Mastering Image Scraping in 2024: Step-by-Step Instructions

Updated on Jan 2

4 min read

Table of contents

What is image scraping?How to scrape images from websites 1. Image scraping with Python 2. No-coding Image Scrapers Best practices for image scraping to avoid common challenges Further reading

Web scraping is a common technique businesses, and individuals use to search and extract valuable data from web sources. Image scraping is one aspect of web scraping that is gaining popularity. Scraping images has become a powerful method for collecting data and insights with the increasing importance of visual content.

In this article, we’ll explain how to scrape images using Python and cover other techniques for extracting images, such as no-code image scrapers. We will also provide best practices for ethical and legal image scraping.

What is image scraping?

Image scraping is a technique used in web scraping to extract image data from web sources in various formats, including JPEG, PNG, and GIF. The term typically refers to automated processes implemented using a Python library, such as Beautiful Soup, or a no-code scraper.

How to scrape images from websites

The web scraping method you choose depends on your specific image scraping needs and requirements. For instance, a no-code image scraper may be the best option to collect data from several pages quickly and easily. However, an in-house image scraper may be more suitable if you need a more customized solution.

Whether you are using an in-house or a no-code web scraper, it is important to ensure that you only scrape publicly available images you have permission to use.

1. Image scraping with Python

You can scrape images from a web page using Python by following these steps:

Install the necessary libraries: The scraping library you choose will depend on your specific data collection requirements. Beautiful Soup and Requests are typically the easiest for basic image scraping tasks. At the same time, Scrapy and Pillow libraries provide more advanced functions for web scraping images. Selenium is generally used for scraping dynamic web pages, which requires user interaction, such as clicking buttons or navigating menus.
You can install the desired library using the pip command, the Python package installer. For example, to install Requests, type the “pip install requests” command into your prompt or terminal.
Identify the image URLs on a web page you wish to scrape: You can inspect the HTML source code of a page using developer tools in your browser. Image URLs are generally included in the src attribute of a <img> tag in the HTML content (Figure 1). Copy the image URL from the src attribute to use a Python library.

Figure 1: Showing how to locate the data you wish to scrape

Request the target web page: Once you’ve identified the target URLs, you can send a request to the web page containing the images you want to scrape. For instance, if you are using the Requests library to scrape an Amazon product image, you can use the following code.
url = ‘https://amazon.com/xyz’
response = requests.get(url)
Parse the HTML content: You can use a Python library like Beautiful Soup or lxml to parse the HTML content of the response.
Extract the image URLs : To extract the image URLs from all image tags, you can use the ‘src’ attribute to specify the URL of the image file that needs to be downloaded.
Download all the images: Once you have the image URLs, you must download the images from the URLs. Python includes several built-in modules for downloading images from web pages, such as urllib, urllib2 and Requests.
- urllib: It is part of the Python standard library. You can download all the images using the “urlretrieve()” function.
- urllib2: It provides more advanced features for sending HTTP requests. You can use the “urlopen()” function to open a connection to the image URL and use the “read()” method to read the image data.
- Requests: It is a third-party Python library. You can use the “get()” function to send a request to the target URL and use the content attribute to access the image data.
Save the downloaded image data: Finally, save the downloaded images to your local file system. For example, you can use the “os” module to save an image to the directory /path/to/images. It keeps the image data in a file called image.jpg in the directory path, but you can change the image filename to suit your needs.

2. No-coding Image Scrapers

No-code image scrapers can extract images from a web page without any programming knowledge. No-code web scrapers typically offer a graphical user interface (GUI) that allows users to locate and select the image elements they want to extract from a web page.

A no-code image scraper may be more suitable depending on the volume and complexity of the data to be extracted. For instance, some no-code web scrapers may include proxy servers and anti-scraping solutions (e.g. CAPTCHA solving) to help users circumvent anti-scraping measures.

Bright Data’s Image Scraper is accessible to a wider range of users, including those with limited or no programming experience. The image scraping tool enables users to scrape data from any online source and track the rankings of images. It complies with all relevant data protection laws, including GDPR and CCPA.

Figure 2: Bright Data’s Image Scraper

Bright Data's image scraper enable businesses and individuals collect image data without writing a single line of code.

Best practices for image scraping to avoid common challenges

It is essential to scrape image data cautiously and follow best practices in order to avoid technical and legal issues. Here are some best practices for image scraping:

Check image formats and sizes: Images can come in various formats, such as JPEG, GIF, and sizes, such as small thumbnails. Ensure that your image scraper can handle all of these formats and different image sizes.
Follow ethical and legal guidelines: Image scraping may be illegal under certain conditions, such as when it violates copyright laws. Check the terms of service and the Robots.txt file of the website you intend to scrape to ensure your data collection activity does not violate any rules or policies. For example, most websites employ rate limits to manage crawling traffic and prevent the overuse of APIs. Check for any rate limits imposed by the website’s API and comply with them to avoid being blocked.
Respecting the website’s server and bandwidth: Limit the frequency and volume of your requests or add time delays between your requests. You can also use caching techniques to avoid requesting the same image data multiple times.