Learning OpenCV

Distance Transform

The distance transform of an image is defined as a new image in which every output pixel is set to a value equal to the distance to the nearest zero pixel in the input image. It should be immediately obvious that the typical input to a distance transform should be some kind of edge image. In most applications the input to the distance transform is an output of an edge detector such as the Canny edge detector that has been inverted (so that the edges have value zero and the non-edges are nonzero).

In practice, the distance transform is carried out by using a mask that is typically a 3-by-3 or 5-by-5 array. Each point in the array defines the "distance" to be associated with a point in that particular position relative to the center of the mask. Larger distances are built up (and thus approximated) as sequences of "moves" defined by the entries in the mask. This means that using a larger mask will yield more accurate distances.

Depending on the desired distance metric, the appropriate mask is automatically selected from a set known to OpenCV. It is also possible to tell OpenCV to compute "exact" distances according to some formula appropriate to the selected metric, but of course this is much slower.

The distance metric can be any of several different types, including the classic L2 (Cartesian) distance metric; see Table 6-2 for a listing. In addition to these you may define a custom metric and associate it with your own custom mask.

Table 6-2. Possible values for distance_type ...