i was following a tutorial about shapes detection using opencv ,numpy and python ,and it was this function i know the reason from it but i do not know how to modify it so i can use it as i want the total bubble's number are 320 but the function detect 303 only i tried to modify this line but the max i get is 303 (len(approx) > 8 and w / h <= 1.1 and w / h >= 0.8) i want someone to explain to me this function please

this is the code

    def getOvalContours(self, adaptiveFrame):contours, hierarchy = cv2.findContours(adaptiveFrame, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)ovalContours = []for contour in contours:approx = cv2.approxPolyDP(contour, 0, True)ret = 0x, y, w, h = cv2.boundingRect(contour)# eliminating not ovals by approx lenghtif (len(approx) > 8 and w / h <= 1.1 and w / h >= 0.8):mask = np.zeros(adaptiveFrame.shape, dtype="uint8")cv2.drawContours(mask, [contour], -1, 255, -1)ret = cv2.matchShapes(mask, contour, 1, 0.0)if (ret < 1):ovalContours.append(contour)self.bubbleWidthAvr += wself.bubbleHeightAvr += hself.bubbleWidthAvr = self.bubbleWidthAvr / len(ovalContours)self.bubbleHeightAvr = self.bubbleHeightAvr / len(ovalContours)return ovalContours

this is the image

Here is one way to do that using Hough Circles in Python/OpenCV.

Input

import cv2
import numpy as np# Read image
img = cv2.imread('multichoice_test.jpg')
hh, ww = img.shape[:2]# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# get Hough circles
min_dist = 30
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, minDist=min_dist, param1=150, param2=20, minRadius=10, maxRadius=15)
print("circles:", circles)
print("")# draw circles
img_circle = img.copy()
count = 0
for circle in circles[0]:# draw the circle in the output image, then draw a rectangle# corresponding to the center of the circle(x,y,r) = circlex = int(x)y = int(y)r = int(r)cv2.circle(img_circle, (x, y), r, (0, 0, 255), 1)count = count + 1# print number of circles
print("number of circles:", count)# save results
cv2.imwrite('multichoice_test_circles.jpg', img_circle)# show images
cv2.imshow('circles', img_circle)
cv2.waitKey(0)
cv2.destroyAllWindows()

Result:

number of circles: 320