寻找颗粒并确定其直径

Question

问题是确定尽可能多且更精确的颗粒，以便将来按直径对它们进行分类

目前，我已经尝试过定义圆的代码，但不够准确，并且确定了很多直径

import cv2
import numpy as np

img = cv2.imread(r'D:\Downloads\bezsveta.jpg', cv2.IMREAD_GRAYSCALE)

img = cv2.equalizeHist(img)
img = cv2.medianBlur(img, 5)

kernel = np.ones((3,3),np.uint8)
img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

retval, threshold = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY_INV)

params = cv2.SimpleBlobDetector_Params()


params.minThreshold = 30
params.maxThreshold = 255

blur = cv2.GaussianBlur(img,(9,9),3)

params.filterByCircularity = True
params.minCircularity = 0.2

params.filterByArea = True
params.minArea = 20

ver = (cv2.__version__).split('.')
if int(ver[0]) < 3 :
    detector = cv2.SimpleBlobDetector(params)
else :
    detector = cv2.SimpleBlobDetector_create(params)


keypoints = detector.detect(threshold)


im_with_keypoints = cv2.drawKeypoints(img, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

# Show keypoints
cv2.imshow("Keypoints", im_with_keypoints)
cv2.waitKey(0)

结果

Answer 1

我对参数进行了迭代，似乎有效，但也许有人会提供有关如何优化的提示？

import cv2
import numpy as np


img = cv2.imread(r'D:\Downloads\bezsveta.jpg', cv2.IMREAD_GRAYSCALE)
original_img = cv2.imread(r'D:\Downloads\bezsveta.jpg')


img = cv2.equalizeHist(img)


img = cv2.bilateralFilter(img, 9, 75, 75)


img = cv2.medianBlur(img, 5)


kernel = np.ones((3,3), np.uint8)
img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)


params = cv2.SimpleBlobDetector_Params()
params.minThreshold = 5
params.maxThreshold = 255
params.filterByCircularity = True
params.minCircularity = 0.1 
params.filterByArea = True
params.minArea = 50 


detector = cv2.SimpleBlobDetector_create(params)


img_with_keypoints = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)


detected_circles = []


for retval in range(80, 255):  

    _, threshold = cv2.threshold(img, retval, 255, cv2.THRESH_BINARY_INV)
    

    keypoints = detector.detect(threshold)
    

    filtered_keypoints = []
    for keypoint in keypoints:
        is_overlap = False
        for circle in detected_circles + filtered_keypoints:
            distance = np.sqrt((keypoint.pt[0] - circle.pt[0])**2 + (keypoint.pt[1] - circle.pt[1])**2)
            if distance < (circle.size * 0.5 + keypoint.size * 0.5):  # Проверяем перекрытие кругов
                is_overlap = True
                break
        if not is_overlap:
            filtered_keypoints.append(keypoint)
    

    detected_circles.extend(filtered_keypoints)
    

    img_with_keypoints = cv2.drawKeypoints(img_with_keypoints, filtered_keypoints, np.array([]), (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)


    cv2.imshow("Keypoints", img_with_keypoints)
    cv2.waitKey(1)  


print("How much:", len(detected_circles))


cv2.waitKey(0) 
cv2.destroyAllWindows()