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89 lines
2.8 KiB
89 lines
2.8 KiB
from PIL import Image
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from multiprocessing import Process
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import sim.histogram as htg
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import sim.aHash as ah
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import sim.pHash as ph
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import sim.dHash as dh
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import os
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import feature
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from flask import redirect,url_for
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# if __name__ == '__main__':
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class simi:
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def similarity(self, path):
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print("begin")
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# print(m)
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# print(path)
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folder_path = "upload/" + path
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dirs = os.listdir(folder_path)
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# 文件夹下有tmp张图片
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tmp = 0
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for item in dirs:
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if os.path.isfile(os.path.join(folder_path, item)):
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tmp += 1
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# print(tmp)
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list = [[] for j in range(int(tmp*(tmp-1)*0.5))] # [[], [], [], [], [], []]
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index = 0
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for i in range(1,tmp+1):
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for j in range(i+1,tmp+1):
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str1 = folder_path + '/img' + str(i) + '.png'
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str2 = folder_path + '/img' + str(j) + '.png'
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# print(str1)
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# print(index)
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# print('------------------')
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list[index].append(str1)
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list[index].append(str2)
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img1 = Image.open(str1)
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img2 = Image.open(str2)
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img1_htg = htg.regularizeImage(img1)
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img2_htg = htg.regularizeImage(img2)
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hg1 = img1_htg.histogram()
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hg2 = img2_htg.histogram()
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# draw the histogram in a no-blocking way
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sub_thread = Process(target=htg.drawHistogram, args=(hg1, hg2,))
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sub_thread.start()
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# print the histogram similarity
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htg_result = htg.calMultipleHistogramSimilarity(img1_htg, img2_htg)
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list[index].append(htg_result)
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print('依据图片直方图距离计算相似度：{}'.format(htg_result))
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# aHash Calculation
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ah_result = ah.calaHashSimilarity(img1, img2)
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list[index].append(ah_result)
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print('依据平均哈希算法计算相似度：{}/{}'.format(ah_result, 64))
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# pHash Calculation
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ph_result = ph.calpHashSimilarity(img1, img2)
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list[index].append(ph_result)
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print('依据感知哈希算法计算相似度：{}/{}'.format(ph_result, 64))
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# dHash Calculation
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dh_result = dh.caldHashSimilarity(img1, img2)
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list[index].append(dh_result)
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print('依据差异哈希算法计算相似度：{}/{}'.format(dh_result, 64))
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index = index + 1
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# print(list)
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# print(index)
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# print(tmp)
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the_feature = feature.feat()
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message = the_feature.call_feature_extraction_1(folder_path,list,index,tmp)
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# return redirect(url_for('call_feature',alist = list))
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return message
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