Yuantao Feng
commited on
Commit
·
1fb6b1a
0
Parent(s):
Add PPResNet from PaddleHub conversion (#4)
Browse files* add PPResNet impl and demo
* add benchmark impl and results for PPResNet
- LICENSE +203 -0
- README.md +22 -0
- demo.py +41 -0
- imagenet_labels.txt +1000 -0
- ppresnet.py +67 -0
LICENSE
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README.md
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# ResNet
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Deep Residual Learning for Image Recognition
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This model is ported from [PaddleHub](https://github.com/PaddlePaddle/PaddleHub) using [this script from OpenCV](https://github.com/opencv/opencv/blob/master/samples/dnn/dnn_model_runner/dnn_conversion/paddlepaddle/paddle_resnet50.py).
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## Demo
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Run the following command to try the demo:
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```shell
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python demo.py --input /path/to/image
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```
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## License
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All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
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## Reference
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- https://arxiv.org/abs/1512.03385
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- https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/paddlepaddle
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- https://github.com/PaddlePaddle/PaddleHub
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demo.py
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# This file is part of OpenCV Zoo project.
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# It is subject to the license terms in the LICENSE file found in the same directory.
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#
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# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
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# Third party copyrights are property of their respective owners.
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import argparse
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import numpy as np
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import cv2 as cv
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from ppresnet import PPResNet
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def str2bool(v):
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if v.lower() in ['on', 'yes', 'true', 'y', 't']:
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return True
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elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
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return False
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else:
|
| 20 |
+
raise NotImplementedError
|
| 21 |
+
|
| 22 |
+
parser = argparse.ArgumentParser(description='Deep Residual Learning for Image Recognition (https://arxiv.org/abs/1512.03385, https://github.com/PaddlePaddle/PaddleHub)')
|
| 23 |
+
parser.add_argument('--input', '-i', type=str, help='Path to the input image.')
|
| 24 |
+
parser.add_argument('--model', '-m', type=str, default='image_classification_ppresnet50.onnx', help='Path to the model.')
|
| 25 |
+
args = parser.parse_args()
|
| 26 |
+
|
| 27 |
+
if __name__ == '__main__':
|
| 28 |
+
# Instantiate ResNet
|
| 29 |
+
