multiple-rectangles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-11T02:31:23.705000",
     "start_time": "2016-07-11T02:31:23.164000"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-11T02:31:26.922000",
     "start_time": "2016-07-11T02:31:24.029000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000L, 8L, 8L), (50000L, 3L, 4L))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_imgs = 50000\n",
    "\n",
    "img_size = 8\n",
    "min_rect_size = 1\n",
    "max_rect_size = 4\n",
    "num_objects = 3\n",
    "\n",
    "bboxes = np.zeros((num_imgs, num_objects, 4))\n",
    "imgs = np.zeros((num_imgs, img_size, img_size))\n",
    "\n",
    "for i_img in range(num_imgs):\n",
    "    for i_object in range(num_objects):\n",
    "        width, height = np.random.randint(min_rect_size, max_rect_size, size=2)\n",
    "        x = np.random.randint(0, img_size - width)\n",
    "        y = np.random.randint(0, img_size - height)\n",
    "        imgs[i_img, x:x+width, y:y+height] = 1.\n",
    "        bboxes[i_img, i_object] = [x, y, width, height]\n",
    "        \n",
    "        \n",
    "    # Prevent overlap between rectangles.\n",
    "    # Makes results (i.e. mean IOU) slightly worse; probably because the shapes are smaller \n",
    "    # (i.e. they are harder to recognize and capture within a bbox) \n",
    "    # and further apart (i.e. the algorithm can't set all bboxes to the same location to get a good IOU).    \n",
    "#     i_object = 0\n",
    "#     while i_object < num_objects:\n",
    "#         width, height = np.random.randint(min_rect_size, max_rect_size, size=2)\n",
    "#         x = np.random.randint(0, img_size - width)\n",
    "#         y = np.random.randint(0, img_size - height)\n",
    "#         overlap = False        \n",
    "# #         print 'Creating object', i_object, '- checking', len(bboxes[i_img, :i_object]), 'bboxes for overlap',\n",
    "#         for bbox in bboxes[i_img, :i_object]:  # TODO: Check if this includes all bboxes.\n",
    "#             overlap = bool(IOU(bbox, [x-1, y-1, width+2, height+2]))  # at least one free field between objects\n",
    "#             if overlap: \n",
    "# #                 print '--> Found overlap'\n",
    "#                 break\n",
    "#         if not overlap:\n",
    "#             imgs[i_img, x:x+width, y:y+height] = 1.\n",
    "#             bboxes[i_img, i_object] = [x, y, width, height]\n",
    "#             i_object += 1\n",
    "# #             print '--> Found no overlap'\n",
    "# #     print\n",
    "        \n",
    "imgs.shape, bboxes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-11T02:31:27.836000",
     "start_time": "2016-07-11T02:31:27.576000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x759bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.imshow(imgs[i].T, cmap='Greys', interpolation='none', origin='lower', extent=[0, img_size, 0, img_size])\n",
    "for bbox in bboxes[i]:\n",
    "    plt.gca().add_patch(matplotlib.patches.Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], ec='r', fc='none'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-11T02:31:29.292000",
     "start_time": "2016-07-11T02:31:29.159000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000L, 64L), 4.8849813083506891e-19, 1.0000000000000002)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = (imgs.reshape(num_imgs, -1) - np.mean(imgs)) / np.std(imgs)\n",
    "X.shape, np.mean(X), np.std(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-11T02:31:30.073000",
     "start_time": "2016-07-11T02:31:30.064000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000L, 12L)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = bboxes.reshape(num_imgs, -1) / img_size\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-11T02:31:31.033000",
