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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import itertools
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import argparse
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from easydict import EasyDict as edict
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import numpy as np
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import pandas as pd
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from utils import Raster, time_it
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def _calcOIF(rgb, stds, rho):
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r, g, b = rgb
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s1 = stds[int(r)]
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s2 = stds[int(g)]
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s3 = stds[int(b)]
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r12 = rho[int(r), int(g)]
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r23 = rho[int(g), int(b)]
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r31 = rho[int(b), int(r)]
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return (s1 + s2 + s3) / (abs(r12) + abs(r23) + abs(r31))
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@time_it
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def oif(img_path, topk=5):
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raster = Raster(img_path)
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img = raster.getArray()
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img_flatten = img.reshape([-1, raster.bands])
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stds = np.std(img_flatten, axis=0)
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datas = edict()
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for c in range(raster.bands):
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datas[str(c + 1)] = img_flatten[:, c]
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datas = pd.DataFrame(datas)
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rho = datas.corr().values
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band_combs = edict()
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for rgb in itertools.combinations(list(range(raster.bands)), 3):
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band_combs[str(rgb)] = _calcOIF(rgb, stds, rho)
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band_combs = sorted(
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band_combs.items(), key=lambda kv: (kv[1], kv[0]), reverse=True)
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print("== Optimal band combination ==")
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for i in range(topk):
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k, v = band_combs[i]
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print("Bands: {0}, OIF value: {1}.".format(k, v))
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parser = argparse.ArgumentParser()
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parser.add_argument("--im_path", type=str, required=True, \
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help="Path of HSIs image.")
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parser.add_argument("--topk", type=int, default=5, \
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help="Number of top results. The default value is 5.")
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if __name__ == "__main__":
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args = parser.parse_args()
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oif(args.im_path, args.topk)
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