notebooks/svglib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deepsvg.svglib.geom import Point, Angle\n",
    "from deepsvg.svglib.svg import SVG\n",
    "from deepsvg.svglib.utils import make_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVGLib walk-through"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading & displaying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dolphin = SVG.load_svg(\"/home/i17oonaka/deepsvg/deepsvg/hiragana/svg/1.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"200px\" viewBox=\"0.0 0.0 100.0 100.0\" width=\"200px\" xmlns=\"http://www.w3.org/2000/svg\"><polygon fill=\"none\" points=\"35.0 31.0 35.0 32.0 34.0 33.0 34.0 37.0 32.0 39.0 28.0 39.0 27.0 38.0 26.0 38.0 26.0 41.0 32.0 41.0 33.0 40.0 34.0 41.0 34.0 48.0 30.0 52.0 29.0 52.0 29.0 53.0 26.0 56.0 26.0 57.0 25.0 58.0 25.0 60.0 24.0 61.0 24.0 67.0 26.0 69.0 29.0 69.0 30.0 68.0 31.0 68.0 33.0 66.0 34.0 66.0 35.0 67.0 35.0 69.0 36.0 68.0 37.0 68.0 37.0 66.0 36.0 65.0 36.0 64.0 37.0 63.0 37.0 62.0 40.0 59.0 40.0 58.0 41.0 57.0 41.0 56.0 42.0 55.0 42.0 54.0 43.0 53.0 43.0 51.0 44.0 50.0 44.0 49.0 45.0 48.0 47.0 48.0 52.0 53.0 52.0 62.0 47.0 67.0 46.0 67.0 45.0 68.0 42.0 68.0 41.0 69.0 42.0 70.0 42.0 71.0 45.0 71.0 46.0 70.0 47.0 70.0 48.0 69.0 49.0 69.0 53.0 65.0 53.0 64.0 54.0 63.0 54.0 61.0 55.0 60.0 55.0 56.0 54.0 55.0 54.0 52.0 53.0 51.0 53.0 50.0 51.0 48.0 50.0 48.0 49.0 47.0 48.0 47.0 47.0 46.0 46.0 46.0 45.0 45.0 45.0 43.0 43.0 43.0 43.0 45.0 42.0 46.0 40.0 46.0 39.0 47.0 37.0 47.0 36.0 46.0 36.0 41.0 37.0 40.0 40.0 40.0 41.0 39.0 45.0 39.0 46.0 38.0 48.0 38.0 48.0 35.0 47.0 35.0 46.0 36.0 44.0 36.0 43.0 37.0 40.0 37.0 39.0 38.0 37.0 38.0 36.0 37.0 36.0 35.0 37.0 34.0 37.0 31.0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<polygon fill=\"none\" points=\"31.0 53.0 32.0 52.0 33.0 52.0 34.0 53.0 34.0 63.0 31.0 66.0 30.0 66.0 29.0 67.0 28.0 67.0 26.0 65.0 26.0 62.0 27.0 61.0 27.0 59.0 29.0 57.0 29.0 56.0 31.0 54.0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<polygon fill=\"none\" points=\"38.0 49.0 39.0 48.0 41.0 48.0 42.0 49.0 42.0 50.0 41.0 51.0 41.0 52.0 40.0 53.0 40.0 55.0 39.0 56.0 39.0 57.0 38.0 58.0 38.0 59.0 37.0 60.0 36.0 60.0 35.0 59.0 35.0 56.0 36.0 55.0 36.0 54.0 35.0 53.0 35.0 51.0 37.0 49.0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dolphin.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For proper displaying, SVGlib expects SVGs to be drawn in a normalized viewbox of size 24x24. Stroke-width will appear to thin if a larger viewbox is used and too thick if the viewbox is too small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bbox(0.0 0.0 100.0 100.0)\n"
     ]
    }
   ],
   "source": [
    "print(dolphin.viewbox)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can **normalize** an SVG with the `normalize` method which will properly scale the points to fit a 24x24 window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bbox(0.0 0.0 24.0 24.0)\n"
     ]
    },
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       "<IPython.core.display.SVG object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dolphin.to_path()\n",
    "dolphin.normalize()\n",
    "print(dolphin.viewbox)\n",
    "dolphin.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `with_points` option lets you display the individual points of the path:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.SVG object>"
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     "metadata": {},
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   ],
   "source": [
    "dolphin.draw(with_points=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us \"unzoom\" a little, so that there is a little more free room on the borders of the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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   ],
   "source": [
