Brisbane Event VPR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "\n",
    "import tensorflow as tf\n",
    "import netvlad_tf.nets as nets\n",
    "\n",
    "import itertools\n",
    "import scipy\n",
    "import scipy.spatial\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "from os import listdir, symlink\n",
    "from os.path import join, isfile, basename, abspath\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed\n",
    "import ipywidgets as widgets\n",
    "\n",
    "%aimport code_helpers_public_pr_curve, code_helpers_public, correspondence_event_camera_frame_camera, read_gps\n",
    "from code_helpers_public_pr_curve import getPAt100R, getPR, getPRCurve, getPRCurveWrapper, getRAt99P, get_recall_helper\n",
    "from code_helpers_public import compare_images, get_timestamps, get_timestamp_matches, get_image_sets_on_demand, get_vlad_features\n",
    "from read_gps import get_gps\n",
    "from correspondence_event_camera_frame_camera import traverse_to_name, name_to_consumervideo, video_beginning\n",
    "\n",
    "tqdm.get_lock().locks = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_folder = '/media/sandisk/event_dataset/reconstructed_videos_from_events/'\n",
    "num_events_per_pixel_list = [0.1, 0.3, 0.6, 0.8]\n",
    "window_duration_list = [44, 66, 88, 120, 140]\n",
    "\n",
    "query_traverse = 'dvs_vpr_2020-04-21-17-03-03'\n",
    "reference_traverse = 'dvs_vpr_2020-04-22-17-24-21'\n",
    "\n",
    "comparison_id = query_traverse + '_' + reference_traverse\n",
    "\n",
    "frames_subfolder = 'frames'\n",
    "event_subfolders = ['N_' + str(num_events_per_pixel) for num_events_per_pixel in num_events_per_pixel_list]\n",
    "event_subfolder_preview = 'N_0.1'\n",
    "fixed_event_subfolders = ['t_' + str(window_duration) for window_duration in window_duration_list]\n",
    "fixed_event_subfolder_preview = 't_120'\n",
    "\n",
    "all_subfolders = [frames_subfolder] + event_subfolders + fixed_event_subfolders\n",
    "\n",
    "\n",
    "def get_image_paths(folder1, folder2):\n",
    "    return sorted([join(folder1, f) for f in listdir(folder1) if f.endswith('.png')]), \\\n",
    "           sorted([join(folder2, f) for f in listdir(folder2) if f.endswith('.png')])\n",
    "\n",
    "\n",
    "overall_data_dir = join(dataset_folder, 'overall_data', comparison_id)\n",
    "path_to_plots = overall_data_dir + '/plots'\n",
    "\n",
    "if not os.path.isdir(overall_data_dir):\n",
    "    os.makedirs(overall_data_dir)\n",
    "    os.makedirs(overall_data_dir + '/plots')\n",
    "\n",
    "image_paths_combined1 = {}\n",
    "image_paths_combined2 = {}\n",
    "\n",
    "for subfolder in all_subfolders:\n",
    "    image_paths_combined1[subfolder], image_paths_combined2[subfolder] = get_image_paths(join(dataset_folder, subfolder, query_traverse), join(dataset_folder, subfolder, reference_traverse))\n",
    "    if not os.path.isdir(join(overall_data_dir, subfolder)):\n",
    "        os.makedirs(join(overall_data_dir, subfolder))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestamps_combined1 = {}; timestamps_combined2 = {}\n",
    "\n",
    "for subfolder in all_subfolders:\n",
    "    if subfolder == 'frames':\n",
    "        timestamps_combined1[subfolder] = np.array([float(os.path.splitext(basename(f))[0]) for f in image_paths_combined1[subfolder]])\n",
    "        timestamps_combined2[subfolder] = np.array([float(os.path.splitext(basename(f))[0]) for f in image_paths_combined2[subfolder]])\n",
    "    else:\n",
    "        timestamps_combined1[subfolder], timestamps_combined2[subfolder] = get_timestamps(join(dataset_folder, subfolder, query_traverse), join(dataset_folder, subfolder, reference_traverse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vid_path_1 = join(dataset_folder, 'camera_vids', name_to_consumervideo[traverse_to_name[query_traverse]])\n",
    "vid_path_2 = join(dataset_folder, 'camera_vids', name_to_consumervideo[traverse_to_name[reference_traverse]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = get_gps(vid_path_1 + '.nmea')\n",
