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