    "dolphin.zoom(1.2).draw(with_points=True)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For easier training of DeepSVG, the following \"canonicalization\" strategy is used:\n",
    "- an SVG path is oriented clockwise and starts from the topmost-leftmost point.\n",
    "- the path is simplified, so that points lie at approximately equal distance between each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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   "source": [
    "dolphin.canonicalize().draw(with_points=True)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our simplification heuristic uses a combination of Ramer-Douglas-Peucker and Philip J. Schneider algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dolphin.simplify_heuristic().draw(with_points=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, generating a GIF from the drawing is as easy as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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controls>Sorry, seems like your browser doesn't support HTML5 audio/video</video></div>"
      ],
      "text/plain": [
       "<moviepy.video.io.html_tools.HTML2 object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dolphin.animate(frame_duration=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distinguishing individual paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "svg = SVG.load_svg(\"docs/imgs/rainbow.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `draw_colored` makes it easy to distinguish the individual paths of an SVG image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svg.draw_colored()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rotation and grid displaying"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a code sample showing how one can duplicate an existing icon, rotate it and display both icons side-by-side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "clock = SVG.load_svg(\"docs/imgs/clock.svg\")\n",
    "clock2 = clock.copy().rotate(Angle(45))\n",
    "clock3 = clock.copy().rotate(Angle(90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_grid([clock, clock2, clock3]).draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating individual paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<path d=\"M17.760000228881836 9.600000381469727 L17.760000228881836 9.600000381469727 C18.3551025390625 9.718419075012207 18.8338565826416 9.869729042053223 19.090347290039062 10.488951683044434 C19.47652816772461 11.421273231506348 18.76919937133789 12.479999542236328 17.760000228881836 12.479999542236328 C16.75080108642578 12.479999542236328 16.04347038269043 11.42127513885498 16.42965316772461 10.488951683044434 C16.686141967773438 9.869728088378906 17.164894104003906 9.718420028686523 17.760000228881836 9.600000381469727 L17.760000228881836 9.600000381469727\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<path d=\"M22.559999465942383 12.479999542236328 L22.559999465942383 10.079999923706055 L22.559999465942383 7.680000305175781 C22.38687515258789 7.261876106262207 22.018123626708984 6.893125057220459 21.599998474121094 6.720000267028809 L18.399999618530273 6.720000267028809 L15.199999809265137 6.720000743865967 L12.0 6.720000267028809 L8.800000190734863 6.720000743865967 L5.599999904632568 6.720000267028809 L2.40000057220459 6.720000267028809 C1.9818763732910156 6.893125057220459 1.6131258010864258 7.261876106262207 1.4400005340576172 7.680000305175781 L1.4400005340576172 10.079999923706055 L1.4400005340576172 12.479999542236328\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<path d=\"M13.732800483703613 16.507200241088867 L11.666399955749512 14.253600120544434 L9.600000381469727 12.0 L7.840000629425049 13.920001029968262 L6.080000400543213 15.84000015258789 L4.320000171661377 17.760000228881836\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<path d=\"M19.68000030517578 18.239999771118164 L17.760000228881836 16.31999969482422 L15.84000015258789 14.399999618530273 L13.920000076293945 16.31999969482422 L12.0 18.239999771118164\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = SVG.load_svg(\"docs/imgs/canvas.svg\")\n",
