Fill holes in raster dataset by interpolation from the edges.
fillnodata(image, mask=None, max_search_distance=100.0, smoothing_iterations=0)¶
Fill holes in raster data by interpolation
This algorithm will interpolate values for all designated nodata pixels (marked by zeros in mask). For each pixel a four direction conic search is done to find values to interpolate from (using inverse distance weighting). Once all values are interpolated, zero or more smoothing iterations (3x3 average filters on interpolated pixels) are applied to smooth out artifacts.
This algorithm is generally suitable for interpolating missing regions of fairly continuously varying rasters (such as elevation models for instance). It is also suitable for filling small holes and cracks in more irregularly varying images (like aerial photos). It is generally not so great for interpolating a raster from sparse point data.
image (numpy ndarray) – The source image with holes to be filled. If a MaskedArray, the inverse of its mask will define the pixels to be filled – unless the
maskargument is not None (see below).`
mask (numpy ndarray or None) – A mask band indicating which pixels to interpolate. Pixels to interpolate into are indicated by the value 0. Values > 0 indicate areas to use during interpolation. Must be same shape as image. This array always takes precedence over the image’s mask (see above). If None, the inverse of the image’s mask will be used if available.
max_search_distance (float, optional) – The maxmimum number of pixels to search in all directions to find values to interpolate from. The default is 100.
smoothing_iterations (integer, optional) – The number of 3x3 smoothing filter passes to run. The default is 0.
out – The filled raster array.
- Return type