     "start_time": "2016-07-11T02:31:31.028000"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i = int(0.8 * num_imgs)\n",
    "train_X = X[:i]\n",
    "test_X = X[i:]\n",
    "train_y = y[:i]\n",
    "test_y = y[i:]\n",
    "test_imgs = imgs[i:]\n",
    "test_bboxes = bboxes[i:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-11T02:31:38.581000",
     "start_time": "2016-07-11T02:31:32.040000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout\n",
    "from keras.optimizers import SGD\n",
    "model = Sequential([\n",
    "        Dense(256, input_dim=X.shape[-1]), \n",
    "        Activation('relu'), \n",
    "        Dropout(0.4), \n",
    "        Dense(y.shape[-1])\n",
    "    ])\n",
    "model.compile('adadelta', 'mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-11T02:49:39.783000",
     "start_time": "2016-07-11T02:31:48.430000"
    },
    "code_folding": [
     2,
     17
    ],
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0866 - val_loss: 0.0245\n",
      "Flipped 65.855 % of all elements\n",
      "Mean IOU: 0.132461820317\n",
      "Mean dist: 0.231913900583\n",
      "Mean mse: 0.0215878985146\n",
      "\n",
      "Epoch 1\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0231 - val_loss: 0.0238\n",
      "Flipped 34.025 % of all elements\n",
      "Mean IOU: 0.150481370741\n",
      "Mean dist: 0.217070926166\n",
      "Mean mse: 0.0192820626975\n",
      "\n",
      "Epoch 2\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0201 - val_loss: 0.0251\n",
      "Flipped 25.3166666667 % of all elements\n",
      "Mean IOU: 0.186506946858\n",
      "Mean dist: 0.193612210226\n",
      "Mean mse: 0.0165723888758\n",
      "\n",
      "Epoch 3\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0176 - val_loss: 0.0273\n",
      "Flipped 19.0266666667 % of all elements\n",
      "Mean IOU: 0.229205227308\n",
      "Mean dist: 0.171791710465\n",
      "Mean mse: 0.0142965778992\n",
      "\n",
      "Epoch 4\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0157 - val_loss: 0.0286\n",
      "Flipped 12.25 % of all elements\n",
      "Mean IOU: 0.254165113817\n",
      "Mean dist: 0.159449935378\n",
      "Mean mse: 0.0129885621856\n",
      "\n",
      "Epoch 5\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0146 - val_loss: 0.0292\n",
      "Flipped 9.37833333333 % of all elements\n",
      "Mean IOU: 0.275962168732\n",
      "Mean dist: 0.150136571577\n",
      "Mean mse: 0.0120152972037\n",
      "\n",
      "Epoch 6\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0138 - val_loss: 0.0296\n",
      "Flipped 7.45416666667 % of all elements\n",
      "Mean IOU: 0.289729962462\n",
      "Mean dist: 0.143686297112\n",
      "Mean mse: 0.0113528223482\n",
      "\n",
      "Epoch 7\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0131 - val_loss: 0.0303\n",
      "Flipped 6.405 % of all elements\n",
      "Mean IOU: 0.303117180378\n",
      "Mean dist: 0.137789827337\n",
      "Mean mse: 0.0107846302155\n",
      "\n",
      "Epoch 8\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0127 - val_loss: 0.0302\n",
      "Flipped 5.86 % of all elements\n",
      "Mean IOU: 0.31079770748\n",
      "Mean dist: 0.134372677222\n",
      "Mean mse: 0.0103704550109\n",
      "\n",
      "Epoch 9\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0122 - val_loss: 0.0309\n",
      "Flipped 5.50583333333 % of all elements\n",
      "Mean IOU: 0.322171889577\n",
      "Mean dist: 0.129881629799\n",
      "Mean mse: 0.0099642509257\n",
      "\n",
