Finding bright spots in a image using opencv

Question

I want to find the bright spots in the above image and tag them using some symbol. For this i have tried using the Hough Circle Transform algorithm that OpenCV already provides. But it is giving some kind of assertion error when i run the code. I also tried the Canny edge detection algorithm which is also provided in OpenCV but it is also giving some kind of assertion error. I would like to know if there is some method to get this done or if i can prevent those error messages.

I am new to OpenCV and any help would be really appreciated.

P.S. - I can also use Scikit-image if necessary. So if this can be done using Scikit-image then please tell me how.

Below is my preprocessing code:

import cv2
import numpy as np



image = cv2.imread("image1.png")

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

binary_image = np.where(gray_image > np.mean(gray_image),1.0,0.0)

binary_image = cv2.Laplacian(binary_image, cv2.CV_8UC1)

Answer 1

If you are just going to work with simple images like your example where you have black background, you can use same basic preprocessing/thresholding then find connected components. Use this example code to draw a circle inside all circles in the image.

import cv2 
import numpy as np

image = cv2.imread("image1.png")

#  constants
BINARY_THRESHOLD = 20
CONNECTIVITY = 4
DRAW_CIRCLE_RADIUS = 4

#  convert to gray
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

#  extract edges
binary_image = cv2.Laplacian(gray_image, cv2.CV_8UC1)

#  fill in the holes between edges with dilation
dilated_image = cv2.dilate(binary_image, np.ones((5, 5)))

#  threshold the black/ non-black areas
_, thresh = cv2.threshold(dilated_image, BINARY_THRESHOLD, 255, cv2.THRESH_BINARY)

#  find connected components
components = cv2.connectedComponentsWithStats(thresh, CONNECTIVITY, cv2.CV_32S)

#  draw circles around center of components
#see connectedComponentsWithStats function for attributes of components variable
centers = components[3]
for center in centers:
    cv2.circle(thresh, (int(center[0]), int(center[1])), DRAW_CIRCLE_RADIUS, (255), thickness=-1)

cv2.imwrite("res.png", thresh)
cv2.imshow("result", thresh)
cv2.waitKey(0)

Here is resulting image:

Edit: connectedComponentsWithStats takes a binary image as input, and returns connected pixel groups in that image. If you would like to implement that function yourself, naive way would be:
1- Scan image pixels from top left to bottom right until you encounter a non-zero pixel that does not have a label (id).
2- When you encounter a non-zero pixel, search all its neighbours recursively( If you use 4 connectivity you check UP-LEFT-DOWN-RIGHT, with 8 connectivity you also check diagonals) until you finish that region. Assign each pixel a label. Increase your label counter.
3- Continue scanning from where you left.