Quick test of Amazon rekognition output

notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas \n",
    "from random import randrange\n",
    "from IPython.core.display import Image, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATAFILE = 'notebook_data.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(DATAFILE, 'r') as infile:\n",
    "    LABELS = json.loads(infile.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Abies',\n",
       " 'Acanthaceae',\n",
       " 'Accessories',\n",
       " 'Adapter',\n",
       " 'Adorable',\n",
       " 'Adventure',\n",
       " 'Aerial View',\n",
       " 'Affenpinscher',\n",
       " 'Afro Hairstyle',\n",
       " 'Agavaceae',\n",
       " 'Aircraft',\n",
       " 'Aircraft Carrier',\n",
       " 'Airedale',\n",
       " 'Airfield',\n",
       " 'Airmail',\n",
       " 'Airplane',\n",
       " 'Airport',\n",
       " 'Aisle',\n",
       " 'Alcohol',\n",
       " 'Algae']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(LABELS.keys())[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "432"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(LABELS['Document'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_rand_image(key):\n",
    "    return LABELS[key][randrange(0, (len(LABELS[key])))]['image']\n",
    "\n",
    "def display_image(img):    \n",
    "    url = 'https://s3.amazonaws.com/{0}'.format(img)\n",
    "    print(url)\n",
    "    display(Image(url, width=800, unconfined=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://s3.amazonaws.com/mith-lakeland-media/0007.jpg\n"
     ]
    },
    {
     "data": {

notebook_data.json