During image segmentation, you should be able to find the center pixel of a given blob of signal with cv2.moments(contour_object). Segmenting objects from one another can be done with watershed algoritms and cv2.findcountours(image) does a pretty good job finding contours in the image. Signal dilation and contraction may be useful for your segmentation purposes if it doesn't work first shot, but yours is a pretty simple image, so have hope.
Below is a boiler from learnopencv.com that should perform in your case:
# read image through command line img = cv2.imread(args["ipimage"]) # convert the image to grayscale gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # convert the grayscale image to binary image ret,thresh = cv2.threshold(gray_image,127,255,0) # find contours in the binary image im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) for c in contours: # calculate moments for each contour M = cv2.moments(c) # calculate x,y coordinate of center cX = int(M["m10"] / M["m00"]) cY = int(M["m01"] / M["m00"]) cv2.circle(img, (cX, cY), 5, (255, 255, 255), -1) cv2.putText(img, "centroid", (cX - 25, cY - 25),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) # display the image cv2.imshow("Image", img) cv2.waitKey(0)
