multiple-rectangles-or-triangles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:03:53.170000",
     "start_time": "2016-07-03T16:03:52.500000"
    },
    "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": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:16:34.666000",
     "start_time": "2016-07-03T16:16:28.832000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000L, 16L, 16L), (50000L, 2L, 4L))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_imgs = 50000\n",
    "\n",
    "img_size = 16\n",
    "min_rect_size = 3\n",
    "max_rect_size = 8\n",
    "num_objects = 2\n",
    "\n",
    "bboxes = np.zeros((num_imgs, num_objects, 4))\n",
    "imgs = np.zeros((num_imgs, img_size, img_size))\n",
    "shapes = np.zeros((num_imgs, num_objects, 1))\n",
    "\n",
    "for i_img in range(num_imgs):\n",
    "    for i_object in range(num_objects):\n",
    "        if np.random.choice([True, False]):\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",
    "            shapes[i_img, i_object] = [0]\n",
    "        else:\n",
    "            size = np.random.randint(min_rect_size, max_rect_size)\n",
    "            x, y = np.random.randint(0, img_size - size, size=2)\n",
    "            mask = np.tril_indices(size)\n",
    "            imgs[i_img, x + mask[0], y + mask[1]] = 1.\n",
    "            bboxes[i_img, i_object] = [x, y, size, size]\n",
    "            shapes[i_img, i_object] = [1]\n",
    "            \n",
    "imgs.shape, bboxes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:16:38.997000",
     "start_time": "2016-07-03T16:16:38.662000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQQAAAEACAYAAABVmQgcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqTEQJDUGgqTGQJDU/C/OL5JxEGZl9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15b57eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "# TODO: Why does the array have to be transposed?\n",
    "plt.imshow(imgs[i].T, cmap='Greys', interpolation='none', origin='lower', extent=[0, img_size, 0, img_size])\n",
    "for bbox, shape in zip(bboxes[i], shapes[i]):\n",
    "    plt.gca().add_patch(matplotlib.patches.Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], ec='r' if shape[0] == 0 else 'y', fc='none'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:16:41.668000",
     "start_time": "2016-07-03T16:16:41.115000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000L, 256L), 5.7734927949582015e-17, 1.0000000000000007)"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:16:42.640000",
     "start_time": "2016-07-03T16:16:42.627000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000L, 10L)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.concatenate([bboxes / img_size, shapes], axis=-1).reshape(num_imgs, -1)\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:16:43.445000",
     "start_time": "2016-07-03T16:16:43.437000"
    },
    "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": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:16:44.266000",
     "start_time": "2016-07-03T16:16:44.145000"
    },
    "collapsed": false
   },
   "outputs": [],
   "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": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:32:02.176000",
     "start_time": "2016-07-03T16:16:45.436000"
    },
    "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",
      "4s - loss: 0.1505 - val_loss: 0.0559\n",
      "Flipped 49.14 % of all elements\n",
      "Mean IOU: 0.234211369772\n",
      "Mean dist: 0.200727835348\n",
      "Mean mse: 0.0169208188335\n",
      "\n",
      "Epoch 1\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0558 - val_loss: 0.0499\n",
      "Flipped 26.935 % of all elements\n",
      "Mean IOU: 0.25961828597\n",
      "Mean dist: 0.187458868204\n",
      "Mean mse: 0.0144943950131\n",
      "\n",
      "Epoch 2\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0501 - val_loss: 0.0479\n",
      "Flipped 19.915 % of all elements\n",
      "Mean IOU: 0.285665079779\n",
      "Mean dist: 0.172386849031\n",
      "Mean mse: 0.01265891626\n",
      "\n",
      "Epoch 3\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0463 - val_loss: 0.0474\n",
      "Flipped 14.9175 % of all elements\n",
      "Mean IOU: 0.319045391304\n",
      "Mean dist: 0.157677244358\n",
      "Mean mse: 0.0111588802368\n",
      "\n",