      "Epoch 10\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0119 - val_loss: 0.0311\n",
      "Flipped 4.87583333333 % of all elements\n",
      "Mean IOU: 0.329181492757\n",
      "Mean dist: 0.127271776721\n",
      "Mean mse: 0.00964884362597\n",
      "\n",
      "Epoch 11\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0116 - val_loss: 0.0312\n",
      "Flipped 4.84333333333 % of all elements\n",
      "Mean IOU: 0.335827488776\n",
      "Mean dist: 0.124212681314\n",
      "Mean mse: 0.00934633646606\n",
      "\n",
      "Epoch 12\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0113 - val_loss: 0.0313\n",
      "Flipped 4.36583333333 % of all elements\n",
      "Mean IOU: 0.342465824678\n",
      "Mean dist: 0.121495686817\n",
      "Mean mse: 0.00905107642108\n",
      "\n",
      "Epoch 13\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0111 - val_loss: 0.0312\n",
      "Flipped 3.935 % of all elements\n",
      "Mean IOU: 0.345173104879\n",
      "Mean dist: 0.119905279937\n",
      "Mean mse: 0.00885077815354\n",
      "\n",
      "Epoch 14\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0109 - val_loss: 0.0314\n",
      "Flipped 3.79583333333 % of all elements\n",
      "Mean IOU: 0.353772143711\n",
      "Mean dist: 0.117217271799\n",
      "Mean mse: 0.00860863516413\n",
      "\n",
      "Epoch 15\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0106 - val_loss: 0.0314\n",
      "Flipped 3.6225 % of all elements\n",
      "Mean IOU: 0.356139135119\n",
      "Mean dist: 0.115841729945\n",
      "Mean mse: 0.00843889112026\n",
      "\n",
      "Epoch 16\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0105 - val_loss: 0.0318\n",
      "Flipped 3.63333333333 % of all elements\n",
      "Mean IOU: 0.362196710225\n",
      "Mean dist: 0.113977171787\n",
      "Mean mse: 0.00824554206167\n",
      "\n",
      "Epoch 17\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0103 - val_loss: 0.0318\n",
      "Flipped 3.0325 % of all elements\n",
      "Mean IOU: 0.3645361806\n",
      "Mean dist: 0.112971440364\n",
      "Mean mse: 0.00811978970344\n",
      "\n",
      "Epoch 18\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0102 - val_loss: 0.0320\n",
      "Flipped 3.35 % of all elements\n",
      "Mean IOU: 0.368902931532\n",
      "Mean dist: 0.111346367841\n",
      "Mean mse: 0.0079878372689\n",
      "\n",
      "Epoch 19\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0101 - val_loss: 0.0319\n",
      "Flipped 3.34 % of all elements\n",
      "Mean IOU: 0.37178507275\n",
      "Mean dist: 0.109925694281\n",
      "Mean mse: 0.00784679623512\n",
      "\n",
      "Epoch 20\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0099 - val_loss: 0.0320\n",
      "Flipped 3.275 % of all elements\n",
      "Mean IOU: 0.374334554438\n",
      "Mean dist: 0.109261254548\n",
      "Mean mse: 0.00777803286095\n",
      "\n",
      "Epoch 21\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0099 - val_loss: 0.0321\n",
      "Flipped 3.11166666667 % of all elements\n",
      "Mean IOU: 0.375755354454\n",
      "Mean dist: 0.108772271417\n",
      "Mean mse: 0.00768379051524\n",
      "\n",
      "Epoch 22\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0098 - val_loss: 0.0318\n",
      "Flipped 2.82 % of all elements\n",
      "Mean IOU: 0.375104486639\n",
      "Mean dist: 0.108117571671\n",
      "Mean mse: 0.00761972946442\n",
      "\n",
      "Epoch 23\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0097 - val_loss: 0.0322\n",
      "Flipped 2.84416666667 % of all elements\n",
      "Mean IOU: 0.38249765076\n",
      "Mean dist: 0.106568802747\n",
      "Mean mse: 0.0074875522475\n",
      "\n",
