shapes.py

import cv2
import numpy as np
from matplotlib import pyplot as plt

# reading image
img = cv2.imread(r'C:\Users\End User\PycharmProjects\cloudchamber\tracks.png')

# converting image into grayscale image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# setting threshold of gray image
_, threshold = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# using a findContours() function
contours, _ = cv2.findContours(
    threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

i = 0

# list for storing names of shapes
for contour in contours:

    # here we are ignoring first counter because
    # findcontour function detects whole image as shape
    if i == 0:
        i = 1
        continue

    # cv2.approxPloyDP() function to approximate the shape
    approx = cv2.approxPolyDP(
        contour, 0.01 * cv2.arcLength(contour, True), True)

    # using drawContours() function
    cv2.drawContours(img, [contour], 0, (0, 0, 255), 5)

    # finding center point of shape
    M = cv2.moments(contour)
    if M['m00'] != 0.0:
        x = int(M['m10'] / M['m00'])
        y = int(M['m01'] / M['m00'])

    # putting shape name at center of each shape
    if len(approx) == 3:
        cv2.putText(img, 'alpha', (x, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)

    elif len(approx) == 4:
        cv2.putText(img, 'muon', (x, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)

    elif len(approx) == 5:
        cv2.putText(img, 'electron', (x, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)

    elif len(approx) == 6:
        cv2.putText(img, 'photoelectron', (x, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)

    else:
        cv2.putText(img, 'circle', (x, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)

# displaying the image after drawing contours
cv2.imshow('shapes', img)

cv2.waitKey(0)
cv2.destroyAllWindows()