Tracker-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#import argparse\n",
    "import cv2, csv, glob, os, sys\n",
    "import pickle\n",
    "import scipy.spatial as sps\n",
    "import pdb\n",
    "import time\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%gui qt\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Run norm_videos.py first.\n",
    "\n",
    "COLOR_BOUND = {\n",
    "'blue':((0,50,0), (130,225,136)),\\\n",
    "'red': ((0,100,70), (10,255,255)),\\\n",
    "'yellow': ((13,100,100),(24,255,255))\n",
    "}\n",
    "#'yellow'20:31\n",
    "#red 0:12\n",
    "color_loc = {\n",
    "'red': [],\\\n",
    "'blue': [],\\\n",
    "'yellow': [],\\\n",
    "}\n",
    "\n",
    "color_data = {\n",
    "'yellow_output': [],\\\n",
    "'red_output':[],\\\n",
    "}\n",
    "\n",
    "frameCenter = []\n",
    "setColor = []\n",
    "setColorMin = []\n",
    "setColorMax = []\n",
    "refPt = np.zeros((4))\n",
    "position = []\n",
    "\n",
    "firstFrame = True\n",
    "\n",
    "kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def histNorm(frame,plotting=False):\t\n",
    "\t\"\"\"this function does some shit\n",
    "\tFrame must be cropped (tracking area only)\"\"\"\n",
    "\n",
    "\thist, bins = np.histogram(frame.flatten(),256,[0,256])\n",
    "\t\n",
    "\tcdf = hist.cumsum()\n",
    "\tcdf_normalized = cdf * hist.max()/cdf.max()\n",
    "\t\n",
    "\tif plotting:\n",
    "\t\tplt.plot(cdf_normalized, color = 'b')\n",
    "\t\tplt.hist(frame.flatten(),256,[0,256])\n",
    "\t\tplt.xlim([0,256])\n",
    "\t\tplt.legend(('cdf','histogram'), loc = 'upper left')\n",
    "\t\tplt.show()\n",
    "\t\t\n",
    "\t\t#TODO: delete image with keystroke\n",
    "\t\n",
    "\t#mask and scale histogram\n",
    "\tcdf_m = np.ma.masked_equal(cdf,0)\n",
    "\tcdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())\n",
    "\tcdf = np.ma.filled(cdf_m,0).astype('uint8')\n",
    "\t\n",
    "\tnew_image = cdf[frame]\n",
    "\t\n",
    "\treturn new_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#NO TRACKING PREDICTION\n",
    "class colortracker():#, numcolor=1, colornames='blue', tracers = 'off'):\n",
    "\t\"\"\"Finds object of specified color(s), tracks pixel position and \n",
    "\t\tradius of the largets object in the frame.\n",
    "\n",
    "\tAttributes:\n",
    "\t\tcolornames: user-specified colors to track (red,green,blue)\n",
    "\t\ttracers: draw trails of tracked points on image (may slow tracking)\n",
    "\t\tposition: (x,y) pixel position of center of largest centroid\n",
    "\t\tradius: pixel radius of largest centroid\n",
    "\t\"\"\"\n",
    "\t\n",
    "#\tif numcolor is None:\n",
    "#\t\tnumcolor = raw_input('Specify number of colors to track. ')\n",
    "#\telif len(colornames) != numcolor:\n",
    "#\t\tprint('Please specify the correct number of color names to track')\n",
    "#\t\tcolornames = str(raw_input('Enter % color names: ') %numcolor) \n",
    "\n",
    "\tdef __init__(self, color, camera, flag, draw='on',tracers='off'):\n",
    "\t\t#Thread.__init__(self)\n",
    "\t\t#global color#global imported\n",
    "\t\tself.color = color\n",
    "\t\tself.draw = draw\n",
    "\t\tself.tracers = tracers\n",
    "\t\tself.position = []\n",
    "\t\tself.radius = []\n",
    "\t\tself.hsv_lower = np.uint8(COLOR_BOUND[color][0])\n",
    "\t\tself.hsv_upper = np.uint8(COLOR_BOUND[color][1])\n",
    "\t\tself.found = []\n",
    "\t\tself.camera = camera\n",
    "\t\tself.flag = flag\n",
    "\t\tself.csvname = []\n",
    "\t\tself.loadfilename = filename[:-4] + '_data.p'\n",
    "\n",
    "\t\tself.refPt = []\n",
    "\t\tself.sizePt = []\n",
    "        \n",
    "\t\tself.imported = False\n",
    "\n",
    "\t\tif tracers != 'off':\n",