model = PPResNet(modelPath=args.model)
|
| 30 |
+
|
| 31 |
+
# Read image and get a 224x224 crop from a 256x256 resized
|
| 32 |
+
image = cv.imread(args.input)
|
| 33 |
+
image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
|
| 34 |
+
image = cv.resize(image, dsize=(256, 256))
|
| 35 |
+
image = image[16:240, 16:240, :]
|
| 36 |
+
|
| 37 |
+
# Inference
|
| 38 |
+
result = model.infer(image)
|
| 39 |
+
|
| 40 |
+
# Print result
|
| 41 |
+
print('label: {}'.format(result))
|
imagenet_labels.txt
ADDED
|
@@ -0,0 +1,1000 @@
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|
|
|
| 1 |
+
tench
|
| 2 |
+
goldfish
|
| 3 |
+
great white shark
|
| 4 |
+
tiger shark
|
| 5 |
+
hammerhead
|
| 6 |
+
electric ray
|
| 7 |
+
stingray
|
| 8 |
+
cock
|
| 9 |
+
hen
|
| 10 |
+
ostrich
|
| 11 |
+
brambling
|
| 12 |
+
goldfinch
|
| 13 |
+
house finch
|
| 14 |
+
junco
|
| 15 |
+
indigo bunting
|
| 16 |
+
robin
|
| 17 |
+
bulbul
|
| 18 |
+
jay
|
| 19 |
+
magpie
|
| 20 |
+
chickadee
|
| 21 |
+
water ouzel
|
| 22 |
+
kite
|
| 23 |
+
bald eagle
|
| 24 |
+
vulture
|
| 25 |
+
great grey owl
|
| 26 |
+
European fire salamander
|
| 27 |
+
common newt
|
| 28 |
+
eft
|
| 29 |
+
spotted salamander
|
| 30 |
+
axolotl
|
| 31 |
+
bullfrog
|
| 32 |
+
tree frog
|
| 33 |
+
tailed frog
|
| 34 |
+
loggerhead
|
| 35 |
+
leatherback turtle
|
| 36 |
+
mud turtle
|
| 37 |
+
terrapin
|
| 38 |
+
box turtle
|
| 39 |
+
banded gecko
|
| 40 |
+
common iguana
|
| 41 |
+
American chameleon
|
| 42 |
+
whiptail
|
| 43 |
+
agama
|
| 44 |
+
frilled lizard
|
| 45 |
+
alligator lizard
|
| 46 |
+
Gila monster
|
| 47 |
+
green lizard
|
| 48 |
+
African chameleon
|
| 49 |
+
Komodo dragon
|
| 50 |
+
African crocodile
|
| 51 |
+
American alligator
|
| 52 |
+
triceratops
|
| 53 |
+
thunder snake
|
| 54 |
+
ringneck snake
|
| 55 |
+
hognose snake
|
| 56 |
+
green snake
|
| 57 |
+
king snake
|
| 58 |
+
garter snake
|
| 59 |
+
water snake
|
| 60 |
+
vine snake
|
| 61 |
+
night snake
|
| 62 |
+
boa constrictor
|
| 63 |
+
rock python
|
| 64 |
+
Indian cobra
|
| 65 |
+
green mamba
|
| 66 |
+
sea snake
|
| 67 |
+
horned viper
|
| 68 |
+
diamondback
|
| 69 |
+
sidewinder
|
| 70 |
+
trilobite
|
| 71 |
+
harvestman
|
| 72 |
+
scorpion
|
| 73 |
+
black and gold garden spider
|
| 74 |
+
barn spider
|
| 75 |
+
garden spider
|
| 76 |
+
black widow
|
| 77 |
+
tarantula
|
| 78 |
+
wolf spider
|
| 79 |
+
tick
|
| 80 |
+
centipede
|
| 81 |
+
black grouse
|
| 82 |
+
ptarmigan
|
| 83 |
+
ruffed grouse
|
| 84 |
+
prairie chicken
|
| 85 |
+
peacock
|
| 86 |
+
quail
|
| 87 |
+
partridge
|
| 88 |
+
African grey
|
| 89 |
+
macaw
|
| 90 |
+
sulphur-crested cockatoo
|
| 91 |
+
lorikeet
|
| 92 |
+
coucal
|
| 93 |
+
bee eater
|
| 94 |
+
hornbill
|
| 95 |
+
hummingbird
|
| 96 |
+
jacamar
|
| 97 |
+
toucan
|
| 98 |
+
drake
|
| 99 |
+
red-breasted merganser
|
| 100 |
+
goose
|
| 101 |
+
black swan
|
| 102 |
+
tusker
|
| 103 |
+
echidna
|
| 104 |
+
platypus
|
| 105 |
+
wallaby
|
| 106 |
+
koala
|
| 107 |
+
wombat
|
| 108 |
+
jellyfish
|
| 109 |
+
sea anemone
|
| 110 |
+
brain coral
|
| 111 |
+
flatworm
|
| 112 |
+
nematode
|
| 113 |
+
conch
|
| 114 |
+
snail
|
| 115 |
+
slug
|
| 116 |
+
sea slug
|
| 117 |
+
chiton
|
| 118 |
+
chambered nautilus
|
| 119 |
+
Dungeness crab
|
| 120 |
+
rock crab
|
| 121 |
+
fiddler crab
|
| 122 |
+
king crab
|
| 123 |
+
American lobster
|
| 124 |
+
spiny lobster
|
| 125 |
+
crayfish
|
| 126 |
+
hermit crab
|
| 127 |
+
isopod
|
| 128 |
+
white stork
|
| 129 |
+
black stork
|
| 130 |
+
spoonbill
|
| 131 |
+
flamingo
|
| 132 |
+
little blue heron
|
| 133 |
+
American egret
|
| 134 |
+
bittern
|
| 135 |
+
crane
|
| 136 |
+
limpkin
|
| 137 |
+
European gallinule
|
| 138 |
+
American coot
|
| 139 |
+
bustard
|
| 140 |
+
ruddy turnstone
|
| 141 |
+
red-backed sandpiper
|
| 142 |
+
redshank
|
| 143 |
+
dowitcher
|
| 144 |
+
oystercatcher
|
| 145 |
+
pelican
|
| 146 |
+
king penguin
|
| 147 |
+
albatross
|
| 148 |
+
grey whale
|
| 149 |
+
killer whale
|
| 150 |
+
dugong
|
| 151 |
+
sea lion
|
| 152 |
+
Chihuahua
|
| 153 |
+
Japanese spaniel
|
| 154 |
+
Maltese dog
|
| 155 |
+
Pekinese
|
| 156 |
+
Shih-Tzu
|
| 157 |
+
Blenheim spaniel
|
| 158 |
+
papillon
|
| 159 |
+
toy terrier
|
| 160 |
+
Rhodesian ridgeback
|
| 161 |
+
Afghan hound
|
| 162 |
+
basset
|
| 163 |
+
beagle
|
| 164 |
+
bloodhound
|
| 165 |
+
bluetick
|
| 166 |
+
black-and-tan coonhound
|
| 167 |
+
Walker hound
|
| 168 |
+
English foxhound
|
| 169 |
+
redbone
|
| 170 |
+
borzoi
|
| 171 |
+
Irish wolfhound
|
| 172 |
+
Italian greyhound
|
| 173 |
+
whippet
|
| 174 |
+
Ibizan hound
|
| 175 |
+
Norwegian elkhound
|
| 176 |
+
otterhound
|
| 177 |
+
Saluki
|
| 178 |
+
Scottish deerhound
|
| 179 |
+
Weimaraner
|
| 180 |
+
Staffordshire bullterrier
|
| 181 |
+
American Staffordshire terrier
|
| 182 |
+
Bedlington terrier
|
| 183 |
+
Border terrier
|
| 184 |
+
Kerry blue terrier
|
| 185 |
+
Irish terrier
|
| 186 |
+
Norfolk terrier
|
| 187 |
+
Norwich terrier
|
| 188 |
+
Yorkshire terrier
|
| 189 |
+
wire-haired fox terrier
|
| 190 |
+
Lakeland terrier
|
| 191 |
+
Sealyham terrier
|
| 192 |
+
Airedale
|
| 193 |
+
cairn
|
| 194 |
+
Australian terrier
|
| 195 |
+
Dandie Dinmont
|
| 196 |
+
Boston bull
|
| 197 |
+
miniature schnauzer
|
| 198 |
+
giant schnauzer
|
| 199 |
+
standard schnauzer
|
| 200 |
+
Scotch terrier
|
| 201 |
+
Tibetan terrier
|
| 202 |
+
silky terrier
|
| 203 |
+
soft-coated wheaten terrier
|
| 204 |
+
West Highland white terrier
|
| 205 |
+
Lhasa
|
| 206 |
+
flat-coated retriever
|
| 207 |
+
curly-coated retriever
|
| 208 |
+
golden retriever
|
| 209 |
+
Labrador retriever
|
| 210 |
+
Chesapeake Bay retriever
|
| 211 |
+
German short-haired pointer
|
| 212 |
+
vizsla
|
| 213 |
+
English setter
|
| 214 |
+
Irish setter
|
| 215 |
+
Gordon setter
|
| 216 |
+
Brittany spaniel
|
| 217 |
+
clumber
|
| 218 |
+
English springer
|
| 219 |
+
Welsh springer spaniel
|
| 220 |
+
cocker spaniel
|
| 221 |
+
Sussex spaniel
|
| 222 |
+
Irish water spaniel
|
| 223 |
+
kuvasz
|
| 224 |
+
schipperke
|
| 225 |
+
groenendael
|
| 226 |
+
malinois
|
| 227 |
+
briard
|
| 228 |
+
kelpie
|
| 229 |
+
komondor
|
| 230 |
+
Old English sheepdog
|
| 231 |
+
Shetland sheepdog
|
| 232 |
+
collie
|
| 233 |
+
Border collie
|
| 234 |
+
Bouvier des Flandres
|
| 235 |
+
Rottweiler
|
| 236 |
+
German shepherd
|
| 237 |
+
Doberman
|
| 238 |
+
miniature pinscher
|
| 239 |
+
Greater Swiss Mountain dog
|
| 240 |
+
Bernese mountain dog
|
| 241 |
+
Appenzeller
|
| 242 |
+
EntleBucher
|
| 243 |
+
boxer
|
| 244 |
+
bull mastiff
|
| 245 |
+
Tibetan mastiff
|
| 246 |
+
French bulldog
|
| 247 |
+
Great Dane
|
| 248 |
+
Saint Bernard
|
| 249 |
+
Eskimo dog
|
| 250 |
+
malamute
|
| 251 |
+
Siberian husky
|
| 252 |
+
dalmatian
|
| 253 |
+
affenpinscher
|
| 254 |
+
basenji
|
| 255 |
+
pug
|
| 256 |
+
Leonberg
|
| 257 |
+
Newfoundland
|
| 258 |
+
Great Pyrenees
|
| 259 |
+
Samoyed
|
| 260 |
+
Pomeranian
|
| 261 |
+
chow
|
| 262 |
+
keeshond
|
| 263 |
+
Brabancon griffon
|
| 264 |
+
Pembroke
|
| 265 |
+
Cardigan
|
| 266 |
+
toy poodle
|
| 267 |
+
miniature poodle
|
| 268 |
+
standard poodle
|
| 269 |
+
Mexican hairless
|
| 270 |
+
timber wolf
|
| 271 |
+
white wolf
|
| 272 |
+
red wolf
|
| 273 |
+
coyote
|
| 274 |
+
dingo
|
| 275 |
+
dhole
|
| 276 |
+
African hunting dog
|
| 277 |
+
hyena
|
| 278 |
+
red fox
|
| 279 |
+
kit fox
|
| 280 |
+
Arctic fox
