正如前文所述,内容聚合网站,比如新浪微博、推特、facebook等网站对于网页的缩略图是刚需。为了让分享的内容引人入胜,网页的图片缩略图是必不可少的。年轻人的聚集地、社交新闻网站reddit也是一个这样的网站,由于他们将自己网站的源代码在github上开源,我便很容易了解他们的做法。

寻找网页图片缩略图的算法,可以在这里找到[1]。

实现这一功能的就是_find_thumbnail_image(self)函数,下边我会仔细分析一下他们的代码。

content_type, content = _fetch_url(self.url)

# if it's an image. it's pretty easy to guess what we should thumbnail.
if content_type and "image" in content_type and content:
return self.url if content_type and "html" in content_type and content:
soup = BeautifulSoup.BeautifulSoup(content)
else:
return None

_fetch_url会请求链接url,获取链接文件类型,和链接的内容。
可以从_fetch_url函数看到,文件的类型是通过,http响应的头部获取的。文件类型由多用途互联网邮件扩展类型(Multipurpose Internet Mail Extensions,MIME)指定。
如果url指向文件是图片(image)就直接返回url,如果指向的文件是超文本标记语言(HTML, hypertext markup language)就用BeautifulSoup包对HTML源代码解析,如果是其它文件类型返回None。

Beautiful Soup 是一个可以从HTML或XML文件中提取数据的Python库。它能够通过你喜欢的转换器实现惯用的文档导航、查找、修改文档的方式。

# allow the content author to specify the thumbnail:
# <meta property="og:image" content="http://...">
og_image = (soup.find('meta', property='og:image') or
soup.find('meta', attrs={'name': 'og:image'}))
if og_image and og_image['content']:
return og_image['content'] # <link rel="image_src" href="http://...">
thumbnail_spec = soup.find('link', rel='image_src')
if thumbnail_spec and thumbnail_spec['href']:
return thumbnail_spec['href']

接下来判断,用户(网页的作者)是否指定缩略图。使用的方法便是前文所说的开放图谱计划(Open Graph Protocol)

<meta property="og:image" content="http://...">

<link rel="image_src" href="http://...">

meta标签或者是link便签可以指定网页的缩略图,如果网页包含这两个标签就大功告成了,直接返回图片的源地址即可。这样很方便,但有明显的不足。如此没有检验图片是否有效,有的网站偷工减料返回的并非网页相关图片的缩略图,而是网站的logo,stackoverflow就是一个典型。不过话又说回来,出现这种特殊情况的概率是相当小的。

# ok, we have no guidance from the author. look for the largest
# image on the page with a few caveats. (see below)
max_area = 0
max_url = None
for image_url in self._extract_image_urls(soup):
# When isolated from the context of a webpage, protocol-relative
# URLs are ambiguous, so let's absolutify them now.
if image_url.startswith('//'):
image_url = coerce_url_to_protocol(image_url, self.protocol)
size = _fetch_image_size(image_url, referer=self.url)
if not size:
continue area = size[0] * size[1]

接下来是一个循环,在通过_extract_image_urls找到网页的所有图片后,遍历每一张图片,找到最大的一张图片。

具体来说还加上了一些限制条件

# ignore little images
if area < 5000:
g.log.debug('ignore little %s' % image_url)
continue # ignore excessively long/wide images
if max(size) / min(size) > 1.5:
g.log.debug('ignore dimensions %s' % image_url)
continue # penalize images with "sprite" in their name
if 'sprite' in image_url.lower():
g.log.debug('penalizing sprite %s' % image_url)
area /= 10

图片的面积必须大于5000像素、宽长比必须小于1.5、url如果包含sprite,则进行惩罚,将面积除以10

_fetch_image_size(image_url, referer=self.url)是一个比较困难的地方,为了找到每一张图片的大小,必须对下载图片。一个小技巧是,图片的大小作为图片文件格式的一部分往往写在了图片文件的头部,只需要下载图片的一部分就可以得到大小了。想要具体了解可以分析一下那个函数。

if area > max_area:
max_area = area
max_url = image_url

到这就结束了。reddit的方法用一句话来总结就是,相信网页指定的缩略图,没有就找最大的图片,同时限制最小面积以及宽长比。

这是它们实际的效果:

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alt="" />

facebook等其他一些社交网站的做法也大同小异,这里有一个回答是介绍facebook如何做的[2]。

参考资料

[1] https://github.com/reddit/reddit/blob/0fbea80d45c4ce35e50ae6f8b42e5e60d79743ca/r2/r2/lib/media.py

[2] http://stackoverflow.com/questions/1138460/how-does-facebook-sharer-select-images

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