|
--- |
|
dataset_info: |
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features: |
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- name: label |
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dtype: |
|
class_label: |
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names: |
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'0': The Eiffel Tower |
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'1': The Great Wall of China |
|
'2': The Mona Lisa |
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'3': aircraft carrier |
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'4': airplane |
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'5': alarm clock |
|
'6': ambulance |
|
'7': angel |
|
'8': animal migration |
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'9': ant |
|
'10': anvil |
|
'11': apple |
|
'12': arm |
|
'13': asparagus |
|
'14': axe |
|
'15': backpack |
|
'16': banana |
|
'17': bandage |
|
'18': barn |
|
'19': baseball |
|
'20': baseball bat |
|
'21': basket |
|
'22': basketball |
|
'23': bat |
|
'24': bathtub |
|
'25': beach |
|
'26': bear |
|
'27': beard |
|
'28': bed |
|
'29': bee |
|
'30': belt |
|
'31': bench |
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'32': bicycle |
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'33': binoculars |
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'34': bird |
|
'35': birthday cake |
|
'36': blackberry |
|
'37': blueberry |
|
'38': book |
|
'39': boomerang |
|
'40': bottlecap |
|
'41': bowtie |
|
'42': bracelet |
|
'43': brain |
|
'44': bread |
|
'45': bridge |
|
'46': broccoli |
|
'47': broom |
|
'48': bucket |
|
'49': bulldozer |
|
'50': bus |
|
'51': bush |
|
'52': butterfly |
|
'53': cactus |
|
'54': cake |
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'55': calculator |
|
'56': calendar |
|
'57': camel |
|
'58': camera |
|
'59': camouflage |
|
'60': campfire |
|
'61': candle |
|
'62': cannon |
|
'63': canoe |
|
'64': car |
|
'65': carrot |
|
'66': castle |
|
'67': cat |
|
'68': ceiling fan |
|
'69': cell phone |
|
'70': cello |
|
'71': chair |
|
'72': chandelier |
|
'73': church |
|
'74': circle |
|
'75': clarinet |
|
'76': clock |
|
'77': cloud |
|
'78': coffee cup |
|
'79': compass |
|
'80': computer |
|
'81': cookie |
|
'82': cooler |
|
'83': couch |
|
'84': cow |
|
'85': crab |
|
'86': crayon |
|
'87': crocodile |
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'88': crown |
|
'89': cruise ship |
|
'90': cup |
|
'91': diamond |
|
'92': dishwasher |
|
'93': diving board |
|
'94': dog |
|
'95': dolphin |
|
'96': donut |
|
'97': door |
|
'98': dragon |
|
'99': dresser |
|
'100': drill |
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'101': drums |
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'102': duck |
|
'103': dumbbell |
|
'104': ear |
|
'105': elbow |
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'106': elephant |
|
'107': envelope |
|
'108': eraser |
|
'109': eye |
|
'110': eyeglasses |
|
'111': face |
|
'112': fan |
|
'113': feather |
|
'114': fence |
|
'115': finger |
|
'116': fire hydrant |
|
'117': fireplace |
|
'118': firetruck |
|
'119': fish |
|
'120': flamingo |
|
'121': flashlight |
|
'122': flip flops |
|
'123': floor lamp |
