Certified Computer Vision Expert (2025)

Name: Certified Computer Vision Expert (2025)
Rating: 4.2 (10 reviews)

Master OpenCV and MediaPipe: Learn Image Processing, Object Detection, Gesture Control, and Real-time Applications.

Created byKalyanasundaram K

Last updated 1/2025

English

English [Auto],

What you'll learn

Gain hands-on experience with OpenCV to process and manipulate images and videos.
Master real-time applications such as hand tracking, pose estimation, and gesture recognition with MediaPipe.
Learn advanced techniques like feature detection, object recognition, and image segmentation.
Build and deploy machine learning models for computer vision tasks like object detection and face recognition.

Course content

4 sections • 30 lectures • 2h 13m total length

Day 1: Introduction9:25
Explore the foundations of computer vision with OpenCV and Mediapipe, learning image processing, object detection, tracking, facial and gesture recognition, and sign language recognition in real-time applications.
Day 2: Image Basics5:40
Learn to convert an image to grayscale, HSV, and LAB color spaces and display the original alongside each result in four tabs using OpenCV.
Day 3: Basic Image Operations6:43
Blend two images in OpenCV using cv2.addWeighted with alpha 0.7, beta 0.3, gamma 0, after resizing to 500 by 500, then display and waitKey.
Day 4: Drawing and Editing8:37
Draw shapes on images using OpenCV and numpy to create a canvas, then render lines, rectangles, circles, and text with cv2, and display the result with cv2.imshow.
Day 5: Video Processing Basics4:17
Learn to stream live video with OpenCV by capturing frames with cv2.VideoCapture, displaying them with imshow, and terminating on q while releasing the capture and destroying all windows.
Day 6: Image Thresholding5:16
Explore image thresholding with simple, adaptive mean and adaptive Gaussian methods in OpenCV, including grayscale conversion, threshold comparisons, and a wrap-up of six applications and a forthcoming mini project.
Day 7: Recap and Mini-Project2:13
Create a photo collage from six to eight images, view the combined output, save it as day seven, and upload your collage to the Udemy resources link.

Day 8: Image Filtering and Edge Detection3:53
Apply Gaussian blur, sharpen with filter2d, and detect edges with canny in OpenCV, illustrating techniques for machine learning and AI.
Day 9: Contour Detection6:33
Master contour detection by converting images to grayscale, applying binary threshold, finding contours with cv2.findContours, and drawing them to reveal edges and shapes.
Day 10: Feature Detection and Matching4:50
Apply Harris corner detection to detect and classify edges and corners in images, using grayscale conversion, OpenCV's cornerHarris, dilation, and thresholding to highlight object features.
Day 11: Morphological Operations2:51
Explore morphological operations in computer vision, including erosion and dilation, and learn how opening and closing remove noise and fill gaps using binary kernels with iterations.
Day 12: Image Transformations2:42
Explore affine transformations in image processing using OpenCV and numpy, learning to rotate and skew images by applying a transformation matrix and wrapping into an affine transformation.
Day 13: Histograms and Color Normalization4:03
Explore histograms and color normalization in OpenCV by applying histogram equalization and normalization, comparing the original, equalized, and normalized images, and understanding their density changes.
Day 14: Mini-Project3:08
Day 14 mini-project builds an image filter app with OpenCV, applying grayscale, blur, edge detection, emboss, sharpen; saves results and previews MediaPipe for face and pose detection.

Day 15: Introduction to MediaPipe5:43
Explore Google's MediaPipe, a cross-platform computer vision toolkit with hand gesture, pose, and object detection, image segmentation, and language and audio classification, integrated with task studio and model maker.
Day 16: Hand Tracking12:49
Explore how MediaPipe enables hand tracking and landmarks in computer vision, integrating with OpenCV to analyze video frames and enable applications like sign language, gestures, and virtual mouse control.
Day 17: Pose Estimation4:55
Apply Mediapipe pose estimation to detect body landmarks and compute squat angles using a pre-trained model, using webcam or video inputs to analyze movement.
Day 18: Face Detection and Landmarks2:12
Learn how to detect faces in video using Mediapipe and cv2, then draw face mesh landmarks for eyes, nose, and lips.
Day 19: Integration of MediaPipe Features1:50
Learn to combine MediaPipe and OpenCV to perform hand detection, face detection, and pose detection, using face mesh contours and detection results for applications like suspicious activity and emotion detection.
Day 20: Recap and Mini-Project2:33
Explore media control using media pipes hand detection and hand landmarks to map thumb to forefinger, compute distance, and drive volume via pika w in a mini-project.

