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OpenCV Labs

Lesson Plan: Introduction to OpenCV in Python

Setup

Students should have access to a computer with a webcam and Python installed. We use Rasberry Pi or NIVIDA Nanos with cameras.

Objective: By the end of the lesson, students will be able to explain the purpose and basic functions of OpenCV, and implement some basic image processing tasks.

1. Introduction (10 minutes)

  • Discussion: Ask students if they've ever used Instagram or Snapchat filters, or how computers recognize faces or objects.
  • Explanation: Introduce OpenCV as one of the most powerful libraries used for computer vision tasks.

2. Brief History & Applications (10 minutes)

  • Mention OpenCV's origins and its significance in AI and robotics.
  • Showcase a few applications, e.g., facial recognition, self-driving cars, AR filters.

3. Basics of Image Representation (10 minutes)

  • Discuss how computers see images as matrices of numbers.
  • Quick overview: Images are made of pixels; each pixel has values that determine its color.

4. Installation & Setup (10 minutes)

  • Demo: How to install OpenCV using pip.

    pip install opencv-python

  • Quick navigation of the OpenCV documentation to encourage self-learning.

5. Hands-on Lab 1: Reading, Displaying, and Saving Images (20 minutes)

  • Exercise:
  • Use OpenCV to read an image.
  • Display the image in a window.
  • Convert the image to grayscale.
  • Save the grayscale image.

6. Hands-on Lab 2: Playing with Webcam Feed (20 minutes)

  • Exercise:
  • Access the webcam using OpenCV.
  • Display the live video feed.
  • Apply a grayscale filter to the feed.
  • Bonus: Add a button or a keypress event to capture and save a snapshot from the feed.

7. Introduction to Basic Image Processing Techniques (15 minutes)

  • Explanation:
  • Discuss image thresholding, blurring, and edge detection.
  • Showcase examples of each technique.

8. Hands-on Lab 3: Basic Image Processing (30 minutes)

  • Exercise:
  • Use a sample image (or one they choose).
  • Apply and display thresholding.
  • Apply and display blurring.
  • Apply and display edge detection using the Canny edge detector.

9. Fun Lab: Snapchat-like Filters (45 minutes)

  • Objective: The goal of this lab is to have students use OpenCV to create basic filters for a live webcam feed.

  • Exercise:

  • Access the webcam feed.
  • Overlay cartoon glasses or hats on the user's face using OpenCV's face detection (Haar cascades).
  • Bonus: Let the students get creative, e.g., adding mustaches, changing background, etc.

10. Discussion & Wrap-up (15 minutes)

  • Reflect on the potential of computer vision and its real-world applications.
  • Discuss the ethical implications, such as privacy concerns with facial recognition.
  • Introduce more advanced topics in OpenCV for those interested (like object recognition, machine learning with OpenCV, etc.)
  • Q&A session.

Additional Resources & Take-home Assignments:

  1. Explore More: Give students links to OpenCV tutorials and documentation for further reading.
  2. Project: Ask students to work on a mini-project, like a basic digital photo editor using OpenCV, allowing them to apply filters, rotate, and crop images.
  3. Challenge: For advanced students, introduce them to object detection using pre-trained models in OpenCV.

Notes for the Instructor:

  • Make sure all students have Python installed and guide them in setting up a virtual environment.
  • Visual aids, like slides with images representing pixel values, will help in explaining image representation.
  • Encourage students to collaborate and share their findings or issues during labs. Pair programming can be useful.
  • Make sure to have a few sample images ready for labs, preferably with varying complexities.