Motion-Detector/Motion_detector.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2,time\n",
    "import pandas\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assigning Intial Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_frame=None\n",
    "status_list=[None,None]\n",
    "times=[]\n",
    "\n",
    "df=pandas.DataFrame(columns=[\"Start\",\"End\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Starts Video Capturing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "video=cv2.VideoCapture(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining Frames on Every moment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "while True:\n",
    "    \n",
    "    check,frame=video.read()\n",
    "    status=0\n",
    "    gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
    "    gray=cv2.GaussianBlur(gray,(21,21),0)\n",
    "    \n",
    "    if first_frame is None:\n",
    "        first_frame=gray\n",
    "        continue\n",
    "        \n",
    "    delta_frame=cv2.absdiff(first_frame,gray)\n",
    "    thresh_delta=cv2.threshold(delta_frame,30,255,cv2.THRESH_BINARY)[1]\n",
    "    \n",
    "    thresh_delta=cv2.dilate(thresh_delta,None,iterations=0)\n",
    "    (_,cnts,_)=cv2.findContours(thresh_delta.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n",
    "    \n",
    "    for contour in cnts:\n",
    "        if cv2.contourArea(contour)<1000:\n",
    "            continue\n",
    "        status=1\n",
    "        (x,y,w,h)=cv2.boundingRect(contour)\n",
    "        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),3)\n",
    "        \n",
    "    status_list.append(status)\n",
    "    status_list=status_list[-2:]\n",
    "    \n",
    "    if status_list[-1]==1 and status_list[-2]==0:\n",
    "        times.append(datetime.now())\n",
    "        \n",
    "    if status_list[-1]==0 and status_list[-2]==1:\n",
    "        times.append(datetime.now())\n",
    "        \n",
    "    cv2.imshow(\"Frame\",frame)\n",
    "    cv2.imshow(\"Capturing\",gray)\n",
    "    cv2.imshow('Delta',delta_frame)\n",
    "    cv2.imshow(\"Thresh\",thresh_delta)\n",
    "    \n",
    "    \n",
    "    \n",
    "    key=cv2.waitKey(1)\n",
    "    \n",
    "    if key==ord('q'):\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving all the contents to Times.csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range (0,len(times)-1,2):\n",
    "    df=df.append({\"Start\":times[i],\"End\":times[i+1]},ignore_index=True)\n",
    "    \n",
    "df.to_csv(\"Times.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ending Video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function destroyAllWindows>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video.release()\n",
    "cv2.destroyAllWindows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the moment with Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Time in sec')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "file=pd.read_csv(\"Times.csv\")\n",
    "file.plot.bar(color='orange',label=\"train_score\")\n",
    "plt.xlabel('No. of Frames',fontsize=12)\n",
    "plt.ylabel('Time in sec',fontsize=12)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}