Mastering image scraping in 2023. step by step instructions

Web scraping is a popular technique that businesses and individuals use to search and extract valuable data from web sources. Image scraping is an aspect of web scraping that is gaining popularity. Image scraping has become a powerful method for gathering data and insights with the growing importance of visual content.

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

What is image scraping?

Image scraping is a technique used in web writing to extract image data from web sources in various formats, including JPEG, PNG, and GIF. The term usually 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 example, a codeless image scraper may be the best option for quickly and easily collecting data from multiple pages. However, an internal image scraper may be more convenient if you need a more customized solution.

Whether you’re using an in-house or no-code web scraper, it’s important to ensure that you’re only scraping publicly available images that 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 required libraries. The scraper library you choose will depend on your specific data collection requirements. Nice soup and queries are usually the easiest for basic image scraping tasks. Meanwhile, the Scrapy and Pillow libraries provide more advanced features for web scraping images. Selenium is typically used to scrape dynamic web pages that require 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 “pip install requests” command in your prompt or terminal.
In:identify the URLs of the images on the web page you want to scrape; You can inspect the HTML code of the page using your browser’s developer tools. Image URLs are generally included in the src attribute of the tag in the HTML content (Figure 1). Copy the image URL from the src attribute to use the Python library.

Figure 1: Shows how to find the data you want to scrape

Request the target web page. Once you’ve identified the target URLs, you can send a request to the web page that contains the images you want to scrape. For example, if you’re using the Requests library to scrape an Amazon product image, you could use the following code:
url = ‘https://amazon.com/xyz’
response = requests.get(url)
Parse HTML content. You can use a Python library such as Beautiful Soup or lxml to parse the HTML content of the response.
Export image URLs. To remove image URLs from all image tags, you can use ‘src’ attribute to specify the URL of the image file to download.
Download all images. Once you have the image URLs, you need to 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 images using “urlretrieve()” function.
- urllib2:It provides more advanced features for sending HTTP requests. You can use “urlopen()” function to open and use a link to the image URL “read()” method of reading image data.
- Requests: It is a third-party Python library. You can use “get()” function to send a request to the destination 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 store an image in a directory /path/to/images. It stores the image data in a file called image.jpg in the directory, but you can change the name of the image file 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 find and select the image elements they want to scrape from a web page.

A codeless image scraper may be more suitable depending on the volume and complexity of the data to be extracted. For example, some codeless web scrapers may include proxy servers and anti-scraping solutions (such as a CAPTCHA solution) to help users bypass anti-scraping measures.

Bright Data’s Image Scraper is accessible to a wider range of users, including those with little or no programming experience. Image scraper tool allows users to scrape data from any online source and track image ranking. It complies with all relevant data protection laws, including GDPR and CCPA.

Figure 2: Bright Data’s Image Scraper

Bright Data's image scraper allows businesses and individuals to harvest image data without writing a single line of code.

Best practices for image scraping to avoid common challenges

To avoid technical and legal issues, you need to scrape image data carefully and follow best practices. Here are some best practices for image scratching.

Check image formats and sizes. Images can be in different formats such as JPEG, GIF and sizes such as small thumbnails. Make sure your image scraper can handle all these formats and different image sizes.
Follow ethical and legal guidelines; Image scraping may be illegal under certain circumstances, 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 that your data collection activities do not violate any rules or policies. For example, most websites use rate limiting to manage crawling traffic and prevent overuse of APIs. Check the rate limits set by the site’s API and follow them to avoid getting blocked.
Respecting the site’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.