2# Test implementation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy import misc\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from rugby_tracker import rugby_tracker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "IMAGE_FOLDER_PATH = \"/Users/zackakil/Desktop/capture clean/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "image_labels = pd.Series.from_csv('rugby_image_labels.csv')\n",
    "image_labels = image_labels.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = misc.imread(IMAGE_FOLDER_PATH+image_labels.keys()[10], flatten=True)  \n",
    "plt.imshow(test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "996\n",
      "997\n",
      "998\n",
      "999\n"
     ]
    }
   ],
   "source": [
    "predictions = []\n",
    "\n",
    "for i in range(1000):\n",
    "    print(i)\n",
    "    test = misc.imread(IMAGE_FOLDER_PATH+image_labels.keys()[i], flatten=True)  \n",
    "    prediction = rugby_tracker.predict_action_pos(test)\n",
    "    predictions.append(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       " 0.54313319162591034,\n",
       " 0.65487955801524056,\n",
       " 0.69170590927707587]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.plot(predictions[:1000])\n",
    "plt.plot(np.array(image_labels.values)[:1000]/640)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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