label.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Video labeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this notebook is to generate a dataframe where each row is a frame of a given video with its corresponding ground truth label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dictionary of all phases\n",
    "phase_dict = {\n",
    "    'adhesiolysis' : 0,\n",
    "    'blurry' : 1,\n",
    "    'catheter insertion' : 2,\n",
    "    'mesh placement' : 3,\n",
    "    'mesh positioning' : 4,\n",
    "    'out of body' : 5,\n",
    "    'peritoneal closure' : 6,\n",
    "    'peritoneal scoring' : 7,\n",
    "    'positioning suture' : 8,\n",
    "    'preperioneal dissection' : 9,\n",
    "    'primary hernia repair' : 10,\n",
    "    'reduction of hernia' : 11,\n",
    "    'stationary idle' : 12,\n",
    "    'transitionary idle' : 13,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv with dummy data annotation\n",
    "dummy_annotation_df = pd.read_csv(utils.csv_path)\n",
    "\n",
    "# create dataframe with labels for every frame of all videos\n",
    "labels_df = pd.DataFrame(columns = ['videoname', 'frame', 'label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "videonames, frames, labels = [], [], []\n",
    "\n",
    "for index in range(dummy_annotation_df.shape[0]):\n",
    "\n",
    "    # obtain row values\n",
    "    videoname = dummy_annotation_df['videoName'][index]\n",
    "    phasename = dummy_annotation_df['PhaseName'][index]\n",
    "    start = dummy_annotation_df['Start'][index]\n",
    "    end = dummy_annotation_df['End'][index]\n",
    "\n",
    "    if ':' in start and ':' in end: # verifies if it is indeed a time coded line\n",
    "\n",
    "        # get corresponding label\n",
    "        label = phase_dict[phasename]\n",
    "        frame_start = int(start[-5:-3]) * 60 + int(start[-2:])\n",
    "        frame_end = int(end[-5:-3]) * 60 + int(end[-2:])\n",
    "        # treat cases where the video is more than one hour long\n",
    "        if len(start) > 5: frame_start += int(start[0:2]) * 3600\n",
    "        if len(end) > 5: frame_end += int(end[0:2]) * 3600\n",
    "        for frame in range(frame_start, frame_end):\n",
    "            videonames.append(videoname)\n",
    "            frames.append(frame)\n",
    "            labels.append(label)\n",
    "\n",
    "# generate labels dataframe\n",
    "# we put frames + 1\n",
    "labels_df = pd.DataFrame({'videoname' : videonames, 'frame' : frames, 'label' : labels})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot histogram of labels\n",
    "labels, counts = np.unique(labels_df['label'], return_counts=True)\n",
    "plt.bar(labels, counts, align='center')\n",
    "plt.gca().set_xticks(labels)\n",
    "plt.title(\"frequency of labels\")\n",
    "plt.xlabel(\"label\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Phases')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot phases\n",
    "plt.scatter(x = labels_df['frame'], y = labels_df['videoname'], c = labels_df['label'], label = labels_df['label'], marker = '|')\n",
    "plt.yticks('')\n",
    "plt.xlabel('time (seconds)')\n",
    "plt.title('Phases')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save labels\n",
    "labels_df.to_pickle(utils.labels_path)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}