How to find the average diameter of the drops, which are superimposed on a strip of size 260 mm x 6.2 mm using image processing?

Question

How to find the average diameter of the drops in an image. The drops on the image are raindrops, and the strip on which drops are superimposed has a size of 260 mm x 6.2 mm. This strip length and breadth could be considered a reference (perspective) size and can be used to measure the drop diameter or length of the x and y axes in mm. Also, how to label each drop with its diameter?

image = cv2.imread("1.png")
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# create a binary thresholded image
_, binary = cv2.threshold(gray, 225, 255, cv2.THRESH_BINARY_INV)
# show it

# find the contours from the thresholded image
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# draw all contours

for contour in contours:
    if (cv2.contourArea(contour) < 3000) & (cv2.contourArea(contour) > 4):
        image = cv2.drawContours(image, contours, -1, (1, 1, 1), 1)

        circles = image.copy()
        num_circles = len(contours)
        ave = 0
        rds = []
        center, radius = cv2.minEnclosingCircle(contour)
        cx = int(round(center[0]))
        cy = int(round(center[1]))
        rr = int(round(radius))
        # draw enclosing circle over beads
    #     cv2.circle(circles, (cx,cy), rr, (1,1,1), 1)
        # cumulate radii for average
        ave = ave + radius
        rds.append(rr)

# print average radius
ave_radius = ave / num_circles
print("average radius:", ave_radius)
print ("number of circles:", num_circles)

cv2.imwrite('beads_circles.jpg', circles)
plt.imshow(circles)
plt.show()

Here is the output

Answer 1

import numpy as np
import matplotlib.pyplot as plt
import cv2
import glob
import os

img = cv2.imread("pip_ims/52.png")

image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# create a binary thresholded image
_, binary = cv2.threshold(gray, 225, 255, cv2.THRESH_BINARY_INV)

# find the contours from the thresholded image
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
dia_mm = []
for contour in contours:
    #image = cv2.drawContours(image, contours, -1, (1, 1, 1), 3)
    area = cv2.contourArea(contour)
    dia = 2*(area/np.pi)**(0.5) # calculating diameter from area
    print(dia)
    diamm = dia*(6.2/64) # converting diameter from pixels to mm
    dia_mm.append(diamm)
    print(diamm)
diameter = list(filter(lambda num: num != 0, dia_mm)) # removing zeros
diameter = np.array(diameter)
print(diameter.mean(), diameter.max(), diameter.min())