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Merge pull request #167 from e10harvey/CVE-2023-4863
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Update from opencv 4.5 to 4.8
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@e10harvey
e10harvey authored Dec 10, 2024
2 parents a8f2a54 + 1c830f3 commit a5f6fb7
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Showing 8 changed files with 187 additions and 175 deletions.
5 changes: 3 additions & 2 deletions example/scene_reconstruction/example_make_aruco_markers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
from os.path import dirname, join

import cv2 as cv
import cv2.aruco as aruco
import imageio.v3 as imageio
import numpy as np

Expand All @@ -26,7 +27,7 @@ def make_aruco_images(save_path: str, number: str, size: int = 500, padding: int
Size of white border, pixels, by default 50
"""
# Define marker parameters
dictionary = cv.aruco.Dictionary_get(cv.aruco.DICT_4X4_1000)
dictionary = aruco.getPredefinedDictionary(aruco.DICT_4X4_1000)
side_pixels = size # Side width
font_thickness = 2
pad_width = padding
Expand All @@ -36,7 +37,7 @@ def make_aruco_images(save_path: str, number: str, size: int = 500, padding: int
img = np.ones((side_pixels, side_pixels), dtype='uint8') * 255

# Create marker image
cv.aruco.drawMarker(dictionary, id_, side_pixels, img[-side_pixels:, :])
aruco.drawMarker(dictionary, id_, side_pixels, img[-side_pixels:, :])

