{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate a balanced session from a lapseless session\n",
    "**Use case:** Given a session with a high number of motion images, but an insufficient number of lapse images (i.e. only daily lapse images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Found 32 sessions\n",
      "Session 'Fox_03' at folder: /home/AMMOD_data/camera_traps/BayerWald/Vielkadaver-Projekt/VIELAAS_Spring_Session03-VIELAAS_Fox_03\n",
      "Loaded scans.\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import shutil\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "from py.Dataset import Dataset\n",
    "from py.Session import Session, MotionImage, LapseImage\n",
    "from py.Labels import LABELS\n",
    "\n",
    "DIR = '/home/AMMOD_data/camera_traps/BayerWald/Vielkadaver-Projekt/'\n",
    "TARGET_DIR = \"./GeneratedSessions_NoBackup/\"\n",
    "\n",
    "ds = Dataset(DIR)\n",
    "target_session = ds.create_session(\"fox_03\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will pick capture sets where every image is labeled as normal, until we have selected at least pick_for_lapse images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Picking at least 1200 of 3702 images\n",
      "[2067, 2068, 2069, 2070, 2066, 2059, 2060, 2058, 2056, 2057, 2053, 2054, 2055, 2051, 2052, 2046, 2050, 2047, 2049, 2048, 2063, 2064, 2062, 2065, 2061, 542, 544, 543, 545, 541, 532, 531, 533, 534, 535, 540, 536, 537, 539, 538, 529, 527, 528, 526, 530, 5359, 5356, 5357, 5358, 5360, 5348, 5349, 5346, 5350, 5347, 5353, 5354, 5355, 5352, 5351, 5333, 5335, 5332, 5331, 5334, 5365, 5362, 5361, 5364, 5363, 5329, 5326, 5327, 5328, 5330, 5342, 5345, 5344, 5341, 5343, 5339, 5338, 5340, 5336, 5337, 4826, 4829, 4828, 4827, 4830, 4809, 4807, 4810, 4808, 4806, 4817, 4816, 4820, 4819, 4818, 4832, 4835, 4833, 4834, 4831, 4825, 4822, 4824, 4821, 4823, 4811, 4813, 4815, 4814, 4812, 227, 230, 226, 228, 229, 253, 251, 254, 252, 255, 221, 223, 225, 222, 224, 211, 215, 212, 214, 213, 205, 202, 203, 201, 204, 260, 256, 257, 259, 258, 236, 240, 238, 239, 237, 248, 247, 250, 249, 246, 208, 207, 210, 206, 209, 233, 235, 231, 234, 232, 220, 217, 216, 218, 219, 244, 245, 243, 242, 241, 393, 392, 395, 394, 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642, 645, 650, 648, 647, 649, 646, 661, 662, 663, 664, 665, 639, 636, 637, 638, 640, 653, 655, 651, 654, 652, 674, 675, 671, 673, 672, 660, 658, 659, 656, 657, 669, 667, 666, 668, 670, 131, 135, 132, 134, 133, 137, 139, 140, 136, 138, 143, 145, 142, 141, 144, 148, 149, 150, 146, 147, 123, 122, 124, 125, 121, 126, 127, 130, 128, 129, 151, 154, 153, 152, 155, 157, 160, 158, 156, 159, 4438, 4439, 4437, 4440, 4436, 4448, 4449, 4447, 4446, 4450, 4432, 4435, 4433, 4434, 4431, 4443, 4441, 4444, 4442, 4445, 1123, 1125, 1122, 1121, 1124, 1128, 1130, 1126, 1127, 1129, 1118, 1116, 1117, 1119, 1120, 1138, 1139, 1140, 1136, 1137, 1134, 1132, 1133, 1131, 1135, 1111, 1113, 1115, 1114, 1112, 1110, 1107, 1108, 1106, 1109, 849, 850, 846, 848, 847, 859, 857, 856, 860, 858, 843, 842, 844, 841, 845, 838, 839, 837, 840, 836, 855, 852, 853, 851, 854, 5060, 5057, 5056, 5058, 5059, 5045, 5043, 5044, 5041, 5042, 5052, 5055, 5051, 5053, 5054, 5038, 5040, 5036, 5039, 5037, 5050, 5047, 5046, 5048, 5049, 1447, 1450, 1448, 1446, 1449, 1438, 1440, 1439, 1436, 1437, 1443, 1442, 1444, 1441, 1445, 4902, 4904, 4905, 4901, 4903, 4912, 4913, 4911, 4915, 4914, 4900, 4896, 4897, 4898, 4899, 4908, 4910, 4906, 4909, 4907, 4230, 4226, 4228, 4227, 4229, 4225, 4223, 4221, 4224, 4222, 4232, 4235, 4233, 4231, 4234, 4729, 4728, 4726, 4727, 4730, 4745, 4742, 4741, 4743, 4744, 4735, 4731, 