      "Epoch 24\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0097 - val_loss: 0.0323\n",
      "Flipped 2.76166666667 % of all elements\n",
      "Mean IOU: 0.384270152949\n",
      "Mean dist: 0.105546078306\n",
      "Mean mse: 0.00740746175872\n",
      "\n",
      "Epoch 25\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0096 - val_loss: 0.0321\n",
      "Flipped 2.6525 % of all elements\n",
      "Mean IOU: 0.384989851392\n",
      "Mean dist: 0.105259973708\n",
      "Mean mse: 0.00737142057346\n",
      "\n",
      "Epoch 26\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0095 - val_loss: 0.0322\n",
      "Flipped 2.735 % of all elements\n",
      "Mean IOU: 0.386006757694\n",
      "Mean dist: 0.104785295641\n",
      "Mean mse: 0.00730480348125\n",
      "\n",
      "Epoch 27\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0095 - val_loss: 0.0325\n",
      "Flipped 2.905 % of all elements\n",
      "Mean IOU: 0.390872786059\n",
      "Mean dist: 0.104233661887\n",
      "Mean mse: 0.00725643671926\n",
      "\n",
      "Epoch 28\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0094 - val_loss: 0.0326\n",
      "Flipped 2.96083333333 % of all elements\n",
      "Mean IOU: 0.390284422156\n",
      "Mean dist: 0.103534450824\n",
      "Mean mse: 0.00719094701272\n",
      "\n",
      "Epoch 29\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0094 - val_loss: 0.0325\n",
      "Flipped 2.83416666667 % of all elements\n",
      "Mean IOU: 0.393472719944\n",
      "Mean dist: 0.103066038216\n",
      "Mean mse: 0.00711752687992\n",
      "\n",
      "Epoch 30\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0093 - val_loss: 0.0327\n",
      "Flipped 2.8725 % of all elements\n",
      "Mean IOU: 0.395203153076\n",
      "Mean dist: 0.102387130608\n",
      "Mean mse: 0.00707564675195\n",
      "\n",
      "Epoch 31\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0092 - val_loss: 0.0325\n",
      "Flipped 2.835 % of all elements\n",
      "Mean IOU: 0.397213390699\n",
      "Mean dist: 0.10182798213\n",
      "Mean mse: 0.00703195041112\n",
      "\n",
      "Epoch 32\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0092 - val_loss: 0.0325\n",
      "Flipped 2.55 % of all elements\n",
      "Mean IOU: 0.400643317245\n",
      "Mean dist: 0.101161168722\n",
      "Mean mse: 0.00697616506101\n",
      "\n",
      "Epoch 33\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0091 - val_loss: 0.0327\n",
      "Flipped 2.53666666667 % of all elements\n",
      "Mean IOU: 0.400765350053\n",
      "Mean dist: 0.100619738294\n",
      "Mean mse: 0.0069208690877\n",
      "\n",
      "Epoch 34\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0091 - val_loss: 0.0325\n",
      "Flipped 2.5175 % of all elements\n",
      "Mean IOU: 0.40060261854\n",
      "Mean dist: 0.100895729201\n",
      "Mean mse: 0.00692658306849\n",
      "\n",
      "Epoch 35\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0091 - val_loss: 0.0325\n",
      "Flipped 2.57166666667 % of all elements\n",
      "Mean IOU: 0.39902782318\n",
      "Mean dist: 0.100378804353\n",
      "Mean mse: 0.00688041141176\n",
      "\n",
      "Epoch 36\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0090 - val_loss: 0.0324\n",
      "Flipped 2.72916666667 % of all elements\n",
      "Mean IOU: 0.399904514085\n",
      "Mean dist: 0.100525918709\n",
      "Mean mse: 0.00687954671208\n",
      "\n",
      "Epoch 37\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0090 - val_loss: 0.0326\n",
      "Flipped 2.55416666667 % of all elements\n",
      "Mean IOU: 0.404934890938\n",
      "Mean dist: 0.0991886116917\n",
      "Mean mse: 0.00678688161954\n",
