获取数组交集
a=[2,3,4,5]
b=[2,5,8]
tmp = [val for val in a if val in b]
print tmp
print list(set(a).intersection(set(b)))
获取数组并集 (合并去重)
print list(set(a).union(set(b)))
获取数组差集
print list(set(b).difference(set(a)))
利用PIL比较两个图片的相似度
需要安装PIL图像模块，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, 代码如下：
import Image, sys, os
reload(sys)
sys.setdefaultencoding('utf-8')
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 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()))))
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 = u'E:\\data\\tmp'
toPath = u'E:\\data\\tmp'
path = path.replace('\\', '/')
toPath = toPath.replace('\\', '/')
print 'start compare similar....'
for x in os.listdir(path):
for y in os.listdir(toPath):
if x == y:
continue
degree = calc_similar_by_path(os.path.join(path,x), os.path.join(toPath,y))
if degree > 0.6:
print 'similar so much in test_case (%s <-> %s): %.3f%%'%(x, y, degree * 100)
print 'end compare similar....'
