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+bee keeping +belly dancing +bench pressing +bending back +bending metal +biking through snow +blasting sand +blowing glass +blowing leaves +blowing nose +blowing out candles +bobsledding +bookbinding +bouncing on trampoline +bowling +braiding hair +breading or breadcrumbing +breakdancing +brush painting +brushing hair +brushing teeth +building cabinet +building shed +bungee jumping +busking +canoeing or kayaking +capoeira +carrying baby +cartwheeling +carving pumpkin +catching fish +catching or throwing baseball +catching or throwing frisbee +catching or throwing softball +celebrating +changing oil +changing wheel +checking tires +cheerleading +chopping wood +clapping +clay pottery making +clean and jerk +cleaning floor +cleaning gutters +cleaning pool +cleaning shoes +cleaning toilet +cleaning windows +climbing a rope +climbing ladder +climbing tree +contact juggling +cooking chicken +cooking egg +cooking on campfire +cooking sausages +counting money +country line dancing +cracking neck +crawling baby +crossing river +crying +curling hair +cutting nails +cutting pineapple +cutting watermelon +dancing ballet +dancing charleston +dancing gangnam style +dancing macarena +deadlifting +decorating the christmas tree +digging +dining +disc golfing +diving cliff +dodgeball +doing aerobics +doing laundry +doing nails +drawing +dribbling basketball +drinking +drinking beer +drinking shots +driving car +driving tractor +drop kicking +drumming fingers +dunking basketball +dying hair +eating burger +eating cake +eating carrots +eating chips +eating doughnuts +eating hotdog +eating ice cream +eating spaghetti +eating watermelon +egg hunting +exercising arm +exercising with an exercise ball +extinguishing fire +faceplanting +feeding birds +feeding fish +feeding goats +filling eyebrows +finger snapping +fixing hair +flipping pancake +flying kite +folding clothes +folding napkins +folding paper +front raises +frying vegetables +garbage collecting +gargling +getting a haircut +getting a tattoo +giving or receiving award +golf chipping +golf driving +golf putting +grinding meat +grooming dog +grooming horse +gymnastics tumbling +hammer throw +headbanging +headbutting +high jump +high kick +hitting baseball +hockey stop +holding snake +hopscotch +hoverboarding +hugging +hula hooping +hurdling +hurling (sport) +ice climbing +ice fishing +ice skating +ironing +javelin throw +jetskiing +jogging +juggling balls +juggling fire +juggling soccer ball +jumping into pool +jumpstyle dancing +kicking field goal +kicking soccer ball +kissing +kitesurfing +knitting +krumping +laughing +laying bricks +long jump +lunge +making a cake +making a sandwich +making bed +making jewelry +making pizza +making snowman +making sushi +making tea +marching +massaging back +massaging feet +massaging legs +massaging person's head +milking cow +mopping floor +motorcycling +moving furniture +mowing lawn +news anchoring +opening bottle +opening present +paragliding +parasailing +parkour +passing American football (in game) +passing American football (not in game) +peeling apples +peeling potatoes +petting animal (not cat) +petting cat +picking fruit +planting trees +plastering +playing accordion +playing badminton +playing bagpipes +playing basketball +playing bass guitar +playing cards +playing cello +playing chess +playing clarinet +playing controller +playing cricket +playing cymbals +playing didgeridoo +playing drums +playing flute +playing guitar +playing harmonica +playing harp +playing ice hockey +playing keyboard +playing kickball +playing monopoly +playing organ +playing paintball +playing piano +playing poker +playing recorder +playing saxophone +playing squash or racquetball +playing tennis +playing trombone +playing trumpet +playing ukulele +playing violin +playing volleyball +playing xylophone +pole vault +presenting weather forecast +pull ups +pumping fist +pumping gas +punching bag +punching person (boxing) +push up +pushing car +pushing cart +pushing wheelchair +reading book +reading newspaper +recording music +riding a bike +riding camel +riding elephant +riding mechanical bull +riding mountain bike +riding mule +riding or walking with horse +riding scooter +riding unicycle +ripping paper +robot dancing +rock climbing +rock scissors paper +roller skating +running on treadmill +sailing +salsa dancing +sanding floor +scrambling eggs +scuba diving +setting table +shaking hands +shaking head +sharpening knives +sharpening pencil +shaving head +shaving legs +shearing sheep +shining shoes +shooting basketball +shooting goal (soccer) +shot put +shoveling snow +shredding paper +shuffling cards +side kick +sign language interpreting +singing +situp +skateboarding +ski jumping +skiing (not slalom or crosscountry) +skiing crosscountry +skiing slalom +skipping rope +skydiving +slacklining +slapping +sled dog racing +smoking +smoking hookah +snatch weight lifting +sneezing +sniffing +snorkeling +snowboarding +snowkiting +snowmobiling +somersaulting +spinning poi +spray painting +spraying +springboard diving +squat +sticking tongue out +stomping grapes +stretching arm +stretching leg +strumming guitar +surfing crowd +surfing water +sweeping floor +swimming backstroke +swimming breast stroke +swimming butterfly stroke +swing dancing +swinging legs +swinging on something +sword fighting +tai chi +taking a shower +tango dancing +tap dancing +tapping guitar +tapping pen +tasting beer +tasting food +testifying +texting +throwing axe +throwing ball +throwing discus +tickling +tobogganing +tossing coin +tossing salad +training dog +trapezing +trimming or shaving beard +trimming trees +triple jump +tying bow tie +tying knot (not on a tie) +tying tie +unboxing +unloading truck +using computer +using remote controller (not gaming) +using segway +vault +waiting in line +walking the dog +washing dishes +washing feet +washing hair +washing hands +water skiing +water sliding +watering plants +waxing back +waxing chest +waxing eyebrows +waxing legs +weaving basket +welding +whistling +windsurfing +wrapping present +wrestling +writing +yawning +yoga +zumba diff --git a/data/dnn/classification_classes_ILSVRC2012.txt b/data/dnn/classification_classes_ILSVRC2012.txt new file mode 100644 index 0000000..a509c00 --- /dev/null +++ b/data/dnn/classification_classes_ILSVRC2012.txt @@ -0,0 +1,1000 @@ +tench, Tinca tinca +goldfish, Carassius auratus +great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +tiger shark, Galeocerdo cuvieri +hammerhead, hammerhead shark +electric ray, crampfish, numbfish, torpedo +stingray +cock +hen +ostrich, Struthio camelus +brambling, Fringilla montifringilla +goldfinch, Carduelis carduelis +house finch, linnet, Carpodacus mexicanus +junco, snowbird +indigo bunting, indigo finch, indigo bird, Passerina cyanea +robin, American robin, Turdus migratorius +bulbul +jay +magpie +chickadee +water ouzel, dipper +kite +bald eagle, American eagle, Haliaeetus leucocephalus +vulture +great grey owl, great gray owl, Strix nebulosa +European fire salamander, Salamandra salamandra +common newt, Triturus vulgaris +eft +spotted salamander, Ambystoma maculatum +axolotl, mud puppy, Ambystoma mexicanum +bullfrog, Rana catesbeiana +tree frog, tree-frog +tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +loggerhead, loggerhead turtle, Caretta caretta +leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +mud turtle +terrapin +box turtle, box tortoise +banded gecko +common iguana, iguana, Iguana iguana +American chameleon, anole, Anolis carolinensis +whiptail, whiptail lizard +agama +frilled lizard, Chlamydosaurus kingi +alligator lizard +Gila monster, Heloderma suspectum +green lizard, Lacerta viridis +African chameleon, Chamaeleo chamaeleon +Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +African crocodile, Nile crocodile, Crocodylus niloticus +American alligator, Alligator mississipiensis +triceratops +thunder snake, worm snake, Carphophis amoenus +ringneck snake, ring-necked snake, ring snake +hognose snake, puff adder, sand viper +green snake, grass snake +king snake, kingsnake +garter snake, grass snake +water snake +vine snake +night snake, Hypsiglena torquata +boa constrictor, Constrictor constrictor +rock python, rock snake, Python sebae +Indian cobra, Naja naja +green mamba +sea snake +horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +diamondback, diamondback rattlesnake, Crotalus adamanteus +sidewinder, horned rattlesnake, Crotalus cerastes +trilobite +harvestman, daddy longlegs, Phalangium opilio +scorpion +black and gold garden spider, Argiope aurantia +barn spider, Araneus cavaticus +garden spider, Aranea diademata +black widow, Latrodectus mactans +tarantula +wolf spider, hunting spider +tick +centipede +black grouse +ptarmigan +ruffed grouse, partridge, Bonasa umbellus +prairie chicken, prairie grouse, prairie fowl +peacock +quail +partridge +African grey, African gray, Psittacus erithacus +macaw +sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +lorikeet +coucal +bee eater +hornbill +hummingbird +jacamar +toucan +drake +red-breasted merganser, Mergus serrator +goose +black swan, Cygnus atratus +tusker +echidna, spiny anteater, anteater +platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +wallaby, brush kangaroo +koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +wombat +jellyfish +sea anemone, anemone +brain coral +flatworm, platyhelminth +nematode, nematode worm, roundworm +conch +snail +slug +sea slug, nudibranch +chiton, coat-of-mail shell, sea cradle, polyplacophore +chambered nautilus, pearly nautilus, nautilus +Dungeness crab, Cancer magister +rock crab, Cancer irroratus +fiddler crab +king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +American lobster, Northern lobster, Maine lobster, Homarus americanus +spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +crayfish, crawfish, crawdad, crawdaddy +hermit crab +isopod +white stork, Ciconia ciconia +black stork, Ciconia nigra +spoonbill +flamingo +little blue heron, Egretta caerulea +American egret, great white heron, Egretta albus +bittern +crane +limpkin, Aramus pictus +European gallinule, Porphyrio porphyrio +American coot, marsh hen, mud hen, water hen, Fulica americana +bustard +ruddy turnstone, Arenaria interpres +red-backed sandpiper, dunlin, Erolia alpina +redshank, Tringa totanus +dowitcher +oystercatcher, oyster catcher +pelican +king penguin, Aptenodytes patagonica +albatross, mollymawk +grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +killer whale, killer, orca, grampus, sea wolf, Orcinus orca +dugong, Dugong dugon +sea lion +Chihuahua +Japanese spaniel +Maltese dog, Maltese terrier, Maltese +Pekinese, Pekingese, Peke +Shih-Tzu +Blenheim spaniel +papillon +toy terrier +Rhodesian ridgeback +Afghan hound, Afghan +basset, basset hound +beagle +bloodhound, sleuthhound +bluetick +black-and-tan coonhound +Walker hound, Walker foxhound +English foxhound +redbone +borzoi, Russian wolfhound +Irish wolfhound +Italian greyhound +whippet +Ibizan hound, Ibizan Podenco +Norwegian elkhound, elkhound +otterhound, otter hound +Saluki, gazelle hound +Scottish deerhound, deerhound +Weimaraner +Staffordshire bullterrier, Staffordshire bull terrier +American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +Bedlington terrier +Border terrier +Kerry blue terrier +Irish terrier +Norfolk terrier +Norwich terrier +Yorkshire terrier +wire-haired fox terrier +Lakeland terrier +Sealyham terrier, Sealyham +Airedale, Airedale terrier +cairn, cairn terrier +Australian terrier +Dandie Dinmont, Dandie Dinmont terrier +Boston bull, Boston terrier +miniature schnauzer +giant schnauzer +standard schnauzer +Scotch terrier, Scottish terrier, Scottie +Tibetan terrier, chrysanthemum dog +silky terrier, Sydney silky +soft-coated wheaten terrier +West Highland white terrier +Lhasa, Lhasa apso +flat-coated retriever +curly-coated retriever +golden retriever +Labrador retriever +Chesapeake Bay retriever +German short-haired pointer +vizsla, Hungarian pointer +English setter +Irish setter, red setter +Gordon setter +Brittany spaniel +clumber, clumber spaniel +English springer, English springer spaniel +Welsh springer spaniel +cocker spaniel, English cocker spaniel, cocker +Sussex spaniel +Irish water spaniel +kuvasz +schipperke +groenendael +malinois +briard +kelpie +komondor +Old English sheepdog, bobtail +Shetland sheepdog, Shetland sheep dog, Shetland +collie +Border collie +Bouvier des Flandres, Bouviers des Flandres +Rottweiler +German shepherd, German shepherd dog, German police dog, alsatian +Doberman, Doberman pinscher +miniature pinscher +Greater Swiss Mountain dog +Bernese mountain dog +Appenzeller +EntleBucher +boxer +bull mastiff +Tibetan mastiff +French bulldog +Great Dane +Saint Bernard, St Bernard +Eskimo dog, husky +malamute, malemute, Alaskan malamute +Siberian husky +dalmatian, coach dog, carriage dog +affenpinscher, monkey pinscher, monkey dog +basenji +pug, pug-dog +Leonberg +Newfoundland, Newfoundland dog +Great Pyrenees +Samoyed, Samoyede +Pomeranian +chow, chow chow +keeshond +Brabancon griffon +Pembroke, Pembroke Welsh corgi +Cardigan, Cardigan Welsh corgi +toy poodle +miniature poodle +standard poodle +Mexican hairless +timber wolf, grey wolf, gray