How to Reduce Image Size in Python

In this post, we'll explore how to reduce image size in python without losing quality using two popular libraries in the Python ecosystem (Pillow and OpenCV).

How to Reduce Image Size in Python

Getting Started

Reducing image size is a common task in web development, data processing, or machine learning workflows. Whether you're optimizing images for faster loading or preparing datasets, Python offers powerful tools to resize and compress images efficiently.

Before diving into Python code examples on how to reduce image size, let's discuss the Python libraries we will use for image size reduction.

What is Pillow?

Pillow is a popular Python Imaging Library (PIL) fork used for image processing tasks in Python. It lets you open, manipulate, and save many different image file formats (like JPEG, PNG, BMP, GIF, etc.).

Common Things You Can Do with Pillow:

Open and display images
Resize, crop, rotate, or flip images
Apply filters (like blur, contour, sharpen)
Convert between image formats
Add text or shapes to images
Work with transparency and alpha channels
Perform basic image enhancements (contrast, brightness, etc.)

What is OpenCV?

OpenCV (short for Open Source Computer Vision Library) is an open-source library used primarily for real-time computer vision, image processing, and machine learning. It is much more powerful and comprehensive than Pillow. It can handle:

Image Processing
- Reading/writing images
- Resizing, rotating, cropping
- Color space conversion (RGB ↔ Grayscale ↔ HSV, etc.)
- Blurring, thresholding, edge detection
Video Processing
- Capture video from cameras or files
- Real-time video analysis
- Object tracking
Computer Vision
- Face detection and recognition
- Object detection
- Feature matching
- Optical flow
- Contour detection
Machine Learning
- KNN, SVM, Decision Trees
- Handwriting recognition
- Gesture recognition

Resize and Compress Images Using Pillow

Installation:

 pip install Pillow

Example:

 from PIL import Image  
 # Open an image  
 image = Image.open('example.jpg')  
 # Resize the image  
 resized_image = image.resize((200, 200))  
 # Save the resized image  
 resized_image.save('resized_example.jpg')

Explanation:

resize(...): Shrinks the image dimensions.
optimize=True: Compresses the file size.
quality=85: Ranges from 1 (worst) to 95 (best). Lower means smaller size. Avoid setting quality below 70 to prevent visible artifacts.

Reduce File Size Without Changing Dimensions

If you just want to compress without resizing:

 from PIL import Image  
 img = Image.open("example.jpg")  
 img.save("compressed.jpg", optimize=True, quality=70)

Resize and Compress Images Using OpenCV

Installation:

 pip install opencv-python

Example:

 import cv2  
 # Load image  
 img = cv2.imread("example.jpg")  
 # Calculate new dimensions  
 scale_percent = 50 # percent of original size  
 width = int(img.shape[1] * scale_percent / 100)  
 height = int(img.shape[0] * scale_percent / 100)  
 # Resize  
 resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)  
 # Save with compression  
 cv2.imwrite("example_cv2.jpg", resized, [cv2.IMWRITE_JPEG_QUALITY, 85])

Summary

Reducing image size in Python is easy and effective using libraries like Pillow and OpenCV. These tools provide you with the control and flexibility needed for any workflow.

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How to Reduce Image Size in Python