Jupyter Notebook · from GitHub
Lab Activities of the course Image processing and computer vision (ISE-2505)
This repository contains the laboratory sheets, source code, and resources for the course ISE----: Image Processing and Computer Vision. The materials demonstrate fundamental techniques in computer vision using Python and OpenCV, ranging from basic image manipulation to advanced feature detection.
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The primary goal of this course is to provide practical experience with digital image processing techniques. The labs are structured as Jupyter Notebooks, each focusing on a specific domain of computer vision, such as enhancement, filtering, segmentation, and feature extraction.
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The coursework is divided into 9 Laboratory Sheets:
| Lab Sheet | Topic | Description |
|---|---|---|
| Lab 01 | Introduction to OpenCV | Basics of reading, writing, and displaying images; understanding image data structures and color spaces. |
| Lab 02 | Image Enhancement | Techniques to improve image quality, including brightness/contrast adjustment, histogram equalization, and contrast stretching. |
| Lab 03 | Geometric Transformations | Applying affine transformations such as rotation, translation, scaling, and warping using cv2.warpAffine. |
| Lab 04 | Edge Detection | Implementation of edge detection algorithms including Sobel, Prewitt, and Canny edge detectors. |
| Lab 05 | Frequency Domain Processing | Introduction to the Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), and frequency filtering. |
| Lab 06 | Image Pyramids | Construction of Gaussian and Laplacian pyramids for multi-scale image representation (pyrUp, pyrDown). |
| Lab 07 | Morphological Operations | Processing binary images using erosion, dilation, opening, closing, and morphological gradients. |
| Lab 08 | Image Segmentation | Techniques for separating objects from the background using global, binary, and adaptive thresholding. |
| Lab 09 | Feature Detection & Matching | Advanced feature extraction using algorithms like SIFT (Scale-Invariant Feature Transform) and ORB (Oriented FAST and Rotated BRIEF). |
images/: Contains sample images used throughout the labs (e.g., lena.jpeg, flower-binary.png, colored-balls.jpg) for testing algorithms.03
To run the notebooks in this repository, you need the following software and libraries installed:
Required Python Libraries:
opencv-python (cv2)numpymatplotlib04
Clone the repository:
git clone [https://github.com/your-username/image-processing-and-computer-vision.git](https://github.com/your-username/image-processing-and-computer-vision.git)
cd image-processing-and-computer-vision
Create a virtual environment (Optional but Recommended):
python -m venv venv
# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate
Install dependencies:
pip install opencv-python numpy matplotlib jupyter
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jupyter notebook
Labsheet04.ipynb).Note: Ensure the images/ directory is in the same path as the notebooks so that cv2.imread() can locate the sample files.