Introduction to OpenCV Lecture Notes

Overview of OpenCV

• OpenCV is the most extensive computer vision library, with over 2,500 optimized algorithms.
• It is downloaded between one to two million times a week.
• Essential for building computer vision and AI applications.
• First library to learn if you are interested in computer vision.

Historical Context

• In 2005, "Stanley," an autonomous vehicle, won the DARPA Grand Challenge by traveling 132 miles through the Mojave Desert using OpenCV.

Course Structure

• Target Audience: Absolute beginners with an intermediate level of programming knowledge in Python.
• Format: The course uses Jupyter notebooks to cover material.
• Objective: Provide a foundational understanding of OpenCV and spark interest in computer vision and AI.

Course Content

1. Basics of Images and Videos

   • Understanding what images and videos are.
   • Representation in OpenCV.
   • Functions to read, write, and manipulate photos and videos.

2. Image Enhancement and Filtering

   • Basic techniques for improving image quality.

3. Image Transformations

   • Techniques for aligning images and creating panoramas.

4. Computational Photography

   • Creation of high dynamic range images (HDR).

5. Machine Learning and Deep Learning in OpenCV

   • Classical machine learning algorithms.
   • Deep learning inference module.
   • Face detection and object tracking.

6. Advanced Deep Learning Applications

   • Deep learning module for object detection and pose estimation.

Further Learning Resources

• Check free content at:
  • opencv.org
  • learnopencv.com
• For structured learning and achieving mastery, explore courses at opencv.org/courses

Conclusion

• The series aims to kickstart your journey in computer vision and AI.
• Encourages further exploration through various resources and courses available online.
• Best wishes for success in your learning path.