Web Performance Exploring Video Hosting The Art of Compromise: How Lossy Compression Works Understanding the ‘Image Loading Error’: Comprehensive Guide A Comprehensive Guide to Resizing Images on iOS VBR vs CBR: Understanding Bitrate for Optimal Media Handling Displaying Images with Python’s Top 5 Image Libraries 4 Ways to Add Images to GitHub README + 1 Bonus Method Converting Images with Python JavaScript Image Optimization Techniques Building an Image Picker in React with react-native-image-crop-picker 6 Ways to Save Images in Python 5 Ways to Import Images in React + Bonus Automation Method Extract Text from Images in Python with Pillow and pytesseract Downloading Image from URL in Python: 5 Ways with Code Examples Image.open Python Function: Syntax and Quick Tutorial Complete Guide to Video SEO & Automating It With Cloudinary A Complete Guide To Website Image Optimization Video Encoding: How It Works, Formats & Best Practices The Developer’s Guide to PDFs Integrating Cloudinary With Gatsby For Image Optimization Mastering Image Optimization With Netlify And Cloudinary Seamlessly Integrate Cloudinary With Netlify For Optimised Website Assets Ultimate Guide to Asset Optimization Using Cloudinary and Netlify Adding Video To Magento Understanding Magento Media Adding a Video Widget to Your Website: What You Need to Know SDR vs. HDR: Differences and 5 Considerations for Content Creators Audio Manipulation In PHP Image Management Systems: Key Capabilities and Best Practices Video CDN: Why You Need It and Top 5 Video CDNs Video Optimization: Why You Need It and 5 Critical Best Practices Multi CDN: 8 Amazing Benefits, Methods, and Best Practices What Is an Optimized Website and 6 Ways to Optimize Yours Understanding Image Hosting for Websites Sprite Generation with CSS and 4 Automated Tools 8 Image SEO Optimization Tips to Improve Your Search Rankings Website Speed: 5 Reasons Your Site is Slow and How to Fix It Web Performance: What is it, Trends and Insights for 2024

Converting Images with Python

convert images python

Python is a versatile programming language beloved for its simplicity and efficiency, enabling developers to perform complex tasks with relatively straightforward code. It also excels in image processing—a critical operation for web developers who frequently need to optimize images for speed, size, and compatibility across different web browsers and devices.

Converting images, for example, from JPEG to PNG or resizing them to fit various screen resolutions not only enhances website performance but also significantly improves user experience. This can also be pivotal in content management systems where dynamic image handling is required. In this article, we’ll walk you through a step-by-step process of converting images in Python from one format to another.

Step by Step Image Conversion with Python

In this tutorial, we’ll be using Pillow (a fork of Python Imaging Library) to convert images from one format to another. Pillow can be used for a variety of basic image processing functionalities, including image resizing, point operations, filtering with a set of built-in convolution kernels, color space conversions, and so on.

Create a virtual environment

It’s often a good practice to create every Python project in a virtual environment so the dependencies of every project are isolated from the system and one another. Create a new directory where you’d like to have the project and run the following command to create a virtual environment:

python3 -m venv env
source env/bin/activate

On Windows, the command may slightly vary

python -m venv env
env/Scripts/activate

Run the command below in your terminal to install Pillow:

python3 -m pip install --upgrade pip
python3 -m pip install --upgrade Pillow

Now, let’s shift our focus from setup to action. With just a few lines of code, you can load an image, display it on your screen, and begin exploring the many ways you can manipulate it to suit your needs.

Loading and presenting an image’s properties

Next, create a file named `converter.py` and add the following code to it:

from PIL import Image

# Define the path to the input image
input_image_path = "Image-1.jpg"

# Open the image using Pillow
image = Image.open(input_image_path)

# Display basic properties of the image
print("Image format:", image.format)
print("Image mode:", image.mode)
print("Image size:", image.size)

When you run the above code, it should print out the properties of the images similar to the following:

Image format: JPEG
Image mode: RGB
Image size: (500, 281)

Changing the Format of Every Image in a Directory

First, you’ll need to create a folder structure for the project. Let’s assume you’ll be working with the following folder structure:

image_converter/
    ├── input_images/
    └── output_images/

The images we want to convert will be placed in the `input_images` folder, and the converted images will be saved into the `output_images` folder.

In the root of the `image_converter` directory, create a file named `batch_converter.py` and add the following code to it:

# Step 1
from PIL import Image
import os

# Step 2
input_dir = "input_images/"
output_dir = "output_images/"

# Create output directory if it doesn't exist
if not os.path.exists(output_dir):
    os.makedirs(output_dir)

# Get all files in the input directory
files = os.listdir(input_dir)

# Iterate through each file in the input directory
for file in files:
    # Check if the file is an image
    if file.endswith(".jpg") or file.endswith(".png"):
        # Open the image
        image_path = os.path.join(input_dir, file)
        image = Image.open(image_path)

        # Convert the image format (e.g., from JPEG to PNG)
        new_file = os.path.splitext(file)[0] + ".png"
        output_path = os.path.join(output_dir, new_file)
        image.save(output_path, format="PNG")

        print(f"Converted {file} to PNG")

print("Conversion complete!")

