I want to use the ZED 2 to make a disparity map using Opencv2 in python. Because it is a school assignement I need to implement the algorithm myself, and I cant use the ZED SDK. I can use opencv librarys and functions such as cv2.StereoBM_create() stereo.compute(imgL_gray, imgR_gray).
I followed a few online turtorials like Turtorial and these work for the images they provide, but they do not work with my images from the ZED2.

I used the ZED 2.

Here is the image I got from the ZED converted to png.
Original Image

The best endresult i got is this. As you can see, it has some form of depth, but it is definetly not a nice disparity map.
Disparity map

This is the code I used. I tried a lot of them but none got better results than this. I would love some help on how to improve these results. I also tried a lot of different parameters already.

import cv2
import numpy as np
from matplotlib import pyplot as plt

# Load the image in grayscale
image = cv2.imread('TestImage4.png', 0)

# Split the image into left and right halves
left_right_image = np.split(image, 2, axis=1)

# Display the left and right images
cv2.imshow("Left Image", left_right_image[0])
cv2.imshow("Right Image", left_right_image[1])
cv2.waitKey(0)
cv2.destroyAllWindows()

# Parameters for the StereoSGBM algorithm
block_size = 11
min_disp = 0
max_disp = 255
num_disp = max_disp - min_disp
uniquenessRatio = 5
speckleWindowSize = 200
speckleRange = 2
disp12MaxDiff = 0

# Create the StereoSGBM object
stereo = cv2.StereoSGBM_create(
    minDisparity=min_disp,
    numDisparities=num_disp,
    blockSize=block_size,
    uniquenessRatio=uniquenessRatio,
    speckleWindowSize=speckleWindowSize,
    speckleRange=speckleRange,
    disp12MaxDiff=disp12MaxDiff,
    P1=8 * 3 * block_size ** 2,
    P2=32 * 3 * block_size ** 2,
    mode=cv2.STEREO_SGBM_MODE_SGBM
)

# Compute the disparity map
disparity = stereo.compute(left_right_image[0], left_right_image[1])

# Normalize the disparity map for displaying
disparity = cv2.normalize(disparity, disparity, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
disparity = np.uint8(disparity)

# Resize the disparity map for better viewing
disparity_resized = cv2.resize(disparity, (disparity.shape[1] // 2, disparity.shape[0] // 2))

# Display the disparity map
cv2.imshow("Disparity", disparity_resized)
cv2.waitKey(0)
cv2.destroyAllWindows()