Shortstraw_example.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shortstraw import short_straw\n",
    "import numpy as np \n",
    "from PIL import Image, ImageDraw, ImageFont\n",
    "import json\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"example_sketches.json\") as f:\n",
    "    data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to draw a sketch\n",
    "def draw_sketch(paths,colour=(0,0,0),bg=(255,255,255),strength=4):\n",
    "    \n",
    "    sketch = Image.new('RGBA',(750,750),bg)\n",
    "    draw = ImageDraw.Draw(sketch)\n",
    "    #Draw each path as a continous \n",
    "    for j,path in enumerate(paths):\n",
    "        points = len(path[0])\n",
    "        for i in range(points):\n",
    "            if i > 0:\n",
    "                prev_x = path[0][i-1]\n",
    "                prev_y = path[1][i-1]\n",
    "                x = path[0][i]\n",
    "                y = path[1][i]\n",
    "                if type(colour) is tuple:\n",
    "                    paint = colour\n",
    "                else:\n",
    "                    paint = colour[j][i]\n",
    "                draw.line((prev_x,prev_y,x,y),fill=paint,width=strength)\n",
    "    return sketch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select sketch from example dataset\n",
    "example = data[\"impact\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Run shortstraw algorithm\n",
    "all_corners, all_curves, feature_info, resampled_data, all_raw_angles, all_raw_curves = short_straw(example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Number of straight: 0\nNumber of narrow_curve: 2\nNumber of wide_curve: 7\nNumber of obtuse: 9\nNumber of right: 1\nNumber of acute: 3\n"
     ]
    }
   ],
   "source": [
    "# Display feature information\n",
    "for key in feature_info:\n",
    "    print(\"Number of {key}: {number}\".format(key=key,number=feature_info[key]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Display distrubtions of raw corner and curve data\n",
    "fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(13, 5))\n",
    "axes[0].set_title(\"Corner distribution\")\n",
    "axes[0].set_xlabel(\"Angle\")\n",
    "axes[0].set_ylabel(\"Total occurences\")\n",
    "axes[0].hist(all_raw_angles)\n",
    "axes[1].set_title(\"Curve distribution\")\n",
    "axes[1].set_xlabel(\"Angle\")\n",
    "axes[1].set_ylabel(\"Total occurences\")\n",
    "axes[1].hist(all_raw_curves)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=750x750 at 0x7FECB8084070>"
      ],
      "image/png": "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\n"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "#Draw resampled version of sketch\n",
    "resampled_sketch = draw_sketch(resampled_data)\n",
    "draw = ImageDraw.Draw(resampled_sketch)\n",
    "\n",
    "#Draw start, end, corner and curve points \n",
    "for path_i,path in enumerate(resampled_data):\n",
    "    for point_i,_ in enumerate(path[0]):\n",
    "        colour = False\n",
    "        # Start points are green\n",
    "        if point_i == 0:\n",
    "            colour = (0,255,0)\n",
    "        # End points are blue\n",
    "        elif point_i == len(path[0])-1:\n",
    "            colour = (0,0,255)\n",
    "        # Corner points are red\n",
    "        elif point_i in all_corners[path_i]:\n",
    "            colour = (255,0,0)\n",
    "        # Curve points are yellow\n",
    "        elif point_i in all_curves[path_i]:\n",
    "            colour = (255,255,0)\n",
    "        # Draw points\n",
    "        if colour:\n",
    "            x = path[0][point_i]\n",
    "            y = path[1][point_i]\n",
    "            draw.ellipse((x-5,y-5,x+5,y+5),fill=colour)       \n",
    "        \n",
    "resampled_sketch"
   ]
  }
 ]
}