mode_converter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib.modeconvertor.animation import test_movie\n",
    "from lib.modeconvertor.movie import read_grayscale\n",
    "from lib.modeconvertor.animation import MoviePlayer\n",
    "from lib.modeconvertor import Converter,Cone\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib ipympl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69c1467521ed4373b873172c086d0d54",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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FCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAiumrANy6NTn66GT58mT16uSee2Y+9qabktNOS17yknZbu/a5jwf6l9kBVNM3AXjrrcnGjcnmzcl99yUnnJCsW5c89tj0x999d3LWWcm3vpVs354MDydvfWvy8593dNlAl5kdQEWLmiZNtxcxF1avTt74xuT669vb4+PtYH7ve5NLL/3d5+/b1z6av/76ZMOGA/uaY2PJ4GAyOjqagYGB2S8emJWxsbEMDg5mdDSZ7V3Q7IB65mJ2LHR98Qzg3r3Jvfe2L8VMWLy4vb19+4H9N55+OvnNb5KXvnTmY/bsaQf3b2/AwmV2AFX1RQA+8UT7KHxoaOr+oaFk164D+29ccklyxBFT/yHY35Yt7aP2iW14ePZrBrrP7ACq6osAfL6uvDK55Zbkttvai8BnsmlTMjo6ue3c2bk1Ar3H7AAWqkO6vYC5sGJFsmRJMjIydf/ISLJy5XOfe/XV7RD/5jeT173uuY9dtqzdgP5gdgBV9cUzgEuXJieemNx11+S+8fH29po1M5931VXJxz6WbNuWnHTS/K8T6C1mB1BVXzwDmLQf43Duue0wPvnk5Lrrkt27k/POa399w4bkyCPba3GS5B//Mbn88uQLX2g//2viep/f+712A2owO4CK+iYA169PHn+8Hcy7diWrVrWPzicu7t6xo31334RPfap9B+Bf/MXU/87mzcmHP9ypVQPdZnYAFfXN5wB2g8/ygu5aqJ/lZXZAdy3U2TGX+uIaQAAADpwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMX0VgFu3JkcfnSxfnqxendxzz3Mf/6UvJcce2x5//PHJHXd0ZJlAjzE7gGr6JgBvvTXZuDHZvDm5777khBOSdeuSxx6b/vjvfS8566zkne9M7r8/OfPMdvvBDzq5aqDbzA6gokVNk6bbi5gLq1cnb3xjcv317e3x8WR4OHnve5NLL3328evXJ7t3J1//+uS+P/mTZNWq5MYbD+xrjo0lg4PJ6OhoBgYGnvfvATg4Y2NjGRwczOhoMtu7oNkB9czF7FjoDun2AubC3r3JvfcmmzZN7lu8OFm7Ntm+ffpztm9vH/X/tnXrkq9+deavs2dPu00YHW1/HBsbm9W6gedn4r7XzPJhrNkBNT3f2dEP+iIAn3gi2bcvGRqaun9oKHnooenP2bVr+uN37Zr562zZknzkI8/ePzw8fHALBubU//xP+4zawTI7oLbZzo5+0BcB2CmbNk195P/kk8nLXpbs2LFw/gKNjbUvb+3cuXCe9rbmzliIax4dTY46KnnpS7u9kudmdnSHNXfGQlzzQpkd86kvAnDFimTJkmRkZOr+kZFk5crpz1m58uCOT5Jly9ptf4ODC+cv/YSBAWvuBGvujMWzfDub2XHwFuLfD2vujIW45tnOjn7QF7/1pUuTE09M7rprct/4eHt7zZrpz1mzZurxSXLnnTMfD/QfswOoqi+eAUzal1fOPTc56aTk5JOT665r36l33nntr2/YkBx5ZHstTpK8733Jm9+cXHNN8ra3Jbfcknz/+8mnP9213wLQBWYHUFHfBOD69cnjjyeXX95ejL1qVbJt2+TF2jt2TH2q95RTki98IfngB5MPfCD54z9u38V33HEH/jWXLWs/O2y6l3Z6lTV3hjV3xlys2ew4MNbcGdbcGQtxzXOtbz4HEACAA9MX1wACAHDgBCAAQDECEACgGAEIAFCMAPwdtm5Njj46Wb68/abx99zz3Md/6UvJsce2xx9/fHLHHR1Z5hQHs+abbkpOOy15yUvabe3a3/17nA8H++c84ZZbkkWLkjPPnM/VTe9g1/zkk8lFFyWHH96+8+yYYzr/9+Ng13zddcmrXpUcemj7Sf8XX5z8+tedWGnr299OzjgjOeKI9v/zc32/3Ql335284Q3tn/ErX5l87nPzvMhpLMS5kZgdnWJ2zL+FOjs6qmnS2KbfbrklzdKlaT772TT/+Z9pLrggzYtfnGZkZPrjv/vdNEuWpLnqqjT/9V9pPvjBNC94QZoHH+zdNZ99dpqtW9Pcf3+aH/4wzV//dZrBwTT//d+9u+aJ7dFH0xx5ZJrTTkvz53/e23839uxJc9JJaU4/Pc13vtOu/e670zzwQO+u+V/+Jc2yZe2Pjz6a5hvfSHP44Wkuvrhza77jjjSXXZbmK19Jk6S57bbnPv6RR9K88IVpNm5s74Of/GR7n9y2rXf/nHthbsxm3WZHZ9ZsdsxuW4izo9Nbur2AXt5OPjnNRRdN3t63L80RR6TZsmX64//yL9O87W1T961eneZv/qZ317z/9swzaV70ojSf/3xvr/mZZ9Kcckqaz3wmzbnndn6IH+yaP/WpNC9/eZq9ezu7zuez5osuSvOnfzp138aNaU49tTvrP5Ah/v73p3nta6fuW78+zbp1vfvn3AtzYzbr3n8zO+ZnzWbH898Wyuzo9OYl4Bns3Zvce2/7ssaExYvb29u3T3/O9u1Tj0+SdetmPn6uzWbN+3v66eQ3v+ncN8ie7Zo/+tHksMOSd75z/te4v9ms+Wtfa79V2EUXtR8wfNxxyRVXJPv29e6aTzmlPWfipZ5HHmlfdjr99Plf72wtxPtgt9ecmB2dYnbM/3pnqxfuh53WN98JZK498UR7B5v4bgAThoaShx6a/pxdu6Y/fteu+Vnj/maz5v1dckl7zcT+d4T5Mps1f+c7yc03Jw88MO/Lm9Zs1vzII8m//3vyjne0g/DHP07e8572H8zNm3tzzWef3Z73pjclTZM880zyrne13/2iV810HxwbS371q/Z6pPm0EOdGYnZ0itkx/+udrW7Pjm7wDCD/35VXthdG33Zbe6FvL3rqqeScc9oL0Fes6PZqDtz4ePusw6c/nZx4Yvvtxy67LLnxxm6vbGZ3390+03DDDcl99yVf+Upy++3Jxz7W7ZXRa8yO+WN2MF88AziDFSuSJUuSkZGp+0dGkpUrpz9n5cqDO36uzWbNE66+uh3i3/xm8rrXzd8a93ewa/7JT5Kf/rR9d9eE8fH2x0MOSR5+OHnFK+ZtuUlm9+d8+OHJC17Qnjfh1a9uH3Xu3ZssXTp/601mt+YPfaj9B/P889vbxx+f7N6dXHhh+w/Q4h58+DjTfXBgoDOP4Bfi3EjMDrNjZmZHfz77l3gGcEZLl7aPtu66a3Lf+Hh7e82a6c9Zs2bq8Uly550zHz/XZrPmJLnqqvaR2bZtyUknzf86f9vBrvnYY5MHH2xfwpnY3v725C1vaX8+PNx7a06SU09tX7qZ+AcnSX70o3a4z/cAT2a35qeffvagnvhHqOnR7yC+EO+D3V5zYnaYHTMzO7qzno7o9rtQenm75Zb2reyf+1z7tvALL2zf+r5rV/vr55yT5tJLJ4//7nfTHHJImquvbj8WYfPm7nwMzMGs+cor27f3f/nLaX7xi8ntqad6d837b914J9/BrnnHjvYdkn/7t2kefjjN17+e5rDD0nz847275s2b2zX/67+2H5Hwb/+W5hWvaN+12qk1P/VU+zEj99/fvpPv2mvbn//sZ+2vX3ppu+6J4yc+yuEf/qG9D27d2p2PgVloc2M26zY7OrNms2N220KcHZ3e0u0F9Pr2yU+mOeqodtCdfHKa//iPyV9785vbAfLbx3/xi2mOOaY9/rWvTXP77b295pe9rL1z7L9t3ty7a95/68YQn82av/e99uM9li1rP9bhE59oP5KiV9f8m9+k+fCH28G9fHma4eE073lPmv/9386t91vfmv7v58Q6zz23Xff+56xa1f4eX/7yNP/8z73/d6MX5sbBrtvs6NyazY6D3xbq7Ojktqhp0qNPyAIAMB9cAwgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMQIQAKAYAQgAUIwABAAoRgACABQjAAEAihGAAADFCEAAgGIEIABAMf8POxt9cQedhn4AAAAASUVORK5CYII=",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mov =test_movie(100,256,256)\n",
    "def plot_hook(ax_b,ax_m,video):\n",
    "    SLcoords= np.array([[0,255],[255,0]])\n",