    "canvas.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us scale the mountain on the right and translate the sun to the left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"200px\" viewBox=\"0.0 0.0 24.0 24.0\" width=\"200px\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M21.600000381469727 19.200000762939453 L18.720001220703125 16.31999969482422 L15.84000015258789 13.4399995803833 L12.960000038146973 16.31999969482422 L10.079999923706055 19.200000762939453\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mountain2 = canvas[4]\n",
    "center = mountain2.bbox().center\n",
    "mountain2.translate(-center).scale(1.5).translate(center).draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"200px\" viewBox=\"0.0 0.0 24.0 24.0\" width=\"200px\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M7.760000228881836 9.600000381469727 L7.760000228881836 9.600000381469727 C8.3551025390625 9.718419075012207 8.833856582641602 9.869729042053223 9.090347290039062 10.488951683044434 C9.47652816772461 11.421273231506348 8.76919937133789 12.479999542236328 7.760000228881836 12.479999542236328 C6.750801086425781 12.479999542236328 6.04347038269043 11.42127513885498 6.429653167724609 10.488951683044434 C6.6861419677734375 9.869728088378906 7.164894104003906 9.718420028686523 7.760000228881836 9.600000381469727 L7.760000228881836 9.600000381469727\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sun = canvas[1]\n",
    "sun.translate(Point(-10, 0)).draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"200px\" viewBox=\"0.0 0.0 24.0 24.0\" width=\"200px\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M2.40000057220459 2.40000057220459 L4.559999942779541 2.40000057220459 L6.720000267028809 2.40000057220459 C7.499027729034424 2.9835729598999023 7.868569374084473 3.72873592376709 8.640000343322754 4.320000171661377 L11.880000114440918 4.320000171661377 L15.119999885559082 4.320000171661377 L18.360000610351562 4.320000171661377 L21.599998474121094 4.320000171661377 C22.018123626708984 4.493124961853027 22.38687515258789 4.861876010894775 22.559999465942383 5.28000020980835 L22.560001373291016 8.352001190185547 L22.559999465942383 11.42400074005127 L22.559999465942383 14.496000289916992 L22.560001373291016 17.56800079345703 L22.559999465942383 20.639999389648438 C22.38687515258789 21.058122634887695 22.018123626708984 21.426876068115234 21.599998474121094 21.599998474121094 L18.399999618530273 21.599998474121094 L15.199999809265137 21.599998474121094 L12.0 21.599998474121094 L8.800000190734863 21.599998474121094 L5.599999904632568 21.599998474121094 L2.40000057220459 21.599998474121094 C1.9818763732910156 21.426876068115234 1.6131258010864258 21.058122634887695 1.4400005340576172 20.639999389648438 L1.4400005340576172 17.184001922607422 L1.4400005340576172 13.72800064086914 L1.4400005340576172 10.272001266479492 L1.4400005340576172 6.816000461578369 L1.4400005340576172 3.3600006103515625 C1.6131258010864258 2.9418764114379883 1.9818763732910156 2.5731258392333984 2.40000057220459 2.40000057220459 L2.40000057220459 2.40000057220459\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<path d=\"M7.760000228881836 9.600000381469727 L7.760000228881836 9.600000381469727 C8.3551025390625 9.718419075012207 8.833856582641602 9.869729042053223 9.090347290039062 10.488951683044434 C9.47652816772461 11.421273231506348 8.76919937133789 12.479999542236328 7.760000228881836 12.479999542236328 C6.750801086425781 12.479999542236328 6.04347038269043 11.42127513885498 6.429653167724609 10.488951683044434 C6.6861419677734375 9.869728088378906 7.164894104003906 9.718420028686523 7.760000228881836 9.600000381469727 L7.760000228881836 9.600000381469727\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<path d=\"M22.559999465942383 12.479999542236328 