sanjanaaa17/shape-detection

Shape Detection uses OpenCV to identify and categorize geometric shapes in images. It provides key metrics like area, perimeter, and centroids for triangles, rectangles, squares, pentagons, hexagons, and circles

Shape-Detection

Shape Detection uses OpenCV to identify and categorize geometric shapes in images. It provides key metrics like area, perimeter, and centroids for triangles, rectangles, squares, pentagons, hexagons, and circles.

Description:

The Shape Detection Project is a computer vision application designed to identify and categorize geometric shapes within an image using the OpenCV library. Leveraging contour analysis, the program extracts valuable information such as area, perimeter, and centroid coordinates for each detected shape. The supported shapes include triangles, rectangles, squares, pentagons, hexagons, and circles. The intuitive script provides a practical tool for understanding and analyzing the composition of images, making it particularly useful in fields such as image processing, computer vision research, and educational contexts.

Key Features:

Contour-Based Detection: Utilizes OpenCV's contour analysis to identify distinct shapes within an image. Comprehensive Metrics: Calculates and displays essential metrics, including area, perimeter, and centroid coordinates for each detected shape. Shape Categorization: Categorizes shapes into predefined types, facilitating easy interpretation of the image's geometric composition. User-Friendly Interface: The script prompts users to input the path to their image, making it accessible for both novice and experienced users.

To run the code you can use Jupyter Notebook/Google Colab.