      "\n",
      "Epoch 38\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0090 - val_loss: 0.0326\n",
      "Flipped 2.50416666667 % of all elements\n",
      "Mean IOU: 0.402472642102\n",
      "Mean dist: 0.0997697177019\n",
      "Mean mse: 0.00680914010245\n",
      "\n",
      "Epoch 39\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0090 - val_loss: 0.0325\n",
      "Flipped 2.44833333333 % of all elements\n",
      "Mean IOU: 0.40197228827\n",
      "Mean dist: 0.0992986498558\n",
      "Mean mse: 0.00676366787696\n",
      "\n",
      "Epoch 40\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "2s - loss: 0.0089 - val_loss: 0.0327\n",
      "Flipped 2.51666666667 % of all elements\n",
      "Mean IOU: 0.4054858615\n",
      "Mean dist: 0.0985546213403\n",
      "Mean mse: 0.00672008755158\n",
      "\n",
      "Epoch 41\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0089 - val_loss: 0.0329\n",
      "Flipped 2.65 % of all elements\n",
      "Mean IOU: 0.406429030963\n",
      "Mean dist: 0.0979248777965\n",
      "Mean mse: 0.00666725132437\n",
      "\n",
      "Epoch 42\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0088 - val_loss: 0.0327\n",
      "Flipped 2.5075 % of all elements\n",
      "Mean IOU: 0.409726833946\n",
      "Mean dist: 0.0976724443448\n",
      "Mean mse: 0.00663231498388\n",
      "\n",
      "Epoch 43\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0088 - val_loss: 0.0327\n",
      "Flipped 2.63416666667 % of all elements\n",
      "Mean IOU: 0.407339620479\n",
      "Mean dist: 0.0976801206413\n",
      "Mean mse: 0.00665000738172\n",
      "\n",
      "Epoch 44\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "9s - loss: 0.0088 - val_loss: 0.0328\n",
      "Flipped 2.40916666667 % of all elements\n",
      "Mean IOU: 0.408052970002\n",
      "Mean dist: 0.0977282885998\n",
      "Mean mse: 0.00663271793058\n",
      "\n",
      "Epoch 45\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0088 - val_loss: 0.0327\n",
      "Flipped 2.51416666667 % of all elements\n",
      "Mean IOU: 0.410803439063\n",
      "Mean dist: 0.0971774425186\n",
      "Mean mse: 0.00657374215001\n",
      "\n",
      "Epoch 46\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0088 - val_loss: 0.0327\n",
      "Flipped 2.4425 % of all elements\n",
      "Mean IOU: 0.409305512569\n",
      "Mean dist: 0.097182728018\n",
      "Mean mse: 0.00656134501249\n",
      "\n",
      "Epoch 47\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0087 - val_loss: 0.0329\n",
      "Flipped 2.485 % of all elements\n",
      "Mean IOU: 0.411896049599\n",
      "Mean dist: 0.0963696082076\n",
      "Mean mse: 0.00652188173433\n",
      "\n",
      "Epoch 48\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0087 - val_loss: 0.0328\n",
      "Flipped 2.34 % of all elements\n",
      "Mean IOU: 0.411688699092\n",
      "Mean dist: 0.0964747907714\n",
      "Mean mse: 0.00650162604176\n",
      "\n",
      "Epoch 49\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0087 - val_loss: 0.0328\n",
      "Flipped 2.57833333333 % of all elements\n",
      "Mean IOU: 0.411528881964\n",
      "Mean dist: 0.096555074057\n",
      "Mean mse: 0.00649752850192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Flip bboxes during training.\n",
    "# Note: The validation loss is always quite big here because we don't flip the bounding boxes for the validation data. \n",
    "def IOU(bbox1, bbox2):\n",
    "    '''Calculate overlap between two bounding boxes [x, y, w, h] as the area of intersection over the area of unity'''\n",
    "    x1, y1, w1, h1 = bbox1[0], bbox1[1], bbox1[2], bbox1[3]  # TODO: Check if its more performant if tensor elements are accessed directly below.\n",
