Motion-detector-using-Open-CV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import pandas as pd\n",
    "import time\n",
    "from datetime import datetime\n",
    "\n",
    "#Initialise variables\n",
    "stillImage = None\n",
    "motionImage = [ None, None ]\n",
    "time = []\n",
    "\n",
    "# Initializing the DataFrame with start and end time\n",
    "df = pd.DataFrame(columns = [\"start\", \"end\"])\n",
    "\n",
    "# Capturing video\n",
    "video = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "   # Start reading image from video\n",
    "   check, frame = video.read()\n",
    "   motion = 0\n",
    "\n",
    "   # Convert color image to gray_scale image\n",
    "   gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "   gray = cv2.GaussianBlur(gray, (21, 21), 0)\n",
    "   if stillImage is None:\n",
    "      stillImage = gray\n",
    "      continue\n",
    "   # Still Image and current image.\n",
    "   diff_frame = cv2.absdiff(stillImage, gray)\n",
    "\n",
    "   # change the image to white if static background and current frame is greater than 25.\n",
    "   thresh_frame = cv2.threshold(diff_frame, 25, 255, cv2.THRESH_BINARY)[1]\n",
    "   thresh_frame = cv2.dilate(thresh_frame, None, iterations = 2)\n",
    "   # Finding contour and hierarchy from a moving object.\n",
    "   contours,hierachy = cv2.findContours(thresh_frame.copy(),\n",
    "      cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "   for contour in contours:\n",
    "      if cv2.contourArea(contour) < 10000:\n",
    "         continue\n",
    "      motion = 1\n",
    "      (x, y, w, h) = cv2.boundingRect(contour)\n",
    "      cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)\n",
    "   # Append current status of motion\n",
    "   motionImage.append(motion)\n",
    "   motionImage = motionImage[-2:]\n",
    "   # Append Start time of motion\n",
    "   if motionImage[-1] == 1 and motionImage[-2] == 0:\n",
    "      time.append(datetime.now())\n",
    "\n",
    "   # Append End time of motion\n",
    "   if motionImage[-1] == 0 and motionImage[-2] == 1:\n",
    "      time.append(datetime.now())\n",
    "   # Displaying image in gray_scale\n",
    "   cv2.imshow(\"Gray_Frame\", gray)\n",
    "\n",
    "   # Display black and white frame & if the intensity difference is > 25, it turns white\n",
    "   cv2.imshow(\"Threshold Frame\", thresh_frame)\n",
    "   # Display colored frame\n",
    "   cv2.imshow(\"Colored_Frame\", frame)\n",
    "\n",
    "   key = cv2.waitKey(1)\n",
    "   # Press q to stop the process\n",
    "   if key == ord('q'):\n",
    "      if motion == 1:\n",
    "         time.append(datetime.now())\n",
    "      break\n",
    "\n",
    "# Append time of motion\n",
    "for i in range(0, len(time), 2):\n",
    "   df = df.append({\"Start\":time[i], \"End\":time[i + 1]}, ignore_index = True)\n",
    "\n",
    "# Creating a csv file in which time of movements will be saved\n",
    "df.to_csv(\"FrameInMotion_time.csv\")\n",
    "\n",
    "video.release()\n",
    "\n",
    "# close window\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}