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