This time I will show you how to use python to determine the image similarity. What are the notes for using python to determine the image similarity, as follows: This is a practical case, let’s take a look at it.
python A very simple example of judging the similarity of images. It is just an example and the accuracy may not be very high. Mainly introduce the principle: first convert the picture to a 12x12 pixel grayscale picture--get the average grayscale of the picture--traverse the 10x10 pixels inside the picture (remove the surrounding 1 pixel)--compare each pixel with the average value
from PIL import Image import os #import hashlib def getGray(image_file): tmpls=[] for h in range(0, image_file.size[1]):#h for w in range(0, image_file.size[0]):#w tmpls.append( image_file.getpixel((w,h)) ) return tmpls def getAvg(ls):#获取平均灰度值 return sum(ls)/len(ls) def getMH(a,b):#比较100个字符有几个字符相同 dist = 0; for i in range(0,len(a)): if a[i]==b[i]: dist=dist+1 return dist def getImgHash(fne): image_file = Image.open(fne) # 打开 image_file=image_file.resize((12, 12))#重置图片大小我12px X 12px image_file=image_file.convert("L")#转256灰度图 Grayls=getGray(image_file)#灰度集合 avg=getAvg(Grayls)#灰度平均值 bitls=''#接收获取0或1 #除去变宽1px遍历像素 for h in range(1, image_file.size[1]-1):#h for w in range(1, image_file.size[0]-1):#w if image_file.getpixel((w,h))>=avg:#像素的值比较平均值 大于记为1 小于记为0 bitls=bitls+'1' else: bitls=bitls+'0' return bitls ''' m2 = hashlib.md5() m2.update(bitls) print m2.hexdigest(),bitls return m2.hexdigest() ''' a=getImgHash("./Test/测试图片.jpg")#图片地址自行替换 files = os.listdir("./Test")#图片文件夹地址自行替换 for file in files: b=getImgHash("./Test/"+str(file)) compare=getMH(a,b) print file,u'相似度',str(compare)+'%'
Size--If it is large, then string add 1, otherwise add 0 (I choose string saving here, in fact there are many methods)--The next step is traverse the pictures in the folder , compare the similarity, and return the similarity
I believe you have mastered the method after reading the case in this article. For more exciting information, please pay attention to other related articles on the PHP Chinese website!
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