OpenCV python - detect and measure a gun shot on a target from a camera stream

I would like to realize a project for a gun club. The goal is to detect and measure shots on a target and count the points. My thoughts about this projects are as follows:

1. apply a region of interest to focus onto the target
2. apply filters on the camera stream to get a sharp boundary around the black center
3. define the diameter since it is known
4. get the center of the boundary and store it as a reference point
5. detect shots and get the radial and distance in reference to the diameter and reference point and hence the value of the shot
6. show the last shots with a circle and their values on the screen

What I got so far is this:

edge detection and center point

Explanation radial and distance:

get the radial and distance

Screen with circles and values:

goal

import cv2
import numpy as np
import imutils

# declare variables

framewidth = 1920
frameheight = 1080
RTSP_URL = 'rtsp://xxxxxx:xxxxxxxx@192.168.1.64:554/Streaming/channels/1'
cap = cv2.VideoCapture(RTSP_URL, cv2.CAP_FFMPEG)
cap.set(3, framewidth)
cap.set(4, frameheight)
if not cap.isOpened():
    print('Cannot open RTSP stream')
    exit(-1)

# pseudo function

def empty(a):
    pass

# slider

cv2.namedWindow("Parameters")
cv2.resizeWindow("Parameters", 640,240)
cv2.createTrackbar("Threshold1","Parameters",16,255,empty)
cv2.createTrackbar("Threshold2","Parameters",192,255,empty)
cv2.createTrackbar("Threshold3","Parameters",243,255,empty)
cv2.createTrackbar("Threshold4","Parameters",255,255,empty)

# imagestack

def stackImages(scale,imgArray):
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range ( 0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank]*rows
        hor_con = [imageBlank]*rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor= np.hstack(imgArray)
        ver = hor
    return ver



def getContours(imgDil,imgContour):

    contours, hierarchy = cv2.findContours(imgDil, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        
    for cnt in contours:
        area = cv2.contourArea(cnt)
        # compute the center of the contour
        M = cv2.moments(cnt)
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])
        # draw the contour and center of the shape on the image

        if area > 5000:
            cv2.drawContours(imgContour, cnt, -1, (255, 0 ,255),3)
            cv2.circle(imgContour, (cX, cY), 7, (255, 0, 255), -1)
            cv2.putText(imgContour, "center", (cX - 20, cY - 20),
        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2)


while(True):
    success, img = cap.read()
    imgContour = img.copy()
    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    threshold1 = cv2.getTrackbarPos("Threshold1", "Parameters")
    threshold2 = cv2.getTrackbarPos("Threshold2", "Parameters")
    threshold3 = cv2.getTrackbarPos("Threshold3", "Parameters")
    threshold4 = cv2.getTrackbarPos("Threshold4", "Parameters")
    ret, thresh = cv2.threshold(imgGray,threshold1,threshold2,1)
    imgCanny = cv2.Canny(imgGray,threshold3,threshold4)
    kernel = np.ones((3,3))
    imgDil = cv2.dilate(thresh, kernel, iterations=1)
    getContours(imgDil,imgContour)

    imgStack = stackImages(0.4,([img,imgGray,thresh],[imgCanny,img,imgContour]))

    cv2.imshow('Result',imgStack)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

I really appreciate any suggestions on best practices and of course any help. Shall I better go for a stereo camera with depth recognition like the Oak-D, since I think the detection of a shot in the black target area could be challenging.