{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# fall, winter & spring are aligned to summer, so read summer.csv\n",
    "df=pd.read_csv('summer.csv', sep=',', header=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tids = df.values[:, 0] / 100  # format: hhmmss\n",
    "lats = df.values[:, 1]\n",
    "lons = df.values[:, 2]\n",
    "speeds = df.values[:, 3]\n",
    "courses = df.values[:, 4]\n",
    "alts = df.values[:, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# go from their time-based representation to number of seconds\n",
    "def val_to_sec(val):\n",
    "    if not isinstance(val, np.ndarray):\n",
    "        val = np.array([val])\n",
    "    hours = (val / 10000).astype(np.int)\n",
    "    minutes = ((val % 10000) / 100).astype(np.int)\n",
    "    secs = (val % 100).astype(np.int)\n",
    "    \n",
    "    absolute = hours * 3600 + minutes * 60 + secs\n",
    "    if len(absolute) == 1:\n",
    "        return int(absolute[0])\n",
    "    else:\n",
    "        return hours * 3600 + minutes * 60 + secs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tids_abs_seconds = val_to_sec(tids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_indices = tids_abs_seconds <= val_to_sec(152840) # all data past 15h 28m 40s is not contained in the video, so remove it\n",
    "# check that we have increasing timestamps\n",
    "going_back_in_time = np.diff(tids_abs_seconds) <= 0\n",
    "assert not np.any(going_back_in_time)\n",
    "\n",
    "speeds = speeds[keep_indices]\n",
    "tids_abs_seconds = tids_abs_seconds[keep_indices]\n",
    "start_val = val_to_sec(53806)  # this is when the train starts moving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tids_abs_seconds_off = tids_abs_seconds - start_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tids_abs_seconds_off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.argwhere(np.diff(tids_abs_seconds_off) > 25).flatten()  # just for debugging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.diff(tids_abs_seconds_off[tids_abs_seconds_off > 0]).sum()  # train moves for ~35439 frames so we're not far off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "desired_times = np.arange(-168, 35768-168)  # train starts moving at frame 168 so make everything relative to that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "match_indices = []\n",
    "for desired_time in desired_times:\n",
    "    diffs = np.abs(tids_abs_seconds_off - desired_time)\n",
    "    best_idx = diffs.argmin()\n",
    "    match_indices.append(best_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_img_ids, new_speeds, new_ref_times, new_lats, new_lons, new_courses, new_alts = [], [], [], [], [], [], []\n",
    "for row_idx in range(35768):\n",
    "    new_img_ids.append(row_idx + 1)\n",
    "    new_match_idx = match_indices[row_idx]\n",
    "    new_speeds.append(speeds[new_match_idx])\n",
    "    new_ref_times.append(tids[new_match_idx])\n",
    "    new_lats.append(lats[new_match_idx] / 100000)\n",
    "    new_lons.append(lons[new_match_idx] / 100000)\n",
    "    new_courses.append(courses[new_match_idx])\n",
    "    new_alts.append(alts[new_match_idx])\n",
    "new_img_ids = np.array(new_img_ids)\n",
    "new_speeds = np.array(new_speeds)\n",
    "new_ref_times = np.array(new_ref_times)\n",
    "new_lats = np.array(new_lats)\n",
    "new_lons = np.array(new_lons)\n",
    "new_courses = np.array(new_courses)\n",
    "new_alts = np.array(new_alts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez('nordland_aligned.npz',\n",
    "         img_id=new_img_ids,\n",
    "         speed=new_speeds,\n",
    "         ref_time=new_ref_times,\n",
    "         lat=new_lats,\n",
