Technical considerations for downloading images include understanding file formats (JPEG, PNG , etc.), managing file sizes and compression, and handling potential issues like broken URLs or restricted access. Automating this process through scripts can save developers time and provide more control over how images are downloaded and used.

Before we start

Working with images in Python? There’s an easy, automatic way to resize and crop them

Cloudinary is a cloud-based image management platform with a generous free plan and a Python SDK . Cloudinary can:

You can sign up free and try it for yourself or learn more below.

This is part of a series of articles about image optimization

In this article:

5 Ways to Downloading an Image from a URL with Python

Python offers an elegant solution for many tasks, from web scraping to automation. Whether you’re working on a data collection project for machine learning, building a web scraper, or simply want to automate the download of media files, Python has you covered.

The Python ecosystem is rich with options, from the basic built-in libraries to more sophisticated third-party tools. Here, we outline five different approaches you can consider when faced with the task of downloading images from URLs.

1. Using urllib.request

urllib.request is a Python module used for opening and reading URLs, particularly HTTP URLs. It’s part of the larger urllib package, which provides tools for working with URLs and web communication. It provides functions for opening URLs and can be used to download images or other files from a URL.

Here is a simple code snippet that shows how we can use urllib.request to download an image from a URL:

import urllib.request
def download_image(url, save_as):
    urllib.request.urlretrieve(url, save_as)
image_url = 'http://example.com/image.jpg'
save_as = 'image.jpg'
download_image(image_url, save_as)
In the above code, we define a function called download_image that takes in a URL and a filename as parameters. The urllib.request.urlretrieve function is used to download the file at the specified URL and save it as the given filename.
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2. Using the Requests Library
The Requests library is a popular Python library for sending HTTP requests. It is more user-friendly than urllib and provides a higher-level API for making requests. Requests abstract the complexities of making HTTP requests behind a beautiful, simple API so you can focus on interacting with services and consuming data in your application. It’s often recommended for web scraping, interacting with REST APIs, and general web programming tasks in Python due to its flexibility and ease of use.
Here is how we can use the Requests library to download an image from a URL:
import requests
def download_image(url, save_as):
    response = requests.get(url)
    with open(save_as, 'wb') as file:
        file.write(response.content)
image_url = 'http://example.com/image.jpg'
save_as = 'image.jpg'
download_image(image_url, save_as)
In this example, we send a GET request to the URL using requests.get. The response from this request is an object that contains the server’s response to our request. We then open the file in write mode (wb stands for write binary) and write the response’s content to the file.
3. Using urllib3
urllib3 is another Python module for handling URLs and sending HTTP requests.urllib3 fills in some of the gaps left by the standard libraries and provides a more feature-rich, efficient, and easy-to-use solution for making HTTP requests in Python. It’s used by several other Python libraries, including requests, for handling HTTP and HTTPS requests.
Here’s how we can use urllib3 to download an image from a URL:
import urllib3
def download_image(url, save_as):
    http = urllib3.PoolManager()
    response = http.request('GET', url)
    with open(save_as, 'wb') as file:
        file.write(response.data)
image_url = 'http://example.com/image.jpg'
save_as = 'image.jpg'
download_image(image_url, save_as)
In this code, we first create an instance of the PoolManager class. This class handles all of the details of connection pooling and thread safety, allowing us to focus on the task at hand. We then use the request method to send a GET request to the URL and write the response data to the file.
4. Using wget
The wget library in Python is a lightweight, easy-to-use tool designed for downloading files from the web. It mirrors the functionality of its Unix namesake, wget, offering a simple way to download files via HTTP, HTTPS, and FTP protocols with minimal code. The library is particularly appreciated for its simplicity, allowing one-line commands to retrieve and save files locally. This simplicity makes it an excellent choice for scripts and applications that need to download resources from the Internet without the overhead of more complex libraries.
Here’s an example of how to use wget to download an image from a URL with Python:
import wget
def download_image(url, save_as):
    wget.download(url, save_as)
image_url = 'http://example.com/image.jpg'
save_as = 'image.jpg'
download_image(image_url, save_as)
In the code above, we use the wget.download function to download the image from the URL and save it as the given filename.
5. Using PyCURL
PyCURL is a Python interface to the libcurl library, which provides capabilities for transferring data with URL syntax. It inherits libcurl’s versatility and speed, making it a powerful option for Python developers needing to handle complex network operations, multipart file uploads, form submissions, and session cookies. Despite its powerful capabilities, PyCURL’s interface can be more complicated and lower-level than other HTTP libraries like Requests, making it less accessible for beginners or those looking for simplicity.
Here’s a code snippet that demonstrates how to use PyCURL to download an image from a URL:
import pycurl
def download_image(url, save_as):
    with open(save_as, 'wb') as file:
        curl = pycurl.Curl()
        curl.setopt(curl.URL, url)
        curl.setopt(curl.WRITEDATA, file)
        curl.perform()
        curl.close()
image_url = 'http://example.com/image.jpg'
save_as = 'image.jpg'
download_image(image_url, save_as)
In this code, we first open the file in write mode. We then create an instance of the Curl class and use the setopt method to set the options for the curl object. Finally, we perform the download operation with the perform method and close the curl object with the close method.
Optimizing and Editing Images at Scale with Cloudinary
We delved into how Python facilitates downloading images in bulk from URLs. As a web developer, however, your task doesn’t end there. Often, you need to go a step further—resize, crop, apply filters, or even add watermarks to those images, all while not losing sight of preserving their quality. That’s where Cloudinary, an end-to-end image and video management solution, comes in here.
Cloudinary is a cloud-based service that answers almost every imaginable need for website image and video management. It offers an array of robust capabilities for transforming, optimizing, and delivering media, which can be deployed via APIs, ensuring a seamless integration into your existing project files.
Straight out of the box, Cloudinary allows you to manipulate media files programmatically—a remarkable game changer for those dealing with large-scale transformations. With its Python SDK, you can easily integrate Cloudinary into your Python scripts, adding a whole new layer of functionality to your bulk downloaded images.
But how exactly can you leverage all this power and optimize your media with Cloudinary? Let’s dive into how you can programmatically edit an image using Cloudinary’s impressive suite of transformations.
Editing an Image with Cloudinary’s Programmable Media
Here is an easy-to-follow guide to demonstrate the editing capacity of Cloudinary using Python.
NOTE: This guide assumes you have a Cloudinary account and Python installed on your workspace. If you don’t already have an account, you can sign up for free to get started.
Install Cloudinary’s Python library. First things first, let’s install Cloudinary’s Python library. You can do it with pip:
pip install cloudinary
Configure Cloudinary’s settings. Next, configure your Cloudinary settings. Create a .env file within your project and copy your credentials there, which can be found in your account dashboard.
CLOUDINARY_URL=cloudinary://API_Key:API_Secret@Cloud_Name
Replace ‘API_Key’, ‘API_Secret’, and ‘Cloud_Name’ with your Cloudinary details, which you can all get from your Cloudinary dashboard.
Open and Transform the Image: Now, let’s open and transform an image. Say you want to resize an image to a width of 500px while maintaining its aspect ratio. Here’s how you can do it:
# Set your Cloudinary credentials
# ==============================
from dotenv import load_dotenv
load_dotenv()
import cloudinary.uploader
response = 
cloudinary.uploader.upload('path/to/your/image', width=500, 
crop="scale")
Replace ‘path/to/your/image’ in the above command with the real file path.
You can use cloudinary.uploader.upload() function to upload an image from any web location by providing its URL as parameter or upload images from your local file system.
Check out How to Upload images in Python tutorial for more details.
And voilà! You’ve just programmatically transformed your image. The response dictionary will contain the URL of the updated image stored in your Cloudinary account.
With just Python and Cloudinary’s powerful set of tools, you can handle image editing tasks that would have once required complex maneuvering. The power is at your fingertips—how will you optimize the media on your websites?
Take your digital experience to the next level with Cloudinary’s powerful media optimization tools. Sign up for free today.
Learn more in Cloudinary’s Python quick start guide
                  
