{ "cells": [ { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import cv2\n", "import cv2.aruco as aruco\n", "import time\n", "import pandas as pd\n", "%matplotlib tk \n", "# inline, nbagg" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250)\n", "img = aruco.drawMarker(aruco_dict, 2, 700)\n", "cv2.imwrite(\"test_marker.jpg\", img)\n", " \n", "angle=[]\n", "cv2.waitKey(0)\n", "cv2.destroyAllWindows()\n", "\n", "cap = cv2.VideoCapture(0)\n", "\n", "size_of_marker = 0.045\n", "\n", "while(True):\n", "\n", " ret, frame = cap.read()\n", " gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n", " aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250)\n", " parameters = aruco.DetectorParameters_create()\n", " \n", "\n", " \n", " ''' detectMarkers(...)\n", " detectMarkers(image, dictionary[, corners[, ids[, parameters[, rejectedI\n", " mgPoints]]]]) -> corners, ids, rejectedImgPoints\n", " '''\n", " size_of_marker = 0.045\n", " corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters)\n", "\n", " frame = aruco.drawDetectedMarkers(frame, corners)\n", "\n", " cv2.imshow('frame',frame)\n", " imsize = gray.shape\n", " \n", " dist = np.zeros((5,1))\n", " \n", " mtx = np.array([[ 2000., 0., imsize[0]/2.],\n", " [ 0., 2000., imsize[1]/2.],\n", " [ 0., 0., 1.]])\n", " \n", " rvecs,tvecs, trash = aruco.estimatePoseSingleMarkers(corners, size_of_marker, mtx, dist )\n", " \n", " angle.append(rvecs)\n", " \n", " angle\n", " \n", " if cv2.waitKey(1) & 0xFF == ord('q'):\n", " break\n", " \n", " \n", " # When everything done, release the capture\n", "cap.release()\n", "cv2.destroyAllWindows()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
tRxRyRz
frame
00.00NaNNaNNaN
10.02NaNNaNNaN
20.04NaNNaNNaN
30.06NaNNaNNaN
40.08NaNNaNNaN
50.10NaNNaNNaN
60.12NaNNaNNaN
70.14NaNNaNNaN
80.16NaNNaNNaN
90.18NaNNaNNaN
100.20NaNNaNNaN
110.22NaNNaNNaN
120.24NaNNaNNaN
130.26NaNNaNNaN
140.28NaNNaNNaN
150.30NaNNaNNaN
160.32NaNNaNNaN
170.34NaNNaNNaN
180.36NaNNaNNaN
190.38NaNNaNNaN
200.40NaNNaNNaN
210.42NaNNaNNaN
220.44NaNNaNNaN
230.46NaNNaNNaN
240.48NaNNaNNaN
250.50NaNNaNNaN
260.52NaNNaNNaN
270.54NaNNaNNaN
280.56NaNNaNNaN
290.58NaNNaNNaN
...............
54810.96112.616365123.675515-32.376848
54910.98110.689358125.841417-32.226566
55011.00109.985899126.922315-29.990246
55111.02112.962763129.407285-28.748873
55211.04111.537820127.950517-33.088528
55311.06108.779533127.127758-32.424396
55411.08108.748064127.543609-30.340518
55511.10106.893078126.890596-30.733698
55611.12107.203824128.077016-31.727598
55711.14106.673280128.877379-34.661211
55811.16106.045870129.255417-34.820648
55911.18106.156150130.859854-32.891079
56011.20107.553999131.699757-31.837569
56111.22107.274529130.818850-32.168542
56211.24106.388770129.698420-34.417733
56311.26105.204035128.819671-35.438550
56411.28106.147819129.701578-34.835199
56511.30103.868823127.923984-34.372065
56611.32104.221034128.070224-33.716344
56711.34104.919628128.773843-34.580152
56811.36105.228644128.442850-34.669834
56911.38104.998862127.552461-35.476936
57011.40104.633700127.194306-35.719187
57111.42102.687253126.837849-34.200362
57211.44102.172307126.291363-34.012023
57311.46101.908830127.839283-34.460761
57411.48103.063066129.827542-33.316156
57511.50105.050463132.241574-30.550074
57611.52103.379668128.878511-30.982259
57711.54103.308638128.442769-32.294583
\n", "

578 rows × 4 columns

\n", "
" ], "text/plain": [ " t Rx Ry Rz\n", "frame \n", "0 0.00 NaN NaN NaN\n", "1 0.02 NaN NaN NaN\n", "2 0.04 NaN NaN NaN\n", "3 0.06 NaN NaN NaN\n", "4 0.08 NaN NaN NaN\n", "5 0.10 NaN NaN NaN\n", "6 0.12 NaN NaN NaN\n", "7 0.14 NaN NaN NaN\n", "8 0.16 NaN NaN NaN\n", "9 0.18 NaN NaN NaN\n", "10 0.20 NaN NaN NaN\n", "11 0.22 NaN NaN NaN\n", "12 0.24 NaN NaN NaN\n", "13 0.26 NaN NaN NaN\n", "14 0.28 NaN NaN NaN\n", "15 0.30 NaN NaN NaN\n", "16 0.32 NaN NaN NaN\n", "17 0.34 NaN NaN NaN\n", "18 0.36 NaN NaN NaN\n", "19 0.38 NaN NaN NaN\n", "20 0.40 NaN NaN NaN\n", "21 0.42 NaN NaN NaN\n", "22 0.44 NaN NaN NaN\n", "23 0.46 NaN NaN NaN\n", "24 0.48 NaN NaN NaN\n", "25 0.50 NaN NaN NaN\n", "26 0.52 NaN NaN NaN\n", "27 0.54 NaN NaN NaN\n", "28 0.56 NaN NaN NaN\n", "29 0.58 NaN NaN NaN\n", "... ... ... ... ...\n", "548 10.96 112.616365 123.675515 -32.376848\n", "549 10.98 110.689358 125.841417 -32.226566\n", "550 11.00 109.985899 126.922315 -29.990246\n", "551 11.02 112.962763 129.407285 -28.748873\n", "552 11.04 111.537820 127.950517 -33.088528\n", "553 11.06 108.779533 127.127758 -32.424396\n", "554 11.08 108.748064 127.543609 -30.340518\n", "555 11.10 106.893078 126.890596 -30.733698\n", "556 11.12 107.203824 128.077016 -31.727598\n", "557 11.14 106.673280 128.877379 -34.661211\n", "558 11.16 106.045870 129.255417 -34.820648\n", "559 11.18 106.156150 130.859854 -32.891079\n", "560 11.20 107.553999 131.699757 -31.837569\n", "561 11.22 107.274529 130.818850 -32.168542\n", "562 11.24 106.388770 129.698420 -34.417733\n", "563 11.26 105.204035 128.819671 -35.438550\n", "564 11.28 106.147819 129.701578 -34.835199\n", "565 11.30 103.868823 127.923984 -34.372065\n", "566 11.32 104.221034 128.070224 -33.716344\n", "567 11.34 104.919628 128.773843 -34.580152\n", "568 11.36 105.228644 128.442850 -34.669834\n", "569 11.38 104.998862 127.552461 -35.476936\n", "570 11.40 104.633700 127.194306 -35.719187\n", "571 11.42 102.687253 126.837849 -34.200362\n", "572 11.44 102.172307 126.291363 -34.012023\n", "573 11.46 101.908830 127.839283 -34.460761\n", "574 11.48 103.063066 129.827542 -33.316156\n", "575 11.50 105.050463 132.241574 -30.550074\n", "576 11.52 103.379668 128.878511 -30.982259\n", "577 11.54 103.308638 128.442769 -32.294583\n", "\n", "[578 rows x 4 columns]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt = 0.02 # Temps entre deux image\n", "Nframe = len(angle)\n", "t = np.arange(Nframe) * dt\n", "R = np.zeros((Nframe, 3))\n", "for i in range(Nframe):\n", " if angle[i] is None:\n", " R[i,:] = np.nan\n", " else:\n", " R[i,:] = angle[i]\n", "out = pd.DataFrame()\n", "out[\"t\"] = t\n", "out[\"Rx\"] = np.degrees(R[:, 0])\n", "out[\"Ry\"] = np.degrees(R[:, 1])\n", "out[\"Rz\"] = np.degrees(R[:, 2])\n", "out.index.name = \"frame\"\n", "out" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "plt.figure()\n", "plt.plot(out.t, abs(out.Rz))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'Rz' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mRz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mRz\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mRz\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'Rz' is not defined" ] } ], "source": [ "Rz = [a for a in Rz if a is not None]\n", "Rz\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "x and y must have same first dimension, but have shapes (10,) and (958,)", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mangle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"or-\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mR:\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 3259\u001b[0m mplDeprecation)\n\u001b[0;32m 3260\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3261\u001b[1;33m \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3262\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3263\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mR:\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1715\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[0;32m 1716\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1717\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1718\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1719\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mR:\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1370\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_alias_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1371\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1372\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1373\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1374\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mR:\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 402\u001b[0m \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 403\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 404\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 405\u001b[0m \u001b[1;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 406\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mR:\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m 382\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 383\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 384\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 385\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 386\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'plot'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mR:\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[1;34m(self, x, y)\u001b[0m\n\u001b[0;32m 241\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 242\u001b[0m raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[1;32m--> 243\u001b[1;33m \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[0;32m 244\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 245\u001b[0m raise ValueError(\"x and y can be no greater than 2-D, but have \"\n", "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (10,) and (958,)" ] } ], "source": [ "plt.figure()\n", "x = points[:, 1]\n", "plt.plot(x,angle, \"oscillations\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function detectMarkers:\n", "\n", "detectMarkers(...)\n", " detectMarkers(image, dictionary[, corners[, ids[, parameters[, rejectedImgPoints[, cameraMatrix[, distCoeff]]]]]]) -> corners, ids, rejectedImgPoints\n", " . * @brief Basic marker detection\n", " . *\n", " . * @param image input image\n", " . * @param dictionary indicates the type of markers that will be searched\n", " . * @param corners vector of detected marker corners. For each marker, its four corners\n", " . * are provided, (e.g std::vector > ). For N detected markers,\n", " . * the dimensions of this array is Nx4. The order of the corners is clockwise.\n", " . * @param ids vector of identifiers of the detected markers. The identifier is of type int\n", " . * (e.g. std::vector). For N detected markers, the size of ids is also N.\n", " . * The identifiers have the same order than the markers in the imgPoints array.\n", " . * @param parameters marker detection parameters\n", " . * @param rejectedImgPoints contains the imgPoints of those squares whose inner code has not a\n", " . * correct codification. Useful for debugging purposes.\n", " . * @param cameraMatrix optional input 3x3 floating-point camera matrix\n", " . * \\f$A = \\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\\f$\n", " . * @param distCoeff optional vector of distortion coefficients\n", " . * \\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\\f$ of 4, 5, 8 or 12 elements\n", " . *\n", " . * Performs marker detection in the input image. Only markers included in the specific dictionary\n", " . * are searched. For each detected marker, it returns the 2D position of its corner in the image\n", " . * and its corresponding identifier.\n", " . * Note that this function does not perform pose estimation.\n", " . * @sa estimatePoseSingleMarkers, estimatePoseBoard\n", " . *\n", "\n" ] } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }