Stereo Map Disparity Values

edit

Thankyou for the effort & sorry for giving less information. So i am not using still images, my input are from live video feed from two logitech cameras.

In my observation my rectification was complete okay. No distortion at all so i am posting the code where the disparity map was generated. Note: dispL & dispR are the output from stereo.compute and grayL & grayR are the output from cv2.remap

# Using the WLS filter
filteredImg= wls_filter.filter(dispL,grayL,None,dispR)
filteredImg = cv2.normalize(src=filteredImg, dst=filteredImg, beta=1, alpha=255, 
norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F);
filteredImg = np.uint8(filteredImg)
cv2.imshow('Disparity',filteredImg)

Moreover...

Through a def function using mouse click button i can output the x,y coordinate & its disparity values from filteredImg.

def coords_mouse_disp(event,x,y,flags,param): if event == cv2.EVENT_LBUTTONDBLCLK: print (str(x),str(y),str(disp[y,x]),str(filteredImg[y,x]))

Then from python shell, filteredImg[y,x] or the disparity values in a given point. Is not static. (please see attached image above, ill edit my post) thankyou @HYPEREGO

The algorithms for stereo match and disparity are quite sensitive to frame-by-frame or camera-to-camera illumination changes and imager noise, especially if there are over/underexposure areas. This may cause unstable ripples, waves, and artifacts along edges. Controlling illumination and using L+R images collected under identical illumination (at same time) may help. Pulising illumination, unsynchronized L+R exposure start, glare/reflection/transparency, rolling shutters can contribute to this.

Also, self-similar pattern matching (especially when the window size is too small) can result in all-out confusion (ex: stereo match disparity of a checkerboard or dot pattern, or low contrast areas of the image - a random projector pattern can help here if contrast is adequate).

Oh so the light exposure in the environment or the L+R images from my calibration can be the factor? Can it be adjusted through WLS filter or StereoSGBM parameters?

Since you're using live stream video, it is ok that in a certain point the disparity value change, due to what opalmirror have underlined; algorithm can have some uncertainty, especially SGBM and similar.

Let's try this: run the algorithm using two static images (try Tsukuba on Middlebury dataset, for example) and see if there are changes on values, so you can validate your result and eventually think about how to fix it; since there are only a small difference between the disparity values, maybe just a post-processing will work.