1.7k Views inProxy Server

The Complete Guide to Extracting Image Src with BeautifulSoup in Python

Images are a vital part of most websites. As a developer or data scientist, you‘ll often need to extract and analyze image URLs at scale.

In this comprehensive guide, you‘ll learn how to use Python and BeautifulSoup to easily locate and scrape image src attributes from HTML.

Here‘s what we‘ll cover:

  • Why Image Scraping is Useful
  • An Introduction to BeautifulSoup
  • Locating Images in HTML Documents
  • Accessing the Src Attribute
  • Complete Walkthrough with Code Examples
  • Helpful Tips and Tricks for Image Scraping
  • Advanced Image Scraping Techniques

Let‘s get started!

Why Image Scraping is Useful

First, why would you want to scrape image URLs from a website in the first place?

Here are some common use cases:

  • Building a Product Catalog – scraping product images for an ecommerce catalog.

  • Price Monitoring – tracking changes in product images over time.

  • Visual Search Index – creating a searchable index of image URLs for a vertical.

  • Scraping News Articles – extracting images associated with news stories.

  • Social Media Tracking – analyzing images shared and posted on social media.

  • Scraping Scientific Papers – extracting charts, graphs, and images from papers.

  • Downloading Dataset – building a dataset of labeled images for machine learning.

  • Website Change Detection – monitoring changes to images on a site.

  • Collecting Data for SEO – analyzing image names, alt text, sizes for optimization.

As you can see, there‘s a wide variety of use cases for extracting image URLs at scale across ecommerce, news, academics, social media, and more.

The most common reason is creating a structured catalog of images associated with specific products or pages. This visual data can then be analyzed for machine learning, search indexing, optimization, and more.

But images are also just one part of understanding the broader content of a page. Combining text, data, and images provides a complete view of a website.

So if you want to scrape and understand the images used across a site or set of pages, you‘ll need an effective way to extract the image source URLs.

That‘s where a library like BeautifulSoup comes in handy.

An Introduction to BeautifulSoup

BeautifulSoup is a popular Python library designed for parsing and navigating HTML and XML documents.

It creates a parse tree from page source code that allows you to easily traverse and search the document content.

Some key features of BeautifulSoup that make it great for screen scraping include:

  • Automatic HTML Tree Generation – Beautiful Soup parses document markup into a Python tree structure based on tags, making it easy to traverse and search the content.

  • Flexible Search API – Methods like find(), find_all(), find_parents(), find_next_siblings() allow you to zone in on specific tags and text.

  • Tolerant of Messy HTML – Beautiful Soup gracefully handles real-world messy and poorly formatted markup, making it very robust.

  • Integrates with Popular Parsers – Out of the box support for Python‘s built-in html.parser and 3rd party parsers like lxml.

  • CSS Selector Support – Query HTML elements using jQuery/CSS selector style syntax.

  • Result Sets as Generators – Option to iterate over results without loading full DOM into memory.

  • Prettify – Output cleaned-up formatted HTML to inspect your parse tree.

In 2022, BeautifulSoup4 is the most mature and widely-used version, providing a nice balance of speed and accuracy.

BeautifulSoup removes the headaches of parsing, searching, and iterating over HTML and XML when screen scraping. With just a few lines of code, you can extract the data you need.

Let‘s look at a quick example:

from bs4 import BeautifulSoup

html = """
<html>
<body>

<p>Paragraph</p>
</body> 
</html>
"""

soup = BeautifulSoup(html, ‘html.parser‘)

h1 = soup.find(‘h1‘)
print(h1.text)

# Prints ‘Heading‘

This demonstrates how BeautifulSoup parses the HTML, allows us to search for tags, and extract text.

Now let‘s see how these features can be used to extract image URLs.

Locating Images in HTML Documents

Most of the complexity in scraping image src attributes comes from finding the relevant <img> tags in the first place.

Once you have a reference to the image elements, pulling the URL itself is straightforward.

So let‘s go over different techniques for locating images in HTML pages with BeautifulSoup.

Finding Images by Tag Name

The simplest method is searching for all <img> tags.

You can use the find_all() method, passing the tag name:

images = soup.find_all(‘img‘)

This will return all image tags on the page.

One caveat is that it may also match unrelated uses of <img> for things like icons. So you likely want to apply additional filters.

