connect_image.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autotime extension is already loaded. To reload it, use:\n",
      "  %reload_ext autotime\n",
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fee28e15850>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 678 ms\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autotime\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import cv2\n",
    "import math\n",
    "import os\n",
    "import imutils\n",
    "from connect_image import *\n",
    "\n",
    "img = cv2.imread('images/lebron2.png', 1)\n",
    "\n",
    "# img = cv2.resize(img, None, fx = 0.5, fy = 0.5, interpolation = cv2.INTER_CUBIC)\n",
    "img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "ax.imshow(img_rgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fee297911d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 588 ms\n"
     ]
    }
   ],
   "source": [
    "edges = extract_edges(img, blur=5, thresh1=100, thresh2=200)\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "ax.imshow(edges, cmap='gray')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n",
      "19\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fee28736cd0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 38.1 s\n"
     ]
    }
   ],
   "source": [
    "connected_edges = np.copy(edges)\n",
    "components = find_components(connected_edges)\n",
    "filtered_components = []\n",
    "min_component_size = 15\n",
    "for component in components:\n",
    "    if len(component) >= min_component_size:\n",
    "        filtered_components.append(component)\n",
    "    else:\n",
    "        x = np.array(list(component))\n",
    "        connected_edges[x[:,0],x[:,1]] = False\n",
    "print len(components)\n",
    "components = filtered_components\n",
    "print len(components)\n",
    "# A = connect2(connected_edges, components)\n",
    "# A = np.array(A)\n",
    "# approx_img = np.zeros_like(connected_edges).astype('uint8')\n",
    "# A = np.array(list(components[16]))\n",
    "# approx_img[A[:,0],A[:,1]] = True\n",
    "connected_edges[0,0] = True\n",
    "components = [set([(0,0)])] + components\n",
    "connect(connected_edges, components)\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "ax.imshow(connected_edges, cmap='gray')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "connected_edges\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0\n",
      "time: 33.8 ms\n"
     ]
    }
   ],
   "source": [
    "# 28,227\n",
    "x, y  = local_search(connected_edges, [(28, 227)], set([(28, 227)]), set(), 30)\n",
    "print len(x), len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 7.34 ms\n"
     ]
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduced points from 22042 to 887\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 4.63 s\n"
     ]
    }
   ],
   "source": [
    "(i, j) = np.argwhere(connected_edges)[0]\n",
    "\n",
    "path, costs, paths = nn_tsp(connected_edges, (i, j))\n",
    "path = np.array(path)\n",
    "# path = np.fliplr(path)\n",
    "# B, vid2 = improve_path(connected_edges, approx, path, graph, vid)\n",
    "# approx = graph2path(graph, [(k, l) for (k, l) in path[:2]], vid)\n",
    "\n",
    "# path = graph2path(connected_edges, graph, tuple(path[0]), vid, paths)\n",
    "# path = np.array(path)\n",
    "# approx = np.squeeze(cv2.approxPolyDP(path, 0.9, False))\n",
    "# approx_img = np.zeros_like(connected_edges).astype('uint8')\n",
    "\n",
    "# approx2 = improve_path2(connected_edges, path, costs, paths)\n",
    "approx1 = simplify_path(path, 1.8)\n",
    "approx1 = np.fliplr(approx1)\n",
    "approx_img = np.zeros_like(connected_edges).astype('uint8')\n",
    "cv2.drawContours(approx_img, [approx1], -1, 255, 1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "ax.imshow(approx_img, cmap='gray') \n",
    "print 'Reduced points from %s to %s' % (path.shape[0], approx1.shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "[28, 27] is not in list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-77-e3de2ff1cff2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m27\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: [28, 27] is not in list"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 27.9 ms\n"
     ]
    }
   ],
   "source": [
    "path.tolist().index([28, 27])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'improve_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-79-23bb44df304f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimprove_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconnected_edges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# path = graph2path(connected_edges, graph, tuple(path[0]), vid, paths)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# path = np.array(path)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# approx_img = np.zeros_like(connected_edges).astype('uint8')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'improve_path' is not defined"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 66.8 ms\n"
     ]
    }
   ],
   "source": [
    "improve_path(connected_edges, graph, vid, paths)\n",
    "# path = graph2path(connected_edges, graph, tuple(path[0]), vid, paths)\n",
    "# path = np.array(path)\n",
    "\n",
    "# approx_img = np.zeros_like(connected_edges).astype('uint8')\n",
    "# cv2.drawContours(approx_img, [path], -1, 255, 1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "ax.imshow(approx_img, cmap='gray') \n",
    "print 'Reduced points to %s' % (path.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "approx[354], approx[355]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "paths[(284, 340)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 28 ms\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "m, n = connected_edges.shape\n",
    "with open('lebron2.pts', 'w') as f:\n",
    "    pickle.dump((approx1, m, n), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], shape=(0, 2), dtype=int32)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 10.1 ms\n"
     ]
    }
   ],
   "source": [
    "approx1[870:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 0\n",
    "for i in range(413):\n",
    "    for j in range(413):\n",
    "        c += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "approx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path[:28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "approx[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "approx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "approx2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "approx1.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "approx2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.version.version\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "costs[4450:-3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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