-
Notifications
You must be signed in to change notification settings - Fork 17
/
produce_dsm.py
92 lines (77 loc) · 4.16 KB
/
produce_dsm.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
# ===============================================================================================================
# Copyright (c) 2019, Cornell University. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that
# the following conditions are met:
#
# * Redistributions of source code must retain the above copyright otice, this list of conditions and
# the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and
# the following disclaimer in the documentation and/or other materials provided with the distribution.
#
# * Neither the name of Cornell University nor the names of its contributors may be used to endorse or
# promote products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
# TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY
# OF SUCH DAMAGE.
#
# Author: Kai Zhang (kz298@cornell.edu)
#
# The research is based upon work supported by the Office of the Director of National Intelligence (ODNI),
# Intelligence Advanced Research Projects Activity (IARPA), via DOI/IBC Contract Number D17PC00287.
# The U.S. Government is authorized to reproduce and distribute copies of this work for Governmental purposes.
# ===============================================================================================================
import os
import json
import numpy as np
from visualization.plot_height_map import plot_height_map
from lib.dsm_util import write_dsm_tif
from lib.proj_to_grid import proj_to_grid
import cv2
e_resolution = 0.5 # 0.5 meters per pixel
n_resolution = 0.5
# points is in UTM
def produce_dsm_from_points(work_dir, points, tif_to_write, jpg_to_write=None):
with open(os.path.join(work_dir, 'aoi.json')) as fp:
aoi_dict = json.load(fp)
# write dsm to tif
ul_e = aoi_dict['ul_easting']
ul_n = aoi_dict['ul_northing']
e_size = int(aoi_dict['width'] / e_resolution) + 1
n_size = int(aoi_dict['height'] / n_resolution) + 1
dsm = proj_to_grid(points, ul_e, ul_n, e_resolution, n_resolution, e_size, n_size, propagate=True)
# median filter
dsm = cv2.medianBlur(dsm.astype(np.float32), 3)
write_dsm_tif(dsm, tif_to_write,
(ul_e, ul_n, e_resolution, n_resolution),
(aoi_dict['zone_number'], aoi_dict['hemisphere']), nodata_val=-10000)
# create a preview file
if jpg_to_write is not None:
dsm = np.clip(dsm, aoi_dict['alt_min'], aoi_dict['alt_max'])
plot_height_map(dsm, jpg_to_write, save_cbar=True)
return (ul_e, ul_n, e_size, n_size, e_resolution, n_resolution)
# points is in UTM
def produce_dsm_from_height(work_dir, height, tif_to_write, jpg_to_write=None):
with open(os.path.join(work_dir, 'aoi.json')) as fp:
aoi_dict = json.load(fp)
# write dsm to tif
ul_e = aoi_dict['ul_easting']
ul_n = aoi_dict['ul_northing']
n_size, e_size = height.shape[:2]
write_dsm_tif(height, tif_to_write,
(ul_e, ul_n, e_resolution, n_resolution),
(aoi_dict['zone_number'], aoi_dict['hemisphere']), nodata_val=-10000)
# create a preview file
if jpg_to_write is not None:
height = np.clip(height, aoi_dict['alt_min'], aoi_dict['alt_max'])
plot_height_map(height, jpg_to_write, save_cbar=True)
return (ul_e, ul_n, e_size, n_size, e_resolution, n_resolution)
if __name__ == '__main__':
pass