diff --git a/example/scene_reconstruction/example_make_aruco_markers.py b/example/scene_reconstruction/example_make_aruco_markers.py
index 260c120e1..de32fed8e 100644
--- a/example/scene_reconstruction/example_make_aruco_markers.py
+++ b/example/scene_reconstruction/example_make_aruco_markers.py
@@ -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
 
@@ -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
@@ -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)
diff --git a/opencsp/app/scene_reconstruction/test/data/data_expected/point_locations.csv b/opencsp/app/scene_reconstruction/test/data/data_expected/point_locations.csv
index 5f4ad7ff7..5a2defdd0 100644
--- a/opencsp/app/scene_reconstruction/test/data/data_expected/point_locations.csv
+++ b/opencsp/app/scene_reconstruction/test/data/data_expected/point_locations.csv
@@ -1,157 +1,165 @@
 # Marker ID,Corner ID,x (meter),y (meter),z (meter)
-0,0,-4.39590359,-0.59885688,0.57563430
-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
-10,42,-4.36647764,0.46333650,6.83112734
-10,43,-4.36237725,0.46100424,6.72653910
-11,44,-4.35965851,0.88090363,6.21317532
-11,45,-4.36140949,0.88542678,6.31610308
-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
-12,50,-4.37423638,-0.41343987,6.18963321
-12,51,-4.37326303,-0.41012205,6.08522087
-13,52,-4.36930457,-0.44579572,6.70269804
-13,53,-4.37050325,-0.45249368,6.80645305
-13,54,-4.37070034,-0.34954226,6.81318257
-13,55,-4.36869604,-0.34315376,6.70858825
-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
-16,64,-1.07730509,0.58611818,7.59292421
-16,65,-0.98342717,0.58088947,7.55045139
-16,66,-0.97886274,0.68417568,7.55129463
-16,67,-1.07540581,0.68820336,7.58315695
-17,68,-1.57290878,0.57067127,7.78354562
-17,69,-1.47795677,0.57331146,7.74650623
-17,70,-1.48344112,0.67698588,7.74179068
-17,71,-1.57813624,0.67377835,7.77815058
-18,72,-2.87669278,0.13765061,5.16781305
-18,73,-2.77367456,0.13700884,5.14846275
-18,74,-2.77721178,0.23844053,5.12715523
-18,75,-2.88011771,0.23888748,5.14632372
-19,76,-2.89405004,0.47402156,5.09685325
-19,77,-2.79177597,0.47456525,5.07511860
-19,78,-2.79723440,0.57672353,5.05347175
-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
-29,118,-3.85415691,1.67520801,3.87583283
-29,119,-3.92973398,1.67706272,3.88641195
-30,120,-1.70942114,1.70901489,0.00178225
-30,121,-1.70640127,1.59465952,0.00008796
-30,122,-1.82065554,1.59102503,0.00003321
-30,123,-1.82389069,1.70649144,0.00068406
-31,124,-4.40018934,0.49287676,1.02111476
-31,125,-4.39794590,0.50373948,1.13718361
-31,126,-4.39732514,0.61752133,1.12493014
-31,127,-4.40026149,0.60670071,1.00921095
-32,128,1.69983893,1.69869835,0.00282706
-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
-33,135,-4.38468446,0.53634693,2.53085765
-34,136,-3.06257214,-1.05161201,0.00321346
-34,137,-3.17760748,-1.05454334,0.00042973
-34,138,-3.17922345,-0.93964974,0.00089265
-34,139,-3.06313774,-0.93686508,0.00374676
-35,140,-1.70986706,-1.71396644,0.00047987
-35,141,-1.70904697,-1.59866001,0.00133898
-35,142,-1.59500917,-1.59943334,0.00191896
-35,143,-1.59684635,-1.71293095,0.00194357
-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
-37,151,-3.92205878,-0.93699216,0.00421665
-38,152,-3.88138099,0.14102883,0.00217368
-38,153,-3.99809360,0.14229239,-0.00001708
-38,154,-3.99472901,0.25681861,0.00034841
-38,155,-3.88045830,0.25528680,0.00092538
-39,156,-4.37757095,0.38673701,4.03916785
-39,157,-4.37688655,0.38392074,4.15486335
-39,158,-4.37311645,0.49923025,4.15776491
-39,159,-4.37429118,0.50222124,4.04243972
-40,160,-0.00212024,1.70337834,0.00130138
-40,161,0.00268073,1.58895453,-0.00073928
-40,162,-0.11209235,1.58467892,-0.00017824
-40,163,-0.11616408,1.69984940,0.00149412
-41,164,0.00212024,-1.71196878,0.00129491
