Note
This notebook can be downloaded here: aruco_calibration.ipynb
Camera calibration using CHARUCO¶
import numpy as np
import cv2, PIL, os
from cv2 import aruco
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
%matplotlib nbagg
1. Marker dictionary creation¶
workdir = "data/"
aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250)
fig = plt.figure()
nx = 8
ny = 6
for i in range(1, nx*ny+1):
ax = fig.add_subplot(ny,nx, i)
img = aruco.drawMarker(aruco_dict,i, 700)
plt.imshow(img, cmap = mpl.cm.gray, interpolation = "nearest")
ax.axis("off")
plt.savefig(workdir + "markers.pdf")
plt.show()
#plt.close()
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2. Camera pose estimation using CHARUCO chessboard¶
First, let’s create the board.
board = aruco.CharucoBoard_create(11, 8, 10, 7, aruco_dict)
imboard = board.draw((500, 500))
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
plt.imshow(imboard, cmap = mpl.cm.gray, interpolation = "nearest")
ax.axis("off")
plt.savefig(workdir + "chessboard.pdf")
plt.show()
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And take photos of it from multiple angles, for example:
images = [workdir + f for f in os.listdir(workdir) if f.endswith(".png") and f.startswith("VID_20180314_141424")]
im = PIL.Image.open(images[10])
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
plt.imshow(im)
#ax.axis('off')
plt.show()
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Now, the camera calibration can be done using all the images of the chessboard. Two functions are necessary:
- The first will detect markers on all the images and.
- The second will proceed the detected markers to estimage the camera calibration data.
def read_chessboards(images):
"""
Charuco base pose estimation.
"""
print("POSE ESTIMATION STARTS:")
allCorners = []
allIds = []
decimator = 0
# SUB PIXEL CORNER DETECTION CRITERION
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.0001)
for im in images:
print("=> Processing image {0}".format(im))
frame = cv2.imread(im)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
corners, ids, rejectedImgPoints = cv2.aruco.detectMarkers(gray, aruco_dict)
if len(corners)>0:
# SUB PIXEL DETECTION
for corner in corners:
cv2.cornerSubPix(gray, corner,
winSize = (20,20),
zeroZone = (-1,-1),
criteria = criteria)
res2 = cv2.aruco.interpolateCornersCharuco(corners,ids,gray,board)
if res2[1] is not None and res2[2] is not None and len(res2[1])>3 and decimator%1==0:
allCorners.append(res2[1])
allIds.append(res2[2])
decimator+=1
imsize = gray.shape
return allCorners,allIds,imsize
#%%time
allCorners,allIds,imsize=read_chessboards(images)
POSE ESTIMATION STARTS:
=> Processing image data/VID_20180314_141424_290.png
=> Processing image data/VID_20180314_141424_335.png
=> Processing image data/VID_20180314_141424_175.png
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=> Processing image data/VID_20180314_141424_325.png
def calibrate_camera(allCorners,allIds,imsize):
"""
Calibrates the camera using the dected corners.
"""
print("CAMERA CALIBRATION")
cameraMatrixInit = np.array([[ 2000., 0., imsize[0]/2.],
[ 0., 2000., imsize[1]/2.],
[ 0., 0., 1.]])
distCoeffsInit = np.zeros((5,1))
flags = (cv2.CALIB_USE_INTRINSIC_GUESS + cv2.CALIB_RATIONAL_MODEL)
(ret, camera_matrix, distortion_coefficients0,
rotation_vectors, translation_vectors,
stdDeviationsIntrinsics, stdDeviationsExtrinsics,
perViewErrors) = cv2.aruco.calibrateCameraCharucoExtended(
charucoCorners=allCorners,
charucoIds=allIds,
board=board,
imageSize=imsize,
cameraMatrix=cameraMatrixInit,
distCoeffs=distCoeffsInit,
flags=flags,
criteria=(cv2.TERM_CRITERIA_EPS & cv2.TERM_CRITERIA_COUNT, 10000, 1e-9))
return ret, camera_matrix, distortion_coefficients0, rotation_vectors, translation_vectors
%time ret, mtx, dist, rvecs, tvecs = calibrate_camera(allCorners,allIds,imsize)
CAMERA CALIBRATION
CPU times: user 11.3 s, sys: 10.9 s, total: 22.2 s
Wall time: 5.78 s
ret
10.507478602319868
mtx
array([[1.82907422e+03, 0.00000000e+00, 9.70018381e+02],
[0.00000000e+00, 1.82396375e+03, 5.64679336e+02],
[0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])
dist
array([[ 5.77647646e+00],
[-1.19867510e+02],
[ 2.81138209e-03],
[ 2.04931051e-02],
[ 1.36665086e+03],
[ 5.10336618e+00],
[-1.13575351e+02],
[ 1.33870881e+03],
[ 0.00000000e+00],
[ 0.00000000e+00],
[ 0.00000000e+00],
[ 0.00000000e+00],
[ 0.00000000e+00],
[ 0.00000000e+00]])
Check calibration results¶
i=3 # select image id
plt.figure()
frame = cv2.imread(images[i])
img_undist = cv2.undistort(frame,mtx,dist,None)
plt.subplot(1,2,1)
plt.imshow(frame)
plt.title("Raw image")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(img_undist)
plt.title("Corrected image")
plt.axis("off")
plt.show()
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3 . Use of camera calibration to estimate 3D translation and rotation of each marker on a scene¶
frame = cv2.imread("data/VID_20180314_141424_335.png")
#frame = cv2.undistort(src = frame, cameraMatrix = mtx, distCoeffs = dist)
plt.figure()
plt.imshow(frame, interpolation = "nearest")
plt.show()
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Post processing¶
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250)
parameters = aruco.DetectorParameters_create()
corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict,
parameters=parameters)
# SUB PIXEL DETECTION
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.0001)
for corner in corners:
cv2.cornerSubPix(gray, corner, winSize = (20,20), zeroZone = (-1,-1), criteria = criteria)
frame_markers = aruco.drawDetectedMarkers(frame.copy(), corners, ids)
corners
[array([[[ 621. , 972. ],
[ 701. , 969. ],
[ 709.82715, 1049.7007 ],
[ 619. , 1055. ]]], dtype=float32),
array([[[ 847.2396 , 945.72266],
[ 969.38995, 943.80817],
[ 971.123 , 1068.5813 ],
[ 846.4021 , 1070.483 ]]], dtype=float32),
array([[[244.78148, 828.47906],
[341. , 845. ],
[352.81595, 951.9142 ],
[233.71716, 952.73145]]], dtype=float32),
array([[[726.8156 , 822.9357 ],
[848.2925 , 820.77747],
[847.2396 , 945.72266],
[723.4202 , 948.4776 ]]], dtype=float32),
array([[[1464. , 834. ],
[1549.7858, 817.7192],
[1562.2096, 936.4745],
[1474.44 , 916.0094]]], dtype=float32),
array([[[1202.7676 , 817.2212 ],
[1318.0548 , 817.4417 ],
[1326.7908 , 939.2189 ],
[1209.4231 , 940.84424]]], dtype=float32),
array([[[1191.0787 , 587.5763 ],
[1302.4027 , 587.67444],
[1309.4077 , 696.56885],
[1197.1561 , 698.04144]]], dtype=float32),
array([[[1093. , 500. ],
[1167. , 498. ],
[1191.0786, 587.5763],
[1076.532 , 588.4834]]], dtype=float32),
array([[[284.72723, 374.40637],
[392.8324 , 372.20898],
[385.58038, 478.16342],
[275.34912, 479.37204]]], dtype=float32),
array([[[1423. , 388. ],
[1505.6632 , 376.44638],
[1517.4326 , 474.12766],
[1410.2745 , 476.39386]]], dtype=float32),
array([[[1067.2129 , 269.61798],
[1177.2119 , 269.69016],
[1181.5264 , 374.11893],
[1070.249 , 374.3764 ]]], dtype=float32),
array([[[849.6194 , 268.4913 ],
[960.5578 , 269.36526],
[962.0218 , 374.2559 ],
[849.7227 , 373.83377]]], dtype=float32),
array([[[627.9509 , 266.41553],
[738.15485, 267.64926],
[736.33246, 373.41757],
[624.7238 , 372.56445]]], dtype=float32),
array([[[ 961.74927, 173.74501],
[1061.6177 , 174.64995],
[1067.2128 , 269.61798],
[ 960.5578 , 269.36523]]], dtype=float32),
array([[[742.5307 , 172.02827],
[846.7603 , 172.7219 ],
[849.6194 , 268.49127],
[738.15485, 267.64923]]], dtype=float32),
array([[[318.66827, 184.43867],
[406.767 , 168.85188],
[400.8201 , 264.7916 ],
[293.67133, 265.81296]]], dtype=float32),
array([[[1087.626 , 942.2079 ],
[1209.4231 , 940.84424],
[1215.7225 , 1065.0857 ],
[1092.1993 , 1066.6892 ]]], dtype=float32),
array([[[ 967.6742 , 819.2523 ],
[1083.6823 , 818.21716],
[1087.626 , 942.2079 ],
[ 969.38995, 943.80817]]], dtype=float32),
array([[[ 374. , 971. ],
[ 477.05597, 951.8135 ],
[ 468.81622, 1075.2206 ],
[ 371.48587, 1055.1322 ]]], dtype=float32),
array([[[1350. , 959. ],
[1448.153 , 939.1731],
[1457.3159, 1061.4172],
[1360.1149, 1041.5824]]], dtype=float32),
array([[[483.96588, 826.2725 ],
[607.66144, 824.7236 ],
[602.3229 , 950.1209 ],
[477.05603, 951.8134 ]]], dtype=float32),
array([[[393.4445, 730.7826],
[471. , 726. ],
[483.9658, 826.2725],
[385. , 809. ]]], dtype=float32),
array([[[630. , 725. ],
[711. , 724. ],
[726.81555, 822.93567],
[607.66144, 824.7235 ]]], dtype=float32),
array([[[867. , 722. ],
[949. , 722. ],
[967.67413, 819.2523 ],
[848.2925 , 820.77747]]], dtype=float32),
array([[[1098. , 720. ],
[1189.615 , 721.7594 ],
[1202.7676 , 817.2212 ],
[1083.6823 , 818.21716]]], dtype=float32),
array([[[1333. , 719. ],
[1418.965 , 725.754 ],
[1437.3319, 815.5953],
[1343.6265, 809.7281]]], dtype=float32),
array([[[265.05103, 594.3574 ],
[378.26273, 592.7059 ],
[369.28116, 705.2608 ],
[254.763 , 706.3264 ]]], dtype=float32),
array([[[498.10806, 592.1523 ],
[617.7701 , 591.5943 ],
[612.09393, 703.04694],
[491.22415, 704.63715]]], dtype=float32),
array([[[732.6115 , 591.00323],
[849.3975 , 589.73444],
[848.42834, 700.7467 ],
[730.0566 , 702.22296]]], dtype=float32),
array([[[ 964.55273, 589.08484],
[1076.532 , 588.4834 ],
[1079.3727 , 698.8115 ],
[ 966.80176, 699.71594]]], dtype=float32),
array([[[1441. , 603. ],
[1528.1691 , 588.53204],
[1537.8229 , 695.71027],
[1458.9944 , 685.63495]]], dtype=float32),
array([[[638. , 500. ],
[717. , 499. ],
[732.6115, 591.0032],
[617.7701, 591.5943]]], dtype=float32),
array([[[867. , 499. ],
[963.7391 , 478.3379 ],
[964.5527 , 589.0847 ],
[849.39746, 589.73444]]], dtype=float32),
array([[[406. , 499. ],
[494.02493, 502.7837 ],
[498.10797, 592.1522 ],
[401. , 575. ]]], dtype=float32),
array([[[1317. , 497. ],
[1410.2745 , 476.39386],
[1418.0753 , 585.7022 ],
[1323. , 570. ]]], dtype=float32),
array([[[1181.5265 , 374.11902],
[1288.7448 , 375.0746 ],
[1294.9125 , 476.00323],
[1186.5801 , 477.40228]]], dtype=float32),
array([[[ 962.0219 , 374.25592],
[1070.2489 , 374.37643],
[1072.9188 , 478.17056],
[ 963.7391 , 478.3379 ]]], dtype=float32),
array([[[736.33246, 373.41754],
[849.7227 , 373.8338 ],
[849.0077 , 478.46964],
[735.2992 , 478.96436]]], dtype=float32),
array([[[508.6434 , 371.5545 ],
[624.7238 , 372.56445],
[620.7678 , 478.14 ],
[503.92432, 477.97134]]], dtype=float32),
array([[[1304. , 285. ],
[1394.4258 , 269.19562],
[1400.6348 , 372.73566],
[1309. , 358. ]]], dtype=float32),
array([[[421. , 281. ],
[514.4226 , 265.67953],
[508.6434 , 371.5545 ],
[415. , 355. ]]], dtype=float32),
array([[[1407. , 185. ],
[1474.6414 , 186.77351],
[1497.5245 , 266.7342 ],
[1394.4258 , 269.19562]]], dtype=float32),
array([[[523.3045 , 170.48518],
[628.4742 , 171.3262 ],
[627.9508 , 266.4155 ],
[514.4226 , 265.67953]]], dtype=float32)]
Very fast processing !
Results¶
plt.figure()
plt.imshow(frame_markers, interpolation = "nearest")
plt.show()
<IPython.core.display.Javascript object>
Add local axis on each marker¶
size_of_marker = 0.015 # side lenght of the marker in meter
rvecs,tvecs = aruco.estimatePoseSingleMarkers(corners, size_of_marker , mtx, dist)
length_of_axis = 0.01
imaxis = aruco.drawDetectedMarkers(frame.copy(), corners, ids)
for i in range(len(tvecs)):
imaxis = aruco.drawAxis(imaxis, mtx, dist, rvecs[i], tvecs[i], length_of_axis)
plt.figure()
plt.imshow(imaxis)
plt.show()
<IPython.core.display.Javascript object>
data = pd.DataFrame(data = tvecs.reshape(len(tvecs),3), columns = ["tx", "ty", "tz"],
index = ids.flatten())
data.index.name = "marker"
data.sort_index(inplace= True)
data
| tx | ty | tz | |
|---|---|---|---|
| marker | |||
| 0 | -0.087532 | -0.048772 | 0.257729 |
| 1 | -0.054511 | -0.047499 | 0.251998 |
| 2 | -0.024629 | -0.048055 | 0.257198 |
| 3 | 0.005991 | -0.049758 | 0.268442 |
| 5 | 0.081431 | -0.058824 | 0.326002 |
| 6 | -0.079812 | -0.038866 | 0.288858 |
| 7 | -0.039313 | -0.033350 | 0.250634 |
| 8 | -0.008749 | -0.032765 | 0.247373 |
| 9 | 0.020734 | -0.033026 | 0.251473 |
| 10 | 0.061540 | -0.039601 | 0.304885 |
| 11 | -0.085302 | -0.018672 | 0.244017 |
| 12 | -0.053047 | -0.018335 | 0.240409 |
| 13 | -0.023604 | -0.018494 | 0.243766 |
| 14 | 0.006463 | -0.019159 | 0.253297 |
| 15 | 0.037312 | -0.019558 | 0.260479 |
| 16 | 0.077235 | -0.021539 | 0.296672 |
| 17 | -0.083106 | -0.003714 | 0.293096 |
| 18 | -0.045454 | -0.003068 | 0.284015 |
| 19 | -0.007825 | -0.003470 | 0.240971 |
| 20 | 0.025637 | -0.003440 | 0.293119 |
| 21 | 0.062923 | -0.005190 | 0.299281 |
| 22 | -0.084038 | 0.010719 | 0.233880 |
| 23 | -0.052517 | 0.010378 | 0.232546 |
| 24 | -0.023112 | 0.010349 | 0.235414 |
| 25 | 0.006908 | 0.010603 | 0.246166 |
| 26 | 0.037766 | 0.010496 | 0.252274 |
| 27 | 0.079958 | 0.012114 | 0.291410 |
| 28 | -0.086678 | 0.033446 | 0.295406 |
| 29 | -0.044910 | 0.031067 | 0.274396 |
| 30 | -0.009170 | 0.030199 | 0.270019 |
| 31 | 0.024675 | 0.029263 | 0.266069 |
| 32 | 0.064121 | 0.031595 | 0.293612 |
| 33 | -0.086579 | 0.041780 | 0.231959 |
| 34 | -0.050911 | 0.038219 | 0.218577 |
| 35 | -0.022517 | 0.038873 | 0.225428 |
| 36 | 0.006988 | 0.039363 | 0.231235 |
| 37 | 0.036482 | 0.039194 | 0.234130 |
| 38 | 0.079325 | 0.045981 | 0.278108 |
| 39 | -0.080389 | 0.065231 | 0.266857 |
| 40 | -0.054088 | 0.077878 | 0.323161 |
| 41 | -0.007729 | 0.053539 | 0.225587 |
| 42 | 0.022046 | 0.054264 | 0.231333 |
| 43 | 0.062683 | 0.063713 | 0.274862 |
v = data.loc[:6].values
((v[1:] - v[:-1])**2).sum(axis = 1)**.5
array([0.03353885, 0.03033577, 0.0326638 , 0.09532321, 0.16666463])
fig = plt.figure()
#ax = fig.add_subplot(111, projection='3d')
ax = fig.add_subplot(1,2,1)
ax.set_aspect("equal")
plt.plot(data.tx, data.ty, "or-")
plt.grid()
ax = fig.add_subplot(1,2,2)
plt.imshow(imaxis, origin = "lower")
plt.plot(np.array(corners)[:, 0, 0,0], np.array(corners)[:, 0, 0,1], "or")
plt.show()
<IPython.core.display.Javascript object>
fig = plt.figure()
plt.show()
<IPython.core.display.Javascript object>
a = np.arange(50)
a
array([ 0, 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])
import pickle
f = open("truc.pckl", "wb")
pickle.dump(a, f)
f.close()
f = open("truc.pckl", "rb")
b = pickle.load(f)
b == a
array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True], dtype=bool)
corners = np.array(corners)
data2 = pd.DataFrame({"px": corners[:, 0, 0, 1],
"py": corners[:, 0, 0, 0]}, index = ids.flatten())
data2.sort_index(inplace=True)
data2
| px | py | |
|---|---|---|
| 0 | 177.324295 | 222.723907 |
| 1 | 174.117722 | 448.426971 |
| 5 | 165.167435 | 1385.455933 |
| 6 | 292.872223 | 348.533112 |
| 7 | 290.211761 | 572.901550 |
| 8 | 286.861359 | 800.593140 |
| 9 | 285.043823 | 1029.405640 |
| 10 | 284.054932 | 1261.753418 |
| 11 | 406.743347 | 250.763550 |
| 12 | 405.577484 | 469.121307 |
| 13 | 402.066681 | 691.525330 |
| 14 | 398.973602 | 918.603577 |
| 16 | 397.476105 | 1371.831177 |
| 17 | 514.600769 | 374.230682 |
| 18 | 512.135010 | 590.534302 |
| 19 | 509.453247 | 809.594849 |
| 20 | 507.959595 | 1029.593262 |
| 21 | 507.521088 | 1253.295044 |
| 22 | 615.594482 | 280.054901 |
| 23 | 614.357056 | 490.602081 |
| 24 | 613.074951 | 704.512085 |
| 25 | 611.417297 | 922.586426 |
| 26 | 611.085632 | 1139.391602 |
| 27 | 611.036255 | 1359.634644 |
| 28 | 716.764465 | 397.975067 |
| 29 | 716.205688 | 606.338318 |
| 30 | 714.187927 | 817.897095 |
| 31 | 713.494141 | 1029.665405 |
| 32 | 713.155762 | 1244.999390 |
| 33 | 811.479309 | 305.960754 |
| 34 | 811.358704 | 509.836670 |
| 35 | 810.507996 | 716.540955 |
| 36 | 810.085144 | 926.713257 |
| 37 | 810.013611 | 1135.423462 |
| 38 | 810.014771 | 1347.564697 |
| 39 | 906.448242 | 420.143951 |
| 40 | 906.174988 | 621.917664 |
| 41 | 905.748413 | 825.513733 |
| 42 | 906.083923 | 1029.803955 |
| 43 | 906.387878 | 1237.707520 |
m0 = data2.loc[0]
m43 = data2.loc[43]
d01 = ((m0 - m43).values**2).sum()**.5
d = 42.5e-3 * (3.5**2 + 4.5**2)**.5
factor = d / d01
data2["x"] = data2.px * factor
data2["y"] = data2.py * factor
((data2[["x", "y"]].loc[11] - data2[["x", "y"]].loc[0]).values**2).sum()**.5
0.043476117957396747
c = np.array(corners).astype(np.float64).reshape(44,4,2)
(((c[:, 1:] - c[:, :-1])**2).sum(axis = 2)**.5).mean(axis =1)
array([ 138.33575835, 143.00113377, 142.012097 , 140.69699432,
146.66782406, 144.02442319, 138.67845434, 142.33812925,
143.00229095, 140.33926025, 140.35356753, 146.66786569,
139.34054504, 146.67222201, 140.03570454, 148.01939184,
143.35647769, 142.67236143, 147.01931296, 148.02127735,
137.67392157, 135.35308209, 141.00354688, 143.67946992,
137.67149733, 138.67392207, 145.00112611, 142.33454105,
138.3466791 , 143.00234925, 139.0035972 , 143.00115739,
143.6865917 , 144.67964727, 144.33446711, 141.67253496,
143.67117097, 147.67232772, 150.35663387, 141.70034559,
149.01342342, 146.01949591, 144.34013329, 150.35333222])
c
array([[[ 2406., 1940.],
[ 2546., 1940.],
[ 2545., 2075.],
[ 2405., 2076.]],
[[ 1991., 1938.],
[ 2138., 1939.],
[ 2138., 2076.],
[ 1993., 2076.]],
[[ 1584., 1936.],
[ 1728., 1936.],
[ 1731., 2073.],
[ 1586., 2072.]],
[[ 2619., 1735.],
[ 2759., 1735.],
[ 2754., 1878.],
[ 2615., 1877.]],
[[ 2198., 1734.],
[ 2347., 1734.],
[ 2346., 1878.],
[ 2199., 1878.]],
[[ 973., 1733.],
[ 1117., 1731.],
[ 1121., 1874.],
[ 976., 1875.]],
