image processing using openCV

Project Description: Image Processing using OpenCV (cv2)

Objective:

The goal of this project is to apply basic and advanced image processing techniques using OpenCV (Open Source Computer Vision Library). OpenCV helps in enhancing images, edge detection, filtering, geometric transformations, and more, making it ideal for computer vision applications.

Key Tasks:

Reading and Displaying Images:

Load and display an image using OpenCV to visualize the original input before processing.

Converting to Grayscale:

Convert the image to grayscale to simplify further processing, especially for tasks like object detection.

Resizing Images:

Resize the image to a desired width and height for uniformity or preparing for machine learning models.

Edge Detection:

Use the Canny edge detection method to identify the edges in an image, useful for detecting object boundaries.

Blurring:

Apply Gaussian blur to reduce noise and smooth the image for better results in further analysis.

Drawing Shapes:

Draw geometric shapes like rectangles or circles on images to highlight areas or provide annotations.

Geometric Transformations:

Perform transformations like rotation to adjust the image's orientation.

Saving Processed Image:

Save the modified image to a file after processing.

Technologies:

Programming Language: Python

Libraries: OpenCV (cv2), Numpy

Tools: Jupyter Notebooks

Expected Outcomes:

Implement basic image processing functions like resizing, edge detection, and blurring.

Create a system that processes images and displays the results effectively.

Applications:

Used in object detection, medical image analysis, and image enhancement tasks.