{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "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"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import csv\n",
    "from py.Dataset import Dataset\n",
    "from py.Session import Session, MotionImage, LapseImage\n",
    "from datetime import datetime\n",
    "\n",
    "DIR = '/home/AMMOD_data/camera_traps/BayerWald/Vielkadaver-Projekt/'\n",
    "\n",
    "ds = Dataset(DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_datetime(datetime_str: str) -> datetime:\n",
    "    return datetime.strptime(datetime_str[:-5], \"%Y-%m-%dT%H:%M:%S\")\n",
    "\n",
    "assert parse_datetime(\"2021-05-28T09:55:24+0100\") == datetime(2021, 5, 28, 9, 55, 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Header:  ['observationID', 'deploymentID', 'sequenceID', 'mediaID', 'timestamp', 'observationType', 'cameraSetup', 'taxonID', 'scientificName', 'count', 'countNew', 'lifeStage', 'sex', 'behaviour', 'individualID', 'classificationMethod', 'classifiedBy', 'classificationTimestamp', 'classificationConfidence', 'comments', '_id']\n",
      "Trying to create session VIELAAS_Spring_Session03-VIELAAS_Beaver_03...\n",
      "There are no sessions matching this name: []\n",
      "Trying to create session VIELAAS_Spring_Session03-VIELAAS_Ermine_03...\n",
      "There are no sessions matching this name: []\n",
      "Trying to create session VIELAAS_Spring_Session03-VIELAAS_Fox_03...\n",
      "Session 'Fox_03' at folder: /home/AMMOD_data/camera_traps/BayerWald/Vielkadaver-Projekt/VIELAAS_Spring_Session03-VIELAAS_Fox_03\n",
      "Loaded scans.\n",
      "Generating motion map...\n",
      "Trying to create session VIELAAS_Spring_Session03-VIELAAS_Rat_03...\n",
      "There are no sessions matching this name: []\n",
      "Successfully found 3695 images\n",
      "Skipped 138 images because session was not found\n",
      "For 0 session dates less images were found than specified (inconsistency)\n",
      "For 6 session dates more images were found than specified (ambiguous)\n"
     ]
    }
   ],
   "source": [
    "with open(\"Kadaverbilder_leer.csv\", newline=\"\") as csvfile:\n",
    "    reader = csv.reader(csvfile, delimiter=\";\")\n",
    "    header = next(reader)\n",
    "    print(\"Header: \", header)\n",
    "    session_col = header.index(\"deploymentID\")\n",
    "    datetime_col = header.index(\"timestamp\")\n",
    "    session_name: str = None\n",
    "    session: Session = None\n",
    "    successful = 0\n",
    "    skipped = 0\n",
    "    less_found = 0\n",
    "    ambiguous = 0\n",
    "    results = []\n",
    "    last_date = None\n",
    "    last_date_occurences = 0\n",
    "    for row in reader:\n",
    "        # print(row)\n",
    "        # open session if not already opened\n",
    "        if session_name != row[session_col]:\n",
    "            session_name = row[session_col]\n",
    "            print(f\"Trying to create session {session_name}...\")\n",
    "            try:\n",
    "                session = ds.create_session(session_name)\n",
    "            except ValueError as e:\n",
    "                print(e)\n",
    "                session = None\n",
    "        if session is None:\n",
    "            skipped += 1\n",
    "        else:\n",
    "            # get datetime\n",
    "            date = parse_datetime(row[datetime_col])\n",
    "            if date == last_date:\n",
    "                last_date_occurences += 1\n",
    "            else:\n",
    "                # look for images\n",
    "                images = session.get_motion_images_from_date(last_date)\n",
    "\n",
    "                if len(images) < last_date_occurences:\n",
    "                    less_images += last_date_occurences\n",
    "                elif len(images) > last_date_occurences:\n",
    "                    ambiguous += last_date_occurences\n",
    "                else:\n",
    "                    for image in images:\n",
    "                        results.append([session.name, image.filename])\n",
    "                    successful += last_date_occurences\n",
    "\n",
    "                last_date = date\n",
    "                last_date_occurences = 1\n",
    "print(f\"Successfully found {successful} images\")\n",
    "print(f\"Skipped {skipped} images because session was not found\")\n",
    "print(f\"For {not_found} session dates less images were found than specified (inconsistency)\")\n",
    "print(f\"For {ambiguous} session dates more images were found than specified (ambiguous)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.save(\"test_images.npy\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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