    "         lon=new_lons,\n",
    "         course=new_courses,\n",
    "         alt=new_alts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity check from manually found matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows_frames = np.array([  # manually found matches (when does train start/stop moving) -> frame number in video\n",
    "    168,  1290, 1792, 2211, 2295, 2501, 2655, 3405, 3668, 5072, 5460, 7080, 7277, 7772, 7870, 10050, 10200, 11670, 11880, 13360, 14835, 19740,\n",
    "    20040, 24120, 24390, 26410, 26535, 28975, 29090, 31090, 31185, 32400, 33040, 35130, 35177, 35608,\n",
    "])\n",
    "points_gps = [  # manually found matches -> time stamp in GPS data\n",
    "    5380600, 5564200, 6050800, 6120100, 6133500, 6165300, 6193200, 6315700, 6363000, 6593500, 7061900, 7331200, 7363400, 7444600, 7462800,\n",
    "    8225000, 8250600, 8494500, 8531900, 9175300, 9421800, 11040900, 11092800, 12171300, 12215500, 12552800, 12573600, 13381000,\n",
    "    13400700, 14132000, 14150200, 14350900, 14455900, 15204500, 15213600, 15284000,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows_gps = []\n",
    "for point_gps in points_gps:\n",
    "    diffs = np.abs(tids - point_gps / 100)\n",
    "    best_idx = diffs.argmin()\n",
    "    rows_gps.append(best_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array(rows_gps)  # as we can see we're close enough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array(match_indices)[np.array(rows_frames)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "abs_diff = np.abs(np.array(rows_gps)-np.array(match_indices)[np.array(rows_frames)])\n",
    "np.mean(abs_diff), np.max(abs_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build dbStruct matlab file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys\n",
    "sys.path.append('../pytorch-NetVlad-Nanne')\n",
    "\n",
    "from datasets import parse_db_struct, save_db_struct, dbStruct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tunnels = [(1870, 2029),   (2313, 2333),   (2341, 2355),   (4093, 4097),   (6501, 6506),   (6756, 6773),   (8479, 8484),   (8489, 8494),   (9967, 9979),   (10239, 10268), (10408, 10416), (10944, 10947),\n",
    "           (10985, 10991), (10997, 11003), (11008, 11019), (11022, 11028), (11030, 11032),\n",
    "           (11037, 11048), (11057, 11065), (11101, 11107), (11129, 11146), (11225, 11228), (11280, 11286), (11915, 12036), (12057, 12062), (12074, 12082), (12165, 12168), (12204, 12208), (12319, 12365),\n",
    "           (12409, 12417), (12472, 12481), (13620, 13628), (14320, 14348), (14390, 14400), (16203, 16206), (16472, 16484), (16690, 16695), (16933, 16936), (17054, 17068), (17177, 17183), (17734, 17756),\n",
    "           (17868, 17902), (17974, 17986), (17991, 17996), (18161, 18170), (18330, 18443), (18540, 18550), (18580, 18588), (18661, 18683), (18955, 18966), (18977, 18986), (19019, 19026), (19092, 19100),\n",
    "           (19170, 19185), (20310, 20354), (20540, 20547), (20594, 20599), (20737, 20760), (21058, 21063), (21478, 21499), (21832, 21872), (21947, 21961), (21986, 22003), (22014, 22030), (22037, 22048),\n",
    "           (22149, 22152), (22174, 22197), (22212, 22241), (22249, 22251), (22263, 22269), (22279, 22344), (22358, 22361), (22397, 22430), (22442, 22450), (22483, 22502), (22571, 22578), (22593, 22596),\n",
    "           (22944, 22950), (22999, 23004), (23026, 23029), (23045, 23049), (23141, 23148), (23166, 23171), (23197, 23214), (23402, 23407), (23486, 23493), (23496, 23503), (23519, 23534), (23571, 23577),\n",
    "           (23593, 23598), (23666, 23675), (23691, 23703), (23707, 23711), (23842, 23855), (23950, 23955), (24988, 24997), (25004, 25030), (25037, 25044), (25256, 25320), (25373, 25380), (25398, 25406),\n",
