Notes

# Standard imports
import cv2
import numpy as np;

#------------------------------------------------------------
# Load original image

image_original = cv2.imread('17lp.bmp', cv2.IMREAD_GRAYSCALE)

#------------------------------------------------------------
# Load image to edit, detect edges using Canny, then find contours on Canny and
# draw them with thick (3pt) lines

image = cv2.imread('17lp.bmp', cv2.IMREAD_GRAYSCALE)
edges = cv2.Canny(image,40,80)

# print some images
cv2.imshow("Original and Edges", np.hstack([image_original, edges]))
#

contours, hierarchy = cv2.findContours(edges.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_TC89_KCOS)
cv2.drawContours(image, contours, -1, (0,0,0), 3)

# print some images
cv2.imshow("Original and Contours", np.hstack([image_original, image]))
#

#------------------------------------------------------------
# Setup SimpleBlobDetector parameters.

params = cv2.SimpleBlobDetector_Params()

# Change thresholds
params.minThreshold = 10
params.maxThreshold = 200

# Filter by Area.
params.filterByArea = True
params.minArea = 50

# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.5

# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.87

# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.5

# Create a detector with the parameters
detector = cv2.SimpleBlobDetector(params)

#------------------------------------------------------------
# Detect blobs.

keypoints = detector.detect(image)

# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures
# the size of the circle corresponds to the size of blob

image_with_keypoints = cv2.drawKeypoints(image_original, keypoints, np.array([]), (0,255,0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

#------------------------------------------------------------
# Show blobs

cv2.imshow("Keypoints", image_with_keypoints)
cv2.waitKey(0)