    "\t\t\tcv2.cvNamedWindow('Tracking video',1)\n",
    "\n",
    "\t\t#Make output file\n",
    "\t\tself.savefilename = str(filename[:-4]) + '.p'\n",
    "\t\tsaveit = open(self.savefilename, 'wb')\n",
    "\t\t\t\t\n",
    "\tdef import_settings(self):\n",
    "\t\t\"\"\"Asks user if image settings should be updated for specific color (determined by Thread). If yes, updates color\n",
    "\t\tcenter, reference region and crops image. If no, looks for save file of setting data then bypasses all setting \n",
    "\t\tupdates. BYPASSING SETTING UPDATES IS NOT RECOMMENDED. YOUR SHIT WILL EXPLODE.\"\"\"\n",
    "\t\tglobal param_data, upperPt, lowerPt, frameCenter, refPt, frame\t\t\n",
    "\t\t\t\t\n",
    "\t\tparam_data = {\n",
    "\t\t'red_pos': [],\\\n",
    "\t\t'red_min': [],\\\n",
    "\t\t'red_max': [],\\\n",
    "\t\t'yellow_pos': [],\\\n",
    "\t\t'yellow_min': [],\\\n",
    "\t\t'yellow_max': [],\\\n",
    "\t\t'lowerPt': [],\\\n",
    "\t\t'upperPt': [],\\\n",
    "\t\t'frameCenter': [],\\\n",
    "\t\t'refPt': [],\\\n",
    "\t\t}\n",
    "\t\t\t\t\t\n",
    "\t\tparam_data = pickle.load(open(self.loadfilename,\"rb\"))\n",
    "\n",
    "\t\t#TODO: only reference param_data, get rid of these variables\n",
    "\t\tupperPt = param_data['upperPt']\n",
    "\t\tlowerPt = param_data['lowerPt']\n",
    "\t\tframeCenter = param_data['frameCenter']\n",
    "\t\trefPt = param_data['refPt']\n",
    "\t\t\n",
    "\t\t#TODO: make this another class?\n",
    "\t\t\n",
    "\t\tself.hsv_lower = np.uint8(COLOR_BOUND[self.color][0])\n",
    "\t\tself.hsv_upper = np.uint8(COLOR_BOUND[self.color][1])\n",
    "\t\t\n",
    "#\t\tself.hsv_lower = np.uint8(param_data[self.color+'_min'])\n",
    "#\t\tself.hsv_upper = np.uint8(param_data[self.color+'_max'])\n",
    "\t\t\n",
    "\t\t#check for error in finding colors, set to canonical values\n",
    "\t\tif self.hsv_lower[0]==self.hsv_upper[0]:\n",
    "\t\t\tself.hsv_lower = np.uint8(COLOR_BOUND[self.color][0])\n",
    "\t\t\tself.hsv_upper = np.uint8(COLOR_BOUND[self.color][1])\n",
    "\t\t\n",
    "\t\tself.imported = True\n",
    "\t\tprint('Settings imported')\n",
    "\t\t#return frame\t\n",
    "\t\t\t\n",
    "\tdef search_region(self,frame, color, regionSize=100):\t#make this a hidden method?\n",
    "\t\t\"\"\"Creates region in which colors will be searched based on last-found position. \n",
    "\t\tOutputs adjusted frame (frame) and top left corner of the search region (topleft)\"\"\"\n",
    "\t\tglobal searchCenter, brightness, contrast\n",
    "\t\ttopleft = []\n",
    "\n",
    "\t\t#resize video and adjust color\n",
    "\t\tframe = frame[upperPt[1]:lowerPt[1],upperPt[0]:lowerPt[0]]\n",
    "\t\t#frame = histNorm(frame)\n",
    "\t\tframe = adjBrtCont(frame,brightness,contrast)\n",
    "\t\t#import pdb; pdb.set_trace()\n",
    "        \n",
    "\t\tsearchCenter = self.position[-1]\n",
    "\t\t#print searchCenter\n",
    "        \n",
    "\t\tif isinstance(searchCenter, int):\n",
    "\t\t\tsearchCenter = list(self.position)\n",
    "\t\telif isinstance(searchCenter, tuple):\n",
    "\t\t\tsearchCenter = list(self.position[-1])\n",
    "\t\telif not isinstance(searchCenter[0],int):            \n",
    "\t\t\t#make sure values are int\n",
    "\t\t\tsearchCenter = [int(i) for i in self.position[-1]]\n",
    "            \n",
    "\t\tif searchCenter == (0,0) or searchCenter==(np.nan, np.nan) or not searchCenter:\n",
    "\t\t\t#If the point wasn't found in the previous frame, search the whole frame\n",
    "\t\t\ttopleft = (0,0)\n",
    "            \n",
    "\t\telse:\n",
    "\t\t\ttopleft = (searchCenter[0] - regionSize/2, searchCenter[1]-regionSize/2)\t\t\t\t\t\n",