    "x2 = get_gps(vid_path_2 + '.nmea')\n",
    "\n",
    "match_x1_to_x2 = []\n",
    "for idx1, (latlon, t) in enumerate(zip(x1[:, 0:2], x1[:, 2])):\n",
    "    if len(match_x1_to_x2) < 6:\n",
    "        min_idx2 = 0\n",
    "        max_idx2 = int(0.25 * len(x2))\n",
    "    elif idx1 > 0.5 * len(x1):\n",
    "        min_idx2 = match_x1_to_x2[-5]\n",
    "        max_idx2 = len(x2)\n",
    "    else:\n",
    "        min_idx2 = match_x1_to_x2[-5]\n",
    "        max_idx2 = int(0.75 * len(x2))\n",
    "    best_match = (np.linalg.norm(x2[min_idx2:max_idx2, 0:2] - latlon, axis=1)).argmin() + min_idx2\n",
    "    # print('%d %.4f' % (idx1, np.linalg.norm(x2[best_match, 0:2] - latlon)))\n",
    "    match_x1_to_x2.append(best_match)\n",
    "match_x1_to_x2 = np.array(match_x1_to_x2)\n",
    "\n",
    "t_raw1 = x1[:, 2]\n",
    "t_raw2 = x2[match_x1_to_x2, 2]\n",
    "timestamps_gps1 = np.array([t + video_beginning[traverse_to_name[query_traverse]] for t in t_raw1])\n",
    "timestamps_gps2 = np.array([t + video_beginning[traverse_to_name[reference_traverse]] for t in t_raw2])\n",
    "\n",
    "images_all_combined_set1 = {}; images_all_combined_set2 = {}\n",
    "matches_fixedlength_combined1 = {}; matches_fixedlength_combined2 = {}\n",
    "\n",
    "for subfolder in all_subfolders:\n",
    "    matches_fixedlength_combined1[subfolder] = get_timestamp_matches(timestamps_combined1[subfolder], timestamps_gps1)\n",
    "    matches_fixedlength_combined2[subfolder] = get_timestamp_matches(timestamps_combined2[subfolder], timestamps_gps2)\n",
    "    images_all_combined_set1[subfolder], images_all_combined_set2[subfolder] = \\\n",
    "        get_image_sets_on_demand(image_paths_combined1[subfolder], image_paths_combined2[subfolder], \n",
    "                                 matches_fixedlength_combined1[subfolder], matches_fixedlength_combined2[subfolder])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preview annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "interactive(children=(IntSlider(value=0, continuous_update=False, description='image_num1', max=577), IntSlide…",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "970692862f4443b6b3f601861f0a5a1d"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "fixed_compare_folder = event_subfolder_preview\n",
    "\n",
    "image_num1_fixed_widget = widgets.IntSlider(min = 0, max = len(images_all_combined_set1[fixed_compare_folder]) - 1, step = 1, value = 0, continuous_update = False)\n",
    "image_num2_fixed_widget = widgets.IntSlider(min = 0, max = len(images_all_combined_set2[fixed_compare_folder]) - 1, step = 1, value = 0, continuous_update = False)\n",
    "image_num2_fixed_widget.layout = widgets.Layout(visibility = 'hidden')\n",
    "widgets.jslink((image_num1_fixed_widget, 'value'), (image_num2_fixed_widget, 'value'))\n",
    "\n",
    "my_widgets_fixed_linked = interactive(compare_images, \n",
    "                                      image_paths1=fixed(images_all_combined_set1[fixed_compare_folder].get_path_list()), image_paths2=fixed(images_all_combined_set2[fixed_compare_folder].get_path_list()), \n",
    "                                      image_num1=image_num1_fixed_widget, image_num2=image_num2_fixed_widget)\n",
    "my_widgets_fixed_linked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:From /home/tobias/anaconda3/envs/ddd17gpu/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1635: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:From /home/tobias/robot/netvlad_tf_open/python/netvlad_tf/nets.py:40: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.keras.layers.Conv2D` instead.\n",
      "WARNING:tensorflow:From /home/tobias/anaconda3/envs/ddd17gpu/lib/python3.7/site-packages/tensorflow_core/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n",
      "WARNING:tensorflow:From /home/tobias/robot/netvlad_tf_open/python/netvlad_tf/nets.py:42: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.MaxPooling2D instead.\n",
