Finding Shapes

import cv2
import numpy as np

def classify_shape(contour):
    """Classify a contour as a specific geometric shape."""
    shape = "unknown"

    # Approximate the contour to a polygon
    perimeter = cv2.arcLength(contour, True)
    epsilon = 0.04 * perimeter  # Approximation accuracy
    approx = cv2.approxPolyDP(contour, epsilon, True)
    vertices = len(approx)

    # Classify by number of vertices
    if vertices == 3:
        shape = "triangle"
    elif vertices == 4:
        # Distinguish rectangle from square by aspect ratio
        x, y, w, h = cv2.boundingRect(approx)
        ratio = w / float(h)
        shape = "square" if 0.9 <= ratio <= 1.1 else "rectangle"
    elif vertices == 5:
        shape = "pentagon"
    elif vertices == 6:
        shape = "hexagon"
    else:
        # Many vertices = probably a circle
        area = cv2.contourArea(contour)
        (cx, cy), radius = cv2.minEnclosingCircle(contour)
        circle_area = np.pi * radius ** 2
        if area / circle_area > 0.85:
            shape = "circle"
        else:
            shape = f"polygon ({vertices} sides)"

    return shape

image = cv2.imread("shapes.jpg")  # Use an image with clear shapes
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

result = image.copy()
for contour in contours:
    if cv2.contourArea(contour) > 500:
        shape = classify_shape(contour)
        M = cv2.moments(contour)
        if M["m00"] != 0:
            cx = int(M["m10"] / M["m00"])
            cy = int(M["m01"] / M["m00"])
            cv2.putText(result, shape, (cx-30, cy),
                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)
            cv2.drawContours(result, [contour], -1, (0,255,0), 2)

cv2.imwrite("shapes_detected.jpg", result)
print("Shape detection complete")