Although the example above only converts JPG or JPEG images to PNG, you can modify the code to change the format of any image depending on the format of the input image and the output format you want to convert to.

convert images python

Converting Image with Python with Cloudinary

While Pillow offers a robust toolkit for image conversion, there are scenarios where it may not be efficient or suitable for the task at hand. Cloudinary is a cloud-based software solution that provides image and video management services. Cloudinary allows users to upload, store, manage, manipulate, and deliver images and videos for websites and applications.

One of the advantages of using Cloudinary over Pillow is ease of use. Cloudinary provides a simple to use Python SDK and API for uploading images and performing transformations in the cloud. With Cloudinary, there is no need to write custom or complex image processing code as a wide array of pre-built image manipulation functions are available through simple API calls. This makes Cloudinary much easier to implement than handling image processing manually with Pillow.

Another major instance where Cloudinary outshines Pillow or other Python-based image processing libraries is scalability. Cloudinary performs all of its image processing operations in the cloud, using its own high-performance infrastructure. Pillow, on the other hand, uses your system’s resources to carry out its processing operations. Using Pillow for intensive tasks can negatively impact the performance of your website or application. With Cloudinary, the cloud infrastructure is responsible for processing the load, so your application performance remains unaffected.

In the next sections, we’ll walk you through how you can convert images in Python using the Cloudinary Python SDK.

Step 1 – Create a virtual environment and install package dependencies

First of all, create a new folder where you’d like to have the project and create a virtual environment in the directory.

python3 -m venv env
source env/bin/activate

This will create a new `env` directory and activate the environment. Then, run the following command to install the necessary packages for the application:

python3 -m pip install fastapi python-multipart pydantic-settings python-dotenv uvicorn[standard]

Here’s a quick breakdown of the packages we’re using and what they do:

  • FastAPI – FastAPI is a Python framework for creating web servers and APIs.
  • Python-multipart – A Python library for handling multipart/form-data POST requests.
  • Python-dotenv – A Python library that reads key-value pairs from a `.env` file and adds them to the environment variables.
  • Pydantic-settings – A Python library that provides a way to manage application settings and configurations using Pydantic models.
  • Cloudinary – A cloud-based service that provides an end-to-end image and video management solution. It offers features such as image upload, storage, manipulation, optimization, and delivery.
  • Uvicorn – An ASGI web server implementation for Python. We’ll use it to create a server environment for our application.

Also, create a `.gitignore` file in the root directory and add the following into it:

__pycache__/
*.py[cod]
*$py.class

venv/
env/

Now, create a file named `main.py` in the project root and add the following code in it:

# Step 1 - import the fastapi class
from fastapi import FastAPI

# Step 2- create a `FastAPI` instance:
app = FastAPI()

# Step 3 - define a path operation decorator and function
@app.get("/")
async def root():
return {"message": "Hello World"}

Next, run the following command in the terminal to start up the server:

uvicorn main:app --reload

Now, if you go to http://127.0.0.1:8000 in your browser, you should see the following JSON response:

{ "message": "Hello World" }

Great! Our application is now up and running.

Step 3 – Add environment variables

When accessing Cloudinary programmatically, we must provide certain credentials to authenticate our requests to the service. Log in to your Cloudinary dashboard and copy your API key, Cloud name, and API secret.

convert images python

Next, create a `.env` file in the root of the project and add your credentials as follows:

CLOUDINARY_API_KEY = 
CLOUDINARY_CLOUD_NAME = 
CLOUDINARY_API_SECRET =

Step 4 – Converting an image to another format

Open the `main.py` file and add the following code to it:

from fastapi import FastAPI
from fastapi import FastAPI, File, UploadFile
from pydantic_settings import BaseSettings
import cloudinary
import re
import os


class Settings(BaseSettings):
    CLOUDINARY_CLOUD_NAME: str
    CLOUDINARY_API_KEY: int
    CLOUDINARY_API_SECRET: str
    class Config:
        env_file = ".env"

settings = Settings()

config = cloudinary.config(cloud_name = settings.CLOUDINARY_CLOUD_NAME, api_key = settings.CLOUDINARY_API_KEY, api_secret = settings.CLOUDINARY_API_SECRET)

import cloudinary.uploader
import cloudinary.api

app = FastAPI()

async def cloudinary_upload(file):
    # Regex to check for the file extension format
    match = re.search(r'\.([^.]+)$', file)
    file_format= match.group(1)
    try:
        # If the uploaded image is PNG, convert it to JPG
        if file_format.lower() == "png":
         imageURL =  cloudinary.uploader.upload(file, fetch_format = "jpg")
         return imageURL
        # If the uploaded image is JPG, convert it to PNG
        elif file_format.lower() == "jpg" or file_format.lower() == "jpeg":
         imageURL =  cloudinary.uploader.upload(file, fetch_format = "png")
         return imageURL
        # If the uploaded image is neither PNG nor JPG, convert it to PNG
        elif file_format.lower() not in ["jpg", "png", "jpeg"]:
         imageURL =  cloudinary.uploader.upload(file, fetch_format = "png")
         return imageURL

    except:
        print("An errror occured while converting the file.")