|
| 281 |
+
grey fox
|
| 282 |
+
tabby
|
| 283 |
+
tiger cat
|
| 284 |
+
Persian cat
|
| 285 |
+
Siamese cat
|
| 286 |
+
Egyptian cat
|
| 287 |
+
cougar
|
| 288 |
+
lynx
|
| 289 |
+
leopard
|
| 290 |
+
snow leopard
|
| 291 |
+
jaguar
|
| 292 |
+
lion
|
| 293 |
+
tiger
|
| 294 |
+
cheetah
|
| 295 |
+
brown bear
|
| 296 |
+
American black bear
|
| 297 |
+
ice bear
|
| 298 |
+
sloth bear
|
| 299 |
+
mongoose
|
| 300 |
+
meerkat
|
| 301 |
+
tiger beetle
|
| 302 |
+
ladybug
|
| 303 |
+
ground beetle
|
| 304 |
+
long-horned beetle
|
| 305 |
+
leaf beetle
|
| 306 |
+
dung beetle
|
| 307 |
+
rhinoceros beetle
|
| 308 |
+
weevil
|
| 309 |
+
fly
|
| 310 |
+
bee
|
| 311 |
+
ant
|
| 312 |
+
grasshopper
|
| 313 |
+
cricket
|
| 314 |
+
walking stick
|
| 315 |
+
cockroach
|
| 316 |
+
mantis
|
| 317 |
+
cicada
|
| 318 |
+
leafhopper
|
| 319 |
+
lacewing
|
| 320 |
+
dragonfly
|
| 321 |
+
damselfly
|
| 322 |
+
admiral
|
| 323 |
+
ringlet
|
| 324 |
+
monarch
|
| 325 |
+
cabbage butterfly
|
| 326 |
+
sulphur butterfly
|
| 327 |
+
lycaenid
|
| 328 |
+
starfish
|
| 329 |
+
sea urchin
|
| 330 |
+
sea cucumber
|
| 331 |
+
wood rabbit
|
| 332 |
+
hare
|
| 333 |
+
Angora
|
| 334 |
+
hamster
|
| 335 |
+
porcupine
|
| 336 |
+
fox squirrel
|
| 337 |
+
marmot
|
| 338 |
+
beaver
|
| 339 |
+
guinea pig
|
| 340 |
+
sorrel
|
| 341 |
+
zebra
|
| 342 |
+
hog
|
| 343 |
+
wild boar
|
| 344 |
+
warthog
|
| 345 |
+
hippopotamus
|
| 346 |
+
ox
|
| 347 |
+
water buffalo
|
| 348 |
+
bison
|
| 349 |
+
ram
|
| 350 |
+
bighorn
|
| 351 |
+
ibex
|
| 352 |
+
hartebeest
|
| 353 |
+
impala
|
| 354 |
+
gazelle
|
| 355 |
+
Arabian camel
|
| 356 |
+
llama
|
| 357 |
+
weasel
|
| 358 |
+
mink
|
| 359 |
+
polecat
|
| 360 |
+
black-footed ferret
|
| 361 |
+
otter
|
| 362 |
+
skunk
|
| 363 |
+
badger
|
| 364 |
+
armadillo
|
| 365 |
+
three-toed sloth
|
| 366 |
+
orangutan
|
| 367 |
+
gorilla
|
| 368 |
+
chimpanzee
|
| 369 |
+
gibbon
|
| 370 |
+
siamang
|
| 371 |
+
guenon
|
| 372 |
+
patas
|
| 373 |
+
baboon
|
| 374 |
+
macaque
|
| 375 |
+
langur
|
| 376 |
+
colobus
|
| 377 |
+
proboscis monkey
|
| 378 |
+
marmoset
|
| 379 |
+
capuchin
|
| 380 |
+
howler monkey
|
| 381 |
+
titi
|
| 382 |
+
spider monkey
|
| 383 |
+
squirrel monkey
|
| 384 |
+
Madagascar cat
|
| 385 |
+
indri
|
| 386 |
+
Indian elephant
|
| 387 |
+
African elephant
|
| 388 |
+
lesser panda
|
| 389 |
+
giant panda
|
| 390 |
+
barracouta
|
| 391 |
+
eel
|
| 392 |
+
coho
|
| 393 |
+
rock beauty
|
| 394 |
+
anemone fish
|
| 395 |
+
sturgeon
|
| 396 |
+
gar
|
| 397 |
+
lionfish
|
| 398 |
+
puffer
|
| 399 |
+
abacus
|
| 400 |
+
abaya
|
| 401 |
+
academic gown
|
| 402 |
+
accordion
|
| 403 |
+
acoustic guitar
|
| 404 |
+
aircraft carrier
|
| 405 |
+
airliner
|
| 406 |
+
airship
|
| 407 |
+
altar
|
| 408 |
+
ambulance
|
| 409 |
+
amphibian
|
| 410 |
+
analog clock
|
| 411 |
+
apiary
|
| 412 |
+
apron
|
| 413 |
+
ashcan
|
| 414 |
+
assault rifle
|
| 415 |
+
backpack
|
| 416 |
+
bakery
|
| 417 |
+
balance beam
|
| 418 |
+
balloon
|
| 419 |
+
ballpoint
|
| 420 |
+
Band Aid
|
| 421 |
+
banjo
|
| 422 |
+
bannister
|
| 423 |
+
barbell
|
| 424 |
+
barber chair
|
| 425 |
+
barbershop
|
| 426 |
+
barn
|
| 427 |
+
barometer
|
| 428 |
+
barrel
|
| 429 |
+
barrow
|
| 430 |
+
baseball
|
| 431 |
+
basketball
|
| 432 |
+
bassinet
|
| 433 |
+
bassoon
|
| 434 |
+
bathing cap
|
| 435 |
+
bath towel
|
| 436 |
+
bathtub
|
| 437 |
+
beach wagon
|
| 438 |
+
beacon
|
| 439 |
+
beaker
|
| 440 |
+
bearskin
|
| 441 |
+
beer bottle
|
| 442 |
+
beer glass
|
| 443 |
+
bell cote
|
| 444 |
+
bib
|
| 445 |
+
bicycle-built-for-two
|
| 446 |
+
bikini
|
| 447 |
+
binder
|
| 448 |
+
binoculars
|
| 449 |
+
birdhouse
|
| 450 |
+
boathouse
|
| 451 |
+
bobsled
|
| 452 |
+
bolo tie
|
| 453 |
+
bonnet
|
| 454 |
+
bookcase
|
| 455 |
+
bookshop
|
| 456 |
+
bottlecap
|
| 457 |
+
bow
|
| 458 |
+
bow tie
|
| 459 |
+
brass
|
| 460 |
+
brassiere
|
| 461 |
+
breakwater
|
| 462 |
+
breastplate
|
| 463 |
+
broom
|
| 464 |
+
bucket
|
| 465 |
+
buckle
|
| 466 |
+
bulletproof vest
|
| 467 |
+
bullet train
|
| 468 |
+
butcher shop
|
| 469 |
+
cab
|
| 470 |
+
caldron
|
| 471 |
+
candle
|
| 472 |
+
cannon
|
| 473 |
+
canoe
|
| 474 |
+
can opener
|
| 475 |
+
cardigan
|
| 476 |
+
car mirror
|
| 477 |
+
carousel
|
| 478 |
+
carpenters kit
|
| 479 |
+
carton
|
| 480 |
+
car wheel
|
| 481 |
+
cash machine
|
| 482 |
+
cassette
|
| 483 |
+
cassette player
|
| 484 |
+
castle
|
| 485 |
+
catamaran
|
| 486 |
+
CD player
|
| 487 |
+
cello
|
| 488 |
+
cellular telephone
|
| 489 |
+
chain
|
| 490 |
+
chainlink fence
|
| 491 |
+
chain mail
|
| 492 |
+
chain saw
|
| 493 |
+
chest
|
| 494 |
+
chiffonier
|
| 495 |
+
chime
|
| 496 |
+
china cabinet
|
| 497 |
+
Christmas stocking
|
| 498 |
+
church
|
| 499 |
+
cinema
|
| 500 |
+
cleaver
|
| 501 |
+
cliff dwelling
|
| 502 |
+
cloak
|
| 503 |
+
clog
|
| 504 |
+
cocktail shaker
|
| 505 |
+
coffee mug
|
| 506 |
+
coffeepot
|
| 507 |
+
coil
|
| 508 |
+
combination lock
|
| 509 |
+
computer keyboard
|
| 510 |
+
confectionery
|
| 511 |
+
container ship
|
| 512 |
+
convertible
|
| 513 |
+
corkscrew
|
| 514 |
+
cornet
|
| 515 |
+
cowboy boot
|
| 516 |
+
cowboy hat
|
| 517 |
+
cradle
|
| 518 |
+
crane
|
| 519 |
+
crash helmet
|
| 520 |
+
crate
|
| 521 |
+
crib
|
| 522 |
+
Crock Pot
|
| 523 |
+
croquet ball
|
| 524 |
+
crutch
|
| 525 |
+
cuirass
|
| 526 |
+
dam
|
| 527 |
+
desk
|
| 528 |
+
desktop computer
|
| 529 |
+
dial telephone
|
| 530 |
+
diaper
|
| 531 |
+
digital clock
|
| 532 |
+
digital watch
|
| 533 |
+
dining table
|
| 534 |
+
dishrag
|
| 535 |
+
dishwasher
|
| 536 |
+
disk brake
|
| 537 |
+
dock
|
| 538 |
+
dogsled
|
| 539 |
+
dome
|
| 540 |
+
doormat
|
| 541 |
+
drilling platform
|
| 542 |
+
drum
|
| 543 |
+
drumstick
|
| 544 |
+
dumbbell
|
| 545 |
+
Dutch oven
|
| 546 |
+
electric fan
|
| 547 |
+
electric guitar
|
| 548 |
+
electric locomotive
|
| 549 |
+
entertainment center
|
| 550 |
+
envelope
|
| 551 |
+
espresso maker
|
| 552 |
+
face powder
|
| 553 |
+
feather boa
|
| 554 |
+
file
|
| 555 |
+
fireboat
|
| 556 |
+
fire engine
|
| 557 |
+
fire screen
|
| 558 |
+
flagpole
|
| 559 |
+
flute
|
| 560 |
+
folding chair
|
| 561 |
+
football helmet
|
| 562 |
+
forklift
|
| 563 |
+
fountain
|
| 564 |
+
fountain pen
|
| 565 |
+
four-poster
|
| 566 |
+
freight car
|
| 567 |
+
French horn
|
| 568 |
+
frying pan
|
| 569 |
+
fur coat
|
| 570 |
+
garbage truck
|
| 571 |
+
gasmask
|
| 572 |
+
gas pump
|
| 573 |
+
goblet
|
| 574 |
+
go-kart
|
| 575 |
+
golf ball
|
| 576 |
+
golfcart
|
| 577 |
+
gondola
|
| 578 |
+
gong
|
| 579 |
+
gown
|
| 580 |
+
grand piano
|
| 581 |
+
greenhouse
|
| 582 |
+
grille
|
| 583 |
+
grocery store
|
| 584 |
+
guillotine
|
| 585 |
+
hair slide
|
| 586 |
+
hair spray
|
| 587 |
+
half track
|
| 588 |
+
hammer
|
| 589 |
+
hamper
|
| 590 |
+
hand blower
|
| 591 |
+
hand-held computer
|
| 592 |
+
handkerchief
|
| 593 |
+
hard disc
|
| 594 |
+
harmonica
|
| 595 |
+
harp
|
| 596 |
+
harvester
|
| 597 |
+
hatchet
|
| 598 |
+
holster
|
| 599 |
+
home theater
|
| 600 |
+
honeycomb
|
| 601 |
+
hook
|
| 602 |
+
hoopskirt
|
| 603 |
+
horizontal bar
|
| 604 |
+
horse cart
|
| 605 |
+
hourglass
|
| 606 |
+
iPod
|
| 607 |
+
iron
|
| 608 |
+
jack-o-lantern
|
| 609 |
+
jean
|
| 610 |
+
jeep
|
| 611 |
+
jersey
|
| 612 |
+
jigsaw puzzle
|
| 613 |
+
jinrikisha
|
| 614 |
+
joystick
|
| 615 |
+
kimono
|
| 616 |
+