In my experience, here are tips that can help you better download images from URLs using Python, and streamline your image handling process:
Validate URLs before downloading

Always validate URLs to ensure they are well-formed and accessible before attempting to download images. Use Python’s urlparse module to parse and validate URLs, and implement error handling for potential issues like timeouts, broken URLs, or HTTP errors. This will prevent your scripts from crashing when encountering invalid URLs.
Handle large image downloads efficiently

For downloading large images or numerous images, consider streaming the download in chunks using requests.get(url, stream=True). This prevents memory overflow and allows you to write the image data to disk as it is being downloaded, which is particularly useful for handling high-resolution images or large datasets.
Set user-agent headers

Some websites block non-browser requests to prevent web scraping. Bypass these restrictions by setting a user-agent header in your HTTP requests to mimic a browser. For example, headers = {'User-Agent': 'Mozilla/5.0'} when using the requests library.
Use asynchronous downloads for speed

When downloading multiple images, consider using asynchronous programming with libraries like aiohttp or asyncio. Asynchronous downloads can significantly reduce the time required to download large sets of images by allowing your script to fetch multiple images concurrently.
Check for duplicates before saving

Avoid unnecessary storage of duplicate images by checking if an image already exists in your storage before saving it. Use the image’s URL or content hash (e.g., hashlib.md5(response.content).hexdigest()) as a unique identifier to check for duplicates. This approach is especially useful in web scraping or when downloading images from a frequently updated source.
Incorporate retry mechanisms

Network issues can cause download failures. Implement retry mechanisms in your script to automatically attempt downloading an image again if the first attempt fails. Python’s retrying or tenacity libraries can be used to add retries with exponential backoff, helping to ensure that intermittent network issues do not cause image downloads to fail permanently.
Optimize downloaded images on-the-fly

If you are downloading images for web use, optimize them immediately after downloading to reduce file size and improve loading times. Use Python’s Pillow library for basic optimizations, such as resizing or compressing, or integrate with Cloudinary to perform advanced optimizations like format conversion, cropping, and applying filters directly via URL transformations.
Store images with meaningful names

Instead of saving images with generic filenames, use meaningful names that reflect their content or source. For example, if downloading images from a product page, use the product name and ID in the filename. This practice improves file organization and makes it easier to identify images later.
Manage image metadata

When saving images, consider preserving important metadata, such as EXIF data (which includes information like the camera model, orientation, and timestamp). Tools like Pillow can help you manage and save metadata along with the images, which can be crucial for tasks like photo archiving or detailed image analysis.
Leverage Cloudinary for scalable image management

When handling images at scale, consider integrating Cloudinary into your workflow. Cloudinary’s Python SDK allows you to upload images directly to the cloud, where you can perform various transformations (e.g., resizing, cropping, format conversion) on-the-fly. This not only saves local storage but also automates the optimization and delivery of images through a global CDN, ensuring faster loading times and reduced bandwidth usage.
These tips will help you efficiently download, manage, and optimize images using Python, particularly when working with large datasets or building scalable applications.