Computer Vision with OpenCV and Python: Beginner to Advanced

Name: Computer Vision with OpenCV and Python: Beginner to Advanced
Rating: 4.3 (51 reviews)

Learn OpenCV with Python: Hands-On Projects in Image Manipulation, Face Recognition, and Emotion Detection

Created byGeeta Patwal, Shivyaansh Singh Rawat

Last updated 1/2026

English

English [Auto],

What you'll learn

OpenCV Basics: How to install OpenCV, set up a Python environment, and perform fundamental image operations, including reading, writing, resizing, and rotating
Image Manipulation: Techniques for drawing shapes, adding text, and applying transformations to modify and enhance images.
Real-Time Face Detection: Building a live face detection system using Haar cascades and integrating it with webcam feeds to identify faces in real-time.
Facial Emotion Recognition using OpenCV with Python
More.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

8 sections • 28 lectures • 1h 48m total length

Overview of OpenCV0:34
Explore the full potential of computer vision with OpenCV, from basic image manipulation to advanced facial recognition and emotion detection, through hands-on projects that build practical skills for intelligent systems.
How OpenCV Is Used in Real-World Applications1:13
Are you Ready?
Installing Python on Windows1:24
Installing Python on MacOs0:51
Installing Python on Linux0:59
Installing VS Code on Windows0:59
Installing VS Code on MacOS0:59
Installing VS Code on Linux0:35

Basic Image Manipulation Using OpenCV2:14
Drawing Shapes17:16
Learn to draw shapes on images using OpenCV with Python, including circles, rectangles and lines, by loading sample images, resizing, and displaying results to explore OpenCV capabilities.
Adding Text7:46
Add text to images using cv2.putText, choose fonts like Hershey Simplex, set font scale and color, position text, and display results with OpenCV in Python, using anti-aliasing via cv2.line_aa.
Applying Transformations and Advanced Image Enhancements21:57
Apply transformations such as rotation, scaling, and translation to images, and enhance quality with brightness, contrast, blur, and sharpening using OpenCV and numpy.
Section Recap-Project-12:43

Real-Time Face Detection Using OpenCV1:41
Real-Time Face Detection-Part-18:04
Achieve face detection with OpenCV and Python by accessing webcam, loading a Haar cascade classifier, converting frames to grayscale, and applying detectMultiScale with scale factor 1.1, minNeighbors 5, minSize 30x30.
Real-Time Face Detection-Part-27:38
Learn real-time face detection with OpenCV using a Haar cascade, convert frames to grayscale, draw blue rectangles around detected faces, and manage keyboard input and resource cleanup.
Section Recap- Project-23:57

Facial Emotion Recognition Using FER1:21
Project 4: Facial Emotion Recognition using FER-Part-18:43
Develop a facial expression recognizer in OpenCV using a pre-trained FER model, capture webcam video, detect and display the dominant emotion with a bounding box and label.
Project 4: Hand Gesture Recognition-Part-24:31
Extract face coordinates and draw a bounding box, then detect and display emotions such as neutral, happy, sad, surprised using an emotion detector with OpenCV.
Section Recap-Project-33:13

Requirements

Basic Python Knowledge
Computer and Internet Access
Software Installation
Webcam
Enthusiasm to Learn

Description

What You Will Learn:

Beginner Level: Image Manipulation

Basic Operations: Dive into fundamental image manipulation techniques using OpenCV. Learn how to draw shapes, add text, and apply various transformations to images. These foundational skills are essential for anyone starting in computer vision.
Practical Skills: Through hands-on projects, you’ll gain experience modifying and enhancing images, which will prepare you for more advanced topics in image processing.

Intermediate Level: Real-Time Face Detection

Face Detection Techniques: Learn to implement real-time face detection using Haar cascades. This project will guide you through detecting faces in images and video feeds, a crucial aspect of many real-world applications.
Webcam Integration: Discover how to process webcam feeds to recognize faces in real-time. This skill is useful for creating interactive applications and surveillance systems, making your projects more dynamic and engaging.

Advanced Level: Face Emotion Detection

Emotion Detection Techniques: Explore advanced techniques for detecting emotions from facial expressions. Learn how to use pre-trained models to recognize various emotions like happiness, sadness, anger, and more.
Practical Applications: Apply emotion detection to real-time video feeds to analyze and interpret emotional states, enhancing interactive and responsive systems.

By progressing through these levels, you will build a comprehensive understanding of computer vision with OpenCV, starting from basic image manipulations and moving to advanced applications in face detection and emotion recognition. This course is perfect for learners who wish to start from the basics and advance their skills in computer vision.

Who this course is for:

Python beginners looking to learn computer vision.
Anyone interested in practical, hands-on projects.
Tech enthusiasts wanting to explore OpenCV.
Students and professionals seeking to enhance their skills in image processing and AI.

Computer Vision with OpenCV and Python: Beginner to Advanced

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction to OpenCV8 lectures • 8min

Setting Up Development Environment1 lecture • 3min

Basic of Open CV-Article Version5 lectures • 6min

Project 1: Basic Image Manipulation-Beginner Level5 lectures • 52min

Project 2: Real-Time Face Detection-Intermediate Level4 lectures • 21min

Face Recognition using FER - Advanced Level4 lectures • 18min

Coding Exercises – Practice & Assessment0

Thank You1 lecture • 1min

Requirements

Description

Who this course is for: