examples/quick-draw-loader.ipynb

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  "metadata": {
    "colab": {
      "name": "quick_draw_loader.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "3MA2k7mUCHz0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# A loader for the Quick, Draw! dataset\n",
        "\n",
        "Shows how to read the original files (as a sequence of brush strokes), then converts those strokes to images. Use this loader to build a fun dataset for image classification (say, with a CNN), or modify it to write data in a format suitablable for an RNN."
      ]
    },
    {
      "metadata": {
        "id": "h3jfQ3KJOnAb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import functools, itertools, json, os, re, textwrap\n",
        "import matplotlib.cm as cm\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "from PIL import Image, ImageDraw\n",
        "from six.moves.urllib import request\n",
        "from xml.dom import minidom"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "B3e-XusiPh_5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Get the data"
      ]
    },
    {
      "metadata": {
        "id": "XeAYBs7PPd7Y",
        "colab_type": "code",
        "outputId": "107fff4f-273d-4e53-a9fb-e74a50e3d820",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "cell_type": "code",
      "source": [
        "# Retrieve list of classes.\n",
        "def list_bucket(bucket, regexp='.*'):\n",
        "    \"\"\"Returns a (filtered) list of Keys in specified GCE bucket.\"\"\"\n",
        "    keys = []\n",
        "    fh = request.urlopen('https://storage.googleapis.com/%s' % bucket)\n",
        "    content = minidom.parseString(fh.read())\n",
        "    for e in content.getElementsByTagName('Contents'):\n",
        "        key = e.getElementsByTagName('Key')[0].firstChild.data\n",
        "        if re.match(regexp, key):\n",
        "            keys.append(key)\n",
        "    return keys\n",
        "\n",
        "all_ndjsons = list_bucket('quickdraw_dataset', '.*ndjson$')\n",
        "print('available: (%d)' % len(all_ndjsons))\n",
        "print('\\n'.join(textwrap.wrap(\n",
        "    ' '.join([key.split('/')[-1].split('.')[0] for key in all_ndjsons]),\n",
        "    width=100)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "available: (309)\n",
            "The Eiffel Tower The Great Wall of China The Mona Lisa aircraft carrier airplane alarm clock\n",
            "ambulance angel animal migration ant anvil apple arm asparagus axe backpack banana bandage barn\n",
            "baseball bat baseball basket basketball bat bathtub beach bear beard bed bee belt bench bicycle\n",
            "binoculars bird birthday cake blackberry blueberry book boomerang bottlecap bowtie bracelet brain\n",
            "bread bridge broccoli broom bucket bulldozer bus bush butterfly cactus cake calculator calendar\n",
            "camel camera camouflage campfire candle cannon canoe car carrot castle cat ceiling fan cell phone\n",
            "cello chair chandelier church circle clarinet clock cloud coffee cup compass computer cookie cooler\n",
            "couch cow crab crayon crocodile crown cruise ship cup diamond dishwasher diving board dog dolphin\n",
            "donut door dragon dresser drill drums duck dumbbell ear elbow elephant envelope eraser eye\n",
            "eyeglasses face fan feather fence finger fire hydrant fireplace firetruck fish flamingo flashlight\n",
            "flip flops floor lamp flower flying saucer foot fork frog frying pan garden hose garden giraffe\n",
            "goatee golf club grapes grass guitar hamburger hammer hand harp hat headphones hedgehog helicopter\n",
            "helmet hexagon hockey puck hockey stick horse hospital hot air balloon hot dog hot tub hourglass\n",
            "house plant house hurricane ice cream jacket jail kangaroo key keyboard knee knife ladder lantern\n",
            "laptop leaf leg light bulb lighter lighthouse lightning line lion lipstick lobster lollipop mailbox\n",
            "map marker matches megaphone mermaid microphone microwave monkey moon mosquito motorbike mountain\n",
            "mouse moustache mouth mug mushroom nail necklace nose ocean octagon octopus onion oven owl paint can\n",
            "paintbrush palm tree panda pants paper clip parachute parrot passport peanut pear peas pencil\n",
            "penguin piano pickup truck picture frame pig pillow pineapple pizza pliers police car pond pool\n",
            "popsicle postcard potato power outlet purse rabbit raccoon radio rain rainbow rake remote control\n",
            "rhinoceros rifle river roller coaster rollerskates sailboat sandwich saw saxophone school bus\n",
            "scissors scorpion screwdriver sea turtle see saw shark sheep shoe shorts shovel sink skateboard\n",
            "skull skyscraper sleeping bag smiley face snail snake snorkel snowflake snowman soccer ball sock\n",
            "speedboat spider spoon spreadsheet square squiggle squirrel stairs star steak stereo stethoscope\n",
            "stitches stop sign stove strawberry streetlight string bean submarine suitcase sun swan sweater\n",
            "swing set sword syringe t-shirt table teapot teddy-bear telephone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "imd5ykBpPkxx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Store all data locally in this directory.\n",
        "data_path = 'data'\n",
        "if not os.path.exists(data_path): \n",
        "  os.mkdir(data_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jFdiHzYNoU5V",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Mini group of two animals.\n",
        "pets = ['cat', 'dog']\n",
        "\n",
        "# Somewhat larger group of zoo animals.\n",
        "zoo = ['elephant', 'giraffe', 'kangaroo', 'lion', 'monkey', 'panda',\n",
        "       'penguin', 'rhinoceros', 'tiger', 'zebra']\n",
        "\n",
        "# Even larger group of all animals.\n",
        "animals = ['bat', 'bird', 'butterfly', 'camel', 'cat', 'cow', 'crab',\n",
        "           'crocodile', 'dog', 'dolphin', 'duck', 'elephant', 'fish',\n",
        "           'frog', 'giraffe', 'hedgehog', 'horse', 'kangaroo', 'lion',\n",
        "           'lobster', 'monkey', 'mosquito', 'mouse', 'octopus', 'owl',\n",
        "           'panda', 'parrot', 'penguin', 'pig', 'rabbit', 'raccoon',\n",
        "           'rhinoceros', 'scorpion', 'sea turtle', 'shark', 'sheep',\n",
        "           'snail', 'spider', 'squirrel', 'teddy-bear', 'tiger',\n",
        "           'whale', 'zebra']\n",
        "\n",
        "# Create your own group -- the more classes you include the more challenging\n",
        "# the classification task will be...\n",
        "\n",
        "# Choose one of the above groups.\n",
        "# Note: This will result in ~100MB of download per class.\n",
        "classes, classes_name = zoo, 'zoo'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lQHOtcIgQM3S",
        "colab_type": "code",
        "outputId": "77d62f9f-80e9-4339-d290-3605d71abd0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "cell_type": "code",
      "source": [
        "# Download the chosen group.\n",
        "def valid_ndjson(filename):\n",
        "    \"\"\"Checks presence + completeness of .ndjson file.\"\"\"\n",
        "    try:\n",
        "        json.loads(open(filename).readlines()[-1])\n",
        "        return True\n",
        "    except (ValueError, IOError):\n",
        "        return False\n",
        "\n",
        "def retrieve(bucket, key, filename):\n",
        "    \"\"\"Returns a file specified by its Key from a GCE bucket.\"\"\"\n",
        "    url = 'https://storage.googleapis.com/%s/%s' % (bucket, key)\n",
        "    if not os.path.isfile(filename):\n",
        "        request.urlretrieve(url=url, filename=filename)\n",
        "    while not valid_ndjson(filename):\n",
        "        print('*** Corrupted download (%.2f MB), retrying...' % (os.path.getsize(filename) / 2.**20))\n",
        "        request.urlretrieve(url=url, filename=filename)\n",
        "\n",
        "if not os.path.exists(data_path):\n",
        "    os.mkdir(data_path)\n",
        "\n",
        "print('\\n%d classes:' % len(classes))\n",
        "\n",
        "for name in classes:\n",
        "    print(name, end=' ')\n",
        "    dst = '%s/%s.ndjson' % (data_path, name)\n",
        "    retrieve('quickdraw_dataset', 'full/simplified/%s.ndjson' % name, dst)\n",
        "    print('%.2f MB' % (os.path.getsize(dst) / 2.**20))\n",
        "\n",
        "print('\\nDONE :)')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "10 classes:\n",
            "elephant 70.86 MB\n",
            "giraffe 58.27 MB\n",
            "kangaroo 96.06 MB\n",
            "lion 90.47 MB\n",
            "monkey 91.05 MB\n",
            "panda 97.04 MB\n",
            "penguin 137.90 MB\n",
            "rhinoceros 92.20 MB\n",
            "tiger 86.09 MB\n",
            "zebra 92.10 MB\n",
            "\n",
            "DONE :)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3PBtoAMsQ558",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Inspect the data"
      ]
    },
    {
      "metadata": {
        "id": "-HHhNxZ3QNSW",
        "colab_type": "code",
        "outputId": "ed37ad6f-3a98-45a9-945d-fbd3780a2757",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "# Let's check out the downloaded files...\n",
        "!ls -lh $data_path"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 913M\n",
            "-rw-r--r-- 1 root root  71M Feb  2 19:21 elephant.ndjson\n",
            "-rw-r--r-- 1 root root  59M Feb  2 19:21 giraffe.ndjson\n",
            "-rw-r--r-- 1 root root  97M Feb  2 19:21 kangaroo.ndjson\n",
            "-rw-r--r-- 1 root root  91M Feb  2 19:21 lion.ndjson\n",
            "-rw-r--r-- 1 root root  92M Feb  2 19:21 monkey.ndjson\n",
            "-rw-r--r-- 1 root root  98M Feb  2 19:21 panda.ndjson\n",
            "-rw-r--r-- 1 root root 138M Feb  2 19:21 penguin.ndjson\n",
            "-rw-r--r-- 1 root root  93M Feb  2 19:21 rhinoceros.ndjson\n",
            "-rw-r--r-- 1 root root  87M Feb  2 19:21 tiger.ndjson\n",
            "-rw-r--r-- 1 root root  93M Feb  2 19:21 zebra.ndjson\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lL73Zh1hRDeo",
        "colab_type": "code",
        "outputId": "635313bc-b4c9-48a4-d642-ed327782136a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "cell_type": "code",
      "source": [
        "# What is the NDJSON file format?\n",
        "# Seems to be one JSON dictionary per line...\n",
        "path = sorted(tf.gfile.Glob(os.path.join(data_path, '*.ndjson')))[1]\n",
        "print(open(path).read()[:1000] + '...')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\"word\":\"giraffe\",\"countrycode\":\"BR\",\"timestamp\":\"2017-01-26 17:23:19.3785 UTC\",\"recognized\":true,\"key_id\":\"5807561943023616\",\"drawing\":[[[25,9,0,1,13,35],[32,46,59,65,67,47]],[[64,47,36,25,25,41,53,66,69],[14,15,21,37,42,56,59,41,24]],[[20,39],[8,23]],[[51,52],[0,11]],[[56,46,46,55],[55,91,127,183]],[[74,70,73],[58,95,172]],[[53,79,100,126,149,150,138,131,96,47,39,40],[187,178,174,173,178,190,203,207,215,203,190,182]],[[54,36,22],[204,228,253]],[[73,49],[220,255]],[[118,119,132],[205,223,252]],[[142,143,159],[195,206,238]],[[143,161,165,169,166],[181,168,169,200,214]]]}\n",
            "{\"word\":\"giraffe\",\"countrycode\":\"FR\",\"timestamp\":\"2017-01-29 13:59:05.33086 UTC\",\"recognized\":true,\"key_id\":\"5747753718120448\",\"drawing\":[[[34,35,45,59,65,67,69,74,79,81,79,81,88,96,99,99,103,101,104,121,129,127,137,151,158,166,163,155,177,198,191,161,115,82,65,56,53,56],[101,249,246,231,218,184,179,179,181,186,220,244,245,230,215,183,199,225,249,255,251,187,203,242,246,225,207,186,148,132,129,133,117,98,85,71,54,20]],...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "eY_5NtJXTW5-",
        "colab_type": "code",
        "outputId": "4363b98d-fcaf-4d71-f0bc-f79b1fd2fd1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Parse single line.\n",
        "data_json = json.loads(open(path).readline())\n",
        "data_json.keys()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[u'word', u'countrycode', u'timestamp', u'recognized', u'key_id', u'drawing']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "Uf2G8GZKTlXC",
        "colab_type": "code",
        "outputId": "c9b30479-f0e6-4b1f-d83d-44028d0698f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "# So we have some meta information...\n",
        "for k, v in data_json.items():\n",
        "    if k != 'drawing':\n",
        "        print('%20s   ->   %s' % (k, v))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                word   ->   giraffe\n",
            "         countrycode   ->   BR\n",
            "           timestamp   ->   2017-01-26 17:23:19.3785 UTC\n",
            "          recognized   ->   True\n",
            "              key_id   ->   5807561943023616\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xl1i5GDxTmig",
        "colab_type": "code",
        "outputId": "d72c2888-8dce-4843-c843-a7882e10780d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "cell_type": "code",
      "source": [
        "# ...and the actual drawing.\n",
        "drawing = data_json['drawing']\n",
        "# The drawing consists of a series of strokes:\n",
        "[np.array(stroke).shape for stroke in drawing]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(2, 6),\n",
              " (2, 9),\n",
              " (2, 2),\n",
              " (2, 2),\n",
              " (2, 4),\n",
              " (2, 3),\n",
              " (2, 12),\n",
              " (2, 3),\n",
              " (2, 2),\n",
              " (2, 3),\n",
              " (2, 3),\n",
              " (2, 5)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "ST-U6uEUTnxB",
        "colab_type": "code",
        "outputId": "5a8e5609-4183-4b97-fe5a-9d95450cad5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "# Draw the image -- the strokes all have have shape (2, n)\n",
        "# so the first index seems to be x/y coordinate:\n",
        "\n",
        "def draw_strokes(drawing):\n",
        "  # Use a different color for each stroke\n",
        "  colors = cm.rainbow(np.linspace(0, 1, len(drawing)))\n",
        "  plt.axis('off')\n",
        "  for stroke, c in zip(drawing, colors):\n",
        "    #print(stroke[0], stroke[1])\n",
        "    plt.plot(np.array(stroke[0]), -1 * np.array(stroke[1]), color=c)\n",
        "    \n",
        "draw_strokes(drawing)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aItKRQlsKytRglJMDYZ5LNNLs47amamUqIl1RaEtBsSyLK6JlJDE8m2z0upzj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\nYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "axa3o1aHTpVL",
        "colab_type": "code",
        "outputId": "15145d83-0a41-4ac5-90cd-59c18f352ca7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Some more code to load many sketches at once.\n",
        "# Let's ignore the difficult \"unrecognized\" sketches for now...\n",
        "# (i.e. unrecognized by the official quickdraw classifier)\n",
        "\n",
        "def convert(line):\n",
        "    \"\"\"Converts single line to JSON + converts 'drawing' to list of np.array.\"\"\"\n",
        "    d = json.loads(line)\n",
        "    d['drawing'] = [np.array(stroke) for stroke in d['drawing']]\n",
        "    return d\n",
        "\n",
        "def loaditer(name, unrecognized=False):\n",
        "    \"\"\"Returns iterable of drawings in specified file.\n",
        "\n",
        "    Args:\n",
        "      name: Name of the downloaded object (e.g. \"elephant\").\n",
        "      unrecognized: Whether to include drawings that were not recognized\n",
        "          by Google AI (i.e. the hard ones).\n",
        "    \"\"\"\n",
        "    for line in open('%s/%s.ndjson' % (data_path, name)):\n",
        "        d = convert(line)\n",
        "        if d['recognized'] or unrecognized:\n",
        "            yield d\n",
        "\n",
        "def loadn(name, n, unrecognized=False):\n",
        "    \"\"\"Returns list of drawings.\n",
        "\n",
        "    Args:\n",
        "      name: Name of the downloaded object (e.g. \"elephant\").\n",
        "      n: Number of drawings to load.\n",
        "      unrecognized: Whether to include drawings that were not recognized\n",
        "          by Google AI (i.e. the hard ones).\n",
        "    \"\"\"\n",
        "    it = loaditer(name, unrecognized=unrecognized)\n",
        "    return list(itertools.islice(it, 0, n))\n",
        "\n",
        "print('loading some \"%s\"...' % classes[0])\n",
        "sample = loadn(classes[0], 100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loading some \"elephant\"...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "izCC7K2lV17W",
        "colab_type": "code",
        "outputId": "b6da3df1-b7aa-4715-9046-8203a32da836",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "cell_type": "code",
      "source": [
        "sample[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{u'countrycode': u'RO',\n",
              " u'drawing': [array([[  0,   0,   6,  29,  46,  66,  75,  89, 110, 122, 143, 168, 196,\n",
              "          210, 233, 242, 255, 255, 248, 220, 212, 211, 198, 195, 191, 190,\n",
              "          185, 176, 169, 168, 173, 168, 151, 147, 144, 154, 151, 127, 111,\n",
              "          107, 106,  94,  90,  87,  97,  82,  60],\n",
              "         [113,  98,  79,  36,  14,   0,   0,   6,  44,  54,  55,  45,  45,\n",
              "           40,  39,  51,  76, 100, 109, 122, 148, 173, 180, 180, 172, 147,\n",
              "          128, 128, 142, 154, 176, 181, 181, 179, 172, 128, 124, 120, 127,\n",
              "          134, 169, 169, 166, 156, 121, 117, 118]])],\n",
              " u'key_id': u'4916532524089344',\n",
              " u'recognized': True,\n",
              " u'timestamp': u'2017-01-31 11:22:07.29568 UTC',\n",
              " u'word': u'elephant'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "owBC4VL9WCr4",
        "colab_type": "code",
        "outputId": "816aaecb-6164-4617-e31d-33d9e2e98d2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "cell_type": "code",
      "source": [
        "# Some more drawings...\n",
        "plt.figure(figsize=(10, 10))\n",
        "n = 3\n",
        "for x in range(n):\n",
        "    for y in range(n):\n",
        "        i = x * n + y\n",
        "        plt.subplot(n, n, i + 1)\n",
        "        drawing = sample[i]['drawing']\n",
        "        draw_strokes(drawing)\n",
        "        \n",
        "# Elephants!"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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H4Zh1z2jlUwZAOvG6HjfUCAqhtepd2aomeIWzdB4Fydmw+UspOQc7sekwZR70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\nAsRm2EVVhjS1h4Uf+0ozVaSxmn3kUUJ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4AxYsgMRjPtDKy2WrhFZoc9KTMOb+lkfahEXq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35/7VpYsUI8f9Ik+O53e15Lhg0lfUSr0GGi2zvEKJ8D1jyREK/+NXHX5BkltL5abOGB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\nv670UmhWOK3FyvY9KuE4B17HiK1SfxSh9bq3GeWmv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6beCQUjk3OuHsKUXBs5Vo0F5cGk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\nCIIgJAkRWoIgCIIgCEni/wMyexJ0HOTCnwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "w92hgeIfgCaA",
        "colab_type": "code",
        "outputId": "d15efcf9-4beb-45c0-cbf4-eb7f5d9b1dbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "cell_type": "code",
      "source": [
        "# Let's first check how many [recognized=True] examples we have in each class.\n",
        "# Depending on your choice of classes you could generate >200k examples / class...\n",
        "for name in classes:\n",
        "    print(name, len(list(open('%s/%s.ndjson' % (data_path, name)))), 'recognized', len(list(loaditer(name))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "elephant 126969 recognized 104986\n",
            "giraffe 127182 recognized 122396\n",
            "kangaroo 174470 recognized 152511\n",
            "lion 120949 recognized 98976\n",
            "monkey 127633 recognized 105650\n",
            "panda 113613 recognized 94814\n",
            "penguin 253791 recognized 231826\n",
            "rhinoceros 188484 recognized 166539\n",
            "tiger 121067 recognized 110177\n",
            "zebra 144608 recognized 127159\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qZhJHLGUXp7g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Make images from the brush stroke"
      ]
    },
    {
      "metadata": {
        "id": "ZljwIGDeWXyF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# The sequence information is important! But... for now we'll \n",
        "# convert these sequences to plain old images."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8DpyF28KXnWF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def json_to_img(drawing, img_sz=64, lw=3, maximize=True):\n",
        "    img = Image.new('L', (img_sz, img_sz))\n",
        "    draw = ImageDraw.Draw(img)\n",
        "    lines = np.array([\n",
        "        stroke[0:2, i:i+2]\n",
        "        for stroke in drawing\n",
        "        for i in range(stroke.shape[1] - 1)\n",
        "    ], dtype=np.float32)\n",
        "    if maximize:\n",
        "        for i in range(2):\n",
        "            min_, max_ = lines[:,i,:].min() * 0.95, lines[:,i,:].max() * 1.05\n",
        "            lines[:,i,:] = (lines[:,i,:] - min_) / max(max_ - min_, 1)\n",
        "    else:\n",
        "        lines /= 1024\n",