-41,165,0.00117709,-1.59774449,-0.00115741
-41,166,0.11493152,-1.59857974,-0.00203763
-41,167,0.11532996,-1.71227174,-0.00052006
-42,168,-1.71261671,0.00211969,-0.00129472
-42,169,-1.82795040,0.00068694,-0.00134314
-42,170,-1.82858953,0.11601592,-0.00274694
-42,171,-1.71342692,0.11692692,-0.00162455
-43,172,1.70361581,-0.00211969,-0.00130157
-43,173,1.58819075,-0.00001449,0.00099999
-43,174,1.58827041,0.11370888,0.00230914
-43,175,1.70308969,0.11264783,0.00227009
-44,176,-3.01389856,0.14843015,-0.00019103
-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
+   0,   0,-4.39634657,-0.598923094,0.575381303
+   0,   1,-4.39489288,-0.593298665,0.676934157
+   0,   2,-4.39727944,-0.49092374,0.670901468
+   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
+   5,  21,-4.3882221,-0.61553609,3.29587741
+   5,  22,-4.38892548,-0.511861892,3.29298658
+   5,  23,-4.38978474,-0.514993553,3.18970826
+   6,  24,-4.37714614,0.96370471,3.45793086
+   6,  25,-4.37670337,0.96615966,3.56161164
+   6,  26,-4.37512633,1.06976638,3.55950846
+   6,  27,-4.37491915,1.0671538,3.45545486
+   7,  28,-4.37833398,-0.613313272,4.70648769
+   7,  29,-4.377481,-0.611225613,4.80927977
+   7,  30,-4.37801907,-0.508074817,4.80831364
+   7,  31,-4.37603233,-0.509463742,4.70477382
+   8,  32,-4.36543654,0.362944615,5.4197184
+   8,  33,-4.36654341,0.365075571,5.52302555
+   8,  34,-4.3656788,0.469108675,5.52089839
+   8,  35,-4.36584787,0.466688067,5.41829117
+   9,  36,-4.36550561,0.988386893,4.76906985
+   9,  37,-4.36462496,0.985773043,4.87288071
+   9,  38,-4.36214523,1.08974485,4.87571963
+   9,  39,-4.36432226,1.09247358,4.77256587
+  10,  40,-4.3627764,0.358399089,6.72737478
+  10,  41,-4.36605607,0.359584568,6.83200157
+  10,  42,-4.36627877,0.463648849,6.8315106
+  10,  43,-4.36203543,0.461229839,6.72688361
+  11,  44,-4.35950312,0.881202342,6.21340885
+  11,  45,-4.36124964,0.885732578,6.31635528
+  11,  46,-4.35798999,0.989225581,6.31172303
+  11,  47,-4.35785433,0.984549517,6.20905966
+  12,  48,-4.37393557,-0.512523072,6.08255319
+  12,  49,-4.37405878,-0.51631068,6.18595884
+  12,  50,-4.37453493,-0.413212437,6.18988052
+  12,  51,-4.37316074,-0.410037556,6.08550822
+  13,  52,-4.36908499,-0.445678005,6.70309753
+  13,  53,-4.37031257,-0.452354747,6.80688833
+  13,  54,-4.37093041,-0.349396012,6.81372436
+  13,  55,-4.36865415,-0.343102554,6.70903097
+  14,  56,-1.4448307,0.140399183,7.76640379
+  14,  57,-1.34756852,0.144413426,7.73352732
+  14,  58,-1.35487667,0.247033328,7.72685463
+  14,  59,-1.45125115,0.241636446,7.76244014
+  15,  60,-1.21289353,0.138236351,7.6812012
+  15,  61,-1.11850867,0.136710862,7.639795
+  15,  62,-1.11824362,0.23980723,7.63791994
+  15,  63,-1.21350259,0.241569241,7.67642004
+  16,  64,-1.07670659,0.58619621,7.59232323
+  16,  65,-0.982836886,0.580955633,7.54983271
+  16,  66,-0.978437843,0.683778946,7.54869968
+  16,  67,-1.0748033,0.688280141,7.58253671
+  17,  68,-1.57227629,0.570811636,7.78305619
+  17,  69,-1.4773308,0.573439361,7.7459912
+  17,  70,-1.48280882,0.677114071,7.74125596
+  17,  71,-1.57749723,0.673919474,7.77764351
+  18,  72,-2.87610251,0.137344693,5.16758853
+  18,  73,-2.77325601,0.136988532,5.14786518
+  18,  74,-2.77686157,0.238489404,5.12648936
+  18,  75,-2.87967617,0.238855152,5.14574809
+  19,  76,-2.89354906,0.473804348,5.09659129
+  19,  77,-2.79131103,0.474474499,5.07444712
+  19,  78,-2.79676049,0.576609021,5.05279369
+  19,  79,-2.89863406,0.576055069,5.0746868
+  20,  80,-3.20631418,0.324106707,5.18353996
+  20,  81,-3.10317651,0.323259267,5.16653822
+  20,  82,-3.10561466,0.424962601,5.14509051
+  20,  83,-3.20839397,0.426300664,5.162387
+  29, 116,-3.91566908,1.67455678,3.95712681
+  29, 117,-3.8433117,1.67493626,3.95149114
+  29, 118,-3.85436408,1.67533612,3.87583262