L22.559999465942383 10.079999923706055 L22.559999465942383 7.680000305175781 C22.38687515258789 7.261876106262207 22.018123626708984 6.893125057220459 21.599998474121094 6.720000267028809 L18.399999618530273 6.720000267028809 L15.199999809265137 6.720000743865967 L12.0 6.720000267028809 L8.800000190734863 6.720000743865967 L5.599999904632568 6.720000267028809 L2.40000057220459 6.720000267028809 C1.9818763732910156 6.893125057220459 1.6131258010864258 7.261876106262207 1.4400005340576172 7.680000305175781 L1.4400005340576172 10.079999923706055 L1.4400005340576172 12.479999542236328\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<path d=\"M13.732800483703613 16.507200241088867 L11.666399955749512 14.253600120544434 L9.600000381469727 12.0 L7.840000629425049 13.920001029968262 L6.080000400543213 15.84000015258789 L4.320000171661377 17.760000228881836\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/>\n",
       "<path d=\"M21.600000381469727 19.200000762939453 L18.720001220703125 16.31999969482422 L15.84000015258789 13.4399995803833 L12.960000038146973 16.31999969482422 L10.079999923706055 19.200000762939453\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conversion to PyTorch tensor-format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"200px\" viewBox=\"0.0 0.0 24.0 24.0\" width=\"200px\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M12.0 3.514719009399414 L20.485280990600586 12.0 L12.0 20.485280990600586 L3.514719009399414 12.0 L12.0 3.514719009399414\" fill=\"none\" filling=\"0\" stroke=\"black\" stroke-opacity=\"1.0\" stroke-width=\".3\"/></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "square = SVG.unit_square().normalize().zoom(0.5).rotate(Angle(45))\n",
    "square.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[  0.,  -1.,  -1.,  -1.,  -1.,  -1., 128.,  37.,  -1.,  -1.,  -1.,  -1.,\n",
       "         128.,  37.],\n",
       "        [  1.,  -1.,  -1.,  -1.,  -1.,  -1., 128.,  37.,  -1.,  -1.,  -1.,  -1.,\n",
       "         219., 128.],\n",
       "        [  1.,  -1.,  -1.,  -1.,  -1.,  -1., 219., 128.,  -1.,  -1.,  -1.,  -1.,\n",
       "         128., 219.],\n",
       "        [  1.,  -1.,  -1.,  -1.,  -1.,  -1., 128., 219.,  -1.,  -1.,  -1.,  -1.,\n",
       "          37., 128.],\n",
       "        [  1.,  -1.,  -1.,  -1.,  -1.,  -1.,  37., 128.,  -1.,  -1.,  -1.,  -1.,\n",
       "         128.,  37.]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = square.copy().numericalize().to_tensor()\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`to_tensor` converts the `SVG` instance to a PyTorch tensor of shape $N_C \\times (1 + 13)$, representing the $N_C$ commands that constitute the SVG path. If the SVG has more than one path, commands will either be concatenated or `to_tensor` will output a list of tensors.\n",
    "- the first row represents the command type (`m`, `l`, `c`, `a`, `EOS`, `SOS` or `z`)\n",
    "- the 13 subsequent rows represent respectively: $r_x$, $r_y$, $\\varphi$, $f_A$, $f_S$, $x_1$, $y_1$, $q_{x1}$, $q_{y1}$, $q_{x2}$, $q_{y2}$, $x_2$ and $y_2$. Note that the first 5 rows are for a potential future compatiblity with elliptical arc commands, but aren't used in general.\n",
    "\n",
    "As described in the paper, unused arguments are encoded as -1.\n",
    "\n",
    "After numericalization, coordinates are between 0 and 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deepsvg.difflib.tensor import SVGTensor\n",
    "tensor = SVGTensor.from_data(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`SVGTensor` is a wrapper around this PyTorch raw data, and allows for easy padding/EOS/SOS processing as well as differentiable sampling of points along the path contours. More details in the `notebooks/svgtensor.ipynb` notebook!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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  "pycharm": {
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