Recognition of Various Geometric Shapes in Images

This document outlines the methodology for recognizing various geometric shapes within images. The target shapes include but are not limited to circles, squares, rectangles, hexagons, and diamonds. The recognition process leverages computer vision techniques such as shape detection algorithms (e.g., Hough Transform for circles, contour approximation for polygons) and feature extraction methods (including Hu moments and aspect ratio calculations). Implementation typically involves preprocessing steps like edge detection using Canny or Sobel operators, followed by contour detection through functions like cv2.findContours() in OpenCV. The system evaluates shape characteristics including size, position, and spatial relationships with other elements to verify target geometries. By analyzing geometric properties (e.g., circularity for circles, vertex count for polygons) and comparing them against predefined thresholds, the algorithm achieves high-precision identification. This robust recognition framework enables efficient processing of complex images, establishing a solid foundation for subsequent analysis and processing tasks.