+  29, 119,-3.92992337,1.6771727,3.88640281
+  30, 120,-1.70950305,1.70901955,0.00225359599
+  30, 121,-1.70644135,1.59473162,-3.88333601e-05
+  30, 122,-1.82070795,1.59110156,-0.000109897988
+  30, 123,-1.82394092,1.70657918,0.000538736088
+  31, 124,-4.40059255,0.492926002,1.02090444
+  31, 125,-4.3983428,0.503792513,1.1369869
+  31, 126,-4.39752978,0.617718607,1.12469942
+  31, 127,-4.40066816,0.606759773,1.00899632
+  32, 128,1.69981235,1.6985553,0.00304606721
+  32, 129,1.70427713,1.58428877,-9.77431652e-05
+  32, 130,1.58802394,1.58012495,0.0033397076
+  32, 131,1.58541879,1.69481842,0.0046793067
+  33, 132,-4.38667157,0.420634914,2.53500143
+  33, 133,-4.38645681,0.425419556,2.65066268
+  33, 134,-4.38455964,0.540653895,2.6460201
+  33, 135,-4.38512352,0.536462167,2.53076429
+  34, 136,-3.06278153,-1.05175591,0.00296737565
+  34, 137,-3.17783027,-1.05468596,0.000167518705
+  34, 138,-3.17944395,-0.939783241,0.000631936464
+  34, 139,-3.06334477,-0.937000071,0.00350199854
+  35, 140,-1.70998866,-1.71407967,0.000458566373
+  35, 141,-1.70916549,-1.59877169,0.00131261659
+  35, 142,-1.59489101,-1.60043043,0.000890945028
+  35, 143,-1.59696256,-1.71304358,0.00193085179
+  36, 144,1.70922764,-1.71304733,0.000336747216
+  36, 145,1.70558377,-1.59838092,0.00181122932
+  36, 146,1.82042787,-1.59675022,0.00104675532
+  36, 147,1.82299888,-1.7104985,0.000617907244
+  37, 148,-3.92541836,-1.05125528,0.00394302448
+  37, 149,-4.03935645,-1.04971309,0.0023819428
+  37, 150,-4.03698144,-0.934932214,0.00222934819
+  37, 151,-3.92196424,-0.937130316,0.00414793463
+  38, 152,-3.88171454,0.141008846,0.0017822289
+  38, 153,-3.99844654,0.142275349,-0.000425338464
+  38, 154,-3.99507445,0.256814981,-5.35826381e-05
+  38, 155,-3.88078515,0.255279868,0.00053909782
+  39, 156,-4.377647,0.386797325,4.03916935
+  39, 157,-4.37643427,0.383786659,4.15460598
+  39, 158,-4.37317622,0.499303667,4.15776665
+  39, 159,-4.37436801,0.502289173,4.04243919
+  40, 160,-0.0020579972,1.70333322,0.0013744025
+  40, 161,0.0027384384,1.58890643,-0.000676129357
+  40, 162,-0.112038382,1.58463971,-0.000133984428
+  40, 163,-0.116105268,1.69981436,0.00154644077
+  41, 164,0.00205799721,-1.71209203,0.00136741036
+  41, 165,0.00111854475,-1.59786443,-0.00108601455
+  41, 166,0.114876305,-1.5987015,-0.00196205005
+  41, 167,0.115271383,-1.71239723,-0.000444092643
+  42, 168,-1.71268984,0.00205743843,-0.00136734417
+  42, 169,-1.82803417,0.000626795231,-0.00143219852
+  42, 170,-1.82867144,0.115963679,-0.00284111158
+  42, 171,-1.71349821,0.116872505,-0.00170220899
+  43, 172,1.70368613,-0.00205743798,-0.00137450031
+  43, 173,1.58812467,-4.65435956e-05,0.0013087654
+  43, 174,1.58837613,0.113580548,0.00210997891
+  43, 175,1.70352408,0.112333785,0.00118875136
+  44, 176,-3.01408414,0.148384207,-0.000439966819
+  44, 177,-3.1299233,0.150869116,-0.00178114191
+  44, 178,-3.12646049,0.26554834,-0.00186075146
+  44, 179,-3.01177295,0.263470551,-0.00179165457
+ 275,1100, nan, nan, nan
+ 275,1101, nan, nan, nan
+ 275,1102, nan, nan, nan
+ 275,1103, nan, nan, nan
+ 319,1276, nan, nan, nan
+ 319,1277, nan, nan, nan
+ 319,1278, nan, nan, nan
+ 319,1279, nan, nan, nan
+ 441,1764, nan, nan, nan
+ 441,1765, nan, nan, nan
+ 441,1766, nan, nan, nan
+ 441,1767, nan, nan, nan
+ 970,3880, nan, nan, nan
+ 970,3881, nan, nan, nan
+ 970,3882, nan, nan, nan
+ 970,3883, nan, nan, nan
\ No newline at end of file
diff --git a/opencsp/common/lib/deflectometry/ImageProjection.py b/opencsp/common/lib/deflectometry/ImageProjection.py
index fb2f99548..49fc043c1 100644
--- a/opencsp/common/lib/deflectometry/ImageProjection.py
+++ b/opencsp/common/lib/deflectometry/ImageProjection.py
@@ -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
 
