| """Quick, Draw! Data Set""" | |
| import numpy as np | |
| import datasets | |
| from datasets.tasks import ImageClassification | |
| _CITATION = """\ | |
| @article{DBLP:journals/corr/HaE17, | |
| author = {David Ha and | |
| Douglas Eck}, | |
| title = {A Neural Representation of Sketch Drawings}, | |
| journal = {CoRR}, | |
| volume = {abs/1704.03477}, | |
| year = {2017}, | |
| url = {http://arxiv.org/abs/1704.03477}, | |
| archivePrefix = {arXiv}, | |
| eprint = {1704.03477}, | |
| timestamp = {Mon, 13 Aug 2018 16:48:30 +0200}, | |
| biburl = {https://dblp.org/rec/bib/journals/corr/HaE17}, | |
| bibsource = {dblp computer science bibliography, https://dblp.org} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. | |
| """ | |
| _URL = "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/" | |
| _CLASSES = [ | |
| "aircraft carrier", | |
| "airplane", | |
| "alarm clock", | |
| "ambulance", | |
| "angel", | |
| "animal migration", | |
| "ant", | |
| "anvil", | |
| "apple", | |
| "arm", | |
| "asparagus", | |
| "axe", | |
| "backpack", | |
| "banana", | |
| "bandage", | |
| "barn", | |
| "baseball", | |
| "baseball bat", | |
| "basket", | |
| "basketball", | |
| "bat", | |
| "bathtub", | |
| "beach", | |
| "bear", | |
| "beard", | |
| "bed", | |
| "bee", | |
| "belt", | |
| "bench", | |
| "bicycle", | |
| "binoculars", | |
| "bird", | |
| "birthday cake", | |
| "blackberry", | |
| "blueberry", | |
| "book", | |
| "boomerang", | |
| "bottlecap", | |
| "bowtie", | |
| "bracelet", | |
| "brain", | |
| "bread", | |
| "bridge", | |
| "broccoli", | |
| "broom", | |
| "bucket", | |
| "bulldozer", | |
| "bus", | |
| "bush", | |
| "butterfly", | |
| "cactus", | |
| "cake", | |
| "calculator", | |
| "calendar", | |
| "camel", | |
| "camera", | |
| "camouflage", | |
| "campfire", | |
| "candle", | |
| "cannon", | |
| "canoe", | |
| "car", | |
| "carrot", | |
| "castle", | |
| "cat", | |
| "ceiling fan", | |
| "cello", | |
| "cell phone", | |
| "chair", | |
| "chandelier", | |
| "church", | |
| "circle", | |
| "clarinet", | |
| "clock", | |
| "cloud", | |
| "coffee cup", | |
| "compass", | |
| "computer", | |
| "cookie", | |
| "cooler", | |
| "couch", | |
| "cow", | |
| "crab", | |
| "crayon", | |
| "crocodile", | |
| "crown", | |
| "cruise ship", | |
| "cup", | |
| "diamond", | |
| "dishwasher", | |
| "diving board", | |
| "dog", | |
| "dolphin", | |
| "donut", | |
| "door", | |
| "dragon", | |
| "dresser", | |
| "drill", | |
| "drums", | |
| "duck", | |
| "dumbbell", | |
| "ear", | |
| "elbow", | |
| "elephant", | |
| "envelope", | |
| "eraser", | |
| "eye", | |
| "eyeglasses", | |
| "face", | |
| "fan", | |
| "feather", | |
| "fence", | |
| "finger", | |
| "fire hydrant", | |
| "fireplace", | |
| "firetruck", | |
| "fish", | |
| "flamingo", | |
| "flashlight", | |
| "flip flops", | |
| "floor lamp", | |
| "flower", | |
| "flying saucer", | |
| "foot", | |
| "fork", | |
| "frog", | |
| "frying pan", | |
| "garden", | |
| "garden hose", | |
| "giraffe", | |
| "goatee", | |
| "golf club", | |
| "grapes", | |
| "grass", | |
| "guitar", | |
| "hamburger", | |
| "hammer", | |
| "hand", | |
| "harp", | |
| "hat", | |
| "headphones", | |
| "hedgehog", | |
| "helicopter", | |
| "helmet", | |
| "hexagon", | |
| "hockey puck", | |
| "hockey stick", | |
| "horse", | |
| "hospital", | |
| "hot air balloon", | |
| "hot dog", | |
| "hot tub", | |
| "hourglass", | |
| "house", | |
| "house plant", | |
| "hurricane", | |
| "ice cream", | |
| "jacket", | |
| "jail", | |
| "kangaroo", | |
| "key", | |
| "keyboard", | |
| "knee", | |
| "knife", | |
| "ladder", | |
| "lantern", | |
| "laptop", | |
| "leaf", | |
| "leg", | |
| "light bulb", | |
| "lighter", | |
| "lighthouse", | |
| "lightning", | |
| "line", | |
| "lion", | |
| "lipstick", | |
| "lobster", | |
| "lollipop", | |
| "mailbox", | |
| "map", | |
| "marker", | |
| "matches", | |
| "megaphone", | |
| "mermaid", | |
| "microphone", | |
| "microwave", | |
| "monkey", | |
| "moon", | |
