S16-App-6-Build-a-Webcam-Motion-Detector.ipynb

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   "metadata": {},
   "source": [
    "# The Python Mega Course: Build 10 Real World Applications\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is a summary of [The Python Mega Course: Build 10 Real World Applciations](https://www.udemy.com/the-python-mega-course), a comprehensive online Python course taught by Ardit Sulce. Each lecture name is clickable and takes you to the video lecture in the course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 16: Application 6: Build a Webcam Motion Detector\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lecture:** [Program Demonstration](https://www.udemy.com/the-python-mega-course/learn/v4/t/lecture/4775504?start=0)\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This video lecture shows the finished version of the website running on a browser."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lecture:** [Detecting Objects on Camera](https://www.udemy.com/the-python-mega-course/learn/v4/t/lecture/4775502?start=0)\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2, time, pandas\n",
    "\n",
    "first_frame = None\n",
    "\n",
    "video=cv2.VideoCapture(0)\n",
    "\n",
    "while True:\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_frame = cv2.threshold(delta_frame, 30, 255, cv2.THRESH_BINARY)[1]\n",
    "    thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\n",
    "\n",
    "    (_,cnts,_) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    for contour in cnts:\n",
    "        if cv2.contourArea(contour) < 10000:\n",
    "            continue\n",
    "        (x, y, w, h) = cv2.boundingRect(contour)\n",
    "        cv2.rectangle(frame, (x, y), (x+w, y+h), (0,255,0), 3)\n",
    "\n",
    "    cv2.imshow(\"Gray Frame\", gray)\n",
    "    cv2.imshow(\"Delta Frame\", delta_frame)\n",
    "    cv2.imshow(\"Threshold Frame\", thresh_frame)\n",
    "    cv2.imshow(\"Color Frame\", frame)\n",
    "\n",
    "    key=cv2.waitKey(1)\n",
    "\n",
    "    if key == ord('q'):\n",
    "        if status == 1:\n",
    "            times.append(datetime.now())\n",
    "        break\n",
    "\n",
    "print(status_list)\n",
    "print(times)\n",
    "\n",
    "for i in range(0,len(times), 2):\n",
    "    df=df.append({\"Start\":times[i], \"End\":times[i+1]}, ignore_index=True)\n",
    "\n",
    "df.to_csv(\"Times.csv\")\n",
    "\n",
    "video.release()\n",
    "cv2.destroyAllWindows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lecture:** [Capturing Motion Time](https://www.udemy.com/the-python-mega-course/learn/v4/t/lecture/4775506?start=0)\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code detects the time that an object entered and exited the frame and stores that time as a row in a CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# motion_detector.py\n",
    "\n",
    "import cv2, time, pandas\n",
    "from datetime import datetime\n",
    "\n",
    "first_frame = None\n",
    "status_list = [None,None]\n",
    "times = []\n",
    "df = pandas.DataFrame(columns=[\"Start\",\"End\"])\n",
    "\n",
    "video=cv2.VideoCapture(0)\n",
    "\n",
    "while True:\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_frame = cv2.threshold(delta_frame, 30, 255, cv2.THRESH_BINARY)[1]\n",
    "    thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\n",
    "\n",
    "    (_,cnts,_) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    for contour in cnts:\n",
    "        if cv2.contourArea(contour) < 10000:\n",
    "            continue\n",
    "        status = 1\n",
    "\n",
    "        (x, y, w, h) = cv2.boundingRect(contour)\n",
    "        reci = cv2.rectangle(frame, (x, y), (x+w, y+h), (0,255,0), 3)\n",
    "    status_list.append(status)\n",
    "\n",
    "    status_list=status_list[-2:]\n",
    "\n",
    "\n",
    "    if status_list[-1] == 1 and status_list[-2] == 0:\n",
    "        times.append(datetime.now())\n",
    "    if status_list[-1] == 0 and status_list[-2] == 1:\n",
    "        times.append(datetime.now())\n",
    "\n",
    "\n",
    "    cv2.imshow(\"Gray Frame\", gray)\n",
