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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import itertools
import argparse
import paddlers
import numpy as np
import pandas as pd
from easydict import EasyDict as edict
from utils import Raster, time_it
def _calcOIF(rgb, stds, rho):
r, g, b = rgb
s1 = stds[int(r)]
s2 = stds[int(g)]
s3 = stds[int(b)]
r12 = rho[int(r), int(g)]
r23 = rho[int(g), int(b)]
r31 = rho[int(b), int(r)]
return (s1 + s2 + s3) / (abs(r12) + abs(r23) + abs(r31))
@time_it
def oif(img_path, topk=5):
raster = Raster(img_path)
img = raster.getArray()
img_flatten = img.reshape([-1, raster.bands])
stds = np.std(img_flatten, axis=0)
datas = edict()
for c in range(raster.bands):
datas[str(c + 1)] = img_flatten[:, c]
datas = pd.DataFrame(datas)
rho = datas.corr().values
band_combs = edict()
for rgb in itertools.combinations(list(range(raster.bands)), 3):
band_combs[str(rgb)] = _calcOIF(rgb, stds, rho)
band_combs = sorted(
band_combs.items(), key=lambda kv: (kv[1], kv[0]), reverse=True)
print("== Optimal band combination ==")
for i in range(topk):
k, v = band_combs[i]
print("Bands: {0}, OIF value: {1}.".format(k, v))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--im_path", type=str, required=True, \
help="Path of HSIs image.")
parser.add_argument("--topk", type=int, default=5, \
help="Number of top results. The default value is 5.")
args = parser.parse_args()
oif(args.im_path, args.topk)