    "\t\t\tframe = frame[searchCenter[1]-regionSize/2:searchCenter[1]+regionSize/2, searchCenter[0]-regionSize/2:searchCenter[0]+regionSize/2]\n",
    "\n",
    "\t\treturn frame,topleft\n",
    "\t\n",
    "\tdef find_color(self,frame):\n",
    "\t\tglobal hsv, color_data, upperPt, lowerPt\t\t\n",
    "\t\t#TODO:Somewhere outside of this function, determine if from camera or file\n",
    "\t\t#firstFrame = False\t\n",
    "        \n",
    "\t\tif not self.imported: #camera.get(cv2.CAP_PROP_POS_FRAMES) == 1:\n",
    "\t\t\t#print('importing settings')\n",
    "\t\t\tself.import_settings()\n",
    "\t\t\tself.found = list(param_data[self.color+'_pos'])\n",
    "\t\t\tsearchCenter = list(param_data[self.color+'_pos'])\n",
    "\t\t\tfirstFrame = True\n",
    "\t\t\tother = (np.nan,np.nan)\n",
    "\t\t\tself.imported = True\n",
    "\n",
    "\t\t\tif self.color == 'yellow':\n",
    "\t\t\t\tself.position = list(param_data['yellow_pos'])\n",
    "\t\t\telse: #self.color == 'red':\n",
    "\t\t\t\tself.position = list(param_data['red_pos'])\n",
    "\t\t\t\t\n",
    "\t\t\tif not self.position:\n",
    "\t\t\t\tprint('No position data!')\n",
    "\n",
    "\t\t#define search region\n",
    "\t\tframe,cropRef = self.search_region(frame,self.color)\n",
    "\t\t\n",
    "\t\thsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n",
    "\t\n",
    "\t\t#filter image for specific color\n",
    "\t\tmask = cv2.inRange(hsv, self.hsv_lower, self.hsv_upper)\n",
    "\t\tmask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)    #this function erodes and dilates based on kernel\n",
    "\n",
    "\t\t#import pdb; pdb.set_trace()\t\t\n",
    "\t\t\n",
    "\t\t#for i in range(400):\n",
    "\t\t#camera.set(cv2.CAP_PROP_POS_FRAMES,400)\n",
    "\t\t#(grabbed, frame)=camera.read()\n",
    "        \n",
    "\t\t#find contours in mask\n",
    "\t\tcontours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]\n",
    "        \n",
    "\t\tif len(contours) > 0:\n",
    "            #Find center of largest contour\n",
    "\t\t\tfiltered = max(contours, key=cv2.contourArea)\n",
    "\n",
    "\t\t\t((x, y), radius) = cv2.minEnclosingCircle(filtered)\n",
    "\t\t\tM = cv2.moments(filtered)\n",
    "\t\t\tcenter = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n",
    "\n",
    "\t\t\tif radius > 3: \n",
    "\t\t\t# draw the circle and centroid on the frame,\n",
    "\t\t\t# then update the list of tracked points\n",
    "\t\t\t\tif self.color =='yellow':\n",
    "\t\t\t\t\tcv2.circle(frame, (int(x), int(y)), int(radius),\n",
    "\t\t\t\t\t\t(0, 255,255), 2)\n",
    "\t\t\t\telse:\n",
    "\t\t\t\t\tcv2.circle(frame, (int(x), int(y)), int(radius),\n",
    "\t\t\t\t\t\t(0,0,255),2)\n",
    "\t\t\t\tself.position.append([x+cropRef[0],y+cropRef[1]])\n",
    "\t\t\t\tself.found = self.position[-1]\n",
    "\t\t\t\tself.radius.append(radius)\n",
    "\t\t\telse:\n",
    "\t\t\t\tprint('Object too small')\n",
    "\t\t\t\tself.position.append([np.nan, np.nan])\n",
    "\t\t\t\tself.radius.append(np.nan)\n",
    "\t\telse:\n",
    "\t\t\tprint('{} object not found'.format(self.color))\n",
    "\t\t\tx = y = 0 #np.nan\n",
    "\t\t\tradius = 0# np.nan\t\t\t\t\t\t\t\t\n",
    "\t\t\tself.position.append([0,0])#(np.nan, np.nan))\n",
    "\t\t\tself.radius.append(0) #previously np.nan\n",
    "\n",
    "\t\t#Dump data to pickle \n",
    "\t\t#TODO: this is not fast, augment for live tracking ish\n",
    "\t\tcolor_data[self.color+'_output'].append((self.position[-1], self.radius[-1]))\n",
    "\t\tpickle.dump(color_data,open(self.savefilename,'wb'))\n",
    "\n",
    "\t\tcv2.imshow(\"tracking...\",frame)\n",
    "\t\tcv2.imshow(\"mask\",mask)\n",
    "\t\tif (cv2.waitKey(10) >= 0):\n",
    "\t\t#alternate:\n",