# Add padding
img = np.pad(img, ((pad_width, pad_width), (pad_width, pad_width)), constant_values=255)
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320 changes: 164 additions & 156 deletions opencsp/app/scene_reconstruction/test/data/data_expected/point_locations.csv
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Original file line number Diff line number Diff line change
@@ -1,157 +1,165 @@
# Marker ID,Corner ID,x (meter),y (meter),z (meter)
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0,1,-4.39471692,-0.59310101,0.67717212
0,2,-4.39684418,-0.49087164,0.67114291
0,3,-4.39739805,-0.49756198,0.56869699
1,4,-3.80182988,1.01293916,-0.00189517
1,5,-3.90366129,1.01170377,-0.00138246
1,6,-3.90571006,1.11475413,-0.00233109
1,7,-3.80230992,1.11495135,-0.00093814
2,8,-2.68223415,0.97661494,-0.00192449
2,9,-2.78534081,0.97758290,-0.00307462
2,10,-2.78462724,1.08088506,-0.00257641
2,11,-2.68181343,1.07991602,-0.00201899
3,12,-4.39371667,-0.61754420,1.78807504
3,13,-4.39125985,-0.61765320,1.89207731
3,14,-4.39014661,-0.51438289,1.89344696
3,15,-4.39245217,-0.51416060,1.78941346
4,16,-4.39117120,0.98229612,1.88306705
4,17,-4.38610878,0.97576962,1.98691736
4,18,-4.38597530,1.07934903,1.99222074
4,19,-4.38672671,1.08524257,1.88877515
5,20,-4.38969747,-0.61895749,3.19263187
5,21,-4.38798589,-0.61555473,3.29588993
5,22,-4.38869152,-0.51188881,3.29299945
5,23,-4.38932826,-0.51507717,3.18965840
6,24,-4.37687919,0.96358340,3.45793224
6,25,-4.37645326,0.96603978,3.56160757
6,26,-4.37487980,1.06964460,3.55950668
6,27,-4.37465494,1.06702970,3.45545827
7,28,-4.37847558,-0.61326855,4.70644053
7,29,-4.37759900,-0.61130522,4.80916513
7,30,-4.37832514,-0.50828684,4.80820880
7,31,-4.37625960,-0.50966556,4.70465709
8,32,-4.36550277,0.36282116,5.41956894
8,33,-4.36662067,0.36494268,5.52286115
8,34,-4.36575929,0.46895992,5.52073764
8,35,-4.36591647,0.46654844,5.41814451
9,36,-4.36546375,0.98820626,4.76899477
9,37,-4.36461935,0.98559348,4.87280138
9,38,-4.36213625,1.08954011,4.87564801
9,39,-4.36427468,1.09226440,4.77249838
10,40,-4.36422493,0.35770756,6.72800909
10,41,-4.36584603,0.35938821,6.83154911
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11,44,-4.35965851,0.88090363,6.21317532
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11,46,-4.35815155,0.98890090,6.31147960
11,47,-4.35801193,0.98423217,6.20883474
12,48,-4.37398205,-0.51261057,6.08224559
12,49,-4.37326444,-0.51570409,6.18553062
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13,53,-4.37050325,-0.45249368,6.80645305
13,54,-4.37070034,-0.34954226,6.81318257
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14,56,-1.44543087,0.14027930,7.76683980
14,57,-1.34826551,0.14426149,7.73542735
14,58,-1.35547791,0.24692246,7.72733161
14,59,-1.45185780,0.24181217,7.76291202
15,60,-1.21328827,0.13788532,7.68120289
15,61,-1.11908973,0.13663072,7.64030334
15,62,-1.11883058,0.23972508,7.63844551
15,63,-1.21409593,0.24147539,7.67692449
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17,68,-1.57290878,0.57067127,7.78354562
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17,70,-1.48344112,0.67698588,7.74179068
17,71,-1.57813624,0.67377835,7.77815058
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19,79,-2.89912008,0.57615623,5.07532547
20,80,-3.20676954,0.32412112,5.18402539
20,81,-3.10363084,0.32328142,5.16706660
20,82,-3.10607527,0.42500085,5.14562231
20,83,-3.20885550,0.42632922,5.16287624
29,116,-3.91551587,1.67446641,3.95715345
29,117,-3.84314225,1.67482981,3.95150879
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30,120,-1.70942114,1.70901489,0.00178225
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30,123,-1.82389069,1.70649144,0.00068406
31,124,-4.40018934,0.49287676,1.02111476
31,125,-4.39794590,0.50373948,1.13718361
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31,127,-4.40026149,0.60670071,1.00921095
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32,129,1.70430232,1.58442529,-0.00031341
32,130,1.58804283,1.58025668,0.00313071
32,131,1.58543868,1.69495633,0.00446705
33,132,-4.38623325,0.42052568,2.53509126
33,133,-4.38603482,0.42530895,2.65073802
33,134,-4.38413613,0.54053809,2.64609936
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36,144,1.70922470,-1.71286646,0.00027787
36,145,1.70558199,-1.59820913,0.00174714
36,146,1.82041751,-1.59657349,0.00098528
36,147,1.82298664,-1.71031204,0.00056248
37,148,-3.92539806,-1.05086339,0.00351162
37,149,-4.03890205,-1.04979705,0.00279086
37,150,-4.03654959,-0.93477609,0.00271324
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38,152,-3.88138099,0.14102883,0.00217368
38,153,-3.99809360,0.14229239,-0.00001708
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44,177,-3.12972540,0.15091149,-0.00152110
44,178,-3.12626473,0.26557993,-0.00159971
44,179,-3.01158931,0.26350559,-0.00154083
275,1100,nan,nan,nan
275,1101,nan,nan,nan
275,1102,nan,nan,nan
275,1103,nan,nan,nan
441,1764,nan,nan,nan
441,1765,nan,nan,nan
441,1766,nan,nan,nan
441,1767,nan,nan,nan
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0, 3,-4.39784255,-0.497618428,0.568439963
1, 4,-3.8017037,1.01316994,-0.00203899729
1, 5,-3.90395344,1.011782,-0.00174217943
1, 6,-3.90599763,1.11484349,-0.00268813296
1, 7,-3.80258068,1.11503633,-0.00128249934
2, 8,-2.68237924,0.976647498,-0.00217510715
2, 9,-2.78549591,0.977617641,-0.00333053572
2, 10,-2.78478091,1.08093236,-0.00283571181
2, 11,-2.6819568,1.07996131,-0.00227309771
3, 12,-4.39426239,-0.617502585,1.78789527
3, 13,-4.39179156,-0.617618527,1.89190977
3, 14,-4.39067351,-0.514334609,1.89327946
3, 15,-4.39373671,-0.513982662,1.78918417
4, 16,-4.39163878,0.98242164,1.88290908
4, 17,-4.38658082,0.975899925,1.98676229
4, 18,-4.3864436,1.07947825,1.99206971
4, 19,-4.38719068,1.08536579,1.88862173
5, 20,-4.38994941,-0.618940221,3.19261414
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970,3881, nan, nan, nan
970,3882, nan, nan, nan
970,3883, nan, nan, nan
5 changes: 3 additions & 2 deletions opencsp/common/lib/deflectometry/ImageProjection.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
from typing import Callable, Optional

import cv2 as cv
import cv2.aruco as aruco
import numpy as np
from PIL import Image, ImageTk

Expand Down Expand Up @@ -190,7 +191,7 @@ def __init__(self, root: tkinter.Tk, display_data: ImageProjectionData) -> 'Imag
self.canvas.configure(background='black', highlightthickness=0)

# Save aruco marker dictionary for calibration image generation
self.aruco_dictionary = cv.aruco.Dictionary_get(cv.aruco.DICT_4X4_1000)
self.aruco_dictionary = aruco.getPredefinedDictionary(aruco.DICT_4X4_1000)

# Save active area data
self._x_active_1: int
Expand Down Expand Up @@ -628,5 +629,5 @@ def _make_aruco_marker(self, width: int, id_: int) -> np.ndarray:
img_2d = np.ones((width, width), dtype='uint8') * self.display_data.projector_max_int

# Create marker image
cv.aruco.drawMarker(self.aruco_dictionary, id_, width, img_2d)
aruco.drawMarker(self.aruco_dictionary, id_, width, img_2d)
return np.concatenate([img_2d[:, :, None]] * 3, axis=2)
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