Q:

OpenCv depth estimation from Disparity map

I'm trying to estimate depth from a stereo pair images with OpenCV.  I have disparity map and depth estimation can be obtained as:
             (Baseline*focal)
depth  =     ------------------
           (disparity*SensorSize)

I have used Block Matching technique to find the same points in the two rectificated images. 
OpenCV permits to set some block matching parameter, for example BMState->numberOfDisparities.  
After block matching process:
cvFindStereoCorrespondenceBM( frame1r, frame2r, disp, BMState);
cvConvertScale( disp, disp, 16, 0 );
cvNormalize( disp, vdisp, 0, 255, CV_MINMAX );

I found depth value as:
if(cvGet2D(vdisp,y,x).val[0]>0)
   {
   depth =((baseline*focal)/(((cvGet2D(vdisp,y,x).val[0])*SENSOR_ELEMENT_SIZE)));
   }

But the depth value obtaied is different from the value obtaied with the previous formula due to the value of BMState->numberOfDisparities that changes the result value. 
How can I set this parameter?  what to change this parameter? 
Thanks

A:

The simple formula is valid if and only if the motion from left camera to right one is a pure translation (in particular, parallel to the horizontal image axis). 
In practice this is hardly ever the case.  It is common, for example, to perform the matching after rectifying the images, i.e. after warping them using a known Fundamental Matrix, so that corresponding pixels are constrained to belong to the same row.  Once you have matches on
the rectified images, you can remap them onto the original images using the inverse of the rectifying warp, and then triangulate into 3D space to reconstruct the scene.  OpenCV has a routine to do that: reprojectImageTo3d

