CV之Hog+HamMingDistance：基于Hog提取和汉明距离对比的应用—图像相似度对比之for循环将多个成对图片依次对比并输出相似度

1. def calc_similar(li, ri): 
2.  res=hist_similar(li.histogram(), ri.histogram())*100
3.  return sum(hist_similar(l.histogram(), r.histogram()) for l, r in zip(split_image(li), split_image(ri))) / 16.0
4. 
5. 
6. def calc_similar_by_path(lf, rf):
7.  li, ri = make_regalur_image(Image.open(lf)), make_regalur_image(Image.open(rf))
8.  return calc_similar(li, ri)
9. 
10. def make_doc_data(lf, rf):
11.   li, ri = make_regalur_image(Image.open(lf)), make_regalur_image(Image.open(rf))
12.   li.save(lf + '_regalur.png')
13.   ri.save(rf + '_regalur.png')
14.   fd = open('stat.csv', 'w')
15.   fd.write('\n'.join(l + ',' + r for l, r in zip(map(str, li.histogram()), map(str, ri.histogram()))))
16. # print >>fd, '\n'
17.   fd.write(','.join(map(str, ri.histogram())))
18.   fd.close()
19.   li = li.convert('RGB')
20.   draw = ImageDraw.Draw(li)
21.   for i in range(0, 256, 64):
22.     draw.line((0, i, 256, i), fill = '#ff0000')
23.     draw.line((i, 0, i, 256), fill = '#ff0000')
24.   li.save(lf + '_lines.png')