k=cv2.waitKey(10)
        if(k==32):
            cv2.imwrite("pic.jpg",frame)#the image from the camera
            img = "pic.jpg"
            for i in flags:

                base = cv2.imread(img)
                test1 = cv2.imread(i)#the flags to be compared with
                rows,cols = base.shape[:2]
                basehsv = cv2.cvtColor(base,cv2.COLOR_BGR2HSV)
                test1hsv = cv2.cvtColor(test1,cv2.COLOR_BGR2HSV)

                histbase = cv2.calcHist(basehsv,[0,1],None,[180,256],ranges)
                cv2.normalize(histbase,histbase,0,255,cv2.NORM_MINMAX)

                histtest1 = cv2.calcHist(test1hsv,[0,1],None,[180,256],ranges)
                cv2.normalize(histtest1,histtest1,0,255,cv2.NORM_MINMAX)    

                comHist=cv2.compareHist(histbase,histtest1,3)
                valueCompare.append(comHist)
                picDict={"comhist":comHist,"name":i}
                list_of_pics.append(picDict)

            newlist = sorted(list_of_pics, key=operator.itemgetter('comhist')) #get the max value of all the compared images
    #print newlist
           matched_image=newlist[0]['name']
           print matched_image
        elif k == 27:
            break
    cv2.destroyAllWindows()

用于模板匹配代码。

k=cv2.waitKey(10)
methods = 'cv2.TM_CCOEFF_NORMED'#only the one method to be used
list_of_pics=[]

if(k==32):
    for flag in flags:
        img = cv2.imread('Singapore.jpg',0)
        #img = img.copy()
        template = cv2.imread(flag,0)
        w, h = template.shape[::-1]

        # All the 6 methods for comparison in a list

        method = eval(methods)#
        #print method
            # Apply template Match
        res = cv2.matchTemplate(img,template,method)
        #print res

        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        #print min_val, max_val
        matchVal=res[0][0]
        picDict={"matchVal":matchVal,"name":flag}
        list_of_pics.append(picDict)
        #print res[0][0]
    newlist = sorted(list_of_pics, key=operator.itemgetter('matchVal'),reverse=True) 
    print newlist
    matched_image=newlist[0]['name']
    print matched_image
elif k == 27:
    break
cv2.destroyAllWindows()

Answer 1

计算直方图后，您可以使用直方图匹配功能。

double result = compareHist( image, template, compare_method );

结果的值取决于您使用的compare_method。例如，如果您使用correlation作为比较方法，那么result的值将介于0-1和更高之间，匹配值就越高。

替代方案：

如果您认为数据库中的标志大小和方向与当前图像几乎相似，那么您甚至不需要计算直方图。在这种情况下，您可以直接使用openCV的matchTemplate()。

如何在opencv python中使用颜色直方图比较图像并找到最接近的匹配

1 个答案: