openCV - Generating disparity map from stereo images

Question

I calibrated my cameras and took a picture with each. Have rectified the images, and saved them. these are the images:

despite my best efforts, I cannot produce a decent depthmap out of these.

this is the best I got so far:

The code I use to try different values is this, where Left_Nice and Right_Nice are the images posted here:

import cv2
import numpy as np

cv2.namedWindow('disp', cv2.WINDOW_NORMAL)
cv2.resizeWindow('disp', 800, 1200)

cv2.createTrackbar('numDisparities', 'disp', 1, 50, nothing)
cv2.createTrackbar('blockSize', 'disp', 5, 50, nothing)
cv2.createTrackbar('preFilterType', 'disp', 1, 1, nothing)
cv2.createTrackbar('preFilterSize', 'disp', 2, 25, nothing)
cv2.createTrackbar('preFilterCap', 'disp', 5, 62, nothing)
cv2.createTrackbar('textureThreshold', 'disp', 10, 100, nothing)
cv2.createTrackbar('uniquenessRatio', 'disp', 15, 100, nothing)
cv2.createTrackbar('speckleRange', 'disp', 0, 100, nothing)
cv2.createTrackbar('speckleWindowSize', 'disp', 3, 25, nothing)
cv2.createTrackbar('disp12MaxDiff', 'disp', 0, 100, nothing)
cv2.createTrackbar('minDisparity', 'disp', 0, 25, nothing)

stereo = cv2.StereoBM_create()

Left_nice = cv2.imread('c:/calimages/nice_cam_left_.jpg')
Right_nice = cv2.imread('c:/calimages/nice_cam_right_.jpg')
Left_nice = cv2.cvtColor(Left_nice, cv2.COLOR_BGR2GRAY)
Right_nice = cv2.cvtColor(Right_nice, cv2.COLOR_BGR2GRAY)

while True:
    # Updating the parameters based on the trackbar positions
    numDisparities = cv2.getTrackbarPos('numDisparities', 'disp') * 16
    blockSize = cv2.getTrackbarPos('blockSize', 'disp') * 2 + 5
    preFilterType = cv2.getTrackbarPos('preFilterType', 'disp')
    preFilterSize = cv2.getTrackbarPos('preFilterSize', 'disp') * 2 + 5
    preFilterCap = cv2.getTrackbarPos('preFilterCap', 'disp')
    textureThreshold = cv2.getTrackbarPos('textureThreshold', 'disp')
    uniquenessRatio = cv2.getTrackbarPos('uniquenessRatio', 'disp')
    speckleRange = cv2.getTrackbarPos('speckleRange', 'disp')
    speckleWindowSize = cv2.getTrackbarPos('speckleWindowSize', 'disp') * 2
    disp12MaxDiff = cv2.getTrackbarPos('disp12MaxDiff', 'disp')
    minDisparity = cv2.getTrackbarPos('minDisparity', 'disp')

    # Setting the updated parameters before computing disparity map
    stereo.setNumDisparities(numDisparities)
    stereo.setBlockSize(blockSize)
    stereo.setPreFilterType(preFilterType)
    stereo.setPreFilterSize(preFilterSize)
    stereo.setPreFilterCap(preFilterCap)
    stereo.setTextureThreshold(textureThreshold)
    stereo.setUniquenessRatio(uniquenessRatio)
    stereo.setSpeckleRange(speckleRange)
    stereo.setSpeckleWindowSize(speckleWindowSize)
    stereo.setDisp12MaxDiff(disp12MaxDiff)
    stereo.setMinDisparity(minDisparity)

    # Calculating disparity using the StereoBM algorithm
    disparity = stereo.compute(Left_nice, Right_nice)
    # NOTE: Code returns a 16bit signed single channel image,
    # CV_16S containing a disparity map scaled by 16. Hence it
    # is essential to convert it to CV_32F and scale it down 16 times.

    # Converting to float32
    disparity = disparity.astype(np.float32)

    # Scaling down the disparity values and normalizing them
    disparity = (disparity / 16.0 - minDisparity) / numDisparities

    # Displaying the disparity map
    cv2.imshow("disp", disparity)
    # Close window using esc key
    if cv2.waitKey(1) == 27:
        break

taken from this sample code: https://learnopencv.com/depth-perception-using-stereo-camera-python-c/

any idea on what I am dong wrong?