Welcome to my learning journey through the OpenCV Bootcamp! This course is helping me gain a solid understanding of Computer Vision using OpenCV and Python. In this repository, I’ll share my experiences, insights, and projects as I progress through the course.
- Course Code: OCVBC
- Type: Preparatory
- Language: Python
- Format: Videos, Quizzes, and Colab Notebooks
- Certification: Official OpenCV Certification upon completion
This bootcamp consists of 14 modules, covering fundamental to advanced topics in OpenCV. Below is my current progress and experiences:
| Status | Module Number | Module Name | Description |
|---|---|---|---|
| ✅ | 1 | Getting Started With Images | Learn how to load, display, and save images using OpenCV. Explore different image formats, how OpenCV reads images, and how to visualize them using Matplotlib. |
| ✅ | 2 | Basic Image Manipulation | Practice resizing, rotating, cropping, and enhancing images. |
| ✅ | 3 | Image Annotation | Draw shapes, write text, and add labels or markers to images using OpenCV’s annotation functions. |
| ✅ | 4 | Image Enhancement | Improve image quality through contrast adjustment, histogram equalization, and denoising techniques. |
| ✅ | 5 | Accessing the Camera | Capture live video streams from a camera, display them in real-time, and save frames. |
| ✅ | 6 | Video Writing | Record video from a camera or a sequence of frames and save it to a file using different codecs. |
| ✅ | 7 | Image Filtering | Apply various filters like Gaussian, median, and bilateral for blurring and noise reduction. |
| ✅ | 8 | Image Features and Alignment | Detect keypoints using SIFT, SURF, ORB, and align images using feature matching. |
| ✅ | 9 | Panorama | Stitch multiple images together to create panoramic views. |
| ✅ | 10 | HDR | Create High Dynamic Range (HDR) images by combining multiple exposures of the same scene. |
| ✅ | 11 | Object Tracking | Track moving objects in videos using algorithms like Meanshift, Camshift, and optical flow. |
| ✅ | 12 | Face Detection | Detect faces in images and video using Haar cascades and other pre-trained models. |
| ✅ | 13 | Tensorflow Object Detection | Use TensorFlow’s object detection API to detect and classify objects in images and video. |
| ✅ | 14 | Pose Estimation using OpenPose | Estimate human poses by detecting key body joints with OpenPose or similar deep learning models. |
✅ Completed | ⏳ In Progress | ❌ Not Started
- Google Colab Notebooks – My hands-on coding exercises and notes.
- Quizzes & Challenges – Insights from quizzes and challenges I completed.
- Projects – Small projects I will build during the course.
After completing the course, I aim to receive the Official OpenCV Certification, which will be a valuable addition to my portfolio!
- Basic understanding of Python.
- Familiarity with NumPy and Matplotlib.
- A working environment with OpenCV installed (Google Colab, Jupyter Notebook, or local setup).
So far, the Getting Started With Images module was a great introduction to OpenCV. I enjoyed working with different image formats and understanding how OpenCV processes image data. I'm excited to continue learning and applying these skills in real-world applications.
Stay tuned for updates! 🚀