    "    cv2.imshow(\"Delta Frame\", delta_frame)\n",
    "    cv2.imshow(\"Threshold Frame\", thresh_frame)\n",
    "    cv2.imshow(\"Color Frame\", frame)\n",
    "\n",
    "    key=cv2.waitKey(1)\n",
    "\n",
    "    if key == ord('q'):\n",
    "        if status == 1:\n",
    "            times.append(datetime.now())\n",
    "        break\n",
    "\n",
    "print(status_list)\n",
    "print(times)\n",
    "\n",
    "for i in range(0,len(times), 2):\n",
    "    df=df.append({\"Start\":times[i], \"End\":times[i+1]}, ignore_index=True)\n",
    "\n",
    "df.to_csv(\"Times.csv\")\n",
    "\n",
    "video.release()\n",
    "cv2.destroyAllWindows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lecture:** [Final Code for Application 6]()\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below is the final code for `motion_detector.py` and for the `plotting.py` which we'll build in the next section. The `plotting.py` file is responsible for plotting the time data. Run `python plotting.py` to execute the program. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Your computer needs a camera for this to work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# motion_detector.py\n",
    "\n",
    "import cv2, time, pandas\n",
    "from datetime import datetime\n",
    "\n",
    "first_frame = None\n",
    "status_list = [None,None]\n",
    "times = []\n",
    "df = pandas.DataFrame(columns=[\"Start\",\"End\"])\n",
    "\n",
    "video=cv2.VideoCapture(0)\n",
    "\n",
    "while True:\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_frame = cv2.threshold(delta_frame, 30, 255, cv2.THRESH_BINARY)[1]\n",
    "    thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\n",
    "\n",
    "    (_,cnts,_) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    for contour in cnts:\n",
    "        if cv2.contourArea(contour) < 10000:\n",
    "            continue\n",
    "        status = 1\n",
    "\n",
    "        (x, y, w, h) = cv2.boundingRect(contour)\n",
    "        reci = cv2.rectangle(frame, (x, y), (x+w, y+h), (0,255,0), 3)\n",
    "    status_list.append(status)\n",
    "\n",
    "    status_list=status_list[-2:]\n",
    "\n",
    "\n",
    "    if status_list[-1] == 1 and status_list[-2] == 0:\n",
    "        times.append(datetime.now())\n",
    "    if status_list[-1] == 0 and status_list[-2] == 1:\n",
    "        times.append(datetime.now())\n",
    "\n",
    "\n",
    "    cv2.imshow(\"Gray Frame\", gray)\n",
    "    cv2.imshow(\"Delta Frame\", delta_frame)\n",
    "    cv2.imshow(\"Threshold Frame\", thresh_frame)\n",
    "    cv2.imshow(\"Color Frame\", frame)\n",
    "\n",
    "    key=cv2.waitKey(1)\n",
    "\n",
    "    if key == ord('q'):\n",
    "        if status == 1:\n",
    "            times.append(datetime.now())\n",
    "        break\n",
    "\n",
    "print(status_list)\n",
    "print(times)\n",
    "\n",
    "for i in range(0,len(times), 2):\n",
    "    df=df.append({\"Start\":times[i], \"End\":times[i+1]}, ignore_index=True)\n",
    "\n",
    "df.to_csv(\"Times.csv\")\n",
    "\n",
    "video.release()\n",
    "cv2.destroyAllWindows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plotting.py\n",
    "\n",
    "from motion_detector import df\n",
    "from bokeh.plotting import figure, show, output_file\n",
    "from bokeh.models import HoverTool, ColumnDataSource\n",
    "\n",
    "df[\"Start_string\"] = df[\"Start\"].dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "df[\"End_string\"] = df[\"End\"].dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "print(df)\n",
    "\n",
    "cds = ColumnDataSource(df)\n",
    "\n",
    "p = figure(x_axis_type='datetime', height=100, width=500, sizing_mode=\"scale_width\", title=\"Motion Graph\")\n",
    "p.yaxis.minor_tick_line_color = None\n",
    "p.ygrid[0].ticker.desired_num_ticks = 1\n",
    "\n",
    "hover = HoverTool(tooltips=[(\"Start\",\"@Start_string\"), (\"End\",\"@End_string\")])\n",
    "p.add_tools(hover)\n",
    "\n",
    "q = p.quad(left=\"Start\", right=\"End\", bottom=0, top=1, color=\"green\", source=cds)\n",
    "\n",
    "output_file(\"Graph1.html\")\n",
    "show(p)"
   ]
  }
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