Day 21: Machine Learning Basics2:32
Explore the basics of machine learning with OpenCV, covering training, testing, and validation, and illustrate k nearest neighbors with labeled data and prediction examples.
Day 22: Object Detection3:59
Detect objects with yolov5 in a live webcam, identifying persons and cell phones using ultralytics pre-trained models. Draw colored bounding boxes with OpenCV in a real-time frame loop.
Day 23: Face Recognition2:47
Explore face detection with haarcascade in OpenCV, loading frontal face and eye xml cascades, applying grayscale conversion, and using detectMultiScale to draw result boxes on faces and eyes.
Day 24: Image Segmentation3:11
Segment images via color and threshold methods in OpenCV using k-means with random centers, and visualize results with Matplotlib.
Day 25: Custom Model Integration6:01
Train and integrate a Bert-based sentiment classifier using torch, transformers, and OpenCV to predict neutral, positive, or negative text, with training, validation, and inference workflows.
Day 26: Practice and Mini-Project3:11
Explores live facial detection with Haar cascade in OpenCV, builds a small convolutional neural network in Keras, and demonstrates loading models for real-time emotion analysis.
Day 27: Capstone Project Kickoff5:14
Kick off the capstone project to build a drowsiness reduction dashboard using OpenCV, Mediapipe, and Haarcascade, covering initialization, eye-closure and yawning detection logic, a user interface, and driver alerts.
Day 28: Capstone Project Development1:20
Day two advances the capstone project by using Euclidean distance to compute eye ratio from left and right eye landmarks, triggering a yawning alert stored as wav in Python.
Day 29: Capstone Project Finalization2:18
Finalize the capstone project by implementing a driver alert system for drowsiness detection, including an alarm sound, alert thread, and parallel datasets for drowsiness and yan (mouth) cues.
Day 30: Showcase and Wrap-Up3:10
Learn to build a drowsiness detector and alert system using OpenCV, haarcascade cascades, and frame-by-frame analysis at 30 fps, including eye, ear, and lip distance features.

Requirements

Basic knowledge of Python programming. Familiarity with computer science and programming fundamentals. No prior experience in computer vision is required.

Description

This comprehensive course is designed to guide you through the essentials and advanced concepts of computer vision using OpenCV and MediaPipe. You will start by understanding the basics of image processing, including loading, displaying, and saving images. As you progress, you'll dive into intermediate topics like image filtering, edge detection, and contour analysis. The course also covers advanced applications such as pose estimation, hand tracking, and facial recognition with MediaPipe.

Throughout the course, you will work on practical projects, such as building a document scanner, creating a real-time gesture-controlled media player, and applying machine learning models for object detection and face recognition. By the end of the course, you'll be equipped to integrate OpenCV and MediaPipe in your projects, design custom computer vision applications, and deploy real-time systems for various industries.

The course includes hands-on exercises, mini-projects, and a capstone project that helps solidify your learning. Whether you're an aspiring computer vision developer or an AI enthusiast, this course provides the skills necessary to tackle real-world computer vision problems and enhance your career.
Master OpenCV and MediaPipe: Learn Image Processing, Object Detection, Gesture Control, and Real-time Applications.

"Join 'Master OpenCV & MediaPipe' to learn image processing, object detection, and real-time apps!"

Who this course is for:

Aspiring computer vision developers, AI enthusiasts, and Python programmers. Students or professionals seeking to enhance their skills in computer vision, image processing, and machine learning. Anyone interested in building real-time applications using OpenCV and MediaPipe.

Certified Computer Vision Expert (2025)

What you'll learn

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Course content

Introduction7 lectures • 42min

WEEK27 lectures • 28min

Week 3: MediaPipe and Advanced Applications6 lectures • 30min

Week 4: Machine Learning with OpenCV10 lectures • 34min

Requirements

Description

Who this course is for: