Note

This notebook can be downloaded here: aruco_basics.ipynb

ARUCO markers: basics

1: Marker creation

import numpy as np
import cv2, PIL
from cv2 import aruco
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
%matplotlib nbagg

aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250)

fig = plt.figure()
nx = 4
ny = 3
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("_data/markers.pdf")
plt.show()

<IPython.core.display.Javascript object>

3: Post processing

%%time
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)
frame_markers = aruco.drawDetectedMarkers(frame.copy(), corners, ids)

CPU times: user 420 ms, sys: 20 ms, total: 440 ms
Wall time: 172 ms

Pretty fast processing !

4: Results

plt.figure()
plt.imshow(frame_markers)
for i in range(len(ids)):
    c = corners[i][0]
    plt.plot([c[:, 0].mean()], [c[:, 1].mean()], "o", label = "id={0}".format(ids[i]))
plt.legend()
plt.show()

<IPython.core.display.Javascript object>

def quad_area(data):
    l = data.shape[0]//2
    corners = data[["c1", "c2", "c3", "c4"]].values.reshape(l, 2,4)
    c1 = corners[:, :, 0]
    c2 = corners[:, :, 1]
    c3 = corners[:, :, 2]
    c4 = corners[:, :, 3]
    e1 = c2-c1
    e2 = c3-c2
    e3 = c4-c3
    e4 = c1-c4
    a = -.5 * (np.cross(-e1, e2, axis = 1) + np.cross(-e3, e4, axis = 1))
    return a

corners2 = np.array([c[0] for c in corners])

data = pd.DataFrame({"x": corners2[:,:,0].flatten(), "y": corners2[:,:,1].flatten()},
                   index = pd.MultiIndex.from_product(
                           [ids.flatten(), ["c{0}".format(i )for i in np.arange(4)+1]],
                       names = ["marker", ""] ))

data = data.unstack().swaplevel(0, 1, axis = 1).stack()
data["m1"] = data[["c1", "c2"]].mean(axis = 1)
data["m2"] = data[["c2", "c3"]].mean(axis = 1)
data["m3"] = data[["c3", "c4"]].mean(axis = 1)
data["m4"] = data[["c4", "c1"]].mean(axis = 1)
data["o"] = data[["m1", "m2", "m3", "m4"]].mean(axis = 1)
data
c1 c2 c3 c4 m1 m2 m3 m4 o
marker
1 x 3114.0 2701.0 2467.0 2876.0 2907.5 2584.0 2671.5 2995.0 2789.50
y 1429.0 1597.0 1168.0 1019.0 1513.0 1382.5 1093.5 1224.0 1303.25
2 x 2593.0 2152.0 1939.0 2363.0 2372.5 2045.5 2151.0 2478.0 2261.75
y 1635.0 1804.0 1352.0 1209.0 1719.5 1578.0 1280.5 1422.0 1500.00
3 x 2037.0 1533.0 1350.0 1826.0 1785.0 1441.5 1588.0 1931.5 1686.50
y 1848.0 2032.0 1518.0 1381.0 1940.0 1775.0 1449.5 1614.5 1694.75
4 x 1409.0 822.0 670.0 1231.0 1115.5 746.0 950.5 1320.0 1033.00
y 2076.0 2281.0 1712.0 1553.0 2178.5 1996.5 1632.5 1814.5 1905.50
5 x 2820.0 2415.0 2217.0 2614.0 2617.5 2316.0 2415.5 2717.0 2516.50
y 924.0 1071.0 686.0 550.0 997.5 878.5 618.0 737.0 807.75
6 x 2316.0 1883.0 1705.0 2121.0 2099.5 1794.0 1913.0 2218.5 2006.25
y 1105.0 1248.0 860.0 720.0 1176.5 1054.0 790.0 912.5 983.25
7 x 1779.0 1311.0 1154.0 1603.0 1545.0 1232.5 1378.5 1691.0 1461.75
y 1279.0 1409.0 989.0 886.0 1344.0 1199.0 937.5 1082.5 1140.75
8 x 1193.0 640.0 525.0 1039.0 916.5 582.5 782.0 1116.0 849.25
y 1439.0 1592.0 1133.0 1013.0 1515.5 1362.5 1073.0 1226.0 1294.25
9 x 2561.0 2173.0 1998.0 2374.0 2367.0 2085.5 2186.0 2467.5 2276.50
y 464.0 598.0 272.0 146.0 531.0 435.0 209.0 305.0 370.00
10 x 2068.0 1667.0 1519.0 1902.0 1867.5 1593.0 1710.5 1985.0 1789.00
y 628.0 762.0 428.0 309.0 695.0 595.0 368.5 468.5 531.75
11 x 1563.0 1119.0 987.0 1411.0 1341.0 1053.0 1199.0 1487.0 1270.00
y 797.0 896.0 543.0 449.0 846.5 719.5 496.0 623.0 671.25
12 x 1008.0 501.0 407.0 881.0 754.5 454.0 644.0 944.5 699.25
y 920.0 1033.0 651.0 563.0 976.5 842.0 607.0 741.5 791.75