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How to Scrape Google Images With Python: An In-Depth Tutorial

Do you ever find yourself wanting to extract and collect image data from Google Images for a project? Want to know how you can programmatically download image search results? Then you‘re in the right place!

This step-by-step guide will teach you how to build your own custom web scraper to extract URLs, titles, descriptions for images on Google Images using Python.

We‘ll walk through it all – setting up the environment, making requests, parsing responses, storing data, and more. We‘ve compiled tips from over 10 years of web scraping experience to help you successfully scrape Google Images.

Let‘s get started!

Why Scrape Google Images?

With over 250 billion images indexed, Google Images is the world‘s largest image search engine. Every day, people conduct over 3.5 billion image searches on Google to find visually relevant content.

Whether you need images for training computer vision models, researching visual trends, or building a relevant image library, scraping helps collect image data fast compared to manual saving.

Some examples of what you can use scraped Google Images for:

  • Machine learning training datasets – Compile categorized image datasets for various ML tasks like classification, object detection etc.
  • Content marketing analysis – Find the types of images your audience responds to best for social media and ads.
  • Research – Gather images around topics of academic or professional interest quickly.
  • Website design – Search for creative commons images to use for your projects legally.
  • Product analysis – See how your products are displayed across websites based on search results.

And many more! Scraping opens up programmatic access to Google‘s massive image catalog.

Prerequisites for Scraping Google Images with Python

Before we jump into the code, let‘s ensure you have the right environment setup:

Python Version

We recommend using Python 3.6 or higher. Python 2 reached end-of-life in 2020, so make sure you are on Python 3 for access to the latest libraries and features.

You can check your Python version at the command prompt:

$ python --version
Python 3.8.2

If you don‘t have Python installed, you can get the latest version at

Python Libraries

We will use the following key Python libraries for scraping:

  • Requests – Sends HTTP requests to websites. We will use it to request search results from Google Images.
  • BeautifulSoup – Parses HTML and XML documents. We need it to extract data from Google‘s response.
  • CSV – Allows saving scraped data as CSV files.

Make sure you have the above libraries installed by entering the following into your command prompt:

$ pip install requests beautifulsoup4 csv

This will download and install the necessary libraries and dependencies using the pip package manager.

If you run into permissions errors, try installing with sudo or check the pip documentation on potential issues.

Now we‘re all set up! Let‘s start writing the scraper.

Making Requests to Google Images

The first step is to use Python to mimic a browser requesting images for a particular search term.

For this, we use the requests library to send a GET request to the Google Images URL with our chosen term and parameters.

import requests

search_term = "kittens"
url = f"{search_term}&source=lnms&tbm=isch&sa=X&ved=2ahUKEwie44_AnqHpAhUhBWMBHUFGD90Q_AUoAXoECBUQAw&biw=1920&bih=947"

response = requests.get(url)

Here we are setting the search term to "kittens". The url is built using f-strings to insert the search_term in the Google Images path.

We get back a Response object with the search result HTML content.

Let‘s add some checks:

if response.status_code != 200:
   raise Exception("Error in API request")


This verifies that the response status code is 200 OK and the content type header indicates HTML, before we try parsing.

Parsing Google Images Results with BeautifulSoup

Now that we have the HTML search result content, we need to parse and extract the image data we want. This is where BeautifulSoup comes in!

First, we create a BeautifulSoup object from the HTML response text:

from bs4 import BeautifulSoup

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

soup contains the parsed document tree we can now navigate and search.

Based on analyzing the Google Images result page structure, we find that the images are housed under a <div> with ID "isr_mc".

We can use BeautifulSoup‘s select() method to extract this key element:

results_block = soup.select_one(‘#isr_mc‘)

The images themselves are individual <div>s under the class rg_i. We find all of them:

image_elements = results_block.find_all(‘div‘, {‘class‘: ‘rg_i‘})

Now image_elements contains all the image cards on the first page of results!

Extracting Image Data into a Python Dictionary

With the key page elements extracted, we can now loop through the image_elements and pull out the relevant data using BeautifulSoup:

image_data = []

for img in image_elements:
   url = img.find(‘img‘)[‘src‘]

   title = img.find(‘h3‘).text
   desc = img.find(‘div‘, {‘class‘: ‘VwiC3b‘}).text

   image_item = {
      ‘url‘: url,
      ‘title‘: title,
      ‘desc‘: desc


We find and store the key image attributes:

  • url – The full resolution image URL contained in the <img> tag
  • title – The title extracted from the <h3> tag
  • desc – The image description from the appropriately classed <div>

Finally, we append each result image metadata as a dictionary into a list image_data.