        "    for line in lines:\n",
        "        draw.line(tuple(line.T.reshape((-1,)) * img_sz), fill='white', width=lw)\n",
        "    return img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "b1KrwVjhZHlE",
        "colab_type": "code",
        "outputId": "a4a57bb0-c8a2-4c4c-c18d-1a71ad7e4f51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "cell_type": "code",
      "source": [
        "json_to_img(drawing)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAABlklEQVR4nM1Wy5LDMAhDmfz/L2sP\ncfwUBre7M8ulSR2EAGEbJo3PD/Rqb9fOv/4eA5gZkEPwAJDi7wHkK6ABKm8kchAAnc9HXeDoGVFY\nAEb/mIIs4uAWUFAAcJ5TAFx99hTuKIJrJZQr5RQGFwCRgWflu5DBVo34MgWj2W20PO0xPM3MLhak\nGZuJzcTs2xQeAKg6ZSb51xgcmCB1mWm+y38kKWqbmgUuL63vB8OEDoAV4XrXghwA1KeB1L6IZPly\nkOqAEHWBLahGuBTf7rMl/IzwoZDiLkQ6RleDt7FUru6gvwt9ClRj7WLAxh0JS/DAxi5Y16sGs92o\nxKYKnByKtgK8te0pIIK6lIRo71Yb+k86OLidVQaz/6x710qDhRKT97vKYNRgnfokgZKCc7rkGDwa\n5LxxZA8GNAY2Et+e9C3b+yicsNoFGFVE/9a7zoJSgKOs7vXgXNBJxgDYd/kPT2fRGWBdzDDYKuIk\nBYkSnI3TI2fp5i6aW9u0Ee0SsBH6jkFqb8jfUTn1FacAD8rjxEM3BfJ/7Adx2m6OeITkggAAAABJ\nRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=64x64 at 0x7F0E0BE926D0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "B6bCQ62MZJZW",
        "colab_type": "code",
        "outputId": "e708f7ab-65d4-4b06-d4d8-ff17d4e19d32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "cell_type": "code",
      "source": [
        "# Fetch some images + shuffle order.\n",
        "n_per_class = 10000\n",
        "drawings_matrix = [loadn(name, n_per_class) for name in classes]\n",
        "drawings_list = functools.reduce(lambda x, y: x + y, drawings_matrix, [])\n",
        "np.random.seed(1)\n",
        "drawings_list = np.random.permutation(drawings_list)\n",
        "\n",
        "# Quick test to see how our code works so far\n",
        "draw_strokes(drawings_list[0]['drawing'])\n",
        "print(str(drawings_list[0]['word']))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "monkey\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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20spK5vZgb0Y5TeUUQENq2RKOPRZmz9a/fKijncZZzs71ak/Wq4UPxXxQW5ZF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\npIj9D70lrFUghnXjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "auEjUQCMaFlb",
        "colab_type": "code",
        "outputId": "b0f20b0c-868f-424b-864e-123a566f5258",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "cell_type": "code",
      "source": [
        "json_to_img(drawings_list[0]['drawing'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAAB1ElEQVR4nM1XQbbEIAgDXu9/Zf5i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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=64x64 at 0x7F0E35686F90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "Ngo3cAQUp9uD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Save it to disk"
      ]
    },
    {
      "metadata": {
        "id": "yuaNgBowdg2B",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Save a dataset to disk\n",
        "X, y = [], []\n",
        "for drawing in drawings_list:\n",
        "  X.append(np.array(json_to_img(drawing['drawing'])))\n",
        "  y.append(str(drawing['word']))\n",
        "  \n",
        "dataset_path = 'mini-quickdraw.npz'\n",
        "out = open(dataset_path, 'w')\n",
        "# This may take a moment...\n",
        "np.savez_compressed(out, X, y, X=X, y=y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fDi9jDoBd4tR",
        "colab_type": "code",
        "outputId": "8b375ee6-9516-4653-f42e-216320b3901c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Demonstrate loading it back\n",
        "loaded = np.load(open(dataset_path))\n",
        "X, y = loaded[\"X\"], loaded[\"y\"]\n",
        "print(X.shape, y.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100000, 64, 64) (100000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XcyLOzPWe6zo",
        "colab_type": "code",
        "outputId": "1f6336d2-f9ca-4417-da5b-200fb2581e27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "cell_type": "code",
      "source": [
        "# it worked!\n",
        "plt.imshow(X[0])\n",
        "print(\"Label\", y[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label monkey\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAFMCAYAAABCsp4mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}