      "Epoch 4\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0434 - val_loss: 0.0484\n",
      "Flipped 11.6025 % of all elements\n",
      "Mean IOU: 0.339965326174\n",
      "Mean dist: 0.147024843153\n",
      "Mean mse: 0.0101238561176\n",
      "\n",
      "Epoch 5\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0407 - val_loss: 0.0491\n",
      "Flipped 9.29 % of all elements\n",
      "Mean IOU: 0.361273007504\n",
      "Mean dist: 0.138836304271\n",
      "Mean mse: 0.00939302657044\n",
      "\n",
      "Epoch 6\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0384 - val_loss: 0.0500\n",
      "Flipped 8.9175 % of all elements\n",
      "Mean IOU: 0.382049353548\n",
      "Mean dist: 0.130278105575\n",
      "Mean mse: 0.00863693586602\n",
      "\n",
      "Epoch 7\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0363 - val_loss: 0.0516\n",
      "Flipped 8.715 % of all elements\n",
      "Mean IOU: 0.403091562327\n",
      "Mean dist: 0.121965919527\n",
      "Mean mse: 0.00794884560706\n",
      "\n",
      "Epoch 8\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0342 - val_loss: 0.0521\n",
      "Flipped 6.5375 % of all elements\n",
      "Mean IOU: 0.423869022259\n",
      "Mean dist: 0.115522680278\n",
      "Mean mse: 0.00739532350985\n",
      "\n",
      "Epoch 9\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0325 - val_loss: 0.0533\n",
      "Flipped 5.605 % of all elements\n",
      "Mean IOU: 0.437069564205\n",
      "Mean dist: 0.110202584812\n",
      "Mean mse: 0.00698140109199\n",
      "\n",
      "Epoch 10\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0312 - val_loss: 0.0537\n",
      "Flipped 4.44 % of all elements\n",
      "Mean IOU: 0.447052615104\n",
      "Mean dist: 0.105774030778\n",
      "Mean mse: 0.00660798313925\n",
      "\n",
      "Epoch 11\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0301 - val_loss: 0.0543\n",
      "Flipped 4.27 % of all elements\n",
      "Mean IOU: 0.4546545274\n",
      "Mean dist: 0.103120491849\n",
      "Mean mse: 0.0064044459049\n",
      "\n",
      "Epoch 12\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0292 - val_loss: 0.0552\n",
      "Flipped 3.7225 % of all elements\n",
      "Mean IOU: 0.466439063163\n",
      "Mean dist: 0.0987319624991\n",
      "Mean mse: 0.00606237596082\n",
      "\n",
      "Epoch 13\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0282 - val_loss: 0.0546\n",
      "Flipped 3.765 % of all elements\n",
      "Mean IOU: 0.475150087327\n",
      "Mean dist: 0.0954913356866\n",
      "Mean mse: 0.00585377630302\n",
      "\n",
      "Epoch 14\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0276 - val_loss: 0.0558\n",
      "Flipped 2.9825 % of all elements\n",
      "Mean IOU: 0.481368471988\n",
      "Mean dist: 0.093024954116\n",
      "Mean mse: 0.00565717665834\n",
      "\n",
      "Epoch 15\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0268 - val_loss: 0.0555\n",
      "Flipped 2.7825 % of all elements\n",
      "Mean IOU: 0.485010471326\n",
      "Mean dist: 0.0922291306785\n",
      "Mean mse: 0.00555591658617\n",
      "\n",
      "Epoch 16\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0263 - val_loss: 0.0558\n",
      "Flipped 2.7025 % of all elements\n",
      "Mean IOU: 0.487686503318\n",
      "Mean dist: 0.091808391434\n",
      "Mean mse: 0.00546566474005\n",
      "\n",
      "Epoch 17\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0259 - val_loss: 0.0563\n",
      "Flipped 2.475 % of all elements\n",
      "Mean IOU: 0.492446974249\n",
      "Mean dist: 0.0890210165659\n",
      "Mean mse: 0.00527322347287\n",
      "\n",
      "Epoch 18\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0254 - val_loss: 0.0566\n",
      "Flipped 2.1925 % of all elements\n",
      "Mean IOU: 0.502135327133\n",
      "Mean dist: 0.0872392306197\n",
      "Mean mse: 0.00516366861823\n",
      "\n",
      "Epoch 19\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0249 - val_loss: 0.0565\n",
      "Flipped 2.5675 % of all elements\n",
      "Mean IOU: 0.500652044952\n",
      "Mean dist: 0.0872541219307\n",
      "Mean mse: 0.00511897637667\n",
      "\n",
      "Epoch 20\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0247 - val_loss: 0.0571\n",
      "Flipped 2.2075 % of all elements\n",
      "Mean IOU: 0.504430402018\n",
      "Mean dist: 0.0859760355258\n",
      "Mean mse: 0.00502476157059\n",
      "\n",
      "Epoch 21\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0243 - val_loss: 0.0566\n",
      "Flipped 2.2275 % of all elements\n",
      "Mean IOU: 0.505250990478\n",
      "Mean dist: 0.0851774075079\n",
      "Mean mse: 0.00493283061627\n",
      "\n",
      "Epoch 22\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0241 - val_loss: 0.0566\n",
      "Flipped 2.3075 % of all elements\n",
      "Mean IOU: 0.50436342583\n",
      "Mean dist: 0.0858150751554\n",
      "Mean mse: 0.00496472848654\n",
      "\n",
      "Epoch 23\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0239 - val_loss: 0.0574\n",
      "Flipped 2.3125 % of all elements\n",
      "Mean IOU: 0.50713262664\n",
      "Mean dist: 0.0850357065346\n",
      "Mean mse: 0.00491903417062\n",
      "\n",
      "Epoch 24\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0235 - val_loss: 0.0571\n",
      "Flipped 1.975 % of all elements\n",
      "Mean IOU: 0.512359533089\n",
      "Mean dist: 0.0837795780707\n",
      "Mean mse: 0.00482852842311\n",
      "\n",
      "Epoch 25\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0232 - val_loss: 0.0578\n",
      "Flipped 1.8675 % of all elements\n",
      "Mean IOU: 0.514593360783\n",
      "Mean dist: 0.0828985640586\n",
      "Mean mse: 0.00475285116451\n",
      "\n",
      "Epoch 26\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0230 - val_loss: 0.0574\n",
      "Flipped 2.14 % of all elements\n",
      "Mean IOU: 0.510991699079\n",
      "Mean dist: 0.0839060128803\n",
      "Mean mse: 0.00478503886858\n",
      "\n",
      "Epoch 27\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0229 - val_loss: 0.0575\n",
      "Flipped 2.22 % of all elements\n",
      "Mean IOU: 0.51510657956\n",
      "Mean dist: 0.0818881934601\n",
      "Mean mse: 0.00467044466043\n",
      "\n",
      "Epoch 28\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0227 - val_loss: 0.0572\n",
      "Flipped 2.33 % of all elements\n",
      "Mean IOU: 0.516495987137\n",
      "Mean dist: 0.0817343860442\n",
      "Mean mse: 0.00462676897481\n",
      "\n",
      "Epoch 29\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0224 - val_loss: 0.0573\n",
      "Flipped 2.1225 % of all elements\n",
      "Mean IOU: 0.520348526052\n",
      "Mean dist: 0.0804247005785\n",
      "Mean mse: 0.0045592573646\n",
      "\n",
      "Epoch 30\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0223 - val_loss: 0.0574\n",
      "Flipped 2.0475 % of all elements\n",
      "Mean IOU: 0.525227191345\n",
      "Mean dist: 0.0801656375978\n",
      "Mean mse: 0.0045323463172\n",
      "\n",
      "Epoch 31\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0222 - val_loss: 0.0576\n",
      "Flipped 2.0175 % of all elements\n",
      "Mean IOU: 0.52382312297\n",
      "Mean dist: 0.0798413737855\n",
      "Mean mse: 0.00447079162116\n",
      "\n",
      "Epoch 32\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0221 - val_loss: 0.0576\n",
      "Flipped 2.0425 % of all elements\n",
      "Mean IOU: 0.524894045705\n",
      "Mean dist: 0.0790919121457\n",
      "Mean mse: 0.00445891213109\n",
      "\n",
      "Epoch 33\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0218 - val_loss: 0.0577\n",
      "Flipped 1.725 % of all elements\n",
      "Mean IOU: 0.530355194819\n",
      "Mean dist: 0.0784302623929\n",
      "Mean mse: 0.00441229688292\n",
      "\n",
      "Epoch 34\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0216 - val_loss: 0.0582\n",
      "Flipped 1.885 % of all elements\n",
      "Mean IOU: 0.522705077851\n",
      "Mean dist: 0.0795871824053\n",
      "Mean mse: 0.00443051811762\n",
      "\n",
      "Epoch 35\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "8s - loss: 0.0215 - val_loss: 0.0584\n",
      "Flipped 1.8325 % of all elements\n",
      "Mean IOU: 0.528942697463\n",
      "Mean dist: 0.0785589417474\n",
      "Mean mse: 0.00439488247914\n",
      "\n",
      "Epoch 36\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0216 - val_loss: 0.0581\n",
      "Flipped 1.9875 % of all elements\n",
      "Mean IOU: 0.526685062976\n",
      "Mean dist: 0.0782153995256\n",
      "Mean mse: 0.00437267973193\n",
      "\n",
