How to detect a mouse cursor location in an image/video frame using python and opencv

I am trying to detect the pixel coordinates (in the image frame) of a mouse pointer in images taken from a screen recording using python and opencv. I have tried a few of the examples posted for shape detection using cv2.approxPolyDP and unfortunately none seem to work. The image:

Some approaches that I have tried:

• shape detection

import cv2
import numpy as np
from matplotlib import pyplot as plt
  
# reading image
img = cv2.imread('test.png')
  
# converting image into grayscale image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
  
# setting threshold of gray image
_, threshold = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
  
# using a findContours() function
contours, _ = cv2.findContours(
    threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
  
i = 0
  
# list for storing names of shapes
for contour in contours:
  
    # here we are ignoring first counter because 
    # findcontour function detects whole image as shape
    if i == 0:
        i = 1
        continue
  
    # cv2.approxPloyDP() function to approximate the shape
    approx = cv2.approxPolyDP(
        contour, 0.01 * cv2.arcLength(contour, True), True)
      
    # using drawContours() function
    cv2.drawContours(img, [contour], 0, (0, 0, 255), 5)
  
    # finding center point of shape
    M = cv2.moments(contour)
    if M['m00'] != 0.0:
        x = int(M['m10']/M['m00'])
        y = int(M['m01']/M['m00'])
  
    # putting shape name at center of each shape
    if len(approx) == 3:
        cv2.putText(img, 'Triangle', (x, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
  
    elif len(approx) == 4:
        cv2.putText(img, 'Quadrilateral', (x, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
  
    elif len(approx) == 5:
        cv2.putText(img, 'Pentagon', (x, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
  
    elif len(approx) == 6:
        cv2.putText(img, 'Hexagon', (x, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
  
    else:
        cv2.putText(img, 'circle', (x, y),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
  
# displaying the image after drawing contours
cv2.imshow('shapes', img)
  
cv2.waitKey(0)
cv2.destroyAllWindows()

• triangle detection

import cv2
import numpy as np
image_obj = cv2.imread('test.jpg')

gray = cv2.cvtColor(image_obj, cv2.COLOR_BGR2GRAY)

kernel = np.ones((4, 4), np.uint8)
dilation = cv2.dilate(gray, kernel, iterations=1)

blur = cv2.GaussianBlur(dilation, (5, 5), 0)


thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)

# Now finding Contours         ###################
_, contours, _ = cv2.findContours(
    thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
coordinates = []
for cnt in contours:
        # [point_x, point_y, width, height] = cv2.boundingRect(cnt)
    approx = cv2.approxPolyDP(
        cnt, 0.07 * cv2.arcLength(cnt, True), True)
    if len(approx) == 3:
        coordinates.append([cnt])
        cv2.drawContours(image_obj, [cnt], 0, (0, 0, 255), 3)

cv2.imwrite("result.png", image_obj)

-- shape detection 2 from a learnopencv tutorial

# Standard imports
import cv2
import numpy as np;

# Read image
im = cv2.imread("blob.jpg", cv2.IMREAD_GRAYSCALE)

# Set up the detector with default parameters.
detector = cv2.SimpleBlobDetector()

# Detect blobs.
keypoints = detector.detect(im)

# 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
im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

# Show keypoints
cv2.imshow("Keypoints", im_with_keypoints)
cv2.waitKey(0)

-- color thresholding approaches because mouse pointer is white:

import cv2
import numpy as np

cap = cv2.VideoCapture(0)

while(1):

    _, frame = cap.read()
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # define range of white color in HSV
    # change it according to your need !
    lower_white = np.array([0,0,0], dtype=np.uint8)
    upper_white = np.array([0,0,255], dtype=np.uint8)

    # Threshold the HSV image to get only white colors
    mask = cv2.inRange(hsv, lower_white, upper_white)
    # Bitwise-AND mask and original image
    res = cv2.bitwise_and(frame,frame, mask= mask)

    cv2.imshow('frame',frame)
    cv2.imshow('mask',mask)
    cv2.imshow('res',res)

    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break

cv2.destroyAllWindows()

I would have thought that some combination of blob detection using tuned circularity, convexity, and inertia parameters along with a color thresholding would be pretty simple (mouse cursors seem pretty distinctive white triangle shapes), but has not proven to be easy.

A pre-trained CNN seemed like overkill for something like this, but perhaps not?

Thanks