object_tracking_from_video.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## object_tracking_from_video\n",
    "\n",
    "In this notebook we apply object tracking using SORT to determine the motion of people in a video, and count the number of people crossing a line. The general idea and the video are taken from https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/\n",
    "\n",
    "In the video there are 6 people in total - 4 walk noth-south and 2 south-north"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "video/people-18s.mp4\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import cv2\n",
    "from IPython.display import Image\n",
    "import deepstack.core as ds\n",
    "import json\n",
    "import numpy as np\n",
    "from sort.sort import *\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "videos = list(Path(\"video/\").rglob(\"*.mp4\"))\n",
    "video_path = str(videos[0]) # use video 0\n",
    "print(video_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'video/people-18s.mp4'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the frames and write to a temp folder (contents are gitignored to keep repo small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "FRAME_SAMPLING = 5 # video is at 30 FPS so down sample to limit compute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vidcap = cv2.VideoCapture(video_path)\n",
    "success, frame = vidcap.read()\n",
    "count = 0\n",
    "cv2.imwrite(\"tmp/frame%d.jpg\" % count, frame)     # save frame as JPEG file  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tmp/frame0.jpg\n"
     ]
    }
   ],
   "source": [
    "frames = list(Path(\"tmp/\").rglob(\"*.jpg\"))\n",
    "frame_path = str(frames[0])\n",
    "print(frame_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(frame_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now iterate over all frames, saving if they are at the FRAME_SAMPLING. Use a list of dictionaries to keep info about the frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'true_count': 0, 'frame_count': 0, 'frame_path': 'tmp/frame_0.jpg'}\n",
      "107\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "true_count = 0 # in the original video\n",
    "frame_count = 0 # in the sampled images\n",
    "\n",
    "vidcap = cv2.VideoCapture(video_path)\n",
    "success, frame = vidcap.read()\n",
    "\n",
    "while success:  \n",
    "    success, frame = vidcap.read()\n",
    "    frame_path = f\"tmp/frame_{frame_count}.jpg\"\n",
    "    \n",
    "    if true_count % FRAME_SAMPLING == 0:\n",
    "        cv2.imwrite(frame_path, frame)     # save frame as JPEG file    \n",
    "        frame_info = {}\n",
    "        frame_info['true_count'] = true_count\n",
    "        frame_info['frame_count'] = frame_count\n",
    "        frame_info['frame_path'] = frame_path\n",
    "        results.append(frame_info)\n",
    "        \n",
    "        frame_count += 1\n",
    "    true_count += 1\n",
    "\n",
    "print(results[0])\n",
    "print(len(results))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process frames to extract object bounding boxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deepstack credentials\n",
    "IP_ADDRESS = 'localhost'\n",
    "PORT = 80\n",
    "API_KEY =  \"\" # if you have not set an api_key, just pass an empty string\n",
    "dsobject = ds.DeepstackObject(IP_ADDRESS, PORT, API_KEY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing image number 0 : tmp/frame_0.jpg : 0 persons\n",
      "Processing image number 1 : tmp/frame_1.jpg : 0 persons\n",
      "Processing image number 2 : tmp/frame_2.jpg : 0 persons\n",
      "Processing image number 3 : tmp/frame_3.jpg : 0 persons\n",
      "Processing image number 4 : tmp/frame_4.jpg : 0 persons\n",
      "Processing image number 5 : tmp/frame_5.jpg : 0 persons\n",
      "Processing image number 6 : tmp/frame_6.jpg : 0 persons\n",
      "Processing image number 7 : tmp/frame_7.jpg : 0 persons\n",
      "Processing image number 8 : tmp/frame_8.jpg : 0 persons\n",
      "Processing image number 9 : tmp/frame_9.jpg : 1 persons\n",
      "Processing image number 10 : tmp/frame_10.jpg : 1 persons\n",
      "Processing image number 11 : tmp/frame_11.jpg : 0 persons\n",
      "Processing image number 12 : tmp/frame_12.jpg : 0 persons\n",
      "Processing image number 13 : tmp/frame_13.jpg : 0 persons\n",
      "Processing image number 14 : tmp/frame_14.jpg : 0 persons\n",