knee pad
|
| 617 |
+
knot
|
| 618 |
+
lab coat
|
| 619 |
+
ladle
|
| 620 |
+
lampshade
|
| 621 |
+
laptop
|
| 622 |
+
lawn mower
|
| 623 |
+
lens cap
|
| 624 |
+
letter opener
|
| 625 |
+
library
|
| 626 |
+
lifeboat
|
| 627 |
+
lighter
|
| 628 |
+
limousine
|
| 629 |
+
liner
|
| 630 |
+
lipstick
|
| 631 |
+
Loafer
|
| 632 |
+
lotion
|
| 633 |
+
loudspeaker
|
| 634 |
+
loupe
|
| 635 |
+
lumbermill
|
| 636 |
+
magnetic compass
|
| 637 |
+
mailbag
|
| 638 |
+
mailbox
|
| 639 |
+
maillot
|
| 640 |
+
maillot
|
| 641 |
+
manhole cover
|
| 642 |
+
maraca
|
| 643 |
+
marimba
|
| 644 |
+
mask
|
| 645 |
+
matchstick
|
| 646 |
+
maypole
|
| 647 |
+
maze
|
| 648 |
+
measuring cup
|
| 649 |
+
medicine chest
|
| 650 |
+
megalith
|
| 651 |
+
microphone
|
| 652 |
+
microwave
|
| 653 |
+
military uniform
|
| 654 |
+
milk can
|
| 655 |
+
minibus
|
| 656 |
+
miniskirt
|
| 657 |
+
minivan
|
| 658 |
+
missile
|
| 659 |
+
mitten
|
| 660 |
+
mixing bowl
|
| 661 |
+
mobile home
|
| 662 |
+
Model T
|
| 663 |
+
modem
|
| 664 |
+
monastery
|
| 665 |
+
monitor
|
| 666 |
+
moped
|
| 667 |
+
mortar
|
| 668 |
+
mortarboard
|
| 669 |
+
mosque
|
| 670 |
+
mosquito net
|
| 671 |
+
motor scooter
|
| 672 |
+
mountain bike
|
| 673 |
+
mountain tent
|
| 674 |
+
mouse
|
| 675 |
+
mousetrap
|
| 676 |
+
moving van
|
| 677 |
+
muzzle
|
| 678 |
+
nail
|
| 679 |
+
neck brace
|
| 680 |
+
necklace
|
| 681 |
+
nipple
|
| 682 |
+
notebook
|
| 683 |
+
obelisk
|
| 684 |
+
oboe
|
| 685 |
+
ocarina
|
| 686 |
+
odometer
|
| 687 |
+
oil filter
|
| 688 |
+
organ
|
| 689 |
+
oscilloscope
|
| 690 |
+
overskirt
|
| 691 |
+
oxcart
|
| 692 |
+
oxygen mask
|
| 693 |
+
packet
|
| 694 |
+
paddle
|
| 695 |
+
paddlewheel
|
| 696 |
+
padlock
|
| 697 |
+
paintbrush
|
| 698 |
+
pajama
|
| 699 |
+
palace
|
| 700 |
+
panpipe
|
| 701 |
+
paper towel
|
| 702 |
+
parachute
|
| 703 |
+
parallel bars
|
| 704 |
+
park bench
|
| 705 |
+
parking meter
|
| 706 |
+
passenger car
|
| 707 |
+
patio
|
| 708 |
+
pay-phone
|
| 709 |
+
pedestal
|
| 710 |
+
pencil box
|
| 711 |
+
pencil sharpener
|
| 712 |
+
perfume
|
| 713 |
+
Petri dish
|
| 714 |
+
photocopier
|
| 715 |
+
pick
|
| 716 |
+
pickelhaube
|
| 717 |
+
picket fence
|
| 718 |
+
pickup
|
| 719 |
+
pier
|
| 720 |
+
piggy bank
|
| 721 |
+
pill bottle
|
| 722 |
+
pillow
|
| 723 |
+
ping-pong ball
|
| 724 |
+
pinwheel
|
| 725 |
+
pirate
|
| 726 |
+
pitcher
|
| 727 |
+
plane
|
| 728 |
+
planetarium
|
| 729 |
+
plastic bag
|
| 730 |
+
plate rack
|
| 731 |
+
plow
|
| 732 |
+
plunger
|
| 733 |
+
Polaroid camera
|
| 734 |
+
pole
|
| 735 |
+
police van
|
| 736 |
+
poncho
|
| 737 |
+
pool table
|
| 738 |
+
pop bottle
|
| 739 |
+
pot
|
| 740 |
+
potters wheel
|
| 741 |
+
power drill
|
| 742 |
+
prayer rug
|
| 743 |
+
printer
|
| 744 |
+
prison
|
| 745 |
+
projectile
|
| 746 |
+
projector
|
| 747 |
+
puck
|
| 748 |
+
punching bag
|
| 749 |
+
purse
|
| 750 |
+
quill
|
| 751 |
+
quilt
|
| 752 |
+
racer
|
| 753 |
+
racket
|
| 754 |
+
radiator
|
| 755 |
+
radio
|
| 756 |
+
radio telescope
|
| 757 |
+
rain barrel
|
| 758 |
+
recreational vehicle
|
| 759 |
+
reel
|
| 760 |
+
reflex camera
|
| 761 |
+
refrigerator
|
| 762 |
+
remote control
|
| 763 |
+
restaurant
|
| 764 |
+
revolver
|
| 765 |
+
rifle
|
| 766 |
+
rocking chair
|
| 767 |
+
rotisserie
|
| 768 |
+
rubber eraser
|
| 769 |
+
rugby ball
|
| 770 |
+
rule
|
| 771 |
+
running shoe
|
| 772 |
+
safe
|
| 773 |
+
safety pin
|
| 774 |
+
saltshaker
|
| 775 |
+
sandal
|
| 776 |
+
sarong
|
| 777 |
+
sax
|
| 778 |
+
scabbard
|
| 779 |
+
scale
|
| 780 |
+
school bus
|
| 781 |
+
schooner
|
| 782 |
+
scoreboard
|
| 783 |
+
screen
|
| 784 |
+
screw
|
| 785 |
+
screwdriver
|
| 786 |
+
seat belt
|
| 787 |