@app.get("/")
async def root():
    return {"message": "Hello World"}

@app.post("/upload")
async def create_image(image: UploadFile = File(...)):
    os.makedirs("images", exist_ok=True)

    file_location = f"images/{image.filename}"
    with open(file_location, "wb+") as file_object:
        file_object.write(image.file.read())

    result = await cloudinary_upload(file_location)

    converted_file = result["secure_url"]


    return f"The converted file can be found here: {converted_file}"

Here’s what is going on in the code above.

  • In the first few lines of the code, we imported the necessary modules for creating our application, such as the `File` and `UploadFile` modules from FastAPI for file uploads. We also imported the `BaseSettings`module from `pydantic_settings` for defining the application settings.
  • In the next lines, we defined a `Settings` class that defines the settings required for accessing the Cloudinary service using environment variables.
  • Next, we created a `cloudinary_upload` function for uploading the image file to Cloudinary. The function takes the image’s path as input, checks for its file format using regular expression, and uses the `cloudinary.uploader.upload` method to specify the format we want the image to be returned in (this is the part of the code that changes the image’s file format).
  • Finally, we defined a route handler for the `/upload` endpoint, which expects an image file as input. It saves the uploaded file locally, uploads it to Cloudinary, converts it to another format, and finally returns the URL of the converted file.

Putting it to the test

Now, let’s examine the output of our code so far. To test our application, download Postman. This program allows us to test and send HTTP or API calls for our local projects.

Any image file you upload to the endpoint will be converted to either a PNG or JPG format or as you specify in your code. Just make sure to set up the request parameters as shown below:

convert images python

Once you send the request, you should receive a link to the file that has been converted in the API response. Hooray!

Wrapping Up

Congratulations if you made it this far! This tutorial has just scratched the surface of the many possible things with Cloudinary. You’ll find Cloudinary useful and suitable for many other use cases. To get started, feel free to sign up for an account today to enjoy the world-class features and flexibility that Cloudinary has to offer.

QUICK TIPS
Tamas Piros
Cloudinary Logo Tamas Piros

In my experience, here are tips that can help you better convert and manage images in Python, ensuring efficient and high-quality results:

  1. Use image format detection
    Before converting an image, use format detection to identify the current image format. This prevents unnecessary conversions and ensures that the output format is appropriate for the original content, preserving quality.
  2. Automate batch conversions with multiprocessing
    When converting a large number of images, leverage Python’s multiprocessing module to speed up the process. This allows you to parallelize the conversion tasks, significantly reducing the time required for batch processing.
  3. Optimize images during conversion
    While converting images, apply optimization techniques like adjusting compression levels, removing metadata, or resizing images to target dimensions. This reduces file size without noticeably affecting image quality, improving performance in web applications.
  4. Leverage image mode conversions
    Convert images to the most suitable mode (e.g., RGB, CMYK, Grayscale) during the format conversion process. For example, converting images to grayscale can drastically reduce file size for black-and-white content, optimizing storage and delivery.
  5. Handle different DPI settings during conversion
    Take into account the DPI (dots per inch) of images when converting between formats. This is particularly important for print-quality images, as altering DPI settings during conversion can affect the sharpness and clarity of the final image.
  6. Integrate image validation and error handling
    Implement validation checks to ensure that input images meet required specifications before conversion. Also, add robust error handling to manage issues such as unsupported formats, corrupted files, or failed conversions, improving the reliability of your conversion process.
  7. Use Cloudinary for on-the-fly transformations
    Instead of handling all conversions locally, integrate Cloudinary for real-time, on-the-fly image transformations. This allows you to dynamically convert, resize, and optimize images based on user requests, reducing server load and simplifying image management.
  8. Preserve color profiles in conversions
    When converting images, ensure that color profiles (e.g., sRGB, Adobe RGB) are preserved or correctly mapped to maintain color accuracy across different devices and platforms. This is critical for applications where color fidelity is important, such as in digital art or photography.
  9. Create custom conversion pipelines
    Develop custom image conversion pipelines to handle specific project needs, such as converting images to multiple formats simultaneously or applying a series of transformations (e.g., resizing, watermarking) during the conversion process. This streamlines workflows and ensures consistency.
  10. Monitor and log conversions for quality control
    Implement logging and monitoring for your image conversion process to track success rates, errors, and performance metrics. This data helps you identify bottlenecks, optimize the conversion process, and ensure that the final output meets quality standards.

These tips will help you effectively convert and manage images in Python, leveraging both local libraries like Pillow and cloud-based solutions like Cloudinary to achieve optimal results in various projects.

Last updated: Sep 7, 2024