      "Processing image number 15 : tmp/frame_15.jpg : 0 persons\n",
      "Processing image number 16 : tmp/frame_16.jpg : 0 persons\n",
      "Processing image number 17 : tmp/frame_17.jpg : 0 persons\n",
      "Processing image number 18 : tmp/frame_18.jpg : 0 persons\n",
      "Processing image number 19 : tmp/frame_19.jpg : 0 persons\n",
      "Processing image number 20 : tmp/frame_20.jpg : 3 persons\n",
      "Processing image number 21 : tmp/frame_21.jpg : 1 persons\n",
      "Processing image number 22 : tmp/frame_22.jpg : 0 persons\n",
      "Processing image number 23 : tmp/frame_23.jpg : 1 persons\n",
      "Processing image number 24 : tmp/frame_24.jpg : 2 persons\n",
      "Processing image number 25 : tmp/frame_25.jpg : 1 persons\n",
      "Processing image number 26 : tmp/frame_26.jpg : 2 persons\n",
      "Processing image number 27 : tmp/frame_27.jpg : 1 persons\n",
      "Processing image number 28 : tmp/frame_28.jpg : 1 persons\n",
      "Processing image number 29 : tmp/frame_29.jpg : 0 persons\n",
      "Processing image number 30 : tmp/frame_30.jpg : 1 persons\n",
      "Processing image number 31 : tmp/frame_31.jpg : 1 persons\n",
      "Processing image number 32 : tmp/frame_32.jpg : 2 persons\n",
      "Processing image number 33 : tmp/frame_33.jpg : 1 persons\n",
      "Processing image number 34 : tmp/frame_34.jpg : 1 persons\n",
      "Processing image number 35 : tmp/frame_35.jpg : 1 persons\n",
      "Processing image number 36 : tmp/frame_36.jpg : 0 persons\n",
      "Processing image number 37 : tmp/frame_37.jpg : 0 persons\n",
      "Processing image number 38 : tmp/frame_38.jpg : 0 persons\n",
      "Processing image number 39 : tmp/frame_39.jpg : 0 persons\n",
      "Processing image number 40 : tmp/frame_40.jpg : 0 persons\n",
      "Processing image number 41 : tmp/frame_41.jpg : 0 persons\n",
      "Processing image number 42 : tmp/frame_42.jpg : 0 persons\n",
      "Processing image number 43 : tmp/frame_43.jpg : 0 persons\n",
      "Processing image number 44 : tmp/frame_44.jpg : 0 persons\n",
      "Processing image number 45 : tmp/frame_45.jpg : 0 persons\n",
      "Processing image number 46 : tmp/frame_46.jpg : 0 persons\n",
      "Processing image number 47 : tmp/frame_47.jpg : 0 persons\n",
      "Processing image number 48 : tmp/frame_48.jpg : 0 persons\n",
      "Processing image number 49 : tmp/frame_49.jpg : 0 persons\n",
      "Processing image number 50 : tmp/frame_50.jpg : 0 persons\n",
      "Processing image number 51 : tmp/frame_51.jpg : 0 persons\n",
      "Processing image number 52 : tmp/frame_52.jpg : 0 persons\n",
      "Processing image number 53 : tmp/frame_53.jpg : 0 persons\n",
      "Processing image number 54 : tmp/frame_54.jpg : 0 persons\n",
      "Processing image number 55 : tmp/frame_55.jpg : 0 persons\n",
      "Processing image number 56 : tmp/frame_56.jpg : 0 persons\n",
      "Processing image number 57 : tmp/frame_57.jpg : 0 persons\n",
      "Processing image number 58 : tmp/frame_58.jpg : 0 persons\n",
      "Processing image number 59 : tmp/frame_59.jpg : 0 persons\n",
      "Processing image number 60 : tmp/frame_60.jpg : 0 persons\n",
      "Processing image number 61 : tmp/frame_61.jpg : 0 persons\n",
      "Processing image number 62 : tmp/frame_62.jpg : 0 persons\n",
      "Processing image number 63 : tmp/frame_63.jpg : 0 persons\n",
      "Processing image number 64 : tmp/frame_64.jpg : 0 persons\n",
      "Processing image number 65 : tmp/frame_65.jpg : 0 persons\n",
      "Processing image number 66 : tmp/frame_66.jpg : 1 persons\n",
      "Processing image number 67 : tmp/frame_67.jpg : 2 persons\n",
      "Processing image number 68 : tmp/frame_68.jpg : 2 persons\n",
      "Processing image number 69 : tmp/frame_69.jpg : 2 persons\n",
      "Processing image number 70 : tmp/frame_70.jpg : 2 persons\n",
      "Processing image number 71 : tmp/frame_71.jpg : 0 persons\n",
      "Processing image number 72 : tmp/frame_72.jpg : 1 persons\n",
      "Processing image number 73 : tmp/frame_73.jpg : 0 persons\n",
      "Processing image number 74 : tmp/frame_74.jpg : 0 persons\n",
      "Processing image number 75 : tmp/frame_75.jpg : 0 persons\n",
      "Processing image number 76 : tmp/frame_76.jpg : 0 persons\n",
      "Processing image number 77 : tmp/frame_77.jpg : 0 persons\n",
      "Processing image number 78 : tmp/frame_78.jpg : 0 persons\n",
      "Processing image number 79 : tmp/frame_79.jpg : 2 persons\n",