+
sewing machine
|
| 788 |
+
shield
|
| 789 |
+
shoe shop
|
| 790 |
+
shoji
|
| 791 |
+
shopping basket
|
| 792 |
+
shopping cart
|
| 793 |
+
shovel
|
| 794 |
+
shower cap
|
| 795 |
+
shower curtain
|
| 796 |
+
ski
|
| 797 |
+
ski mask
|
| 798 |
+
sleeping bag
|
| 799 |
+
slide rule
|
| 800 |
+
sliding door
|
| 801 |
+
slot
|
| 802 |
+
snorkel
|
| 803 |
+
snowmobile
|
| 804 |
+
snowplow
|
| 805 |
+
soap dispenser
|
| 806 |
+
soccer ball
|
| 807 |
+
sock
|
| 808 |
+
solar dish
|
| 809 |
+
sombrero
|
| 810 |
+
soup bowl
|
| 811 |
+
space bar
|
| 812 |
+
space heater
|
| 813 |
+
space shuttle
|
| 814 |
+
spatula
|
| 815 |
+
speedboat
|
| 816 |
+
spider web
|
| 817 |
+
spindle
|
| 818 |
+
sports car
|
| 819 |
+
spotlight
|
| 820 |
+
stage
|
| 821 |
+
steam locomotive
|
| 822 |
+
steel arch bridge
|
| 823 |
+
steel drum
|
| 824 |
+
stethoscope
|
| 825 |
+
stole
|
| 826 |
+
stone wall
|
| 827 |
+
stopwatch
|
| 828 |
+
stove
|
| 829 |
+
strainer
|
| 830 |
+
streetcar
|
| 831 |
+
stretcher
|
| 832 |
+
studio couch
|
| 833 |
+
stupa
|
| 834 |
+
submarine
|
| 835 |
+
suit
|
| 836 |
+
sundial
|
| 837 |
+
sunglass
|
| 838 |
+
sunglasses
|
| 839 |
+
sunscreen
|
| 840 |
+
suspension bridge
|
| 841 |
+
swab
|
| 842 |
+
sweatshirt
|
| 843 |
+
swimming trunks
|
| 844 |
+
swing
|
| 845 |
+
switch
|
| 846 |
+
syringe
|
| 847 |
+
table lamp
|
| 848 |
+
tank
|
| 849 |
+
tape player
|
| 850 |
+
teapot
|
| 851 |
+
teddy
|
| 852 |
+
television
|
| 853 |
+
tennis ball
|
| 854 |
+
thatch
|
| 855 |
+
theater curtain
|
| 856 |
+
thimble
|
| 857 |
+
thresher
|
| 858 |
+
throne
|
| 859 |
+
tile roof
|
| 860 |
+
toaster
|
| 861 |
+
tobacco shop
|
| 862 |
+
toilet seat
|
| 863 |
+
torch
|
| 864 |
+
totem pole
|
| 865 |
+
tow truck
|
| 866 |
+
toyshop
|
| 867 |
+
tractor
|
| 868 |
+
trailer truck
|
| 869 |
+
tray
|
| 870 |
+
trench coat
|
| 871 |
+
tricycle
|
| 872 |
+
trimaran
|
| 873 |
+
tripod
|
| 874 |
+
triumphal arch
|
| 875 |
+
trolleybus
|
| 876 |
+
trombone
|
| 877 |
+
tub
|
| 878 |
+
turnstile
|
| 879 |
+
typewriter keyboard
|
| 880 |
+
umbrella
|
| 881 |
+
unicycle
|
| 882 |
+
upright
|
| 883 |
+
vacuum
|
| 884 |
+
vase
|
| 885 |
+
vault
|
| 886 |
+
velvet
|
| 887 |
+
vending machine
|
| 888 |
+
vestment
|
| 889 |
+
viaduct
|
| 890 |
+
violin
|
| 891 |
+
volleyball
|
| 892 |
+
waffle iron
|
| 893 |
+
wall clock
|
| 894 |
+
wallet
|
| 895 |
+
wardrobe
|
| 896 |
+
warplane
|
| 897 |
+
washbasin
|
| 898 |
+
washer
|
| 899 |
+
water bottle
|
| 900 |
+
water jug
|
| 901 |
+
water tower
|
| 902 |
+
whiskey jug
|
| 903 |
+
whistle
|
| 904 |
+
wig
|
| 905 |
+
window screen
|
| 906 |
+
window shade
|
| 907 |
+
Windsor tie
|
| 908 |
+
wine bottle
|
| 909 |
+
wing
|
| 910 |
+
wok
|
| 911 |
+
wooden spoon
|
| 912 |
+
wool
|
| 913 |
+
worm fence
|
| 914 |
+
wreck
|
| 915 |
+
yawl
|
| 916 |
+
yurt
|
| 917 |
+
web site
|
| 918 |
+
comic book
|
| 919 |
+
crossword puzzle
|
| 920 |
+
street sign
|
| 921 |
+
traffic light
|
| 922 |
+
book jacket
|
| 923 |
+
menu
|
| 924 |
+
plate
|
| 925 |
+
guacamole
|
| 926 |
+
consomme
|
| 927 |
+
hot pot
|
| 928 |
+
trifle
|
| 929 |
+
ice cream
|
| 930 |
+
ice lolly
|
| 931 |
+
French loaf
|
| 932 |
+
bagel
|
| 933 |
+
pretzel
|
| 934 |
+
cheeseburger
|
| 935 |
+
hotdog
|
| 936 |
+
mashed potato
|
| 937 |
+
head cabbage
|
| 938 |
+
broccoli
|
| 939 |
+
cauliflower
|
| 940 |
+
zucchini
|
| 941 |
+
spaghetti squash
|
| 942 |
+
acorn squash
|
| 943 |
+
butternut squash
|
| 944 |
+
cucumber
|
| 945 |
+
artichoke
|
| 946 |
+
bell pepper
|
| 947 |
+
cardoon
|
| 948 |
+
mushroom
|
| 949 |
+
Granny Smith
|
| 950 |
+
strawberry
|
| 951 |
+
orange
|
| 952 |
+
lemon
|
| 953 |
+
fig
|
| 954 |
+
pineapple
|
