Calculates the distance to the closest zero pixel for each pixel of the source image

 dst = cv.distanceTransform(src)
 [dst, labels] = cv.distanceTransform(src)
 [...] = cv.distanceTransform(..., 'OptionName',optionValue, ...)

Input

• src 8-bit, single-channel (binary) source image.

Output

• dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src.
• labels Optional output 2D array of labels (the discrete Voronoi diagram). It has the type int32 and the same size as src.

Options

• DistanceType Type of distance, default 'L2'. One of:
  • L1 distance = |x1-x2| + |y1-y2|
  • L2 the simple euclidean distance
  • C distance = max(|x1-x2|,|y1-y2|)
• MaskSize Size of the distance transform mask. The 'Precise' option is not supported by the second variant with labels output. In case of the 'L1' or 'C' distance type, the parameter is forced to 3 because a 3x3 mask gives the same result as 5x5 or any larger aperture. The following options are available:
  • 3 approximate distance transform with 3x3 mask (default)
  • 5 approximate distance transform with 5x5 mask
  • Precise precise distance transform
• LabelType Type of the label array to build, default 'CComp'. Only supported by the second variant with labels output. One of:
  • CComp each connected component of zeros in src (as well as all the non-zero pixels closest to the connected component) will be assigned the same label.
  • Pixel each zero pixel (and all the non-zero pixels closest to it) gets its own label.
• DstType Type of output image dst. It can be uint8 or single. Only supported by the first variant without labels output. Type uint8 can be used only for the first variant of the function and DistanceType = 'L1', otherwise the default single is assumed.

The function cv.distanceTransform calculates the approximate or precise distance from every binary image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.

When MaskSize is 'Precise' and DistanceType is 'L2', the function runs the algorithm described in [Felzenszwalb04]. This algorithm is parallelized with the TBB library.

In other cases, the algorithm [Borgefors86] is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight's move (the latest is available for a 5x5 mask). The overall distance is calculated as a sum of these basic distances. Since the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a), all the diagonal shifts must have the same cost (denoted as b), and all knight's moves must have the same cost (denoted as c). For the 'C' and 'L1' types, the distance is calculated precisely, whereas for 'L2' (Euclidian distance) the distance can be calculated only with a relative error (a 5x5 mask gives more accurate results). For a, b, and c, OpenCV uses the values suggested in the original paper:

• L1
  • 3x3: a = 1, b = 2
• L2
  • 3x3: a = 0.955, b = 1.3693
  • 5x5: a = 1, b = 1.4, c = 2.1969
• C
  • 3x3: a = 1, b = 1

Typically, for a fast, coarse distance estimation 'L2', a 3x3 mask is used. For a more accurate distance estimation 'L2', a 5x5 mask or the precise algorithm is used. Note that both the precise and the approximate algorithms are linear on the number of pixels.

The second variant of the function does not only compute the minimum distance for each pixel (x,y) but also identifies the nearest connected component consisting of zero pixels (LabelType='CComp') or the nearest zero pixel (LabelType='Pixel'). Index of the component/pixel is stored in labels(x,y). When LabelType is 'CComp' the function automatically finds connected components of zero pixels in the input image and marks them with distinct labels. When LabelType is 'Pixel', the function scans through the input image and marks all the zero pixels with distinct labels.

In this mode, the complexity is still linear. That is, the function provides a very fast way to compute the Voronoi diagram for a binary image. Currently, the second variant can use only the approximate distance transform algorithm, i.e. MaskSize='Precise' is not supported yet.

References

[Felzenszwalb04]:

Pedro Felzenszwalb and Daniel Huttenlocher. "Distance transforms of sampled functions". Technical report, Cornell University, 2004.

[Borgefors86]:

Gunilla Borgefors. "Distance transformations in digital images". Computer vision, graphics, and image processing, 34(3):344-371, 1986.