@@ -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
@@ -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)
diff --git a/opencsp/common/lib/photogrammetry/photogrammetry.py b/opencsp/common/lib/photogrammetry/photogrammetry.py
index 7308e496b..f932987fc 100644
--- a/opencsp/common/lib/photogrammetry/photogrammetry.py
+++ b/opencsp/common/lib/photogrammetry/photogrammetry.py
@@ -2,6 +2,7 @@
 """
 
 import cv2 as cv
+import cv2.aruco as aruco
 import matplotlib.pyplot as plt
 import numpy as np
 from numpy import ndarray
@@ -43,9 +44,9 @@ def find_aruco_marker(
     image : ndarray
         2D grayscale image.
     adaptiveThreshConstant : float, optional
-        cv.aruco parameter. The default is 10.
+        aruco parameter. The default is 10.
     minMarkerPerimeterRate : float, optional
-        cv.aruco parameter. The default is 0.05.
+        aruco parameter. The default is 0.01.
 
     Returns
     -------
@@ -57,13 +58,14 @@ def find_aruco_marker(
 
     """
     # Setup detection parameters
-    aruco_dict = cv.aruco.Dictionary_get(cv.aruco.DICT_4X4_1000)
-    aruco_detect_params = cv.aruco.DetectorParameters_create()
+    aruco_dict = aruco.getPredefinedDictionary(aruco.DICT_4X4_1000)
+    aruco_detect_params = aruco.DetectorParameters()
     aruco_detect_params.adaptiveThreshConstant = adaptiveThreshConstant
     aruco_detect_params.minMarkerPerimeterRate = minMarkerPerimeterRate
 
     # Find targets
-    (corners, ids, _) = cv.aruco.detectMarkers(image, aruco_dict, parameters=aruco_detect_params)
+    arcuoDetector = aruco.ArucoDetector(aruco_dict, detectorParams=aruco_detect_params)
+    (corners, ids, _) = arcuoDetector.detectMarkers(image)
 