Saving and Exporting the Scraped Images

Now that we‘ve extracted the images metadata, let‘s save it to a CSV file for further use and analysis:

import csv

with open(‘google_images.csv‘, ‘w‘, newline=‘‘) as csv_file:
   writer = csv.DictWriter(csv_file, fieldnames=[‘url‘, ‘title‘, ‘desc‘])

   for img in image_data:

We open a new CSV for writing, define column headers, and write each image‘s data as a new row.

The result is a google_images.csv containing the metadata for images matching our search term!

Some other ideas for storing the data:

  • JSON – Save as structured JSON objects using json.dump()
  • Database – Insert into a SQL database like PostgreSQL
  • Cloud storage – Upload to cloud platforms like Amazon S3

This covers the core workflow – let‘s recap:

  • Request search results from Google Images with search parameters
  • Parse the HTML response using BeautifulSoup
  • Extract image data like URLs into Python lists/dicts
  • Store structured results in CSV, JSON, database etc.

But we‘ve only scratched the surface of robust web scraping! Next let‘s talk about dealing with some common challenges.

Handling Bot Detection and CAPTCHAs

A downside of scraping is that websites try to detect and block bots with CAPTCHAs and other defenses. So we need ways to mimic human behavior to avoid triggers.

Here are some tips for stealthier scraping:

  • Use a random User-Agent string in the request headers to spoof different devices and browsers.
  • Add time delays between requests to limit speed.
  • Handle CAPTCHAs by integrating a service like Anti-CAPTCHA.
  • Use proxies to make requests from multiple IP addresses.
  • Rotate proxies frequently to vary fingerprints.

Web scraping safely and ethically requires care to avoid overloading sites. But a few simple tricks can help bypass defenses.

Scraping Multiple Pages of Image Results

Often you‘ll need more than one page of search results. We can scrape additional pages by modifying the search URL parameters.

Google Images supports start and ijn for pagination. To get pages 2 and 3:

page1 = f"{search_term}" 

page2 = f"{page1}&start=21&ijn=2"
page3 = f"{page1}&start=42&ijn=3"

We can loop through page ranges to extract data:

pages = range(1, 4)

for page in pages:
   url = f"{page1}&start={21 * (page - 1)}&ijn={page}"
   response = requests.get(url)

   # Parsing and extraction steps

Setting an incrementing start and ijn will enable scraping multiple pages.

Accelerating Scraping with Threaded Parallelism and Async

To speed up scraping for large datasets, we can use techniques like multithreading and async:

  • Threads – Process multiple pages concurrently using threading
  • Async – Use asyncio and aiohttp for async non-blocking requests

Here is an example async scraper to extract data faster:

import asyncio
import aiohttp

async def scrape_page(session, url):
   async with session.get(url) as response:
      # Extract data

async def main():
   async with aiohttp.ClientSession() as session:
      urls = [google_urls]
      await asyncio.gather(*[scrape_page(session, url) for url in urls])

Async web scraping helps speed up the process considerably!

Scraping Responsibly with Google Images

While scraping opens up useful possibilities, keep in mind:

  • Avoid hitting Google servers too aggressively with requests.
  • Extract and use data only for legal and ethical purposes.
  • Review scraped images for copyrights. Avoid saving or reusing protected images.
  • Always check Google‘s Terms of Service for allowed usage.

Balancing speed and volume with responsible scraping is crucial.

Key Takeaways from Scraping Google Images with Python

Let‘s recap what we learned:

  • With some Python code, we can imitate a browser and request search results from Google Images.
  • BeautifulSoup parses the HTML response and allows extracting image data through DOM traversal.
  • We collected image URLs, titles and descriptions into structured Python dicts.
  • Multipage scraping, async requests, proxies etc. help build robust scalable scrapers.
  • However, it‘s critical to scrape ethically and avoid overloading servers.

Scraping opens up automated programmatic access to Google‘s vast visual index. With some Python skills and responsible practices, you can leverage scraping for all kinds of image-based projects!

So go forth and gather image data, train computer vision models, analyze visual trends or whatever else your heart desires! Just remember to scrape kindly.

Happy scraping!


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