    "#\t\tif cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "\t\t\tcamera.release()\n",
    "\t\t\tcv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#os.chdir('/Users/sazamore/BehaviorVids/')\n",
    "ID = \"90_170201_5rpm.mp4\"#str(raw_input('Enter animal ID number: ')) + '_28nov16_1rpm.mp4'\n",
    "filename = ID\n",
    "camera = cv2.VideoCapture(filename)\n",
    "\n",
    "cv2.namedWindow(\"tracking\", cv2.WINDOW_AUTOSIZE)\n",
    "red = colortracker(\"red\",camera,1)\n",
    "yellow = colortracker(\"yellow\",camera,1)\n",
    "\n",
    "camera.set(cv2.CAP_PROP_POS_FRAMES, 300)\n",
    "g,frame = camera.read()\n",
    "if g:\n",
    "    scale = 1300./frame.shape[1]\n",
    "    frame = cv2.resize(frame,None,fx=scale,fy=scale,interpolation = cv2.INTER_CUBIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "upperPt = param_data['upperPt']\n",
    "lowerPt = param_data['lowerPt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#grabbed,frame = camera.read()\n",
    "#yellow.import_settings()\n",
    "#frame = cv2.resize(frame,None,fx=scale,fy=scale,interpolation = cv2.INTER_CUBIC)\n",
    "#frame = frame[upperPt[1]:lowerPt[1],upperPt[0]:lowerPt[0]]\n",
    "\n",
    "#cv2.imshow('Color input image', frame)\n",
    "#cv2.imshow('Histogram equalized', img_output)\n",
    "global brightness, contrast\n",
    "brightness = 1.5 #1.65\n",
    "contrast = -2. #-56.\n",
    "\n",
    "def adjBrtCont(img,brightness=2.,contrast=-50.):\n",
    "    mod_img = cv2.add(img,np.array([contrast]))\n",
    "    mod_img = cv2.multiply(mod_img,np.array([brightness]))\n",
    "    return mod_img\n",
    "\n",
    "def drawMatchColor(img):\n",
    "    \"\"\"Draws circles on image for color matching. Color values are BGR. To make conversions, \n",
    "    use http://http://colorizer.org/. Remember to update the image (run this function)\n",
    "    after every whole-image modification, or color values will also change.\"\"\"\n",
    "    x1,y1 = param_data['red_pos']\n",
    "    cv2.circle(img, (int(x1)+10, int(y1)-10), int(5),(21.32, 36.47,203),-2)#(2.5, 206.5, 204), 2)\n",
    "    \n",
    "    x2,y2 = param_data['yellow_pos']\n",
    "    cv2.circle(img, (int(x2)+10, int(y2)-10), int(5),(0, 183, 255),-2)#(21.5, 255, 127.5), 2)\n",
    "\n",
    "def img_adj(frame):\n",
    "    \"\"\"On-screen live adjustment of brightness and contrast.\"\"\"\n",
    "    brightness = 1.5\n",
    "    contrast = -2.\n",
    "\n",
    "    finished = False\n",
    "    mod_img = frame[:]\n",
    "    prime_img = frame[:]\n",
    "    #prime_img = cv2.add(prime_img,np.array([contrast])) \n",
    "    #prime_img = cv2.multiply(prime_img,np.array([brightness]))\n",
    "        \n",
    "    cv2.imshow('mod',mod_img)\n",
    "    \n",
    "    while not finished:\n",
    "        mod_img = adjBrtCont(prime_img,brightness, contrast)\n",
    "        #mod_img = cv2.add(prime_img,np.array([contrast]))\n",
    "        #mod_img = cv2.multiply(mod_img,np.array([brightness]))\n",
    "        drawMatchColor(mod_img)\n",
    "        cv2.imshow('mod',mod_img)\n",
    "\n",
    "        k = cv2.waitKey(0)\n",
    "        if k == ord('w'): #up arrow key increases contrast\n",
    "            contrast -= 2.\n",
    "            mod_img = cv2.add(prime_img,np.array([contrast])) \n",
    "            \n",
    "        elif k == ord('s'): #down arrow key decreases contrast\n",
    "            contrast += 2.\n",
    "            mod_img = cv2.add(prime_img,np.array([contrast])) \n",
    "            #cv2.imshow('mod',mod_img)\n",
    "            #k = cv2.waitKey()\n",
    "            \n",
    "        elif k == ord('a'): #left arrow key decreases brightness\n",
    "            brightness -= 0.05\n",
    "            mod_img = cv2.multiply(prime_img,np.array([brightness]))\n",