     # Refine corner locations (inaccurate using cv.cornerSubPix)
     #     criteria = (cv.TERM_CRITERIA_EPS + cv.TermCriteria_COUNT, max_iterations, precision)
diff --git a/opencsp/common/lib/photogrammetry/test/test_photogrammetry.py b/opencsp/common/lib/photogrammetry/test/test_photogrammetry.py
index c6da17499..bd9e05936 100644
--- a/opencsp/common/lib/photogrammetry/test/test_photogrammetry.py
+++ b/opencsp/common/lib/photogrammetry/test/test_photogrammetry.py
@@ -27,20 +27,20 @@ def test_load_grayscale():
 
 def test_find_aruco_marker():
     img = ph.load_image_grayscale(join(os.path.dirname(__file__), 'data/image.png'))
-    ids, corners = ph.find_aruco_marker(img, 10, 0.01)
+    ids, corners = ph.find_aruco_marker(img, 7, 0.01)
 
     # Test IDs
-    np.testing.assert_equal(np.array([19, 17, 20, 18, 15, 14, 16]), ids)
+    np.testing.assert_equal(np.array([20, 19, 18, 16, 15, 14, 17]), ids)
     # Test corners
     corns_exp = np.array(
         [
+            [[330.0, 846.0], [470.0, 845.0], [474.0, 984.0], [334.0, 987.0]],
             [[759.0, 1049.0], [897.0, 1050.0], [898.0, 1190.0], [760.0, 1189.0]],
-            [[1015.0, 872.0], [1095.0, 876.0], [1091.0, 957.0], [1011.0, 953.0]],
-            [[330.0, 847.0], [470.0, 845.0], [474.0, 984.0], [334.0, 987.0]],
             [[757.0, 601.0], [892.0, 601.0], [896.0, 737.0], [761.0, 737.0]],
-            [[1305.0, 546.0], [1386.0, 546.0], [1385.0, 627.0], [1305.0, 626.0]],
-            [[1116.0, 543.0], [1196.0, 548.0], [1192.0, 629.0], [1111.0, 623.0]],
-            [[1421.0, 892.0], [1502.0, 889.0], [1505.0, 969.0], [1424.0, 972.0]],
+            [[1421.0, 891.0], [1502.0, 889.0], [1505.0, 969.0], [1424.0, 972.0]],
+            [[1305.0, 546.0], [1386.0, 546.0], [1386.0, 627.0], [1305.0, 626.0]],
+            [[1116.0, 543.0], [1197.0, 549.0], [1192.0, 629.0], [1111.0, 623.0]],
+            [[1015.0, 872.0], [1095.0, 876.0], [1091.0, 957.0], [1011.0, 953.0]],
         ]
     )
     np.testing.assert_equal(corns_exp, np.array(corners))
diff --git a/opencsp/common/lib/render/VideoHandler.py b/opencsp/common/lib/render/VideoHandler.py
index acfeab4e3..4c5cc6321 100644
--- a/opencsp/common/lib/render/VideoHandler.py
+++ b/opencsp/common/lib/render/VideoHandler.py
@@ -5,7 +5,7 @@
 
 """
 
-from cv2 import cv2 as cv
+import cv2 as cv
 import os
 
 import opencsp.common.lib.process.subprocess_tools as subt
diff --git a/opencsp/test/data/dot_location_calibration/data_expected/fixed_pattern_dot_locations.h5 b/opencsp/test/data/dot_location_calibration/data_expected/fixed_pattern_dot_locations.h5
index a53ef5166..60a319675 100644
Binary files a/opencsp/test/data/dot_location_calibration/data_expected/fixed_pattern_dot_locations.h5 and b/opencsp/test/data/dot_location_calibration/data_expected/fixed_pattern_dot_locations.h5 differ
diff --git a/requirements.txt b/requirements.txt
index 8d233dc19..cb9b3fad2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -12,7 +12,6 @@
 h5py >= 3.10.0
 imageio >= 2.34.0
 matplotlib <= 3.8.4
-numpy <= 1.26.4
 pandas >= 2.2.1
 pillow >= 10.2.0
 pyproj >= 3.6.1
@@ -22,7 +21,8 @@ sympy >= 1.12
 
 # packages that can't be installed with conda but can be installed with pip on solo #pippkgs
 ipykernel >= 6.29.3
-opencv-contrib-python == 4.5.5.64
+opencv-contrib-python >= 4.8.1.78, < 4.10.0.84
+numpy < 2.0.0
 pytest >= 8.1.1
 python-pptx >= 0.6.23
 rawpy >= 0.19.1