75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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# \file dfc_daps.py
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# \brief TODO
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# \author Florent Guiotte <florent.guiotte@gmail.com>
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# \version 0.1
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# \date 27 août 2018
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#
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# TODO details
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import numpy as np
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import ld2dap
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def run(rasters, treshold=1e4, areas=None, sd=None, moi=None, split=1, split_dim=0):
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"""DFC Differential Self Dual Attribute Profiles
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Compute description vectors for parameters. Rasters can be splitted along
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`split_dim` before description proceeds.
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"""
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# Parse attribute type
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treshold = float(treshold)
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areas = None if areas is None else np.array(areas).astype(np.float).astype(np.int)
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sd = None if sd is None else np.array(sd).astype(np.float)
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moi = None if moi is None else np.array(moi).astype(np.float)
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# Load and filter
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loader = ld2dap.LoadTIFF(rasters)
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dfc_filter = ld2dap.Treshold(treshold)
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normalize = ld2dap.Normalize(dtype=np.uint8)
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raw_out = ld2dap.RawOutput()
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raw_out.input = normalize
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normalize.input = dfc_filter
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dfc_filter.input = loader
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raw_out.run()
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# Split
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n = split; d = split_dim
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step = int(raw_out.data.shape[d] / n)
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view = np.moveaxis(raw_out.data, d, 0)
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cuts = list()
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for i in range(n):
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cut = np.moveaxis(view[i*step:(i+1)*step+1], 0, d)
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cuts.append(cut)
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# Describe
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dcuts = list()
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for cut in cuts:
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rinp = ld2dap.RawInput(cut, raw_out.metadata)
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aps = ld2dap.SelfDualAttributeProfiles(areas, sd, moi, normalize_to_dtype=False)
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diff = ld2dap.Differential()
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vout = ld2dap.RawOutput()
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vout.input = diff
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diff.input = aps
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aps.input = rinp
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vout.run()
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dcuts.append(vout.data)
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# Merge
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descriptors = np.zeros(raw_out.data.shape[:2] + (dcuts[0].shape[-1],))
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view = np.moveaxis(descriptors, d, 0)
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for i, cut in enumerate(dcuts):
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view[i*step:(i+1)*step+1] = np.moveaxis(cut, 0, d)
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return descriptors
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def version():
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return 'v0.0'
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