      "WARNING:tensorflow:From /home/tobias/robot/netvlad_tf_open/python/netvlad_tf/layers.py:35: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Flatten instead.\n",
      "Device mapping:\n",
      "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n",
      "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1\n",
      "/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from /home/tobias/robot/netvlad_tf_open/checkpoints/vd16_pitts30k_conv5_3_vlad_preL2_intra_white\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "tf.compat.v1.disable_eager_execution(); tf.compat.v1.reset_default_graph()\n",
    "image_batch = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, None, None, 3])\n",
    "\n",
    "net_out = nets.vgg16NetvladPca(image_batch)\n",
    "saver = tf.compat.v1.train.Saver()\n",
    "\n",
    "gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=0.5)\n",
    "config = tf.compat.v1.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1, gpu_options=gpu_options)\n",
    "config.gpu_options.allow_growth = True\n",
    "config.log_device_placement = True\n",
    "sess = tf.compat.v1.Session(config=config)\n",
    "\n",
    "saver.restore(sess, nets.defaultCheckpoint())\n",
    "\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": []
    }
   ],
   "source": [
    "features_all_combined1 = {}; features_all_combined2 = {}; dist_matrix_all_combined = {}\n",
    "\n",
    "with tqdm(total = 2 * len(all_subfolders), position = 0, leave = False) as pbar:\n",
    "    for subfolder in all_subfolders:\n",
    "        features_all_combined1[subfolder]   = get_vlad_features(sess, net_out, image_batch, images_all_combined_set1[subfolder], join(overall_data_dir, subfolder, 'netvlad_features_all_set1' + '.npy')); pbar.update(1)\n",
    "        features_all_combined2[subfolder]   = get_vlad_features(sess, net_out, image_batch, images_all_combined_set2[subfolder], join(overall_data_dir, subfolder, 'netvlad_features_all_set2' + '.npy')); pbar.update(1)\n",
    "        dist_matrix_all_combined[subfolder] = scipy.spatial.distance.cdist(features_all_combined1[subfolder], features_all_combined2[subfolder], 'cosine').T\n",
    "\n",
    "dist_matrix_shape = dist_matrix_all_combined[frames_subfolder].shape\n",
    "assert dist_matrix_shape == (len(images_all_combined_set1['frames']), len(images_all_combined_set2['frames']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_dist_plot = 20\n",
    "plot_threshold_fps = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Dropdown(description='Ensemble init kind:', options=('frames', 'zeros'), value='frames')",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "01e51c9ea3cd4d2b8e54df575ffbf6c0"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "ensemble_init_kind_widget = widgets.Dropdown(options=['frames', 'zeros'], value='frames', description='Ensemble init kind:')\n",
    "ensemble_init_kind_widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Init with zeros\n"
     ]
    }
   ],
   "source": [
    "ensemble_init_kind = ensemble_init_kind_widget.value\n",
    "\n",
    "matchIndsCombined = []\n",
    "\n",
    "if ensemble_init_kind == 'zeros':\n",
    "    print('Init with zeros')\n",
    "    ensemble_init = np.zeros(dist_matrix_shape)\n",
    "    init_len = 0\n",
    "elif ensemble_init_kind == 'frames':\n",
    "    print('Include frames in ensemble')\n",
    "    ensemble_init = dist_matrix_all_combined[frames_subfolder]\n",
    "    init_len = 1\n",
    "    matchIndsCombined.append(np.argmin(dist_matrix_all_combined[frames_subfolder], axis=0))\n",
    "else:\n",
    "    raise ValueError('Wrong init type')\n",
    "\n",
    "dist_matrix_ensemble = ensemble_init.copy()\n",
    "\n",
    "for event_subfolder in event_subfolders:\n",
    "    dist_matrix_ensemble += dist_matrix_all_combined[event_subfolder]\n",
    "\n",
    "for fixed_event_subfolder in fixed_event_subfolders:\n",
    "    dist_matrix_ensemble += dist_matrix_all_combined[fixed_event_subfolder]\n",
    "\n",
    "dist_matrix_ensemble = dist_matrix_ensemble / (len(event_subfolders) + len(fixed_event_subfolders) + init_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "                                             \n",
