{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from pycocotools.coco import COCO\n",
    "from pycocotools.cocoeval import COCOeval\n",
    "import numpy as np\n",
    "import skimage.io as io\n",
    "import pylab\n",
    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running demo for *bbox* results.\n"
     ]
    }
   ],
   "source": [
    "annType = ['segm','bbox','keypoints']\n",
    "annType = annType[1]      #specify type here\n",
    "prefix = 'person_keypoints' if annType=='keypoints' else 'instances'\n",
    "print 'Running demo for *%s* results.'%(annType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=8.01s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "#initialize COCO ground truth api\n",
    "dataDir='../'\n",
    "dataType='val2014'\n",
    "annFile = '%s/annotations/%s_%s.json'%(dataDir,prefix,dataType)\n",
    "cocoGt=COCO(annFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading and preparing results...     \n",
      "DONE (t=0.05s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "#initialize COCO detections api\n",
    "resFile='%s/results/%s_%s_fake%s100_results.json'\n",
    "resFile = resFile%(dataDir, prefix, dataType, annType)\n",
    "cocoDt=cocoGt.loadRes(resFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "imgIds=sorted(cocoGt.getImgIds())\n",
    "imgIds=imgIds[0:100]\n",
    "imgId = imgIds[np.random.randint(100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running per image evaluation...      \n",
      "DONE (t=0.46s).\n",
      "Accumulating evaluation results...   \n",
      "DONE (t=0.38s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.697\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.573\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.586\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.519\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.501\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.387\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.594\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.595\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.640\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.566\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.564\n"
     ]
    }
   ],
   "source": [
    "# running evaluation\n",
    "cocoEval = COCOeval(cocoGt,cocoDt,annType)\n",
    "cocoEval.params.imgIds  = imgIds\n",
    "cocoEval.evaluate()\n",
    "cocoEval.accumulate()\n",
    "cocoEval.summarize()"
   ]
  }
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