# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Quickdraw dataset""" import io import json import os import struct import textwrap from datetime import datetime 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!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located. """ _HOMEPAGE = "https://quickdraw.withgoogle.com/data" _LICENSE = "CC BY 4.0" _NAMES = """\ aircraft carrier,airplane,alarm clock,ambulance,angel animal migration,ant,anvil,apple,arm asparagus,axe,backpack,banana,bandage barn,baseball bat,baseball,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,cell phone,cello,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 hose,garden,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 plant,house 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 paint can,paintbrush,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,t-shirt,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,umbrella,underwear van,vase,violin,washing machine,watermelon waterslide,whale,wheel,windmill,wine bottle wine glass,wristwatch,yoga,zebra,zigzag """ _NAMES = [name for line in _NAMES.strip().splitlines() for name in line.strip().split(",")] _CONFIG_NAME_TO_BASE_URL = { "raw": "https://storage.googleapis.com/quickdraw_dataset/full/raw/{}.ndjson", "preprocessed_simplified_drawings": "https://storage.googleapis.com/quickdraw_dataset/full/binary/{}.bin", "preprocessed_bitmaps": "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/{}.npy", "sketch_rnn": "https://storage.googleapis.com/quickdraw_dataset/sketchrnn/{}.npz", "sketch_rnn_full": "https://storage.googleapis.com/quickdraw_dataset/sketchrnn/{}.full.npz", } class Quickdraw(datasets.GeneratorBasedBuilder): """Quickdraw dataset""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="raw", version=VERSION, description="The raw moderated dataset"), datasets.BuilderConfig( name="preprocessed_simplified_drawings", version=VERSION, description=textwrap.dedent( """\ The simplified version of the dataset with the simplified vectors, removed timing information, and the data positioned and scaled into a 256x256 region. The simplification process was: 1.Align the drawing to the top-left corner, to have minimum values of 0. 2.Uniformly scale the drawing, to have a maximum value of 255. 3.Resample all strokes with a 1 pixel spacing. 4.Simplify all strokes using the Ramer-Douglas-Peucker algorithm with an epsilon value of 2.0. """ ), ), datasets.BuilderConfig( name="preprocessed_bitmaps", version=VERSION, description="The preprocessed dataset where all the simplified drawings have been rendered into a 28x28 grayscale bitmap.", ), datasets.BuilderConfig( name="sketch_rnn", version=VERSION, description=textwrap.dedent( """\ This dataset was used for training the Sketch-RNN model from the paper https://arxiv.org/abs/1704.03477. In this dataset, 75K samples (70K Training, 2.5K Validation, 2.5K Test) has been randomly selected from each category, processed with RDP line simplification with an epsilon parameter of 2.0 """ ), ), datasets.BuilderConfig( name="sketch_rnn_full", version=VERSION, description="Compared to the `sketch_rnn` config, this version provides the full data for each category for training more complex models.", ), ] DEFAULT_CONFIG_NAME = "preprocessed_bitmaps" def _info(self): if self.config.name == "raw": features = datasets.Features( { "key_id": datasets.Value("string"), "word": datasets.ClassLabel(names=_NAMES), "recognized": datasets.Value("bool"), "timestamp": datasets.Value("timestamp[us, tz=UTC]"), "countrycode": datasets.Value("string"), "drawing": datasets.Sequence( { "x": datasets.Sequence(datasets.Value("float32")), "y": datasets.Sequence(datasets.Value("float32")), "t": datasets.Sequence(datasets.Value("int32")), } ), } ) elif self.config.name == "preprocessed_simplified_drawings": features = datasets.Features( { "key_id": datasets.Value("string"), "word": datasets.ClassLabel(names=_NAMES), "recognized": datasets.Value("bool"), "timestamp": datasets.Value("timestamp[us, tz=UTC]"), "countrycode": datasets.Value("string"), "drawing": datasets.Sequence( { "x": datasets.Sequence(datasets.Value("uint8")), "y": datasets.Sequence(datasets.Value("uint8")), } ), } ) elif self.config.name == "preprocessed_bitmaps": features = datasets.Features( { "image": datasets.Image(), "label": datasets.ClassLabel(names=_NAMES), } ) else: # sketch_rnn, sketch_rnn_full features = datasets.Features( { "word": datasets.ClassLabel(names=_NAMES), "drawing": datasets.Array2D(shape=(None, 3), dtype="int16"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, task_templates=[ImageClassification(image_column="image", label_column="label")] if self.config.name == "preprocessed_bitmaps" else None, ) def _split_generators(self, dl_manager): base_url = _CONFIG_NAME_TO_BASE_URL[self.config.name] if not self.config.name.startswith("sketch_rnn"): files = dl_manager.download( {name: url for name, url in zip(_NAMES, [base_url.format(name) for name in _NAMES])} ) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files": files, "split": "train", }, ), ] else: files = dl_manager.download_and_extract( {name: url for name, url in zip(_NAMES, [base_url.format(name) for name in _NAMES])} ) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files": files, "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "files": files, "split": "valid", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "files": files, "split": "test", }, ), ] def _generate_examples(self, files, split): if self.config.name == "raw": idx = 0 for _, file in files.items(): with open(file, encoding="utf-8") as f: for line in f: example = json.loads(line) example["timestamp"] = datetime.strptime(example["timestamp"], "%Y-%m-%d %H:%M:%S.%f %Z") example["drawing"] = [{"x": x, "y": y, "t": t} for x, y, t in example["drawing"]] yield idx, example idx += 1 elif self.config.name == "preprocessed_simplified_drawings": idx = 0 for label, file in files.items(): with open(file, "rb") as f: while True: try: example = process_struct(f) example["word"] = label yield idx, example except struct.error: break idx += 1 elif self.config.name == "preprocessed_bitmaps": idx = 0 for label, file in files.items(): with open(file, "rb") as f: images = np.load(f) for image in images: yield idx, { "image": image.reshape(28, 28), "label": label, } idx += 1 else: # sketch_rnn, sketch_rnn_full idx = 0 for label, file in files.items(): with open(os.path.join(file, f"{split}.npy"), "rb") as f: # read entire file since f.seek is not supported in the streaming mode drawings = np.load(io.BytesIO(f.read()), encoding="latin1", allow_pickle=True) for drawing in drawings: yield idx, { "word": label, "drawing": drawing, } idx += 1 def process_struct(fileobj): """ Process a struct from a binary file object. The code for this function is borrowed from the following link: https://github.com/googlecreativelab/quickdraw-dataset/blob/f0f3beef0fc86393b3771cdf1fc94828b76bc89b/examples/binary_file_parser.py#L19 """ (key_id,) = struct.unpack("Q", fileobj.read(8)) (country_code,) = struct.unpack("2s", fileobj.read(2)) (recognized,) = struct.unpack("b", fileobj.read(1)) (timestamp,) = struct.unpack("I", fileobj.read(4)) (n_strokes,) = struct.unpack("H", fileobj.read(2)) drawing = [] for _ in range(n_strokes): (n_points,) = struct.unpack("H", fileobj.read(2)) fmt = str(n_points) + "B" x = struct.unpack(fmt, fileobj.read(n_points)) y = struct.unpack(fmt, fileobj.read(n_points)) drawing.append({"x": list(x), "y": list(y)}) return { "key_id": str(key_id), "recognized": recognized, "timestamp": datetime.fromtimestamp(timestamp), "countrycode": country_code.decode("utf-8"), "drawing": drawing, }