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56 lines
2.2 KiB
56 lines
2.2 KiB
# 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 os |
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import os.path as osp |
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import numpy as np |
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import argparse |
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import paddlers |
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from sklearn.decomposition import PCA |
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from joblib import dump |
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from utils import Raster, save_geotiff, time_it |
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@time_it |
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def pca_train(img_path, save_dir="output", dim=3): |
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raster = Raster(img_path) |
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im = raster.getArray() |
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n_im = np.reshape(im, (-1, raster.bands)) |
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pca = PCA(n_components=dim, whiten=True) |
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pca_model = pca.fit(n_im) |
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if not osp.exists(save_dir): |
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os.makedirs(save_dir) |
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name = osp.splitext(osp.normpath(img_path).split(os.sep)[-1])[0] |
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model_save_path = osp.join(save_dir, (name + "_pca.joblib")) |
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image_save_path = osp.join(save_dir, (name + "_pca.tif")) |
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dump(pca_model, model_save_path) # Save model |
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output = pca_model.transform(n_im).reshape( |
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(raster.height, raster.width, -1)) |
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save_geotiff(output, image_save_path, raster.proj, raster.geot) # Save tiff |
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print("The output image and the PCA model are saved in {}.".format( |
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save_dir)) |
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if __name__ == "__main__": |
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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("--save_dir", type=str, default="output", \ |
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help="Directory to save PCA params(*.joblib). Default: output.") |
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parser.add_argument("--dim", type=int, default=3, \ |
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help="Dimension to reduce to. Default: 3.") |
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args = parser.parse_args() |
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pca_train(args.im_path, args.save_dir, args.dim)
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