SciPy Filters QGIS 4 compatibility

QGIS plugin providing access to SciPy filters via the processing toolbox. SciPy offers a range of highly optimised algorithms for i.e. multidimensional image processing and signal processing, and some can be useful to analyze raster layers. Includes raster filters such as: - binary/grey morphology (dilation, erosion, closing, opening; tophat etc.) - principal component analysis (PCA) - statistical filters (local variance, std, median, minimum, percentile etc.) - edge detection (sobel, prewitt, gaussian magnitude etc.) - convolution with a custom kernel (both, classic and FFT versions) - sharpening with unsharp mask - noise removal with Wiener filter - blurring (gaussian, box filter etc.) - Pixel statistics (std, mean, min ... of all bands for individual pixels) - Get no data mask, apply no data mask (i.e. set cells to no data) - fill no data cells of all bands with value, band mean or minimum, maximum or central value of the data type Most filters are based on scipy.ndimage, a library to filter images (or arrays, rasters) in n dimensions. These are either applied on each layer seperately in 2D, or in 3D on a 3D datacube consisting of all bands. In most cases, the plugin simply provides a user interface for a single SciPy function, gets the raster data using GDAL, calls the SciPy function with the provided parameters and loads the result back into QGIS. A few filters (PCA, unsharp mask, pixel statistics etc.) use custom functions that where implemented using SciPy and/or Numpy. For many filters, a custom footprint and/or structure or kernel can be provided, adjusting the size and shape of the filter. Python users get helpers.RasterWizard to quickly get the data of a raster layer as numpy array and the processing result back into QGIS as a new raster layer.