-
How to extract images from a website during scraping?
Extracting images from a website involves identifying the HTML tags where the image URLs are stored. Most images are found in img elements with src attributes that point to the image file. Using Python’s BeautifulSoup, you can easily extract these URLs for static pages. For dynamic sites, tools like Puppeteer or Selenium can help load all images before scraping.
Here’s an example using BeautifulSoup:import requests from bs4 import BeautifulSoup url = "https://example.com/gallery" headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(url, headers=headers) if response.status_code == 200: soup = BeautifulSoup(response.content, "html.parser") images = soup.find_all("img") for idx, img in enumerate(images, 1): src = img.get("src") print(f"Image {idx}: {src}") else: print("Failed to fetch the page.")For saving images locally, you can use the requests library to download each image. Dynamic content, such as lazy-loaded images, requires browser automation tools to ensure all images are fully loaded before extraction. How do you handle large-scale image scraping efficiently?
-
3 Replies
-
I use the requests library to download images directly after extracting their URLs. It’s fast and simple for static sites.
-
For lazy-loaded images, I rely on Selenium to scroll through the page and ensure all images are loaded before scraping.
-
Batch downloading images with multithreading speeds up the process, especially for large datasets.
Log in to reply.