Finding Images by CSS Class

If the images you want to scrape have a common CSS class, you can search by class name:

product_images = soup.find_all(‘img‘, class_=‘product-image‘)

This will match only <img> tags with a class of product-image.

Finding Images by CSS Selector

For more complex queries, you can use CSS selectors with the select() method:

thumbnail_images = soup.select(‘div.products img.thumbnail‘)

This will find <img> tags with a class of thumbnail only inside <div class="products"> elements.

CSS selectors give you the power to locate elements based on classes, IDs, attributes, hierarchy, and more.

Limiting Results

To avoid overloading memory, you may want to limit the number of results:

product_images = soup.find_all(‘img‘, class=‘product‘, limit=10)

This will return only the first 10 matching image elements.

In summary, using a combination of search by tag name, CSS class, CSS selectors, and result limits allows you to hone in on the particular image tags you want to extract.

BeautifulSoup has additional search tools like keyword arguments and lambda expressions which we don‘t have space to cover fully here.

The key point is that you have many options to precisely target the image content needed for your scraper.

Accessing the Src Attribute

Once you have your list of matching <img> tag elements, you can iterate through them and access the attributes.

The src attribute contains the full URL path to the image file.

Here‘s an example to extract src from all images on a page:

images = soup.find_all(‘img‘)

for img in images:
   print(img[‘src‘])

We locate all images, then loop through printing the src attribute of each.

You can also condense this using a list comprehension:

image_srcs = [img[‘src‘] for img in images]

This gives you a list containing the src URL for each image on the page.

Alternative, you may want a dictionary with src mapped to other attributes:

image_data = [{‘src‘: img[‘src‘], ‘alt‘: img.get(‘alt‘, ‘‘), ‘height‘: img.get(‘height‘, 0)} for img in images]

This extracts the src, alt text, and height for each image into a dictionary.

With just a few lines of Beautiful Soup, you have easy access to image attributes for scraping.

Practical Example: Scraping Product Images

To tie everything together, let‘s walk through a real-world example extracting product images from an ecommerce site.

We‘ll scrape Books to Scrape, an example book store, and build a catalog of product images.

Import Libraries

We‘ll use Requests to download the page HTML and Beautiful Soup to parse it:

import requests
from bs4 import BeautifulSoup

Download the Page

Use Requests to download the HTML of the site‘s homepage:

url = ‘http://books.toscrape.com‘
response = requests.get(url)
html = response.text

This downloads the raw HTML we will parse.

Create the BeautifulSoup Object

Next, parse the HTML into a BeautifulSoup object:

soup = BeautifulSoup(html, ‘html.parser‘)

The html.parser is Python‘s built-in parser – you can also use lxml for speed and accuracy.

Find Product Images

We need to locate the <img> tags specific to the products:

images = soup.find_all(‘img‘, class_=‘thumbnail‘)

Here we search for all images with the thumbnail class – this targets only the product photos.

Extract the Src URLs

With the image elements, we can loop through and grab the src attribute from each:

for image in images:
    url = image[‘src‘]
    print(url)

This prints the relative path for each product image on the page.

Full Script

Putting it all together, here is the complete script to scrape product images:

import requests
from bs4 import BeautifulSoup

url = ‘http://books.toscrape.com‘
response = requests.get(url)
html = response.text

soup = BeautifulSoup(html, ‘html.parser‘) 
images = soup.find_all(‘img‘, class_=‘thumbnail‘)

for image in images:
    url = image[‘src‘]
    print(url)

In around 10 lines of code, we were able to extract image src URLs using BeautifulSoup and Requests!

This gives you the raw materials to build a catalog of product images. Some possible next steps:

  • Download the images locally
  • Store in a database or CSV
  • Build out a complete product catalog with data + images
  • Create a search index using the image src URLs

As you can see, combining Requests for downloading and BeautifulSoup for parsing enables simple yet powerful scraping scripts with Python.

Helpful Tips for Scraping Images

Here are some additional helpful tips for scraping images successfully:

Handle Absolute vs Relative URLs

Image src attributes may contain relative or absolute URLs. You‘ll often want to construct full absolute URLs:

base = ‘http://books.toscrape.com/‘

for image in images:
   src = base + image[‘src‘]

This prepends the base URL to relative links.