    "            #cv2.imshow('mod',mod_img)\n",
    "            #k = cv2.waitKey()\n",
    "            \n",
    "        elif k == ord('d'): #right arrow key increases brightness\n",
    "            brightness += 0.05\n",
    "            mod_img = cv2.divide(prime_img,np.array([brightness]))\n",
    "            #cv2.imshow('mod',mod_img)\n",
    "            \n",
    "            #k = cv2.waitKey()\n",
    "        elif k == ord('o'): #show original\n",
    "            cv2.imshow('original',frame)\n",
    "            #k = cv2.waitKey()\n",
    "        elif k == ord('x'): #d exits adjustment\n",
    "            #ex = input('Are you sure? (Y/n) ')\n",
    "            #if ex.lower == 'y':\n",
    "                #continue\n",
    "            #else:\n",
    "            finished = True\n",
    "            #x,y = param_data['red_pos']\n",
    "            #red = \n",
    "            cv2.destroyAllWindows()\n",
    "                #TODO: get color values at color loc\n",
    "\n",
    "    return brightness, contrast\n",
    "\n",
    "def update_colors(frame, brightness,contrast):\n",
    "    \"\"\"Updates upper and lower HSV values for masking\"\"\"\n",
    "    \n",
    "    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n",
    "    x1,y1 = param_data['red_pos']\n",
    "    red = hsv[y1-2:y1+2,x1-2:x1+2,:]\n",
    "    red = np.uint8(red)\n",
    "    redMin = (np.min(red[:,:,0]),np.min(red[:,:,1]),np.min(red[:,:,2]))\t \t#not sure this will gake min of each column\n",
    "    redMax = (np.max(red[:,:,0]),np.max(red[:,:,1]),np.max(red[ :,:,2]))\n",
    "    \n",
    "    x2,y2 = param_data['yellow_pos']\n",
    "    yellow = hsv[y2-2:y2+2,x2-2:x2+2,:]\n",
    "    yellow = np.uint8(yellow)\n",
    "    yellowMin = (np.min(yellow[:,:,0]),np.min(yellow[:,:,1]),np.min(yellow[:,:,2]))\t \t#not sure this will gake min of each column\n",
    "    yellowMax = (np.max(yellow[:,:,0]),np.max(yellow[:,:,1]),np.max(yellow[ :,:,2]))\n",
    "    \n",
    "    return np.uint8(redMin), np.uint8(redMax), np.uint8(yellowMin), np.uint8(yellowMax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Settings imported\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'diameter': [],\n",
       " 'frameCenter': (329, 340),\n",
       " 'lowerPt': (990, 699),\n",
       " 'red_max': (10, 255, 123),\n",
       " 'red_min': (1, 213, 4),\n",
       " 'red_pos': (382, 295),\n",
       " 'refPt': [(44, 9), (124, 89)],\n",
       " 'upperPt': (309, 24),\n",
       " 'yellow_max': (16, 252, 240),\n",
       " 'yellow_min': (2, 246, 83),\n",
       " 'yellow_pos': (386, 305)}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get param_data\n",
    "yellow = colortracker(\"yellow\",camera,1)\n",
    "yellow.import_settings()\n",
    "param_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "camera.set(cv2.CAP_PROP_POS_FRAMES, 556)\n",
    "grabbed,frame = camera.read()\n",
    "\n",
    "frame = cv2.resize(frame,None,fx=scale,fy=scale,interpolation = cv2.INTER_CUBIC)\n",
    "frame = frame[upperPt[1]:lowerPt[1],upperPt[0]:lowerPt[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#bright,contr=img_adj(frame)\n",
    "mod_img = adjBrtCont(frame,bright,contr)\n",
    "cv2.imshow('mod',mod_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "brightness = bright; contrast = contr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mask_check(mod_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#QUICK CHECK\n",
    "\n",
    "#mod_img = cv2.add(frame,np.array([contrast]))\n",
    "#mod_img = cv2.multiply(mod_img,np.array([brightness]))\n",
    "\n",
    "def mask_check(mod_img):\n",
    "    \"\"\"Filters for yellow and red colors in augmented image (\"mod_img\"). Comparison to original (\"frame\") can be \n",
