Detect drops of water using OpenCV

I am trying to detect drops inside the water, where at first I will detect the edges, but there are light spots in the image, which are also detected as drops.

Noting that the drops are white surrounded by a dark layer.

My code :

import cv2
import numpy as np

def unsharp_mask(img, blur_size = (5,5), imgWeight = 1.5, gaussianWeight = -0.5):
    gaussian = cv2.GaussianBlur(img, (5,5), 0)
    return cv2.addWeighted(img, imgWeight, gaussian, gaussianWeight, 0)

def clahe(img, clip_limit = 2.0):
    clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=(5,5))
    return clahe.apply(img)


def get_sobel(img, size = -1):
    sobelx64f = cv2.Sobel(img,cv2.CV_64F,2,0,size)
    abs_sobel64f = np.absolute(sobelx64f)
    return np.uint8(abs_sobel64f)

img = cv2.imread("img_brightened.jpg")
# save color copy for visualizing
imgc = img.copy()
# resize image to make the analytics easier (a form of filtering)
resize_times = 1.5
img = cv2.resize(img, None, img, fx = 1 / resize_times, fy = 1 / resize_times)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("Input", img)

# use sobel operator to evaluate high frequencies
sobel = get_sobel(img)
# experimentally calculated function - needs refining
clip_limit = (-2.556) * np.sum(sobel)/(img.shape[0] * img.shape[1]) + 26.557
# don't apply clahe if there is enough high freq to find blobs
if(clip_limit < 1.0):
    clip_limit = 0.1
# limit clahe if there's not enough details - needs more tests
if(clip_limit > 8.0):
    clip_limit = 8

# apply clahe and unsharp mask to improve high frequencies as much as possible
img = clahe(img, clip_limit)
img = unsharp_mask(img)

# filter the image to ensure edge continuity and perform Canny
img_blurred = (cv2.GaussianBlur(img, (2*2+1,2*2+1), 0))
canny = cv2.Canny(img_blurred, 100, 255)
cv2.imshow("Output", canny)
cv2.waitKey(0)

Result

I used codes from https://github.com/kavyamusty/Shading-removal-of-images/blob/master/Article%20submission.ipynb, which works for removing shadows first, then the cv2.HoughCircles to find the circles.

The codes as below :

import cv2
import numpy as np
import matplotlib.pyplot as plt
def max_filtering(N, I_temp):
    wall = np.full((I_temp.shape[0]+(N//2)*2, I_temp.shape[1]+(N//2)*2), -1)
    wall[(N//2):wall.shape[0]-(N//2), (N//2):wall.shape[1]-(N//2)] = I_temp.copy()
    temp = np.full((I_temp.shape[0]+(N//2)*2, I_temp.shape[1]+(N//2)*2), -1)
    for y in range(0,wall.shape[0]):
        for x in range(0,wall.shape[1]):
            if wall[y,x]!=-1:
                window = wall[y-(N//2):y+(N//2)+1,x-(N//2):x+(N//2)+1]
                num = np.amax(window)
                temp[y,x] = num
    A = temp[(N//2):wall.shape[0]-(N//2), (N//2):wall.shape[1]-(N//2)].copy()
    return A

def min_filtering(N, A):
    wall_min = np.full((A.shape[0]+(N//2)*2, A.shape[1]+(N//2)*2), 300)
    wall_min[(N//2):wall_min.shape[0]-(N//2), (N//2):wall_min.shape[1]-(N//2)] = A.copy()
    temp_min = np.full((A.shape[0]+(N//2)*2, A.shape[1]+(N//2)*2), 300)
    for y in range(0,wall_min.shape[0]):
        for x in range(0,wall_min.shape[1]):
            if wall_min[y,x]!=300:
                window_min = wall_min[y-(N//2):y+(N//2)+1,x-(N//2):x+(N//2)+1]
                num_min = np.amin(window_min)
                temp_min[y,x] = num_min
    B = temp_min[(N//2):wall_min.shape[0]-(N//2), (N//2):wall_min.shape[1]-(N//2)].copy()
    return B

def background_subtraction(I, B):
    O = I - B
    norm_img = cv2.normalize(O, None, 0,255, norm_type=cv2.NORM_MINMAX)
    return norm_img

def min_max_filtering(M, N, I):
    if M == 0:
        #max_filtering
        A = max_filtering(N, I)
        #min_filtering
        B = min_filtering(N, A)
        #subtraction
        normalised_img = background_subtraction(I, B)
    elif M == 1:
        #min_filtering
        A = min_filtering(N, I)
        #max_filtering
        B = max_filtering(N, A)
        #subtraction
        normalised_img = background_subtraction(I, B)
    return normalised_img

# Read Image 
img = cv2.imread(r"D:/Image.jpg")
# Copy origin image 
cimg = img.copy()

# Initialization array of uint8 
img_remove_shadow = np.zeros(np.shape(img), dtype="uint8")

for i in range(np.shape(img)[2]):
    img_remove_shadow[:, :, i] = np.array(min_max_filtering(M = 0, N = 20, I = img[:, :, i]))

# Using median blur 
img = cv2.medianBlur(img_remove_shadow,5)

# Change to gray image
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

cv2.imshow("Removing Shadow", img)



# Find circles 
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 5, np.array([]), 40, 23, 5,20)

circles = np.uint16(np.around(circles))

for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
    # draw the center of the circle
    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
    
cv2.imshow('detected circles',cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

The result is as below:

PS: The codes take 11.74s running time, I would appreciate it if someone could optimize the code.