Computing a disparity map in OpenCV

A disparity map contains information related to the distance of the objects of a scene from a viewpoint. In this example we will see how to compute a disparity map from a stereo pair and how to use the map to cut the objects far from the cameras.

The stereo pair is represented by two input images, these images are taken with two cameras separated by a distance and the disparity map is derived from the offset of the objects between them. There are various algorithm to compute a disparity map, the one implemented in OpenCV is the graph cut algorithm. To use it we have to call the function CreateStereoGCState() to initialize the data structure needed by the algorithm and use the function FindStereoCorrespondenceGC() to get the disparity map. Let's see the code:

def cut(disparity, image, threshold):
 for i in range(0, image.height):
  for j in range(0, image.width):
   # keep closer object
   if cv.GetReal2D(disparity,i,j) > threshold:
    cv.Set2D(disparity,i,j,cv.Get2D(image,i,j))

# loading the stereo pair
left  = cv.LoadImage('scene_l.bmp',cv.CV_LOAD_IMAGE_GRAYSCALE)
right = cv.LoadImage('scene_r.bmp',cv.CV_LOAD_IMAGE_GRAYSCALE)

disparity_left  = cv.CreateMat(left.height, left.width, cv.CV_16S)
disparity_right = cv.CreateMat(left.height, left.width, cv.CV_16S)

# data structure initialization
state = cv.CreateStereoGCState(16,2)
# running the graph-cut algorithm
cv.FindStereoCorrespondenceGC(left,right,
                          disparity_left,disparity_right,state)

disp_left_visual = cv.CreateMat(left.height, left.width, cv.CV_8U)
cv.ConvertScale( disparity_left, disp_left_visual, -20 );
cv.Save( "disparity.pgm", disp_left_visual ); # save the map

# cutting the object farthest of a threshold (120)
cut(disp_left_visual,left,120)

cv.NamedWindow('Disparity map', cv.CV_WINDOW_AUTOSIZE)
cv.ShowImage('Disparity map', disp_left_visual)
cv.WaitKey()

These are the two input image I used to test the program (respectively left and right):



Result using threshold = 100


Result using threshold = 120


Result using threshold = 180




Source: http://glowingpython.blogspot.com/2011/11/computing-disparity-map-in-opencv.html