| 955 |
+
banana
|
| 956 |
+
jackfruit
|
| 957 |
+
custard apple
|
| 958 |
+
pomegranate
|
| 959 |
+
hay
|
| 960 |
+
carbonara
|
| 961 |
+
chocolate sauce
|
| 962 |
+
dough
|
| 963 |
+
meat loaf
|
| 964 |
+
pizza
|
| 965 |
+
potpie
|
| 966 |
+
burrito
|
| 967 |
+
red wine
|
| 968 |
+
espresso
|
| 969 |
+
cup
|
| 970 |
+
eggnog
|
| 971 |
+
alp
|
| 972 |
+
bubble
|
| 973 |
+
cliff
|
| 974 |
+
coral reef
|
| 975 |
+
geyser
|
| 976 |
+
lakeside
|
| 977 |
+
promontory
|
| 978 |
+
sandbar
|
| 979 |
+
seashore
|
| 980 |
+
valley
|
| 981 |
+
volcano
|
| 982 |
+
ballplayer
|
| 983 |
+
groom
|
| 984 |
+
scuba diver
|
| 985 |
+
rapeseed
|
| 986 |
+
daisy
|
| 987 |
+
yellow ladys slipper
|
| 988 |
+
corn
|
| 989 |
+
acorn
|
| 990 |
+
hip
|
| 991 |
+
buckeye
|
| 992 |
+
coral fungus
|
| 993 |
+
agaric
|
| 994 |
+
gyromitra
|
| 995 |
+
stinkhorn
|
| 996 |
+
earthstar
|
| 997 |
+
hen-of-the-woods
|
| 998 |
+
bolete
|
| 999 |
+
ear
|
| 1000 |
+
toilet tissue
|
ppresnet.py
ADDED
|
@@ -0,0 +1,67 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# This file is part of OpenCV Zoo project.
|
| 2 |
+
# It is subject to the license terms in the LICENSE file found in the same directory.
|
| 3 |
+
#
|
| 4 |
+
# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
|
| 5 |
+
# Third party copyrights are property of their respective owners.
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
import numpy as np
|
| 9 |
+
import cv2 as cv
|
| 10 |
+
|
| 11 |
+
class PPResNet:
|
| 12 |
+
def __init__(self, modelPath, labelPath):
|
| 13 |
+
self._modelPath = modelPath
|
| 14 |
+
self._model = cv.dnn.readNet(self._modelPath)
|
| 15 |
+
self._labelPath = labelPath
|
| 16 |
+
|
| 17 |
+
self._inputNames = ''
|
| 18 |
+
self._outputNames = ['save_infer_model/scale_0.tmp_0']
|
| 19 |
+
self._inputSize = [224, 224]
|
| 20 |
+
self._mean = np.array([0.485, 0.456, 0.406])[np.newaxis, np.newaxis, :]
|
| 21 |
+
self._std = np.array([0.229, 0.224, 0.225])[np.newaxis, np.newaxis, :]
|
| 22 |
+
|
| 23 |
+
# load labels
|
| 24 |
+
self._labels = self._load_labels()
|
| 25 |
+
|
| 26 |
+
def _load_labels(self):
|
| 27 |
+
labels = []
|
| 28 |
+
with open(self._labelPath, 'r') as f:
|
| 29 |
+
for line in f:
|
| 30 |
+
labels.append(line.strip())
|
| 31 |
+
return labels
|
| 32 |
+
|
| 33 |
+
@property
|
| 34 |
+
def name(self):
|
| 35 |
+
return self.__class__.__name__
|
| 36 |
+
|
| 37 |
+
def setBackend(self, backend_id):
|
| 38 |
+
self._model.setPreferableBackend(backend_id)
|
| 39 |
+
|
| 40 |
+
def setTarget(self, target_id):
|
| 41 |
+
self._model.setPreferableTarget(target_id)
|
| 42 |
+
|
| 43 |
+
def _preprocess(self, image):
|
| 44 |
+
image = image.astype(np.float32, copy=False) / 255.0
|
| 45 |
+
image -= self._mean
|
| 46 |
+
image /= self._std
|
| 47 |
+
return cv.dnn.blobFromImage(image)
|
| 48 |
+
|
| 49 |
+
def infer(self, image):
|
| 50 |
+
assert image.shape[0] == self._inputSize[1], '{} (height of input image) != {} (preset height)'.format(image.shape[0], self._inputSize[1])
|
| 51 |
+
assert image.shape[1] == self._inputSize[0], '{} (width of input image) != {} (preset width)'.format(image.shape[1], self._inputSize[0])
|
| 52 |
+
|
| 53 |
+
# Preprocess
|
| 54 |
+
inputBlob = self._preprocess(image)
|
| 55 |
+
|
| 56 |
+
# Forward
|
| 57 |
+
self._model.setInput(inputBlob, self._inputNames)
|
| 58 |
+
outputBlob = self._model.forward(self._outputNames)
|
| 59 |
+
|
| 60 |
+
# Postprocess
|
| 61 |
+
results = self._postprocess(outputBlob)
|
| 62 |
+
|
| 63 |
+
return results
|
| 64 |
+
|
| 65 |
+
def _postprocess(self, outputBlob):
|
| 66 |
+
class_id = np.argmax(outputBlob[0])
|
| 67 |
+
return self._labels[class_id]
|