[[ 572., 1732.],
[ 710., 1732.],
[ 713., 1874.],
[ 577., 1873.]],
[[ 2410., 1533.],
[ 2554., 1533.],
[ 2552., 1672.],
[ 2408., 1672.]],
[[ 1373., 1326.],
[ 1519., 1325.],
[ 1519., 1463.],
[ 1374., 1464.]],
[[ 1785., 1326.],
[ 1926., 1324.],
[ 1927., 1463.],
[ 1786., 1463.]],
[[ 2627., 1323.],
[ 2767., 1324.],
[ 2763., 1464.],
[ 2622., 1464.]],
[[ 2200., 1324.],
[ 2350., 1324.],
[ 2349., 1463.],
[ 2198., 1463.]],
[[ 760., 1128.],
[ 901., 1127.],
[ 903., 1265.],
[ 764., 1266.]],
[[ 1988., 1123.],
[ 2138., 1121.],
[ 2138., 1261.],
[ 1988., 1262.]],
[[ 547., 920.],
[ 687., 918.],
[ 692., 1058.],
[ 552., 1059.]],
[[ 2203., 910.],
[ 2354., 908.],
[ 2351., 1050.],
[ 2200., 1052.]],
[[ 2631., 908.],
[ 2775., 906.],
[ 2771., 1050.],
[ 2629., 1050.]],
[[ 750., 708.],
[ 890., 707.],
[ 892., 855.],
[ 752., 855.]],
[[ 2419., 695.],
[ 2565., 693.],
[ 2563., 842.],
[ 2417., 845.]],
[[ 946., 494.],
[ 1093., 491.],
[ 1096., 642.],
[ 950., 643.]],
[[ 1181., 1936.],
[ 1319., 1935.],
[ 1321., 2073.],
[ 1184., 2072.]],
[[ 780., 1935.],
[ 916., 1935.],
[ 920., 2070.],
[ 785., 2070.]],
[[ 1788., 1731.],
[ 1928., 1732.],
[ 1929., 1876.],
[ 1790., 1875.]],
[[ 1378., 1731.],
[ 1521., 1730.],
[ 1524., 1873.],
[ 1379., 1874.]],
[[ 771., 1533.],
[ 909., 1533.],
[ 911., 1671.],
[ 774., 1671.]],
[[ 1176., 1533.],
[ 1315., 1532.],
[ 1317., 1669.],
[ 1177., 1670.]],
[[ 1989., 1532.],
[ 2137., 1532.],
[ 2137., 1671.],
[ 1989., 1670.]],
[[ 1581., 1531.],
[ 1726., 1531.],
[ 1727., 1669.],
[ 1583., 1669.]],
[[ 560., 1329.],
[ 700., 1328.],
[ 703., 1465.],
[ 565., 1466.]],
[[ 966., 1328.],
[ 1112., 1327.],
[ 1113., 1465.],
[ 968., 1465.]],
[[ 1169., 1127.],
[ 1309., 1126.],
[ 1310., 1264.],
[ 1171., 1265.]],
[[ 1579., 1124.],
[ 1723., 1123.],
[ 1723., 1263.],
[ 1578., 1263.]],
[[ 2415., 1120.],
[ 2560., 1119.],
[ 2556., 1261.],
[ 2412., 1261.]],
[[ 956., 919.],
[ 1103., 918.],
[ 1106., 1058.],
[ 959., 1059.]],
[[ 1367., 917.],
[ 1514., 916.],
[ 1514., 1056.],
[ 1368., 1056.]],
[[ 1784., 914.],
[ 1926., 912.],
[ 1926., 1053.],
[ 1784., 1054.]],
[[ 1160., 706.],
[ 1302., 706.],
[ 1304., 854.],
[ 1163., 854.]],
[[ 1574., 703.],
[ 1722., 702.],
[ 1722., 850.],
[ 1575., 852.]],
[[ 1991., 699.],
[ 2142., 697.],
[ 2138., 847.],
[ 1988., 848.]],
[[ 539., 499.],
[ 677., 496.],
[ 681., 644.],
[ 542., 646.]],
[[ 1360., 490.],
[ 1508., 488.],
[ 1510., 639.],
[ 1362., 641.]],
[[ 1784., 486.],
[ 1928., 483.],
[ 1926., 635.],
[ 1784., 637.]],
[[ 2637., 479.],
[ 2778., 480.],
[ 2776., 630.],
[ 2634., 629.]],
[[ 2207., 481.],
[ 2356., 478.],
[ 2356., 629.],
[ 2205., 632.]]])
help(cv2.aruco.detectMarkers)
Help on built-in function detectMarkers:
detectMarkers(...)
detectMarkers(image, dictionary[, corners[, ids[, parameters[, rejectedImgPoints]]]]) -> corners, ids, rejectedImgPoints