本文实例讲述了Python比较两个图片相似度的方法。分享给大家供大家参考。具体分析如下:
这段代码实用pil模块比较两个图片的相似度,根据实际实用,代码虽短但效果不错,还是非常靠谱的,前提是图片要大一些,太小的图片不好比较。附件提供完整测试代码和对比用的图片。
#!/usr/bin/python
# Filename: histsimilar.py
# -*- coding: utf-8 -*-
import Image
def make_regalur_image(img,size = (256,256)):
return img.resize(size).convert('RGB')
def split_image(img,part_size = (64,64)):
w,h = img.size
pw,ph = part_size
assert w % pw == h % ph == 0
return [img.crop((i,j,i+pw,j+ph)).copy() \
for i in xrange(0,w,pw) \
for j in xrange(0,h,ph)]
def hist_similar(lh,rh):
assert len(lh) == len(rh)
return sum(1 - (0 if l == r else float(abs(l - r))/max(l,r)) for l,r in zip(lh,rh))/len(lh)
def calc_similar(li,ri):
# return hist_similar(li.histogram(),ri.histogram())
return sum(hist_similar(l.histogram(),r.histogram()) for l,r in zip(split_image(li),split_image(ri))) / 16.0
def calc_similar_by_path(lf,rf):
li,ri = make_regalur_image(Image.open(lf)),make_regalur_image(Image.open(rf))
return calc_similar(li,ri)
def make_doc_data(lf,make_regalur_image(Image.open(rf))
li.save(lf + '_regalur.png')
ri.save(rf + '_regalur.png')
fd = open('stat.csv','w')
fd.write('\n'.join(l + ',' + r for l,r in zip(map(str,li.histogram()),map(str,ri.histogram()))))
# print >>fd,'\n'
# fd.write(','.join(map(str,ri.histogram())))
fd.close()
import ImageDraw
li = li.convert('RGB')
draw = ImageDraw.Draw(li)
for i in xrange(0,256,64):
draw.line((0,i,i),fill = '#ff0000')
draw.line((i,256),fill = '#ff0000')
li.save(lf + '_lines.png')
if __name__ == '__main__':
path = r'testpic/TEST%d/%d.JPG'
for i in xrange(1,7):
print 'test_case_%d: %.3f%%'%(i,\
calc_similar_by_path('testpic/TEST%d/%d.JPG'%(i,1),'testpic/TEST%d/%d.JPG'%(i,2))*100)
# make_doc_data('test/TEST4/1.JPG','test/TEST4/2.JPG')
# Filename: histsimilar.py
# -*- coding: utf-8 -*-
import Image
def make_regalur_image(img,size = (256,256)):
return img.resize(size).convert('RGB')
def split_image(img,part_size = (64,64)):
w,h = img.size
pw,ph = part_size
assert w % pw == h % ph == 0
return [img.crop((i,j,i+pw,j+ph)).copy() \
for i in xrange(0,w,pw) \
for j in xrange(0,h,ph)]
def hist_similar(lh,rh):
assert len(lh) == len(rh)
return sum(1 - (0 if l == r else float(abs(l - r))/max(l,r)) for l,r in zip(lh,rh))/len(lh)
def calc_similar(li,ri):
# return hist_similar(li.histogram(),ri.histogram())
return sum(hist_similar(l.histogram(),r.histogram()) for l,r in zip(split_image(li),split_image(ri))) / 16.0
def calc_similar_by_path(lf,rf):
li,ri = make_regalur_image(Image.open(lf)),make_regalur_image(Image.open(rf))
return calc_similar(li,ri)
def make_doc_data(lf,make_regalur_image(Image.open(rf))
li.save(lf + '_regalur.png')
ri.save(rf + '_regalur.png')
fd = open('stat.csv','w')
fd.write('\n'.join(l + ',' + r for l,r in zip(map(str,li.histogram()),map(str,ri.histogram()))))
# print >>fd,'\n'
# fd.write(','.join(map(str,ri.histogram())))
fd.close()
import ImageDraw
li = li.convert('RGB')
draw = ImageDraw.Draw(li)
for i in xrange(0,256,64):
draw.line((0,i,i),fill = '#ff0000')
draw.line((i,256),fill = '#ff0000')
li.save(lf + '_lines.png')
if __name__ == '__main__':
path = r'testpic/TEST%d/%d.JPG'
for i in xrange(1,7):
print 'test_case_%d: %.3f%%'%(i,\
calc_similar_by_path('testpic/TEST%d/%d.JPG'%(i,1),'testpic/TEST%d/%d.JPG'%(i,2))*100)
# make_doc_data('test/TEST4/1.JPG','test/TEST4/2.JPG')
希望本文所述对大家的Python程序设计有所帮助。