    "    mc = Converter(anchor_idx=len(video)//2,movies=video,SLcoords=SLcoords,SR=1,W=128,A=0,S=0,R=0)\n",
    "    amm= mc.amm()\n",
    "    \n",
    "    bSL = mc.perSL()\n",
    "    mSL= mc.mSL()\n",
    "    mpred_coords_y = np.array([80,120,150,200]).reshape(4,)\n",
    "    mpred_coords_x = np.full((4,),mc.mSLx())\n",
    "    mpred_coords   = np.stack([mpred_coords_y,mpred_coords_x],axis=-1)\n",
    "    bpred_coords   = mc.pred_bcoords(mpred_coords)\n",
    "\n",
    "    ax_m.imshow(amm,cmap='gray')\n",
    "    ax_b.scatter(bSL[:,1],bSL[:,0],marker='d',color='0.8')\n",
    "    ax_b.plot(bpred_coords[:,1],bpred_coords[:,0],'-x',color='red',markersize=10)\n",
    "\n",
    "    ax_m.scatter(mSL[:,1],mSL[:,0],marker='d',color='0.8')\n",
    "    ax_m.plot(mpred_coords[:,1],mpred_coords[:,0],'-x',color='red',markersize=10)\n",
    "    \n",
    "\n",
    "player=MoviePlayer(mov)\n",
    "player.register(plot_hook)\n",
    "player.play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8cee6e59d464f74b77b5d305fd8c629",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mov = read_grayscale('./data/echotest.avi')\n",
    "def plot_hook(ax_b,ax_m,video):\n",
    "    c = Cone(video)\n",
    "    SLcoords = c.left(angle=30)\n",
    "    mc = Converter(anchor_idx=len(video)//2,movies=video,SLcoords=SLcoords,SR=1,W=128,A=0,S=0,R=0,num_pts=600)\n",
    "    amm= mc.amm()\n",
    "    \n",
    "    bSL = mc.perSL()\n",
    "    mSL= mc.mSL()\n",
    "    mpred_coords_y = np.array([200,300,400,500]).reshape(4,)\n",
    "    mpred_coords_x = np.full((4,),mc.mSLx())\n",
    "    mpred_coords   = np.stack([mpred_coords_y,mpred_coords_x],axis=-1)\n",
    "    bpred_coords   = mc.pred_bcoords(mpred_coords)\n",
    "\n",
    "    ax_m.imshow(amm,cmap='gray')\n",
    "    ax_b.scatter(bSL[:,1],bSL[:,0],marker='d',color='0.8')\n",
    "    ax_b.plot(bpred_coords[:,1],bpred_coords[:,0],'-x',color='red',markersize=10)\n",
    "\n",
    "    ax_m.scatter(mSL[:,1],mSL[:,0],marker='d',color='0.8')\n",
    "    ax_m.plot(mpred_coords[:,1],mpred_coords[:,0],'-x',color='red',markersize=10)\n",
    "    \n",
    "\n",
    "player=MoviePlayer(mov)\n",
    "player.register(plot_hook)\n",
    "player.play()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "workspace",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}