It is a python package of scalespace. The scale space is built by L_k = G( L_{k+1} ), H_k = L_{k+1} - L_k, where G represents a gaussian blur. If you specify level=Q, L_Q is to be an input data and L_0 is lowest frequency data.
import imagescalespace.imagescalespace as scalespace
Decompose the input to scale space data.
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input numpy.ndarray Three dimensional array of [height, width, channel]. Note that it should be three dimensional array even if it is a gray image data.
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blur_sigma float It specifies the standard deviation of Gaussian blur kernel.
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dims_outputs int It specifies dimensions of output data.
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output numpy.ndarray If dims_outputs is 4, 4th dimension represents level of scalespace. If dims_output is 3, the four-dimensional output data is reshaped with (height, width, channel*level).
Compose the scalespace data to the image.
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input numpy.ndarray It should be three dimensional or four dimensional data.
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nb_channels float If the input data is the three dimensional data, nb_channels and/or level should be specified. For the four dimensional input data, it is ignored.
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level numpy.ndarray If the input data is the three dimensional data, nb_channels and/or level should be specified. For the four dimensional input data, it is ignored.
% pip install git+https://github.com/mastnk/imagescalespace