Вот ответ, который я когда-то опубликовал go в Python / OpenCV для выполнения анализа BLOB-объектов. Возможно, это вам поможет.
Ввод:
import numpy as np
import cv2
import math
# read image
img = cv2.imread("particles.jpg")
# convert to grayscale
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# apply Gaussian Blur
smoothed = cv2.GaussianBlur(gray, (0,0), sigmaX=9, sigmaY=9, borderType = cv2.BORDER_DEFAULT)
# do adaptive threshold on gray image
thresh = cv2.adaptiveThreshold(smoothed, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 65, 10)
cv2.imshow("Threshold", thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Set up the SimpleBlobdetector with default parameters.
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = 0
params.maxThreshold = 256
# Filter by Area.
params.filterByArea = True
params.minArea = 30
params.maxArea = 10000
# Filter by Color (black=0)
params.filterByColor = True
params.blobColor = 0
# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.5
params.maxCircularity = 1
# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.5
params.maxConvexity = 1
# Filter by InertiaRatio
params.filterByInertia = True
params.minInertiaRatio = 0
params.maxInertiaRatio = 1
# Distance Between Blobs
params.minDistBetweenBlobs = 0
# Do detecting
detector = cv2.SimpleBlobDetector_create(params)
# Get keypoints
keypoints = detector.detect(thresh)
print(len(keypoints))
print('')
# Get keypoint locations and radius
for keypoint in keypoints:
x = int(keypoint.pt[0])
y = int(keypoint.pt[1])
s = keypoint.size
r = int(math.floor(s/2))
print (x,y,r)
#cv2.circle(img, (x, y), r, (0, 0, 255), 2)
# Draw blobs
blobs = cv2.drawKeypoints(thresh, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow("Keypoints", blobs)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Save result
cv2.imwrite("particle_blobs.jpg", blobs)