How to Scrape Google Images with Python

Images are a crucial part of modern businesses, whether for machine learning, content generation, digital marketing, or research. Companies across various industries require large datasets of images to train AI models, analyze trends, or enrich their content. Manually collecting these images is time-consuming, which is where Google Image Scraping comes into play.

In this guide, we’ll explore how to scrape Google Images with Python using various methods, including requests, BeautifulSoup, and Selenium. Additionally, we will introduce an easier and more efficient alternative—Google Image Search API—for seamless image extraction at scale.

Setting Up Your Python Environment for Web Scraping

Before we start coding, let’s install the necessary libraries for scraping Google Images.

Install Required Python Libraries

Open your terminal or command prompt and install the following packages:

pip install requests
pip install beautifulsoup4
pip install selenium
pip install pillow
pip install webdriver-manager

What Do These Libraries Do?

• requests – Fetches the HTML content of web pages.
• BeautifulSoup4 – Parses HTML and extracts useful information like image URLs.
• Selenium – Automates browser interaction to scrape dynamic content.
• Pillow – Handles image processing and storage.
• webdriver-manager – Automatically manages the installation of WebDriver for Selenium.

Python Code for Scraping Static Google Images

import requests
from bs4 import BeautifulSoup
import os

# Define the search query
query = "sunset images"
search_url = f"https://www.google.com/search?q={query}&tbm=isch"

# Set headers to mimic a real browser
headers = {"User-Agent": "Mozilla/5.0"}

# Fetch the page content
response = requests.get(search_url, headers=headers)
soup = BeautifulSoup(response.text, "html.parser")

# Extract image URLs
image_tags = soup.find_all("img")

# Create directory to store images
os.makedirs("images", exist_ok=True)

# Download and save images
for i, img in enumerate(image_tags):
    img_url = img.get("src")
    if img_url:
        img_data = requests.get(img_url).content
        with open(f"images/image_{i}.jpg", "wb") as f:
            f.write(img_data)

print("Images downloaded successfully!")

Limitations of This Approach

• Google dynamically loads images using JavaScript, which requests alone cannot handle.
• Many images are stored as thumbnails; high-resolution versions need additional requests.

To handle these limitations, we use Selenium.

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
import time
import os

# Set up Selenium WebDriver
options = webdriver.ChromeOptions()
options.add_argument("--headless")  # Run in headless mode
options.add_argument("--disable-blink-features=AutomationControlled")
driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)

# Define search query
query = "sunset images"
search_url = f"https://www.google.com/search?q={query}&tbm=isch"

# Open Google Images
driver.get(search_url)
time.sleep(2)  # Wait for images to load

# Scroll down multiple times to load more images
for _ in range(5):
    driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
    time.sleep(2)

# Extract image URLs
image_elements = driver.find_elements(By.CSS_SELECTOR, "img")
image_urls = [img.get_attribute("src") for img in image_elements if img.get_attribute("src")]

# Create directory and save images
os.makedirs("selenium_images", exist_ok=True)

for i, img_url in enumerate(image_urls):
    img_data = requests.get(img_url).content
    with open(f"selenium_images/image_{i}.jpg", "wb") as f:
        f.write(img_data)

print("Images downloaded successfully!")

driver.quit()

pip install requests pandas

import requests
import pandas as pd

payload = {
    "source": "google_images",
    "domain": "com",
    "query": "sunset",
    "parse": "true",
    "geo_location": "United States",
    "context": [
        {
            "key": "search_operators",
            "value": [
                {"key": "filetype", "value": "jpg"},
                {"key": "inurl", "value": "image"},
            ],
        }
    ],
}

USERNAME = ""
PASSWORD = ""

response = requests.post(
    "https://realtime.oxylabs.io/v1/queries",
    auth=(USERNAME, PASSWORD),
    json=payload
)

# Extract results
data = response.json()

# Extracting image details
results = data["results"][0]["content"]
image_results = results["results"]["organic"]

# Create a DataFrame to store the images
df = pd.DataFrame(columns=["Image Title", "Image Description", "Image URL"])

for img in image_results:
    title = img.get("title", "No Title")
    description = img.get("desc", "No Description")
    url = img.get("url")

    df = pd.concat(
        [pd.DataFrame([[title, description, url]], columns=df.columns), df],
        ignore_index=True,
    )

# Save the data to CSV and JSON files
df.to_csv("google_images.csv", index=False)
df.to_json("google_images.json", orient="split", index=False)

print("Image data saved successfully!")

Step 6 — Example Use Case

payload = {
    "source": "google_images",
    "domain": "com",
    "query": "https://upload.wikimedia.org/wikipedia/commons/a/a3/June_odd-eyed-cat.jpg",
    "parse": "true",
    "geo_location": "United States",
    "context": [
        {
            "key": "search_operators",
            "value": [
                {"key": "site", "value": "wikipedia.org"},
                {"key": "filetype", "value": "jpg"},
                {"key": "inurl", "value": "image"},
            ],
        }
    ],
}

While Python-based scraping methods using Selenium and BeautifulSoup can work, they come with significant challenges:

• IP blocks and CAPTCHAs require constant workaround.
• Google’s dynamic loading makes scraping unreliable.
• Frequent website changes break scrapers, requiring ongoing maintenance.

Advantages of Oxylabs’ API Over Manual Scraping

• Bypasses CAPTCHA & Bot Detection — No need for proxies or CAPTCHA solvers.
• Scalable Solution — Handles large-scale image scraping without IP bans.
• Fast & Efficient — Returns high-resolution images with minimal delays.
• Structured Data Output — Eliminates the need for manual HTML parsing.
• Easy Integration — Simple POST requests provide ready-to-use results.

Instead of worrying about Google’s bot protection, Oxylabs’ Google Image Search API provides a hassle-free, scalable, and legal way to scrape Google Images efficiently.

Final Thoughts

Scraping Google Images with Python is a good choice if you using Selenium, BeautifulSoup, and Requests, but it comes with limitations. IP blocks, CAPTCHA challenges, and JavaScript rendering issues make manual scraping time-consuming and unreliable.

For a faster, more efficient, and scalable solution, Oxylabs’ Google Image Search API is the best choice. With ready-to-use structured results, CAPTCHA-free access, and full automation, it’s the ideal tool for businesses and developers who need high-quality image data.