{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import threading\n",
    "import time, os\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import multiprocessing as mp\n",
    "\n",
    "import nbimporter\n",
    "from utils import FPSTimes\n",
    "from camera import WebcamStream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionTracker(FPSTimes):\n",
    "\n",
    "    default_cfg = {\n",
    "        'background_file': 'background.png',\n",
    "        'arena_x': 512,\n",
    "        'arena_y': 384,\n",
    "        'arena_radius': 350,\n",
    "        'log_file': 'test_position_log.csv'\n",
    "    }\n",
    "    \n",
    "    def __init__(self, status, video_stream, cfg):\n",
    "        super(PositionTracker, self).__init__()\n",
    "        \n",
    "        self.status = status\n",
    "        self.cfg = cfg\n",
    "        self.video_stream = video_stream\n",
    "        self.background = cv2.imread(cfg['background_file'], 1)\n",
    "        self.x, self.y = None, None\n",
    "        self.contour = []\n",
    "        self.stopped = False\n",
    "\n",
    "        self.mask = np.zeros(shape=self.background.shape, dtype=\"uint8\")\n",
    "        cv2.circle(self.mask, (cfg['arena_x'], cfg['arena_y']), cfg['arena_radius'], (255,255,255), -1)\n",
    "\n",
    "        with open(cfg['log_file'], 'w') as f:\n",
    "            f.write(\"time,x,y\\n\")\n",
    "\n",
    "    def start(self):\n",
    "        self._th = threading.Thread(target=self.update, args=())\n",
    "        self._th.start()\n",
    "    \n",
    "    def stop(self):\n",
    "        self.stopped = True\n",
    "        self._th.join()\n",
    "        print('Position tracker stopped')\n",
    "        \n",
    "    def update(self):\n",
    "        while not self.stopped:\n",
    "            frame = self.video_stream.read()\n",
    "            if frame is None:\n",
    "                time.sleep(0.05)\n",
    "                continue\n",
    "                \n",
    "            self.count()  # count FPS\n",
    "            self.detect_position(frame)\n",
    "\n",
    "            if self.status.value == 2:\n",
    "                with open(self.cfg['log_file'], 'a') as f:\n",
    "                    f.write(\",\".join([str(x) for x in (self.frame_times[-1], self.x, self.y)]) + \"\\n\")         \n",
    "       \n",
    "    def detect_position(self, frame):\n",
    "        masked_frame = cv2.bitwise_and(src1=frame, src2=self.mask)\n",
    "\n",
    "        # Substracts background from current frame\n",
    "        subject = cv2.subtract(masked_frame, self.background)\n",
    "\n",
    "        # Converts subject to grey scale\n",
    "        subject_gray = cv2.cvtColor(subject, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        # Applies blur and thresholding to the subject\n",
    "        kernel_size = (25,25)\n",
    "        frame_blur = cv2.GaussianBlur(subject_gray, kernel_size, 0)\n",
    "        _, thresh = cv2.threshold(frame_blur, 40, 255, cv2.THRESH_BINARY)\n",
    "\n",
    "        # Finds contours and selects the contour with the largest area\n",
    "        contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "        if len(contours) == 0:\n",
    "            return\n",
    "        contour = contours[np.argmax(list(map(cv2.contourArea, contours)))]\n",
    "        M = cv2.moments(contour)\n",
    "        if (M['m00'] == 0):\n",
    "            return\n",
    "\n",
    "        self.x = int(M['m10'] / M['m00'])\n",
    "        self.y = int(M['m01'] / M['m00'])\n",
    "        self.contour = contour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing position detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Webcam stream 960.0:720.0 at 20.00 FPS started\n",
      "Camera released\n",
      "Position tracker stopped\n"
     ]
    }
   ],
   "source": [
    "# controller status: 1 - detecting, 2 - detecting + logging\n",
    "status = mp.Value('i', 1)\n",
    "\n",
    "# let's use a webcam stream\n",
    "vs = WebcamStream(WebcamStream.default_cfg)\n",
    "vs.start()  # stream runs in a separate thread\n",
    "\n",
    "# init controller\n",
    "pt_cfg = PositionTracker.default_cfg\n",
    "pt_cfg['background_file'] = os.path.join('..', 'assets', 'background.png')\n",
    "pt = PositionTracker(status, vs, pt_cfg)\n",
    "pt.start()\n",
    "\n",
    "try:\n",
    "    while True:\n",
    "        frame = vs.read()\n",
    "        if frame is not None:\n",
    "            masked_frame = cv2.bitwise_and(src1=frame, src2=pt.mask)\n",
    "            #masked_frame = cv2.subtract(masked_frame, pt.background)\n",
    "            #masked_frame = cv2.cvtColor(masked_frame, cv2.COLOR_BGR2GRAY)\n",
    "            \n",
    "            if pt.x is not None:\n",
    "                color = (127, 255, 0) if status.value == 1 else (0, 0, 255)\n",
    "                cv2.circle(masked_frame, (pt.x, pt.y), 10, color, -1)\n",
    "                cv2.drawContours(masked_frame, [pt.contour], 0, color, 1, cv2.LINE_AA)\n",
    "                \n",
    "            cv2.imshow('Webcam', masked_frame)\n",
    "\n",
    "        k = cv2.waitKey(33)\n",
    "        if k == ord('q'):\n",
    "            break\n",
    "            \n",
    "        if k == ord('s'):\n",
    "            status.value = 2 if status.value == 1 else 1\n",
    "finally:\n",
    "    cv2.destroyAllWindows()\n",
    "    vs.stop(), pt.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}