#! /usr/bin/env python3
#
# Copyright 2019 California Institute of Technology
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# ISOFIT: Imaging Spectrometer Optimal FITting
# Author: David R Thompson, david.r.thompson@jpl.nasa.gov
#
import numpy as np
from spectral.io import envi
from isofit.core.common import envi_header
[docs]
def remap(inputfile, labels, outputfile, flag, chunksize):
"""."""
ref_file = inputfile
lbl_file = labels
out_file = outputfile
nchunk = chunksize
ref_img = envi.open(envi_header(ref_file), ref_file)
ref_meta = ref_img.metadata
ref_mm = ref_img.open_memmap(interleave="source", writable=False)
ref = np.array(ref_mm[:, :])
lbl_img = envi.open(envi_header(lbl_file), lbl_file)
lbl_meta = lbl_img.metadata
labels = lbl_img.read_band(0)
nl = int(lbl_meta["lines"])
ns = int(lbl_meta["samples"])
nb = int(ref_meta["bands"])
out_meta = dict([(k, v) for k, v in ref_meta.items()])
out_meta["samples"] = ns
out_meta["bands"] = nb
out_meta["lines"] = nl
out_meta["data type"] = ref_meta["data type"]
out_meta["interleave"] = "bil"
out_img = envi.create_image(
envi_header(out_file), metadata=out_meta, ext="", force=True
)
out_mm = out_img.open_memmap(interleave="source", writable=True)
# Iterate through image "chunks," restoring as we go
for lstart in np.arange(0, nl, nchunk):
print(lstart)
del out_mm
out_mm = out_img.open_memmap(interleave="source", writable=True)
# Which labels will we extract? ignore zero index
lend = min(lstart + nchunk, nl)
lbl = labels[lstart:lend, :]
out = flag * np.ones((lbl.shape[0], nb, lbl.shape[1]))
for row in range(lbl.shape[0]):
for col in range(lbl.shape[1]):
out[row, :, col] = np.squeeze(ref[int(lbl[row, col]), :])
out_mm[lstart:lend, :, :] = out