I'm trying to implement the Hough circle by using python without cv2 - ("OpenCV")

I have checked the python implementation of hough circles in "detectCircles" function in

https://github.com/PavanGJ/Circle-Hough-Transform/blob/master/main.py

and my code below is based on implementation in above

I have some problems adjusting my function with simple parameters

My original function parameters is : image_input, minimum_radius, max_radius,

I must return a list with contains the center(x,y) and the radius of the circle

the list return is like the from (x,y,r) the (x,y) for center and r for radius

def AlgoHoughCircle(input: np.ndarray, minimum_radius: float, maximum_radius: float) -> list:
img_h, img_w = input.shape
input = cv2.GaussianBlur(input, (5, 5), 0)
input = input.astype('uint8')
input = cv2.Canny(input, 50, 100)
x_y_edges = np.argwhere(input > 0)
A = np.zeros((maximum_radius, img_h + 2 * maximum_radius, img_w + 2 * maximum_radius))
theta = np.arange(0, 360) * np.pi / 180
for r in range(round(minimum_radius), round(maximum_radius)):
    bprint = np.zeros((2 * (r + 1), 2 * (r + 1)))
    (x_0, y_0) = (r + 1, r + 1)
    for angle in theta:
        x = int(np.round(r * np.cos(angle)))
        y = int(np.round(r * np.sin(angle)))
        bprint[x_0 + x, y_0 + y] = 1
    constant = np.argwhere(bprint).shape[0]

    for x, y in x_y_edges:
        A[r, x - x_0 + maximum_radius:x + x_0 + maximum_radius, y - y_0 + maximum_radius:y + y_0 + maximum_radius] += bprint
    threshold = 7
    A[r][A[r] < threshold * constant / r] = 0

B = np.zeros((maximum_radius, img_h + 2 * maximum_radius, img_w + 2 * maximum_radius))
region = 15
for r, x, y in np.argwhere(A):
    environment = A[r - region:r + region, x - region:x + region, y - region:y + region]
    p, a, b = np.unravel_index(np.argmax(environment), environment.shape)
    B[r + (p - region), x + (a - region), y + (b - region)] = 1
circleCoordinates = np.argwhere(B[:, maximum_radius:-maximum_radius, maximum_radius:-maximum_radius])
return circleCoordinates