OpenCV Tutorial in Python
Last Updated :
12 Jul, 2025
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning library. It allows us to process images and videos, detect objects, faces and even handwriting. This tutorial will guide us through image and video processing from the basics to advanced topics using Python and OpenCV. We'll learn how to handle image transformations, feature extraction, object detection and more.
Why Learn OpenCV?
- Comprehensive Image Processing: OpenCV has a range of functions to manipulate and analyze images helps in making it ideal for various applications.
- Real-Time Video Processing: It supports video capture and real-time video processing.
- Cross-Platform: Works on multiple platforms like Windows, Linux, macOS and Android.
- Open-Source: It is free to use and has a large community support.
- Integration with Deep Learning: It integrates with popular deep learning libraries like TensorFlow and PyTorch.
Introduction to OpenCV
Before moving into OpenCV, make sure we have set it up correctly in our environment. Whether we're working on Windows, Linux, macOS or using Anaconda.
Working with Images
Here we see how to manipulate and process images with OpenCV in Python. This section will introduce basic image operations like loading, saving and displaying images followed by more advanced image processing tasks.
1. Image Processing and Enhancement
This includes techniques that transform or enhance images for better visual quality and manipulation:
2. Image Segmentation and Thresholding
Techniques that help in dividing an image into meaningful regions or objects:
3. Morphological Operations & Filtering
Operations focused on structuring objects and reducing noise or unwanted features:
4. Advanced Image Manipulation & Background Subtraction
More advanced techniques for handling complex image manipulations and background removal:
5. Feature Detection and Description
Techniques to detect key features like lines, corners and shapes in an image:
6. Feature Detection & Analysis
Techniques to detect and analyze key features or patterns in an image:
Working with Videos
This section focuses on how to handle videos using OpenCV. We’ll learn everything from loading videos to advanced processing and creating new videos.
1. Basic Video Operations
These are foundational tasks for handling video input and output including loading and saving video files and capturing video from a webcam.
These tasks involve modifying the video content such as adjusting speed, blending videos and converting between color spaces.
3. Video Processing and Advanced Tasks
Advanced video processing techniques such as creating videos from images, extracting frames and saving key event clips using OpenCV.
Important functions in OpenCV
Lets see some important functions in OpenCV that are important for performing various image and video operations such as handling images, applying transformations and detecting features.
1. Image Handling Functions:
These functions allow us to load and display images which helps in basic image operations in OpenCV.
These functions help in resizing, rotating and changing the color space of images which are important for image manipulation in OpenCV.
3. Drawing Functions:
These functions allow us to draw various shapes and text on images which is useful for visualizing data, marking regions of interest and annotation.
4. Image Processing:
These functions are used to enhance images, detect edges and apply various transformations to refine visual data and extract meaningful features.
5. Object Detection:
These functions are important for detecting objects and contours within images.
6. Feature Detection:
These functions are used to detect and match keypoints, descriptors and patterns in images for tasks like object recognition and tracking.
7. Tracking and Motion:
These functions helps in tracking of moving objects and background subtraction used in motion detection and real-time video analysis.
Applications of OpenCV
OpenCV is used in various real-world applications like object detection, facial recognition and real-time video analysis.
For more applications refer to Some Amazing Applications of OpenCV Library
Projects of OpenCV
For more Projects refer to 15 OpenCV Projects Ideas for Beginners to Practice in 2025
With these foundational OpenCV skills, we're ready to get more advanced projects and upgrade our image and video processing capabilities.
OpenCV Tutorial in Python