4733, 4732, 4734, 4722, 4725, 4721, 4723, 4724, 4739, 4737, 4738, 4740, 4736, 4119, 4117, 4120, 4116, 4118, 4128, 4126, 4129, 4130, 4127, 4110, 4109, 4106, 4108, 4107, 4134, 4135, 4131, 4133, 4132, 4121, 4122, 4124, 4123, 4125, 4113, 4114, 4111, 4112, 4115, 2225, 2224, 2223, 2222, 2221, 2226, 2230, 2229, 2227, 2228, 4651, 4654, 4653, 4652, 4655, 1509, 1510, 1507, 1506, 1508, 1504, 1503, 1501, 1502, 1505, 1522, 1521, 1523, 1525, 1524, 1514, 1515, 1513, 1511, 1512, 1520, 1517, 1519, 1518, 1516, 1497, 1496, 1500, 1498, 1499, 346, 348, 349, 350, 347, 351, 355, 353, 354, 352, 344, 345, 342, 343, 341, 930, 928, 926, 929, 927, 925, 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     ]
    }
   ],
   "source": [
    "pick_for_lapse = 1200\n",
    "\n",
    "normal_list = LABELS[target_session.name][\"normal\"]\n",
    "print(f\"Picking at least {pick_for_lapse} of {len(normal_list)} images\")\n",
    "assert pick_for_lapse < len(normal_list)\n",
    "\n",
    "lapse_img_nrs = []\n",
    "while len(lapse_img_nrs) < pick_for_lapse:\n",
    "    img_set = target_session.get_random_motion_image_set()\n",
    "    set_nrs = []\n",
    "    for img in img_set:\n",
    "        img_nr = int(img.filename[-9:-4])\n",
    "        if img_nr not in normal_list:\n",
    "            # at least one image in set is anomalous, discard whole set\n",
    "            set_nrs = []\n",
    "            break\n",
    "        elif img_nr not in lapse_img_nrs:\n",
    "            set_nrs.append(img_nr)\n",
    "    # whole set contains empty images, so add the whole set\n",
    "    lapse_img_nrs.extend(set_nrs)\n",
    "print(lapse_img_nrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1210"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lapse_img_nrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now copy the Motion images to either Lapse or Motion, depending on whether they are in lapse_img_nrs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5495/5495 [03:08<00:00, 29.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copied 1210 files to Lapse, 4285 to Motion\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# copy motion images to Motion and Lapse folders\n",
    "motion_folder = os.path.join(TARGET_DIR, os.path.basename(target_session.folder), \"Motion\")\n",
    "lapse_folder = os.path.join(TARGET_DIR, os.path.basename(target_session.folder), \"Lapse\")\n",
    "os.makedirs(motion_folder, exist_ok=True)\n",
    "os.makedirs(lapse_folder, exist_ok=True)\n",
    "lapse = 0\n",
    "motion = 0\n",
    "for motion_img in tqdm(list(target_session.generate_motion_images())):\n",
    "    img_nr = int(motion_img.filename[-9:-4])\n",
    "    if img_nr in lapse_img_nrs:\n",
    "        shutil.copy(motion_img.get_full_path(), lapse_folder)\n",
    "        lapse += 1\n",
    "    else:\n",
    "        shutil.copy(motion_img.get_full_path(), motion_folder)\n",
    "        motion += 1\n",
    "print(f\"Copied {lapse} files to Lapse, {motion} to Motion\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full folder is copied without changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./GeneratedSessions_NoBackup/VIELAAS_Spring_Session03-VIELAAS_Fox_03/Full'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# copy full folder as-is\n",
    "full_folder = os.path.join(TARGET_DIR, os.path.basename(target_session.folder), \"Full\")\n",
    "shutil.copytree(target_session.get_full_folder(), full_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright &copy; 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
   ]
  }
 ],
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