    "    x2, y2, w2, h2 = bbox2[0], bbox2[1], bbox2[2], bbox2[3]\n",
    "\n",
    "    w_I = min(x1 + w1, x2 + w2) - max(x1, x2)\n",
    "    h_I = min(y1 + h1, y2 + h2) - max(y1, y2)\n",
    "    if w_I <= 0 or h_I <= 0:  # no overlap\n",
    "        return 0\n",
    "    I = w_I * h_I\n",
    "\n",
    "    U = w1 * h1 + w2 * h2 - I\n",
    "\n",
    "    return I / U\n",
    "\n",
    "def distance(bbox1, bbox2):\n",
    "    return np.sqrt(np.sum(np.square(bbox1[:2] - bbox2[:2])))\n",
    "\n",
    "num_epochs = 50\n",
    "flipped_train_y = np.array(train_y)\n",
    "flipped = np.zeros((len(flipped_train_y), num_epochs))\n",
    "ious_epoch = np.zeros((len(flipped_train_y), num_epochs))\n",
    "dists_epoch = np.zeros((len(flipped_train_y), num_epochs))\n",
    "mses_epoch = np.zeros((len(flipped_train_y), num_epochs))\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    print 'Epoch', epoch\n",
    "    model.fit(train_X, flipped_train_y, nb_epoch=1, validation_data=(test_X, test_y), verbose=2)\n",
    "    pred_y = model.predict(train_X)\n",
    "\n",
    "    for sample, (pred_bboxes, exp_bboxes) in enumerate(zip(pred_y, flipped_train_y)):\n",
    "        \n",
    "        pred_bboxes = pred_bboxes.reshape(num_objects, -1)\n",
    "        exp_bboxes = exp_bboxes.reshape(num_objects, -1)\n",
    "\n",
    "        ious = np.zeros((num_objects, num_objects))\n",
    "        dists = np.zeros((num_objects, num_objects))\n",
    "        mses = np.zeros((num_objects, num_objects))\n",
    "        for i, exp_bbox in enumerate(exp_bboxes):\n",
    "            for j, pred_bbox in enumerate(pred_bboxes):\n",
    "                ious[i, j] = IOU(exp_bbox, pred_bbox)\n",
    "                dists[i, j] = distance(exp_bbox, pred_bbox)\n",
    "                mses[i, j] = np.mean(np.square(exp_bbox - pred_bbox))\n",
    "                \n",
    "        new_order = np.zeros(num_objects, dtype=int)\n",
    "        \n",
    "        for i in range(num_objects):\n",
    "            # Find pred and exp bbox with minimum mse and assign them to each other (i.e. switch the positions of the exp bboxes in y).\n",
    "            ind_exp_bbox, ind_pred_bbox = np.unravel_index(mses.argmin(), mses.shape)\n",
    "            ious_epoch[sample, epoch] += ious[ind_exp_bbox, ind_pred_bbox]\n",
    "            dists_epoch[sample, epoch] += dists[ind_exp_bbox, ind_pred_bbox]\n",
    "            mses_epoch[sample, epoch] += mses[ind_exp_bbox, ind_pred_bbox]\n",
    "            mses[ind_exp_bbox] = 10000000#-1  # set mse of assigned bboxes to a large number, so they don't get assigned again\n",
    "            mses[:, ind_pred_bbox] = 10000000#-1\n",
    "            new_order[ind_pred_bbox] = ind_exp_bbox\n",
    "        \n",
    "        # Write flipped bboxes to training data.\n",
    "        flipped_train_y[sample] = exp_bboxes[new_order].flatten()\n",
    "        \n",
    "        flipped[sample, epoch] = 1. - np.mean(new_order == np.arange(num_objects, dtype=int))#np.array_equal(new_order, np.arange(num_objects, dtype=int))  # TODO: Change this to reflect the number of flips.\n",
    "        ious_epoch[sample, epoch] /= num_objects\n",
    "        dists_epoch[sample, epoch] /= num_objects\n",
    "        mses_epoch[sample, epoch] /= num_objects\n",
    "        \n",
    "            \n",
    "    print 'Flipped {} % of all elements'.format(np.mean(flipped[:, epoch]) * 100.)\n",
    "    print 'Mean IOU: {}'.format(np.mean(ious_epoch[:, epoch]))\n",
    "    print 'Mean dist: {}'.format(np.mean(dists_epoch[:, epoch]))\n",
    "    print 'Mean mse: {}'.format(np.mean(mses_epoch[:, epoch]))\n",