    "           (25507, 25521), (25825, 25846), (26086, 26091), (26120, 26135), (26890, 26897), (26997, 27012), (27408, 27423), (27432, 27435), (27926, 27943), (28687, 28693), (29321, 29331), (29384, 29421),\n",
    "           (29525, 29532), (29693, 29707), (29974, 29981), (29994, 30010), (30073, 30091), (30103, 30106), (30137, 30142), (30174, 30179), (30204, 30211), (31301, 31325), (31332, 31340), (31396, 31410),\n",
    "           (31433, 31437), (31448, 31482), (31492, 31551), (31611, 31628), (31666, 31712), (31748, 31796), (31823, 31828), (31831, 31836), (31848, 31865), (31903, 31965), (31998, 32062), (32102, 32128),\n",
    "           (32143, 32165), (32214, 32242), (32344, 32348), (33317, 33328), (33341, 33346), (33370, 33386), (33430, 33513), (33717, 33721), (33754, 33781), (33917, 33923), (34234, 34242), (34631, 34655),\n",
    "           (34742, 34757), (34775, 34811), (34849, 34857), (34978, 34992), (35362, 35366), (35386, 35390), (35395, 35400), (35430, 35440), (35464, 35466)]\n",
    "filter_tunnels = np.array(np.ones(len(new_img_ids)), dtype=np.bool)\n",
    "last = 0\n",
    "for tunnel in tunnels:\n",
    "    # print(tunnel[1]-tunnel[0])\n",
    "    # print(tunnel[1])\n",
    "    assert tunnel[0] > last\n",
    "    last = tunnel[1]\n",
    "    filter_tunnels[tunnel[0]-1:tunnel[1]-1] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_speed = new_speeds > 1500\n",
    "all_filters = np.logical_and(filter_speed, filter_tunnels)\n",
    "max_im_num = 10000000000000  # 10000000000 for all\n",
    "\n",
    "whichSet = 'test'\n",
    "dataset = 'nordland'\n",
    "dbImage = ['images-%05d.png' % img_id for img_id in new_img_ids[all_filters][:max_im_num]]\n",
    "qImage = dbImage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numDb = len(dbImage)\n",
    "numQ = len(qImage)\n",
    "\n",
    "posDistThr = 2\n",
    "posDistSqThr = posDistThr**2\n",
    "nonTrivPosDistSqThr = 100\n",
    "\n",
    "gpsDb = np.vstack((new_lats[all_filters][:max_im_num], new_lons[all_filters][:max_im_num])).T\n",
    "gpsQ = gpsDb\n",
    "\n",
    "utmDb = np.vstack((range(numDb), range(numDb))).T\n",
    "utmQ = utmDb\n",
    "# utmQ = None; utmDb = None; \n",
    "\n",
    "dbTimeStamp = None; qTimeStamp = None\n",
    "\n",
    "db = dbStruct(whichSet, dataset, dbImage, utmDb, qImage, utmQ, numDb, numQ, posDistThr,\n",
    "              posDistSqThr, nonTrivPosDistSqThr, dbTimeStamp, qTimeStamp, gpsDb, gpsQ)\n",
    "\n",
    "save_db_struct('nordland.mat', db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "knn = NearestNeighbors(n_jobs=-1)\n",
    "knn.fit(db.utmDb)\n",
    "distances, positives = knn.radius_neighbors(db.utmQ, radius=db.posDistThr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "positives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "source_dir = '/media/storage_hdd/Datasets/nordland/640x320-color/'\n",
    "dest_dir = '/media/storage_hdd/Datasets/nordland/640x320-color-filtered/'\n",
    "for season in ['summer', 'spring', 'fall', 'winter']:\n",
    "    os.makedirs(os.path.join(dest_dir, season))\n",
    "    for idx, im in enumerate(dbImage):\n",
    "        os.symlink(os.path.join(source_dir, season, im), os.path.join(dest_dir, season, 'filtered-%05d.png' % idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('nordland_matches.txt', 'w') as outfile:\n",
    "    for im_name1 in dbImage:\n",
    "        for im_name2 in dbImage:\n",
    "            outfile.write('summer/' + im_name1 + ' ' + 'winter/' + im_name2 + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__End__"
   ]
  }
 ],
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