| "mosquito", | |
| "motorbike", | |
| "mountain", | |
| "mouse", | |
| "moustache", | |
| "mouth", | |
| "mug", | |
| "mushroom", | |
| "nail", | |
| "necklace", | |
| "nose", | |
| "ocean", | |
| "octagon", | |
| "octopus", | |
| "onion", | |
| "oven", | |
| "owl", | |
| "paintbrush", | |
| "paint can", | |
| "palm tree", | |
| "panda", | |
| "pants", | |
| "paper clip", | |
| "parachute", | |
| "parrot", | |
| "passport", | |
| "peanut", | |
| "pear", | |
| "peas", | |
| "pencil", | |
| "penguin", | |
| "piano", | |
| "pickup truck", | |
| "picture frame", | |
| "pig", | |
| "pillow", | |
| "pineapple", | |
| "pizza", | |
| "pliers", | |
| "police car", | |
| "pond", | |
| "pool", | |
| "popsicle", | |
| "postcard", | |
| "potato", | |
| "power outlet", | |
| "purse", | |
| "rabbit", | |
| "raccoon", | |
| "radio", | |
| "rain", | |
| "rainbow", | |
| "rake", | |
| "remote control", | |
| "rhinoceros", | |
| "rifle", | |
| "river", | |
| "roller coaster", | |
| "rollerskates", | |
| "sailboat", | |
| "sandwich", | |
| "saw", | |
| "saxophone", | |
| "school bus", | |
| "scissors", | |
| "scorpion", | |
| "screwdriver", | |
| "sea turtle", | |
| "see saw", | |
| "shark", | |
| "sheep", | |
| "shoe", | |
| "shorts", | |
| "shovel", | |
| "sink", | |
| "skateboard", | |
| "skull", | |
| "skyscraper", | |
| "sleeping bag", | |
| "smiley face", | |
| "snail", | |
| "snake", | |
| "snorkel", | |
| "snowflake", | |
| "snowman", | |
| "soccer ball", | |
| "sock", | |
| "speedboat", | |
| "spider", | |
| "spoon", | |
| "spreadsheet", | |
| "square", | |
| "squiggle", | |
| "squirrel", | |
| "stairs", | |
| "star", | |
| "steak", | |
| "stereo", | |
| "stethoscope", | |
| "stitches", | |
| "stop sign", | |
| "stove", | |
| "strawberry", | |
| "streetlight", | |
| "string bean", | |
| "submarine", | |
| "suitcase", | |
| "sun", | |
| "swan", | |
| "sweater", | |
| "swing set", | |
| "sword", | |
| "syringe", | |
| "table", | |
| "teapot", | |
| "teddy-bear", | |
| "telephone", | |
| "television", | |
| "tennis racquet", | |
| "tent", | |
| "The Eiffel Tower", | |
| "The Great Wall of China", | |
| "The Mona Lisa", | |
| "tiger", | |
| "toaster", | |
| "toe", | |
| "toilet", | |
| "tooth", | |
| "toothbrush", | |
| "toothpaste", | |
| "tornado", | |
| "tractor", | |
| "traffic light", | |
| "train", | |
| "tree", | |
| "triangle", | |
| "trombone", | |
| "truck", | |
| "trumpet", | |
| "t-shirt", | |
| "umbrella", | |
| "underwear", | |
| "van", | |
| "vase", | |
| "violin", | |
| "washing machine", | |
| "watermelon", | |
| "waterslide", | |
| "whale", | |
| "wheel", | |
| "windmill", | |
| "wine bottle", | |
| "wine glass", | |
| "wristwatch", | |
| "yoga", | |
| "zebra", | |
| "zigzag", | |
| ] | |
| class QuickDraw(datasets.GeneratorBasedBuilder): | |
| """QuickDraw Data Set""" | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name="quickdraw", | |
| version=datasets.Version("1.0.0"), | |
| description=_DESCRIPTION, | |
| ) | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "image": datasets.Image(), | |
| "label": datasets.features.ClassLabel(names=_CLASSES), | |
| } | |
| ), | |
| supervised_keys=("image", "label"), | |
| homepage="https://github.com/googlecreativelab/quickdraw-dataset", | |
| citation=_CITATION, | |
| task_templates=[ | |
| ImageClassification( | |
| image_column="image", | |
| label_column="label", | |
| ) | |
| ], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| urls_to_download = {c: _URL + c + ".npy" for c in _CLASSES} | |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepaths": [downloaded_files[c] for c in _CLASSES], | |
| "labels": _CLASSES | |
| } | |
| ) | |
| ] | |
| def _generate_examples(self, filepaths, labels): | |
| """This function returns the examples in the raw form.""" | |
| for filepath, label in zip(filepaths, labels): | |
| data = np.load(filepath, mmap_mode='r') | |
| for i, ex in enumerate(data): | |
| yield i, {"image": ex.reshape(28, 28), "label": label} | |