本文實例講述了Python比較兩個圖片相似度的方法。分享給大家供大家參考。具體分析如下：
這段代碼實用pil模塊比較兩個圖片的相似度，根據實際實用，代碼雖短但效果不錯，還是非?？孔V的，前提是圖片要大一些，太小的圖片不好比較。附件提供完整測試代碼和對比用的圖片。
復制代碼代碼如下:
#!/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, rf):
li, ri = make_regalur_image(Image.open(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, 256, i), fill = '#ff0000')
draw.line((i, 0, 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, rf):
li, ri = make_regalur_image(Image.open(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, 256, i), fill = '#ff0000')
draw.line((i, 0, 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')
完整實例代碼點擊此處本站下載。
希望本文所述對大家的Python程序設計有所幫助。
