Documentation: http://icrawler.readthedocs.io/
This package is a mini framework of web crawlers. With modularization design, it can be extended conveniently. It supports media data like images and videos very well, and can also be applied to texts and other type of files. Scrapy is heavy and powerful, while icrawler is tiny and flexible.
With this package, you can write a multiple thread crawler easily by focusing on the contents you want to crawl, avoiding some troublesome problems like exception handling, thread scheduling and communication.
It also provides built-in crawlers for popular image sites like flickr and search engines such as Google, Bing and Baidu. If you want to add your own crawlers into built-in, welcome for pull requests.
Python 2.7+ or 3.4+ (recommended).
Using builtin crawlers is very simple.
from icrawler.builtin import GoogleImageCrawler
google_crawler = GoogleImageCrawler(parser_threads=2, downloader_threads=4,
storage={'root_dir': 'your_image_dir'})
google_crawler.crawl(keyword='sunny', max_num=1000,
date_min=None, date_max=None,
min_size=(200,200), max_size=None)Writing your own crawlers with this framework is also convenient, see the tutorials.
A crawler consists of 3 main components (Feeder, Parser and Downloader), they are connected with each other with FIFO queues. The workflow is shown in the following figure.
url_queuestores the url of pages which may contain imagestask_queuestores the image url as well as any meta data you like, each element in the queue is a dictionary and must contain the fieldimg_url- Feeder puts page urls to
url_queue - Parser requests and parses the page, then extracts the image urls and
puts them into
task_queue - Downloader gets tasks from
task_queueand requests the images, then saves them in the given path.
Feeder, parser and downloader are all thread pools, so you can specify the number of threads they use.