forked from Open-CT/openbrain
69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
import cv2
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#采用均值hash函数进行图像相似度判断
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# Hash值对比
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from PIL import UnidentifiedImageError
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from textSimilar import find_student_picture, picture_cut
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def cmpHash(hash1, hash2, shape=(10, 10)):
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n = 0
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# hash长度不同则返回-1代表传参出错
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if len(hash1) != len(hash2):
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return -1
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# 遍历判断
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for i in range(len(hash1)):
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# 相等则n计数+1，n最终为相似度
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if hash1[i] == hash2[i]:
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n = n + 1
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return n / (shape[0] * shape[1])
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# 均值哈希算法
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def aHash(img, shape=(10, 10)):
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# 缩放为10*10
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img=cv2.imread(img)
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img = cv2.resize(img, shape)
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# 转换为灰度图
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# s为像素和初值为0，hash_str为hash值初值为''
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s = 0
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hash_str = ''
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# 遍历累加求像素和
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for i in range(shape[0]):
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for j in range(shape[1]):
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s = s + gray[i, j]
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# 求平均灰度
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avg = s / 100
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# 灰度大于平均值为1相反为0生成图片的hash值
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for i in range(shape[0]):
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for j in range(shape[1]):
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if gray[i, j] > avg:
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hash_str = hash_str + '1'
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else:
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hash_str = hash_str + '0'
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return hash_str
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def hash_similar(hashResult):
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hashList=[]
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for i in range(len(hashResult)-1):
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n = cmpHash(hashResult[i], hashResult[i+1])
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hashList.append(n)
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print("hash相似度："+str(n))
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return hashList
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def picture_to_hash(filePath):
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studentPicture =find_student_picture(filePath)
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result=[]
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for singlePicture in studentPicture:
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try:
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picture_cut(filePath,filePath + "/" + singlePicture)
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hashResult = aHash(filePath+"/tempPicture.jpg")
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result.append(hashResult)
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except UnidentifiedImageError:
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print(singlePicture)
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print(result)
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hashSimilarList=hash_similar(result)
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return hashSimilarList |