      "\n",
      "Frame:                        0.836\n",
      "Best individual method        0.832 (t_120)\n",
      "Ensemble:                     0.888\n"
     ]
    }
   ],
   "source": [
    "with tqdm(total=1 + len(all_subfolders), position=0, leave=False) as pbar:\n",
    "    recalls_combined = {}; tps_combined = {}; best_matches_combined = {}\n",
    "    recalls_best_individual = None; tps_best_individual=None; best_matches_individual=None\n",
    "\n",
    "    recall_ensemble, tps_ensemble, best_matches_ensemble = get_recall_helper(dist_matrix_ensemble)\n",
    "    pbar.update(1)\n",
    "\n",
    "    for subfolder in all_subfolders:\n",
    "        recalls_combined[subfolder], tps_combined[subfolder], best_matches_combined[subfolder] = get_recall_helper(dist_matrix_all_combined[subfolder])\n",
    "        pbar.update(1)\n",
    "\n",
    "        if subfolder!='frames' and (recalls_best_individual is None or recalls_combined[subfolder][plot_threshold_fps] > recalls_best_individual[plot_threshold_fps]):\n",
    "            recalls_best_individual, tps_best_individual, best_matches_individual = recalls_combined[subfolder], tps_combined[subfolder], best_matches_combined[subfolder]\n",
    "            best_individual_name = subfolder\n",
    "\n",
    "print('\\n')\n",
    "print('Frame:                        %.3f' % recalls_combined['frames'][plot_threshold_fps])\n",
    "print('Best individual method        %.3f (%s)' % (recalls_best_individual[plot_threshold_fps], best_individual_name))\n",
    "print('Ensemble:                     %.3f' % recall_ensemble[plot_threshold_fps])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(recall_ensemble, '-', label='Ensemble method', color='black')\n",
    "plt.plot(recalls_combined[frames_subfolder], '-', label='Frame-based')\n",
    "\n",
    "style=itertools.cycle([\"-\", \"--\", \"-.\", \":\"])\n",
    "\n",
    "for event_subfolder in event_subfolders:\n",
    "    plt.plot(recalls_combined[event_subfolder], \":\")  # , label='Event-based ' + event_subfolder\n",
    "\n",
    "for fixed_event_subfolder in fixed_event_subfolders:\n",
    "    plt.plot(recalls_combined[fixed_event_subfolder], \":\")  # , label='Event-based ' + fixed_event_subfolder\n",
    "\n",
    "custom_line = Line2D([], [], color='brown', ls=':', label='Individual models')\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "handles.append(custom_line)\n",
    "labels.append('Individual models')\n",
    "\n",
    "plt.xlabel(\"Time threshold [s]\")\n",
    "plt.ylabel(\"Precision\\n@ 100\\% recall\")\n",
    "plt.xlim(0, max_dist_plot)\n",
    "plt.ylim(0, 1.0)\n",
    "\n",
    "plt.savefig(join(path_to_plots, 'recall_curve_' + ensemble_init_kind + '.pdf'), bbox_inches='tight')\n",
    "plt.savefig(join(path_to_plots, 'recall_curve_' + ensemble_init_kind + '.pgf'), bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_ensemble = getPRCurveWrapper(dist_matrix_ensemble, plot_threshold_fps)\n",
    "pr_frames = getPRCurveWrapper(dist_matrix_all_combined['frames'], plot_threshold_fps)\n",
    "\n",
    "pr_event_combined = {}\n",
    "for subfolder in all_subfolders:\n",
    "    pr_event_combined[subfolder] = getPRCurveWrapper(dist_matrix_all_combined[subfolder], plot_threshold_fps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "PAt100R ensemble: 0.886\n"
     ]
    }
   ],
   "source": [
    "print('PAt100R ensemble: %.3f' % getPAt100R(np.abs(np.argmin(dist_matrix_ensemble, axis=0)-np.arange(len(dist_matrix_ensemble))), plot_threshold_fps)[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "getRAt99P ensemble: 0.437\n"
     ]
    }
   ],
   "source": [
    "print('getRAt99P ensemble: %.3f' % getRAt99P(pr_ensemble))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "PAt100R best    : 0.827\n"
     ]
    }
   ],
   "source": [