      "Epoch 37\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0213 - val_loss: 0.0586\n",
      "Flipped 1.86 % of all elements\n",
      "Mean IOU: 0.530983843794\n",
      "Mean dist: 0.0772202077916\n",
      "Mean mse: 0.00428697422964\n",
      "\n",
      "Epoch 38\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0213 - val_loss: 0.0580\n",
      "Flipped 1.63 % of all elements\n",
      "Mean IOU: 0.531960873355\n",
      "Mean dist: 0.0774240931569\n",
      "Mean mse: 0.00428354157969\n",
      "\n",
      "Epoch 39\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0211 - val_loss: 0.0588\n",
      "Flipped 1.8725 % of all elements\n",
      "Mean IOU: 0.532681479934\n",
      "Mean dist: 0.0770623142474\n",
      "Mean mse: 0.00424637828159\n",
      "\n",
      "Epoch 40\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0210 - val_loss: 0.0578\n",
      "Flipped 1.95 % of all elements\n",
      "Mean IOU: 0.533979040391\n",
      "Mean dist: 0.077404630551\n",
      "Mean mse: 0.00425770390895\n",
      "\n",
      "Epoch 41\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0209 - val_loss: 0.0584\n",
      "Flipped 1.8525 % of all elements\n",
      "Mean IOU: 0.534955115217\n",
      "Mean dist: 0.0765883346279\n",
      "Mean mse: 0.00421227174575\n",
      "\n",
      "Epoch 42\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0209 - val_loss: 0.0581\n",
      "Flipped 1.805 % of all elements\n",
      "Mean IOU: 0.534320987971\n",
      "Mean dist: 0.0765066308489\n",
      "Mean mse: 0.00422701742572\n",
      "\n",
      "Epoch 43\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0207 - val_loss: 0.0585\n",
      "Flipped 1.8825 % of all elements\n",
      "Mean IOU: 0.535369959754\n",
      "Mean dist: 0.0763960186563\n",
      "Mean mse: 0.00418211300553\n",
      "\n",
      "Epoch 44\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0205 - val_loss: 0.0584\n",
      "Flipped 1.9625 % of all elements\n",
      "Mean IOU: 0.538506318522\n",
      "Mean dist: 0.0758882617551\n",
      "Mean mse: 0.00415130026949\n",
      "\n",
      "Epoch 45\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0204 - val_loss: 0.0589\n",
      "Flipped 1.88 % of all elements\n",
      "Mean IOU: 0.536553771922\n",
      "Mean dist: 0.0763552270766\n",
      "Mean mse: 0.00416935595785\n",
      "\n",
      "Epoch 46\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "6s - loss: 0.0204 - val_loss: 0.0578\n",
      "Flipped 1.69 % of all elements\n",
      "Mean IOU: 0.537272648614\n",
      "Mean dist: 0.0758712798596\n",
      "Mean mse: 0.00414251295481\n",
      "\n",
      "Epoch 47\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0203 - val_loss: 0.0584\n",
      "Flipped 1.825 % of all elements\n",
      "Mean IOU: 0.541206450914\n",
      "Mean dist: 0.0750306102421\n",
      "Mean mse: 0.0040954257016\n",
      "\n",
      "Epoch 48\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "5s - loss: 0.0203 - val_loss: 0.0581\n",
      "Flipped 1.91 % of all elements\n",
      "Mean IOU: 0.534911588734\n",
      "Mean dist: 0.0765635474\n",
      "Mean mse: 0.00412608518256\n",
      "\n",
      "Epoch 49\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0202 - val_loss: 0.0583\n",
      "Flipped 1.9 % of all elements\n",
      "Mean IOU: 0.535998163817\n",
      "Mean dist: 0.0759283639398\n",
      "Mean mse: 0.00412869736146\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 dist(bbox1, bbox2):\n",
    "    return np.sqrt(np.sum(np.square(bbox1[:2] - bbox2[:2])))\n",
    "\n",
    "num_epochs_flipping = 50\n",
    "num_epochs_no_flipping = 0  # has no significant effect\n",
    "flipped_train_y = np.array(train_y)\n",
    "flipped = np.zeros((len(flipped_train_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "ious_epoch = np.zeros((len(flipped_train_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "dists_epoch = np.zeros((len(flipped_train_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "mses_epoch = np.zeros((len(flipped_train_y), num_epochs_flipping + num_epochs_no_flipping))\n",
    "\n",
    "# TODO: Calculate ious directly for all samples (using slices of the array pred_y for x, y, w, h).\n",
    "for epoch in range(num_epochs_flipping):\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, exp) in enumerate(zip(pred_y, flipped_train_y)):\n",