      "Processing image number 80 : tmp/frame_80.jpg : 0 persons\n",
      "Processing image number 81 : tmp/frame_81.jpg : 0 persons\n",
      "Processing image number 82 : tmp/frame_82.jpg : 0 persons\n",
      "Processing image number 83 : tmp/frame_83.jpg : 0 persons\n",
      "Processing image number 84 : tmp/frame_84.jpg : 0 persons\n",
      "Processing image number 85 : tmp/frame_85.jpg : 0 persons\n",
      "Processing image number 86 : tmp/frame_86.jpg : 1 persons\n",
      "Processing image number 87 : tmp/frame_87.jpg : 0 persons\n",
      "Processing image number 88 : tmp/frame_88.jpg : 0 persons\n",
      "Processing image number 89 : tmp/frame_89.jpg : 0 persons\n",
      "Processing image number 90 : tmp/frame_90.jpg : 0 persons\n",
      "Processing image number 91 : tmp/frame_91.jpg : 0 persons\n",
      "Processing image number 92 : tmp/frame_92.jpg : 1 persons\n",
      "Processing image number 93 : tmp/frame_93.jpg : 0 persons\n",
      "Processing image number 94 : tmp/frame_94.jpg : 0 persons\n",
      "Processing image number 95 : tmp/frame_95.jpg : 0 persons\n",
      "Processing image number 96 : tmp/frame_96.jpg : 0 persons\n",
      "Processing image number 97 : tmp/frame_97.jpg : 0 persons\n",
      "Processing image number 98 : tmp/frame_98.jpg : 1 persons\n",
      "Processing image number 99 : tmp/frame_99.jpg : 0 persons\n",
      "Processing image number 100 : tmp/frame_100.jpg : 0 persons\n",
      "Processing image number 101 : tmp/frame_101.jpg : 0 persons\n",
      "Processing image number 102 : tmp/frame_102.jpg : 0 persons\n",
      "Processing image number 103 : tmp/frame_103.jpg : 0 persons\n",
      "Processing image number 104 : tmp/frame_104.jpg : 0 persons\n",
      "Processing image number 105 : tmp/frame_105.jpg : 0 persons\n",
      "Processing image number 106 : tmp/frame_106.jpg : 0 persons\n",
      "CPU times: user 478 ms, sys: 126 ms, total: 604 ms\n",
      "Wall time: 47.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "for i, frame_info in enumerate(results):\n",
    "    image = str(frame_info['frame_path'])\n",
    "    try:\n",
    "        with open(image, 'rb') as image_bytes:\n",
    "            predictions = dsobject.detect(image_bytes)\n",
    "            frame_info['predictions'] = predictions\n",
    "            frame_info['persons'] = len([p for p in predictions if p['label']=='person'])\n",
    "            print(f\"Processing image number {i} : {image} : {frame_info['persons']} persons\")\n",
    "    except Exception as exc:\n",
    "        print(exc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write results to json for safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('results.json', 'w') as fp:\n",
    "    json.dump(results, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the person count and visualise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "persons = {p['frame_count']:p['persons'] for p in results}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Person count')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(persons).plot.bar(figsize=(15,5)).set_ylabel('Person count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks approximately correct but appears to be poor at identifying the individuals. We could use more frames to improve our chance of detection as maybe the sampled frames are not the best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Track with sort\n",
    "* https://towardsdatascience.com/detect-and-track-baseball-using-detectron2-and-sort-6dd92a46e6f2\n",
    "* https://github.com/abewley/sort\n",
    "Note I have just placed the contents of the sort repo in the folder `sort`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install -r sort/requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we iterate over the frames, and for each frame pass in the bounding box coordinates, and get back the object tracked ID. \n",
    "\n",
    "* Sort expects a numpy array of detections in the format of `[[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],…]`\n",
    "* Use `np.empty((0, 5))` for frames without detections\n",
    "\n",
    "Write a helper to extract this info from result[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_prediction = [{'confidence': 0.97203445,\n",
    "   'label': 'person',\n",
    "   'y_min': 0,\n",
    "   'x_min': 104,\n",
    "   'y_max': 108,\n",
    "   'x_max': 162},\n",
