基于OpenCV的水中水滴检测：光斑误检问题及优化需求

我尝试使用OpenCV检测水中的水滴，初始采用边缘检测方案，但图像中的光斑会被误识别为水滴。待检测的水滴特征为白色区域外围环绕深色层。

我的实现代码如下：

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, 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)

当前检测结果如下：

内容的提问来源于stack exchange，提问作者Rachid Ben abdelmalek