This tool will estimate the ** Euclidean distance** (i.e.
straight-line distance) between each grid cell and the nearest
'target cell' in the input image. Target cells are all non-zero,
non-NoData grid cells. Distance in the output image is measured in
the same units as the horizontal units of the input image.

The algorithm is based on the highly efficient distance transform of
Shih and Wu (2003). It makes four passes of the image; the first pass
initializes the output image; the second and third passes calculate the
minimum squared Euclidean distance by examining the 3 x 3 neighbourhood
surrounding each cell; the last pass takes the ** square root**
of cell values, transforming them into true

Two temporary images are created during the calculation and should be
automatically deleted upon completion.

Shih FY and Wu Y-T (2004), Fast Euclidean distance transformation in two scans using a 3 x3 neighborhood, Computer Vision and Image Understanding, 93: 195-205.

The following is an example of a Python script that uses this tool:

```
wd = pluginHost.getWorkingDirectory()
```

inputFile = wd + "input.dep"

outputFile = wd + "output.dep"

args = [inputFile, outputFile]

pluginHost.runPlugin("EuclideanDistance", args, False)

This is a Groovy script also using this tool:

```
def wd = pluginHost.getWorkingDirectory()
```

def inputFile = wd + "input.dep"

def outputFile = wd + "output.dep"

String[] args = [inputFile, outputFile]

pluginHost.runPlugin("EuclideanDistance", args, false)

- John Lindsay (2012) email: jlindsay@uoguelph.ca