    "  {'confidence': 0.8538234,\n",
    "   'label': 'person',\n",
    "   'y_min': 2,\n",
    "   'x_min': 185,\n",
    "   'y_max': 84,\n",
    "   'x_max': 235}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'confidence': 0.97203445,\n",
       "  'label': 'person',\n",
       "  'y_min': 0,\n",
       "  'x_min': 104,\n",
       "  'y_max': 108,\n",
       "  'x_max': 162},\n",
       " {'confidence': 0.8538234,\n",
       "  'label': 'person',\n",
       "  'y_min': 2,\n",
       "  'x_min': 185,\n",
       "  'y_max': 84,\n",
       "  'x_max': 235}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[104.        ,   0.        , 162.        , 108.        ,\n",
       "          0.97203445],\n",
       "       [185.        ,   2.        , 235.        ,  84.        ,\n",
       "          0.8538234 ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_detections(prediction : list) -> np.array:\n",
    "    detections = []\n",
    "    people = [p for p in prediction if p['label']=='person']\n",
    "    if len(people) == 0:\n",
    "        return np.empty((0, 5))\n",
    "    for p in people:\n",
    "        detection = np.array([p['x_min'], p['y_min'], p['x_max'], p['y_max'], p['confidence']])\n",
    "        detections.append(detection)\n",
    "    return np.array(detections)\n",
    "\n",
    "get_detections(test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 []\n",
      "1 []\n",
      "2 []\n",
      "3 []\n",
      "4 []\n",
      "5 []\n",
      "6 []\n",
      "7 []\n",
      "8 []\n",
      "9 []\n",
      "10 []\n",
      "11 []\n",
      "12 []\n",
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      "14 []\n",
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      "17 []\n",
      "18 []\n",
      "19 []\n",
      "20 []\n",
      "21 []\n",
      "22 []\n",
      "23 []\n",
      "24 []\n",
      "25 [[183.96008252  44.48918636 253.9329312  165.70161142   2.        ]]\n",
      "26 [[187.51927306  63.42292068 260.85094848 187.80326276   2.        ]]\n",
      "27 [[187.91299682  86.18878162 264.61184814 215.06813559   2.        ]]\n",
      "28 [[184.38561766 105.32894448 261.83471226 226.89267335   2.        ]]\n",
      "29 []\n",
      "30 []\n",
      "31 []\n",
      "32 []\n",
      "33 []\n",
      "34 [[194.44007995 232.20141163 266.79182515 305.72407341   7.        ]]\n",
      "35 [[195.91589067 250.91134354 262.48308501 307.04828682   7.        ]]\n",
      "36 []\n",
      "37 []\n",
      "38 []\n",
      "39 []\n",
      "40 []\n",
      "41 []\n",
      "42 []\n",
      "43 []\n",
      "44 []\n",
      "45 []\n",
      "46 []\n",
      "47 []\n",
      "48 []\n",
      "49 []\n",
      "50 []\n",
      "51 []\n",
      "52 []\n",
      "53 []\n",
      "54 []\n",
      "55 []\n",
      "56 []\n",
      "57 []\n",
      "58 []\n",
      "59 []\n",
      "60 []\n",
      "61 []\n",
      "62 []\n",
      "63 []\n",
      "64 []\n",
      "65 []\n",
      "66 []\n",
      "67 []\n",
      "68 []\n",
      "69 [[222.88807956 194.25703272 310.70742558 287.16672684   8.        ]]\n",
      "70 [[303.4908146  189.9329321  373.6862038  269.61225669   9.        ]\n",
      " [220.52678672 170.09929987 310.26744733 275.23143929   8.        ]]\n",
      "71 []\n",
      "72 []\n",
      "73 []\n",
      "74 []\n",
      "75 []\n",
      "76 []\n",
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      "78 []\n",
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      "90 []\n",
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      "94 []\n",
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      "98 []\n",
      "99 []\n",
      "100 []\n",
      "101 []\n",
      "102 []\n",
      "103 []\n",
      "104 []\n",
      "105 []\n",
      "106 []\n"
     ]
    }
   ],
   "source": [
    "#create instance of SORT. Note that rerunning this cell increments the track id each time\n",
    "mot_tracker = Sort()\n",
    "\n",
    "for i, frame_info in enumerate(results):\n",
    "    image = str(frame_info['frame_path'])\n",
    "    detections = get_detections(frame_info['predictions'])\n",
    "    track_bbs_ids = mot_tracker.update(detections)\n",
    "    print(i, track_bbs_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort is identifying 4 unique people, but we know there are 6, try tweaking parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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