Download Images with Requests

You can download images locally using the Requests library:

for image in images:
   url = base + image[‘src‘]
   r = requests.get(url)
   with open(f"images/{image[‘src‘]}", ‘wb‘) as f:
      f.write(r.content)

This writes the image bytes to disk.

Check for Broken Images

Test if images return 404 errors before scraping:

for image in images:
   url = base + image[‘src‘]  
   r = requests.head(url)
   if r.status_code == 200:
     # image exists

This saves bandwidth on dead links.

Follow Robots.txt Rules

Respect crawling limits in the site‘s robots.txt file to avoid bans.

Use Threaded Downloads

Use threading or async libraries like aiohttp to speed up scraping.

Limit Your Scrape Rate

Set delays between requests and limit concurrency to avoid overwhelming servers.

Store Data in CSV/JSON/Database

Retain structured data in files or databases for future analysis.

Track Changes Over Time

Periodically re-scrape and compare old vs new data to detect changes.

By following best practices like these, you can build reliable and scalable scrapers that avoid issues.

Advanced Image Scraping Techniques

Here are some more advanced tactics for challenging image scraping scenarios:

Extract Lazy-Loaded Images

Some sites use lazy loading – detecting when images are fetched via JavaScript and scrolling accordingly.

Scrape Paginated Galleries

Navigate through page number or "Next" links to scrape all images across multiple pages.

Handling Watermarked Images

Detect and filter out watermarked images if the originals are needed.

Scraping Scaled Images

Look for multiple sizes of images and extract original high resolution sources.

Scraping Multi-page PDFs

Extract images from PDFs by converting to HTML first using tools like pdfminer.

Executing JavaScript Rendering

Use Selenium, Playwright, or browser automation to load pages and expand JS-generated image content.

Leveraging Computer Vision

Use libraries like OpenCV and image hashing to identify duplicate images.

Scraping Image-Heavy SPAs

Use API scraping techniques to harvest images from modern JavaScript web apps.

Scraping Images from Posts

Analyze text and HTML content around images for context.

Downloading Image Datasets

Build scripts to scrape image sources and labels for machine learning model training.

As you can see, advanced use cases introduce new complexity – but the core principles remain similar. The key is understanding how images are loaded in the page and adapting your scraping script accordingly.

With some creativity, Beautiful Soup provides the toolset to extract images in virtually any scenario.

Conclusion

Extracting image src URLs is a key scraping skill needed across a variety of domains and use cases.

In this guide, you learned foundational techniques for locating <img> tags in HTML and accessing their attributes using Python and Beautiful Soup.

Here are some key takeaways:

  • BeautifulSoup parses HTML/XML into a navigable tree for easy searching and data extraction.

  • Use find_all() and select() to locate image tags based on criteria like class names.

  • The src attribute contains the path to the image file which you can extract.

  • Construct absolute URLs from relative paths when needed.

  • Download images locally with Requests and store in databases.

  • Follow best practices around polite scraping, error handling, and data storage.

  • Advanced techniques like lazy loading, PDFs, and automation may be needed for complex sites.

Being able to harvest image sources at scale opens up many possibilities for building visual datasets, change monitoring, ecommerce catalogs, and more.

Hopefully this guide provided a solid foundation for using Python and BeautifulSoup to extract images from websites. Let me know if you have any other questions!

Avatar photo

Written by Python Scraper

As an accomplished Proxies & Web scraping expert with over a decade of experience in data extraction, my expertise lies in leveraging proxies to maximize the efficiency and effectiveness of web scraping projects. My journey in this field began with a fascination for the vast troves of data available online and a passion for unlocking its potential.

Over the years, I've honed my skills in Python, developing sophisticated scraping tools that navigate complex web structures. A critical component of my work involves using various proxy services, including BrightData, Soax, Smartproxy, Proxy-Cheap, and Proxy-seller. These services have been instrumental in my ability to obtain multiple IP addresses, bypass IP restrictions, and overcome geographical limitations, thus enabling me to access and extract data seamlessly from diverse sources.

My approach to web scraping is not just technical; it's also strategic. I understand that every scraping task has unique challenges, and I tailor my methods accordingly, ensuring compliance with legal and ethical standards. By staying up-to-date with the latest developments in proxy technologies and web scraping methodologies, I continue to provide top-tier services in data extraction, helping clients transform raw data into actionable insights.