    "        \n",
    "        # TODO: Make this simpler.\n",
    "        pred = pred.reshape(num_objects, -1)\n",
    "        exp = exp.reshape(num_objects, -1)\n",
    "        \n",
    "        pred_bboxes = pred[:, :4]\n",
    "        exp_bboxes = exp[:, :4]\n",
    "        \n",
    "        # TODO: Try flipping array and see if results differ.\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] = dist(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 maximum iou 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] = 1000000#-1  # set iou of assigned bboxes to -1, 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",
    "        flipped_train_y[sample] = exp[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",
    "    \n",
    "# print '------------------------------------'\n",
    "# print 'Training now without flipping bboxes'\n",
    "# print '------------------------------------'\n",
    "    \n",
    "# for epoch in range(num_epochs_flipping, num_epochs_flipping + num_epochs_no_flipping):\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",
    "#     # Calculate iou/dist, but don't flip.\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",
    "#         for exp_bbox, pred_bbox in zip(exp_bboxes, pred_bboxes):\n",
    "#             ious_epoch[sample, epoch] += IOU(exp_bbox, pred_bbox)\n",
    "#             dists_epoch[sample, epoch] += dist(exp_bbox, pred_bbox)\n",
    "#             mses_epoch[sample, epoch] += np.mean(np.square(exp_bbox - pred_bbox))\n",
    "            \n",
    "#         ious_epoch[sample, epoch] /= num_objects\n",
    "#         dists_epoch[sample, epoch] /= num_objects \n",
    "#         mses_epoch[sample, epoch] /= num_objects \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",
    "    \n",
    "# TODO: Calculate IOU and dist for validation data during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:32:11.172000",
     "start_time": "2016-07-03T16:32:07.209000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x16edc978>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5kTQUWTuu7Pe47sVDrbgm1hqsYiLhQcBZwOH8/4mEb8ybubqk9sFtw+tpewup6Jetzgq6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f18fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolor(flipped[:1000], cmap='Greys', vmax=1.)\n",
    "# plt.axvline(num_epochs_flipping, c='r')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training sample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:32:15.670000",
     "start_time": "2016-07-03T16:32:15.223000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KK8nPz6dv375MnTqVZ599tlXP3570QFVEGiUUhtlrqtdee419+/YBkJSUhDGGiIgIrrvu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f1f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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')  # relative to image size\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.axvline(num_epochs_flipping, c='r')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "plt.ylim(0, 1)\n",
    "\n",
    "# plt.savefig('plots/bw-multiple-rectangles-mse.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:32:21.325000",
     "start_time": "2016-07-03T16:32:20.874000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000L, 2L, 4L), (10000L, 2L, 1L))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y = model.predict(test_X)\n",
    "pred_y = pred_y.reshape(len(pred_y), num_objects, -1)\n",
    "pred_bboxes = pred_y[..., :4] * img_size\n",
    "pred_shapes = pred_y[..., 4:5]\n",
    "pred_bboxes.shape, pred_shapes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-03T16:33:28.201000",
     "start_time": "2016-07-03T16:33:26.046000"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bjELP5tk196jbPbe1dlNVfVuSX0nyosOuWFVt2cnBNmmt1YDDySgsSEZh3GQU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\n0/kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x272ad2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "for i_subplot in range(1, 9):\n",
    "    plt.subplot(2, 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, pred_shape in zip(pred_bboxes[i], test_bboxes[i], pred_shapes[i]):\n",
    "        plt.gca().add_patch(matplotlib.patches.Rectangle((pred_bbox[0], pred_bbox[1]), pred_bbox[2], pred_bbox[3], ec='r' if pred_shape[0] <= 0.5 else 'y', 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
}