    "    made, but modifications to script are necessary. Displays augmented image with locations of \"found\" colors, \n",
    "    and images of masks for yellow and red colors. If the colors are well defined, there should be only small circle\n",
    "    for red and yellow in the mask, and appropriately sized circles around the found colors on the input image \n",
    "    (mod_img).\"\"\"\n",
    "    hsv_mod = cv2.cvtColor(mod_img, cv2.COLOR_BGR2HSV)\n",
    "    #hsv_raw = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n",
    "\n",
    "    #'red': ((0,100,70), (10,255,255)),\\\n",
    "    #'yellow': ((12,100,100),(31,255,255))\n",
    "\n",
    "    #check updated color masks\n",
    "    red_mask = cv2.inRange(hsv_mod, (0,100,70),(9,255,255))#red_min, red_max)\n",
    "    red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_OPEN, kernel) \n",
    "\n",
    "    yellow_mask = cv2.inRange(hsv_mod, (12,100,100),(26,255,255))#yellow_min, yellow_max)\n",
    "    yellow_mask = cv2.morphologyEx(yellow_mask, cv2.MORPH_OPEN, kernel) \n",
    "\n",
    "    contours = cv2.findContours(red_mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]\n",
    "    #pdb.set_trace()\n",
    "    if contours:\n",
    "        filtered = max(contours, key=cv2.contourArea)\n",
    "        ((x, y), radius) = cv2.minEnclosingCircle(filtered)\n",
    "        M = cv2.moments(filtered)\n",
    "        center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n",
    "        cv2.circle(mod_img, (int(x), int(y)), int(radius),\n",
    "                                        (0, 0, 255), 2)\n",
    "    else:\n",
    "        print('No yellow found')\n",
    "\n",
    "    contours = cv2.findContours(yellow_mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]\n",
    "    filtered = max(contours, key=cv2.contourArea)\n",
    "    ((x, y), radius) = cv2.minEnclosingCircle(filtered)\n",
    "    M = cv2.moments(filtered)\n",
    "    center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n",
    "    cv2.circle(mod_img, (int(x), int(y)), int(radius),\n",
    "                                        (0, 255, 255), 2)\n",
    "\n",
    "    cv2.imshow('image',mod_img)\n",
    "    cv2.imshow('red mask',red_mask)\n",
    "    cv2.imshow('yellow mask',yellow_mask)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-189-a890181d1e08>:2: SyntaxWarning: name 'filename' is assigned to before global declaration\n",
      "  global filename\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Settings imported\n",
      "Settings imported\n",
      "Object too small\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "cannot convert float NaN to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-189-a890181d1e08>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m                 \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minterpolation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mINTER_CUBIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m                 \u001b[0myellow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m                 \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m                 \u001b[0mred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-181-437d36124823>\u001b[0m in \u001b[0;36mfind_color\u001b[0;34m(self, frame)\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m                 \u001b[0;31m#define search region\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m                 \u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcropRef\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch_region\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m                 \u001b[0mhsv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOLOR_BGR2HSV\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-181-437d36124823>\u001b[0m in \u001b[0;36msearch_region\u001b[0;34m(self, frame, color, regionSize)\u001b[0m\n\u001b[1;32m    110\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msearchCenter\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m                         \u001b[0;31m#make sure values are int\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m                         \u001b[0msearchCenter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mposition\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0msearchCenter\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0msearchCenter\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msearchCenter\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
     ]
    }
   ],
   "source": [
    "for filename in glob.glob(ID):\t\n",
    "\tglobal filename\n",
    "\tcamera = cv2.VideoCapture(filename)\n",
    "\n",
    "\t#TODO:figure out live camera capture business. NEEDED?\n",
    "\t#TODO: toggle tracking \"preview\" (showing the tracking as it's happening)\n",
    "\tcv2.namedWindow(\"tracking\", cv2.WINDOW_AUTOSIZE)\n",
    "\tcamera.set(cv2.CAP_PROP_POS_FRAMES, 400)\n",
    "\n",
    "\tred = colortracker(\"red\",camera,1)\n",
    "\tyellow = colortracker(\"yellow\",camera,1)\n",
    "\n",
    "\twhile True:\n",
    "\t\t#grab and scale frame\n",
    "\t\t(grabbed, frame) = camera.read()\t#check args for filename\n",
    "\t\t#print('Frame {}'.format(camera.get(cv2.CAP_PROP_POS_FRAMES)))\n",
    "\t\t#import pdb; pdb.set_trace()\n",
    "\t\tscale = 1300./frame.shape[1]\n",
    "\t\tframe = cv2.resize(frame,None,fx=scale,fy=scale,interpolation = cv2.INTER_CUBIC)\n",
    "\n",
    "\t\tyellow.find_color(frame)\n",
    "\t\ttime.sleep(.2)\n",
    "\t\tred.find_color(frame)\n",
    "\t\t\t#blue.find_color(frame)\n",
    "#\t\t\tif cv2.waitKey(10) >= 0:\n",
    "#\t\t\t\tcamera.release()\n",
    "#\t\t\t\tcv2.destroyAllWindows()\n",
    "#\t\t\t\tprint('Quitting tracker..')\n",
    "#\t\t\t\tsys.exit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "620.0"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "camera.get(cv2.CAP_PROP_POS_FRAMES)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(x_y,y_y,'yo',x_r,y_r,'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "red.position[0] = [red.position[0],red.position[1]]\n",
    "del red.position[1]\n",
    "yellow.position[0] = [yellow.position[0],yellow.position[1]]\n",
    "del yellow.position[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_r = []\n",
    "y_r = []\n",
    "for row in red.position:\n",
    "    x_r.append(row[0])\n",
    "    y_r.append(row[1])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def recenter(x,y):\n",
    "    \"\"\"This function adjusts the x,y outputs of all tracked data and moves the origine to the center determined\n",
    "    by get_center()\"\"\"\n",
    "    new_x = np.zeros(np.size(x))\n",
    "    new_y = np.zeros(np.size(y))\n",
    "    #default origin is the upper left corner of an image.\n",
    "    if frameCenter == []:\n",
    "        print ('Center not found')\n",
    "        cv2.imshow('Click on center',frame)\n",
    "        cv2.setMouseCallback('Click on center',self.get_center)\n",
    "        cv2.waitKey(5000)\n",
    "    for i in range(len(x)):\n",
    "        new_x[i] = x[i] - param_data['frameCenter'][0] \t#x_center,y_center are global\n",
    "        new_y[i] = param_data['frameCenter'][1] - y[i]\n",
    "    return new_x,new_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_r, y_r = recenter(x_r,y_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}