    "    print\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:51:28.611000",
     "start_time": "2016-07-03T16:51:25.540000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x26a47a58>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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05fw0RtJuDILHZyLi6iK5t+UBEBGPA7PAsfSzLI4EjpP0d8CfAm+R9BlgRw/L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\nnZ+tup9rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14178a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show flippings for a few training samples.\n",
    "plt.pcolor(flipped[:300], cmap='Greys', vmax=1.)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training sample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:51:37.618000",
     "start_time": "2016-07-03T16:51:37.062000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J1zlfV8uTl+e6CTjzTNdrY8+eTf00EZHg1KxvqBpjzgOmAmfXlWfmzJn++czM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\nIhKE/j+q8W5rMUGDpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25954898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot metrics on the training data. \n",
    "mean_ious_epoch = np.mean(ious_epoch, axis=0)\n",
    "mean_dists_epoch = np.mean(dists_epoch, axis=0)\n",
    "mean_mses_epoch = np.mean(mses_epoch, axis=0)\n",
    "plt.plot(mean_ious_epoch, label='Mean IOU')  # between predicted and assigned true bboxes\n",
    "plt.plot(mean_dists_epoch, label='Mean distance')  # relative to image size\n",
    "plt.plot(mean_mses_epoch, label='Mean mse')\n",
    "plt.annotate(np.round(np.max(mean_ious_epoch), 3), (len(mean_ious_epoch)-1, mean_ious_epoch[-1]+0.03), horizontalalignment='right', color='b')\n",
    "plt.annotate(np.round(np.min(mean_dists_epoch), 3), (len(mean_dists_epoch)-1, mean_dists_epoch[-1]+0.03), horizontalalignment='right', color='g')\n",
    "plt.annotate(np.round(np.min(mean_mses_epoch), 3), (len(mean_mses_epoch)-1, mean_mses_epoch[-1]+0.03), horizontalalignment='right', color='r')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "plt.ylim(0, 1)\n",
    "\n",
    "# plt.savefig('plots/bw-three-rectangles-50k.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:34:24.949000",
     "start_time": "2016-07-03T16:34:24.511000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000L, 3L, 4L)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y = model.predict(test_X)\n",
    "pred_bboxes = pred_y * img_size\n",
    "pred_bboxes = pred_bboxes.reshape(len(pred_bboxes), num_objects, -1)\n",
    "pred_bboxes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:34:55.948000",
     "start_time": "2016-07-03T16:34:54.428000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9/zb//tj2x7Dt7bcmo9rf7Ptv2x/D2O1vkIxqfycew1QyOqTA/HyS762qb6qq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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x268f7908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 3))\n",
    "for i_subplot in range(1, 5):\n",
    "    plt.subplot(1, 4, i_subplot)\n",
    "    i = np.random.randint(len(test_X))\n",
    "    plt.imshow(test_imgs[i].T, cmap='Greys', interpolation='none', origin='lower', extent=[0, img_size, 0, img_size])\n",
    "    for pred_bbox, exp_bbox in zip(pred_bboxes[i], test_bboxes[i]):\n",
    "        plt.gca().add_patch(matplotlib.patches.Rectangle((pred_bbox[0], pred_bbox[1]), pred_bbox[2], pred_bbox[3], ec='r', fc='none'))\n",
    "        # TODO: Calculate max IOU with all expected bounding boxes.\n",
    "#         plt.annotate('IOU: {:.2f}'.format(IOU(pred_bbox, exp_bbox)), (pred_bbox[0], pred_bbox[1]+pred_bbox[3]+0.4), color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}