|
'124': flower |
|
'125': flying saucer |
|
'126': foot |
|
'127': fork |
|
'128': frog |
|
'129': frying pan |
|
'130': garden |
|
'131': garden hose |
|
'132': giraffe |
|
'133': goatee |
|
'134': golf club |
|
'135': grapes |
|
'136': grass |
|
'137': guitar |
|
'138': hamburger |
|
'139': hammer |
|
'140': hand |
|
'141': harp |
|
'142': hat |
|
'143': headphones |
|
'144': hedgehog |
|
'145': helicopter |
|
'146': helmet |
|
'147': hexagon |
|
'148': hockey puck |
|
'149': hockey stick |
|
'150': horse |
|
'151': hospital |
|
'152': hot air balloon |
|
'153': hot dog |
|
'154': hot tub |
|
'155': hourglass |
|
'156': house |
|
'157': house plant |
|
'158': hurricane |
|
'159': ice cream |
|
'160': jacket |
|
'161': jail |
|
'162': kangaroo |
|
'163': key |
|
'164': keyboard |
|
'165': knee |
|
'166': knife |
|
'167': ladder |
|
'168': lantern |
|
'169': laptop |
|
'170': leaf |
|
'171': leg |
|
'172': light bulb |
|
'173': lighter |
|
'174': lighthouse |
|
'175': lightning |
|
'176': line |
|
'177': lion |
|
'178': lipstick |
|
'179': lobster |
|
'180': lollipop |
|
'181': mailbox |
|
'182': map |
|
'183': marker |
|
'184': matches |
|
'185': megaphone |
|
'186': mermaid |
|
'187': microphone |
|
'188': microwave |
|
'189': monkey |
|
'190': moon |
|
'191': mosquito |
|
'192': motorbike |
|
'193': mountain |
|
'194': mouse |
|
'195': moustache |
|
'196': mouth |
|
'197': mug |
|
'198': mushroom |
|
'199': nail |
|
'200': necklace |
|
'201': nose |
|
'202': ocean |
|
'203': octagon |
|
'204': octopus |
|
'205': onion |
|
'206': oven |
|
'207': owl |
|
'208': paint can |
|
'209': paintbrush |
|
'210': palm tree |
|
'211': panda |
|
'212': pants |
|
'213': paper clip |
|
'214': parachute |
|
'215': parrot |
|
'216': passport |
|
'217': peanut |
|
'218': pear |
|
'219': peas |
|
'220': pencil |
|
'221': penguin |
|
'222': piano |
|
'223': pickup truck |
|
'224': picture frame |
|
'225': pig |
|
'226': pillow |
|
'227': pineapple |
|
'228': pizza |
|
'229': pliers |
|
'230': police car |
|
'231': pond |
|
'232': pool |
|
'233': popsicle |
|
'234': postcard |
|
'235': potato |
|
'236': power outlet |
|
'237': purse |
|
'238': rabbit |
|
'239': raccoon |
|
'240': radio |
|
'241': rain |
|
'242': rainbow |
|
'243': rake |
|
'244': remote control |
|
'245': rhinoceros |
|
'246': rifle |
|
'247': river |
|
'248': roller coaster |
|
'249': rollerskates |
|
'250': sailboat |
|
'251': sandwich |
|
'252': saw |
|
'253': saxophone |
|
'254': school bus |
|
'255': scissors |
|
'256': scorpion |
|
'257': screwdriver |
|
'258': sea turtle |
|
'259': see saw |
|
'260': shark |
|
'261': sheep |
|
'262': shoe |
|
'263': shorts |
|
'264': shovel |
|
'265': sink |
|
'266': skateboard |
|
'267': skull |
|
'268': skyscraper |
|
'269': sleeping bag |
|
'270': smiley face |
|
'271': snail |
|
'272': snake |
|
'273': snorkel |
|
'274': snowflake |
|
'275': snowman |
|
'276': soccer ball |
|
'277': sock |
|
'278': speedboat |
|
'279': spider |
|
'280': spoon |
|
'281': spreadsheet |
|
'282': square |
|
'283': squiggle |
|
'284': squirrel |
|
'285': stairs |
|
'286': star |
|
'287': steak |
|
'288': stereo |
|
'289': stethoscope |
|
'290': stitches |
|
'291': stop sign |
|
'292': stove |
|
'293': strawberry |