    "print('PAt100R best    : %.3f' % getPAt100R(np.abs(np.argmin(dist_matrix_all_combined[best_individual_name], axis=0)-np.arange(len(dist_matrix_ensemble))), plot_threshold_fps)[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(pr_ensemble[:, 1], pr_ensemble[:, 0], '-', label='Ensemble method', color='black')\n",
    "plt.plot(pr_event_combined[frames_subfolder][:, 1], pr_event_combined[frames_subfolder][:, 0], '-', label='Frame-based')\n",
    "\n",
    "style=itertools.cycle([\"-\", \"--\", \"-.\", \":\"])\n",
    "\n",
    "for event_subfolder in event_subfolders:\n",
    "    plt.plot(pr_event_combined[event_subfolder][:, 1], pr_event_combined[event_subfolder][:, 0], \":\", label='Event-based ' + event_subfolder)\n",
    "\n",
    "for fixed_event_subfolder in fixed_event_subfolders:\n",
    "    plt.plot(pr_event_combined[fixed_event_subfolder][:, 1], pr_event_combined[fixed_event_subfolder][:, 0], \":\", label='Event-based ' + fixed_event_subfolder)\n",
    "\n",
    "custom_line = Line2D([], [], color='brown', ls=':', label='Individual models')\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "handles.append(custom_line)\n",
    "labels.append('Individual models')\n",
    "\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.xlim(0, 1.0)\n",
    "plt.ylim(0.0, 1.005)\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(join(path_to_plots, 'pr_curve_' + ensemble_init_kind + '.pdf'), bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "avg_window_durations = []\n",
    "num_events_per_pixels = []\n",
    "xs_events, ys_events = [], []\n",
    "xs_events_window, ys_events_window = [], []\n",
    "xs_fixed_events, ys_fixed_events = [], []\n",
    "rat99ps = []\n",
    "\n",
    "perf_frames = recalls_combined[frames_subfolder][plot_threshold_fps]\n",
    "perf_ensemble = recall_ensemble[plot_threshold_fps]\n",
    "\n",
    "for event_subfolder in event_subfolders:\n",
    "    perf = recalls_combined[event_subfolder][plot_threshold_fps]\n",
    "    num_events_per_pixel = float(event_subfolder[event_subfolder.find('_')+1:])\n",
    "    num_events_per_pixels.append(num_events_per_pixel)\n",
    "    # TODO: Remove unique stuff which is/was for super low pixels per event\n",
    "    avg_window_duration = (np.median(np.diff(np.unique(timestamps_combined1[event_subfolder]))) + np.median(np.diff(np.unique(timestamps_combined2[event_subfolder])))) / 2.0 * 1000.0\n",
    "    avg_window_durations.append(avg_window_duration)\n",
    "    xs_events.append(num_events_per_pixel); xs_events_window.append(avg_window_duration)\n",
    "    ys_events.append(perf); ys_events_window.append(perf)\n",
    "    rat99ps.append(getRAt99P(pr_event_combined[event_subfolder]))\n",
    "\n",
    "for fixed_event_subfolder in fixed_event_subfolders:\n",
    "    perf = recalls_combined[fixed_event_subfolder][plot_threshold_fps]\n",
    "    window_duration = float(fixed_event_subfolder[fixed_event_subfolder.find('_')+1:])\n",
    "    xs_fixed_events.append(window_duration)\n",
    "    ys_fixed_events.append(perf)\n",
    "    rat99ps.append(getRAt99P(pr_event_combined[fixed_event_subfolder]))\n",
    "\n",
    "plt.ylabel(\"Recall (threshold=%d)\" % (plot_threshold_fps-1))\n",
    "\n",
    "plt.ylim(0.0, 1.0)\n",
    "\n",
    "plt.axhline(perf_ensemble, linestyle='-', color='black', label='{:<32}- {:.3f}'.format('ensemble', perf_ensemble))\n",
    "\n",
    "plt.scatter([1000.0 / 30.0], [perf_frames], color='green', label='{:<32}- {:.3f}'.format('Frame-based approach', perf_frames))\n",
    "plt.plot(xs_events_window, ys_events_window, marker='D', markersize=7, label='{:<32}- {:.3f}'.format('Fixed num events; best', np.max(ys_events_window)))\n",
    "plt.plot(xs_fixed_events, ys_fixed_events, marker='*', markersize=10, label='{:<32}- {:.3f}'.format('Fixed time window; best', np.max(ys_fixed_events)))\n",
    "\n",
    "plt.xlabel(\"Window duration [milliseconds]\")\n",
    "plt.legend(prop={'family': 'monospace'}, bbox_to_anchor=(0.96, -0.23))\n",
    "\n",
    "plt.savefig(join(path_to_plots, 'perf_joint.pdf'), bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__End of notebook__"
   ]
  }
 ],
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