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96 lines
3.7 KiB
96 lines
3.7 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 argparse |
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import numpy as np |
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import cv2 |
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from utils import Raster, raster2uint8, save_geotiff, time_it |
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class MatchError(Exception): |
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def __str__(self): |
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return "Cannot match the two images." |
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def _calcu_tf(im1, im2): |
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orb = cv2.AKAZE_create() |
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kp1, des1 = orb.detectAndCompute(im1, None) |
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kp2, des2 = orb.detectAndCompute(im2, None) |
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bf = cv2.BFMatcher() |
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mathces = bf.knnMatch(des2, des1, k=2) |
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good_matches = [] |
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for m, n in mathces: |
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if m.distance < 0.75 * n.distance: |
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good_matches.append([m]) |
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if len(good_matches) < 4: |
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raise MatchError() |
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src_automatic_points = np.float32([kp2[m[0].queryIdx].pt \ |
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for m in good_matches]).reshape(-1, 1, 2) |
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den_automatic_points = np.float32([kp1[m[0].trainIdx].pt \ |
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for m in good_matches]).reshape(-1, 1, 2) |
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H, _ = cv2.findHomography(src_automatic_points, den_automatic_points, |
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cv2.RANSAC, 5.0) |
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return H |
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def _get_match_img(raster, bands): |
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if len(bands) not in [1, 3]: |
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raise ValueError("The lenght of bands must be 1 or 3.") |
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band_array = [] |
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for b in bands: |
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band_i = raster.GetRasterBand(b).ReadAsArray() |
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band_array.append(band_i) |
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if len(band_array) == 1: |
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ima = raster2uint8(band_array[0]) |
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else: |
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ima = raster2uint8(np.stack(band_array, axis=-1)) |
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ima = cv2.cvtColor(ima, cv2.COLOR_RGB2GRAY) |
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return ima |
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@time_it |
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def match(im1_path, |
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im2_path, |
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save_path, |
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im1_bands=[1, 2, 3], |
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im2_bands=[1, 2, 3]): |
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im1_ras = Raster(im1_path) |
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im2_ras = Raster(im2_path) |
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im1 = _get_match_img(im1_ras._src_data, im1_bands) |
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im2 = _get_match_img(im2_ras._src_data, im2_bands) |
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H = _calcu_tf(im1, im2) |
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im2_arr_t = cv2.warpPerspective(im2_ras.getArray(), H, |
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(im1_ras.width, im1_ras.height)) |
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save_geotiff(im2_arr_t, save_path, im1_ras.proj, im1_ras.geot, |
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im1_ras.datatype) |
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parser = argparse.ArgumentParser(description="input parameters") |
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parser.add_argument('--im1_path', type=str, required=True, \ |
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help="Path of time1 image (with geoinfo).") |
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parser.add_argument('--im2_path', type=str, required=True, \ |
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help="Path of time2 image.") |
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parser.add_argument('--save_path', type=str, required=True, \ |
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help="Path to save matching result.") |
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parser.add_argument('--im1_bands', type=int, nargs="+", default=[1, 2, 3], \ |
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help="Bands of im1 to be used for matching, RGB or monochrome. The default value is [1, 2, 3].") |
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parser.add_argument('--im2_bands', type=int, nargs="+", default=[1, 2, 3], \ |
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help="Bands of im2 to be used for matching, RGB or monochrome. The default value is [1, 2, 3].") |
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if __name__ == "__main__": |
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args = parser.parse_args() |
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match(args.im1_path, args.im2_path, args.save_path, args.im1_bands, |
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args.im2_bands)
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