|
'294': streetlight |
|
'295': string bean |
|
'296': submarine |
|
'297': suitcase |
|
'298': sun |
|
'299': swan |
|
'300': sweater |
|
'301': swing set |
|
'302': sword |
|
'303': syringe |
|
'304': t-shirt |
|
'305': table |
|
'306': teapot |
|
'307': teddy-bear |
|
'308': telephone |
|
'309': television |
|
'310': tennis racquet |
|
'311': tent |
|
'312': tiger |
|
'313': toaster |
|
'314': toe |
|
'315': toilet |
|
'316': tooth |
|
'317': toothbrush |
|
'318': toothpaste |
|
'319': tornado |
|
'320': tractor |
|
'321': traffic light |
|
'322': train |
|
'323': tree |
|
'324': triangle |
|
'325': trombone |
|
'326': truck |
|
'327': trumpet |
|
'328': umbrella |
|
'329': underwear |
|
'330': van |
|
'331': vase |
|
'332': violin |
|
'333': washing machine |
|
'334': watermelon |
|
'335': waterslide |
|
'336': whale |
|
'337': wheel |
|
'338': windmill |
|
'339': wine bottle |
|
'340': wine glass |
|
'341': wristwatch |
|
'342': yoga |
|
'343': zebra |
|
'344': zigzag |
|
- name: packed_drawing |
|
dtype: binary |
|
splits: |
|
- name: train |
|
num_bytes: 51960652.42514169 |
|
num_examples: 403410 |
|
- name: test |
|
num_bytes: 12990227.508075692 |
|
num_examples: 100853 |
|
download_size: 62877590 |
|
dataset_size: 64950879.933217384 |
|
--- |
|
# Quick!Draw! 1pct Sample (per-row bin format) |
|
|
|
This is a sample 1-percent of the entire 50M-row [QuickDraw! dataset](https://github.com/googlecreativelab/quickdraw-dataset). The row for each drawing contains a byte-encoded packed representation of the drawing and data, which you can unpack using the following snippet: |
|
|
|
``` |
|
def unpack_drawing(file_handle): |
|
key_id, = unpack('Q', file_handle.read(8)) |
|
country_code, = unpack('2s', file_handle.read(2)) |
|
recognized, = unpack('b', file_handle.read(1)) |
|
timestamp, = unpack('I', file_handle.read(4)) |
|
n_strokes, = unpack('H', file_handle.read(2)) |
|
image = [] |
|
n_bytes = 17 |
|
for i in range(n_strokes): |
|
n_points, = unpack('H', file_handle.read(2)) |
|
fmt = str(n_points) + 'B' |
|
x = unpack(fmt, file_handle.read(n_points)) |
|
y = unpack(fmt, file_handle.read(n_points)) |
|
image.append((x, y)) |
|
n_bytes += 2 + 2*n_points |
|
result = { |
|
'key_id': key_id, |
|
'country_code': country_code, |
|
'recognized': recognized, |
|
'timestamp': timestamp, |
|
'image': image, |
|
} |
|
return result |
|
``` |
|
|
|
The `image` in the above is still in line vector format. To convert render this to a raster image (I recommend you do this on-the-fly in a pre-processor): |
|
|
|
``` |
|
# packed bin -> RGB PIL |
|
def binToPIL(packed_drawing): |
|
padding = 8 |
|
radius = 7 |
|
scale = (224.0-(2*padding)) / 256 |
|
|
|
unpacked = unpack_drawing(io.BytesIO(packed_drawing)) |
|
unpacked_image = unpacked['image'] |
|
image = np.full((224,224), 255, np.uint8) |
|
for stroke in unpacked['image']: |
|
prevX = round(stroke[0][0]*scale) |
|
prevY = round(stroke[1][0]*scale) |
|
for i in range(1, len(stroke[0])): |
|
x = round(stroke[0][i]*scale) |
|
y = round(stroke[1][i]*scale) |
|
cv2.line(image, (padding+prevX, padding+prevY), (padding+x, padding+y), 0, radius, -1) |
|
prevX = x |
|
prevY = y |
|
pilImage = Image.fromarray(image).convert("RGB") |
|
return pilImage |
|
``` |
