visualize_coco.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pycocotools.coco import COCO\n",
    "import numpy as np\n",
    "import skimage.io as io\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_directory = './train/shapes_train2018/'\n",
    "annotation_file = './train/annotations/instances_shape_train2018.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=0.01s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "example_coco = COCO(annotation_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custom COCO categories: \n",
      "square circle triangle\n",
      "\n",
      "Custom COCO supercategories: \n",
      "shape\n"
     ]
    }
   ],
   "source": [
    "categories = example_coco.loadCats(example_coco.getCatIds())\n",
    "category_names = [category['name'] for category in categories]\n",
    "print('Custom COCO categories: \\n{}\\n'.format(' '.join(category_names)))\n",
    "\n",
    "category_names = set([category['supercategory'] for category in categories])\n",
    "print('Custom COCO supercategories: \\n{}'.format(' '.join(category_names)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "category_ids = example_coco.getCatIds(catNms=['triangle'])\n",
    "image_ids = example_coco.getImgIds(catIds=category_ids)\n",
    "image_data = example_coco.loadImgs(image_ids[np.random.randint(0, len(image_ids))])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'coco_url': u'',\n",
       " u'date_captured': u'2018-04-13 03:25:06.134240',\n",
       " u'file_name': u'1276.jpeg',\n",
       " u'flickr_url': u'',\n",
       " u'height': 128,\n",
       " u'id': 415,\n",
       " u'license': 1,\n",
       " u'width': 128}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ACGUrk6fQ7A5ieHDyA06FZ7gsfSGPfuvFdb+tn2L9FMTXU5bTk6z4OnS6/twtD8ZZTryi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\noigdVCgoitJBhYKiKB1UKCiK0uH/AwQHEwH5jJbEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load and display instance annotations\n",
    "image = io.imread(image_directory + image_data['file_name'])\n",
    "plt.imshow(image); plt.axis('off')\n",
    "pylab.rcParams['figure.figsize'] = (8.0, 10.0)\n",
    "annotation_ids = example_coco.getAnnIds(imgIds=image_data['id'], catIds=category_ids, iscrowd=None)\n",
    "annotations = example_coco.loadAnns(annotation_ids)\n",
    "example_coco.showAnns(annotations)"
   ]
  },
  {
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