wolf, Canis lupus +white wolf, Arctic wolf, Canis lupus tundrarum +red wolf, maned wolf, Canis rufus, Canis niger +coyote, prairie wolf, brush wolf, Canis latrans +dingo, warrigal, warragal, Canis dingo +dhole, Cuon alpinus +African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +hyena, hyaena +red fox, Vulpes vulpes +kit fox, Vulpes macrotis +Arctic fox, white fox, Alopex lagopus +grey fox, gray fox, Urocyon cinereoargenteus +tabby, tabby cat +tiger cat +Persian cat +Siamese cat, Siamese +Egyptian cat +cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +lynx, catamount +leopard, Panthera pardus +snow leopard, ounce, Panthera uncia +jaguar, panther, Panthera onca, Felis onca +lion, king of beasts, Panthera leo +tiger, Panthera tigris +cheetah, chetah, Acinonyx jubatus +brown bear, bruin, Ursus arctos +American black bear, black bear, Ursus americanus, Euarctos americanus +ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +sloth bear, Melursus ursinus, Ursus ursinus +mongoose +meerkat, mierkat +tiger beetle +ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +ground beetle, carabid beetle +long-horned beetle, longicorn, longicorn beetle +leaf beetle, chrysomelid +dung beetle +rhinoceros beetle +weevil +fly +bee +ant, emmet, pismire +grasshopper, hopper +cricket +walking stick, walkingstick, stick insect +cockroach, roach +mantis, mantid +cicada, cicala +leafhopper +lacewing, lacewing fly +dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +damselfly +admiral +ringlet, ringlet butterfly +monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +cabbage butterfly +sulphur butterfly, sulfur butterfly +lycaenid, lycaenid butterfly +starfish, sea star +sea urchin +sea cucumber, holothurian +wood rabbit, cottontail, cottontail rabbit +hare +Angora, Angora rabbit +hamster +porcupine, hedgehog +fox squirrel, eastern fox squirrel, Sciurus niger +marmot +beaver +guinea pig, Cavia cobaya +sorrel +zebra +hog, pig, grunter, squealer, Sus scrofa +wild boar, boar, Sus scrofa +warthog +hippopotamus, hippo, river horse, Hippopotamus amphibius +ox +water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +bison +ram, tup +bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +ibex, Capra ibex +hartebeest +impala, Aepyceros melampus +gazelle +Arabian camel, dromedary, Camelus dromedarius +llama +weasel +mink +polecat, fitch, foulmart, foumart, Mustela putorius +black-footed ferret, ferret, Mustela nigripes +otter +skunk, polecat, wood pussy +badger +armadillo +three-toed sloth, ai, Bradypus tridactylus +orangutan, orang, orangutang, Pongo pygmaeus +gorilla, Gorilla gorilla +chimpanzee, chimp, Pan troglodytes +gibbon, Hylobates lar +siamang, Hylobates syndactylus, Symphalangus syndactylus +guenon, guenon monkey +patas, hussar monkey, Erythrocebus patas +baboon +macaque +langur +colobus, colobus monkey +proboscis monkey, Nasalis larvatus +marmoset +capuchin, ringtail, Cebus capucinus +howler monkey, howler +titi, titi monkey +spider monkey, Ateles geoffroyi +squirrel monkey, Saimiri sciureus +Madagascar cat, ring-tailed lemur, Lemur catta +indri, indris, Indri indri, Indri brevicaudatus +Indian elephant, Elephas maximus +African elephant, Loxodonta africana +lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +barracouta, snoek +eel +coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +rock beauty, Holocanthus tricolor +anemone fish +sturgeon +gar, garfish, garpike, billfish, Lepisosteus osseus +lionfish +puffer, pufferfish, blowfish, globefish +abacus +abaya +academic gown, academic robe, judge's robe +accordion, piano accordion, squeeze box +acoustic guitar +aircraft carrier, carrier, flattop, attack aircraft carrier +airliner +airship, dirigible +altar +ambulance +amphibian, amphibious vehicle +analog clock +apiary, bee house +apron +ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +assault rifle, assault gun +backpack, back pack, knapsack, packsack, rucksack, haversack +bakery, bakeshop, bakehouse +balance beam, beam +balloon +ballpoint, ballpoint pen, ballpen, Biro +Band Aid +banjo +bannister, banister, balustrade, balusters, handrail +barbell +barber chair +barbershop +barn +barometer +barrel, cask +barrow, garden cart, lawn cart, wheelbarrow +baseball +basketball +bassinet +bassoon +bathing cap, swimming cap +bath towel +bathtub, bathing tub, bath, tub +beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +beacon, lighthouse, beacon light, pharos +beaker +bearskin, busby, shako +beer bottle +beer glass +bell cote, bell cot +bib +bicycle-built-for-two, tandem bicycle, tandem +bikini, two-piece +binder, ring-binder +binoculars, field glasses, opera glasses +birdhouse +boathouse +bobsled, bobsleigh, bob +bolo tie, bolo, bola tie, bola +bonnet, poke bonnet +bookcase +bookshop, bookstore, bookstall +bottlecap +bow +bow tie, bow-tie, bowtie +brass, memorial tablet, plaque +brassiere, bra, bandeau +breakwater, groin, groyne, mole, bulwark, seawall, jetty +breastplate, aegis, egis +broom +bucket, pail +buckle +bulletproof vest +bullet train, bullet +butcher shop, meat market +cab, hack, taxi, taxicab +caldron, cauldron +candle, taper, wax light +cannon +canoe +can opener, tin opener +cardigan +car mirror +carousel, carrousel, merry-go-round, roundabout, whirligig +carpenter's kit, tool kit +carton +car wheel +cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +cassette +cassette player +castle +catamaran +CD player +cello, violoncello +cellular telephone, cellular phone, cellphone, cell, mobile phone +chain +chainlink fence +chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +chain saw, chainsaw +chest +chiffonier, commode +chime, bell, gong +china cabinet, china closet +Christmas stocking +church, church building +cinema, movie theater, movie theatre, movie house, picture palace +cleaver, meat cleaver, chopper +cliff dwelling +cloak +clog, geta, patten, sabot +cocktail shaker +coffee mug +coffeepot +coil, spiral, volute, whorl, helix +combination lock +computer keyboard, keypad +confectionery, confectionary, candy store +container ship, containership, container vessel +convertible +corkscrew, bottle screw +cornet, horn, trumpet, trump +cowboy boot +cowboy hat, ten-gallon hat +cradle +crane +crash helmet +crate +crib, cot +Crock Pot +croquet ball +crutch +cuirass +dam, dike, dyke +desk +desktop computer +dial telephone, dial phone +diaper, nappy, napkin +digital clock +digital watch +dining table, board +dishrag, dishcloth +dishwasher, dish washer, dishwashing machine +disk brake, disc brake +dock, dockage, docking facility +dogsled, dog sled, dog sleigh +dome +doormat, welcome mat +drilling platform, offshore rig +drum, membranophone, tympan +drumstick +dumbbell +Dutch oven +electric fan, blower +electric guitar +electric locomotive +entertainment center +envelope +espresso maker +face powder +feather boa, boa +file, file cabinet, filing cabinet +fireboat +fire engine, fire truck +fire screen, fireguard +flagpole, flagstaff +flute, transverse flute +folding chair +football helmet +forklift +fountain +fountain pen +four-poster +freight car +French horn, horn +frying pan, frypan, skillet +fur coat +garbage truck, dustcart +gasmask, respirator, gas helmet +gas pump, gasoline pump, petrol pump, island dispenser +goblet +go-kart +golf ball +golfcart, golf cart +gondola +gong, tam-tam +gown +grand piano, grand +greenhouse, nursery, glasshouse +grille, radiator grille +grocery store, grocery, food market, market +guillotine +hair slide +hair spray +half track +hammer +hamper +hand blower, blow dryer, blow drier, hair dryer, hair drier +hand-held computer, hand-held microcomputer +handkerchief, hankie, hanky, hankey +hard disc, hard disk, fixed disk +harmonica, mouth organ, harp, mouth harp +harp +harvester, reaper +hatchet +holster +home theater, home theatre +honeycomb +hook, claw +hoopskirt, crinoline +horizontal bar, high bar +horse cart, horse-cart +hourglass +iPod +iron, smoothing iron +jack-o'-lantern +jean, blue jean, denim +jeep, landrover +jersey, T-shirt, tee shirt +jigsaw puzzle +jinrikisha, ricksha, rickshaw +joystick +kimono +knee pad +knot +lab coat, laboratory coat +ladle +lampshade, lamp shade +laptop, laptop computer +lawn mower, mower +lens cap, lens cover +letter opener, paper knife, paperknife +library +lifeboat +lighter, light, igniter, ignitor +limousine, limo +liner, ocean liner +lipstick, lip rouge +Loafer +lotion +loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +loupe, jeweler's loupe +lumbermill, sawmill +magnetic compass +mailbag, postbag +mailbox, letter box +maillot +maillot, tank suit +manhole cover +maraca +marimba, xylophone +mask +matchstick +maypole +maze, labyrinth +measuring cup +medicine chest, medicine cabinet +megalith, megalithic structure +microphone, mike +microwave, microwave oven +military uniform +milk can +minibus +miniskirt, mini +minivan +missile +mitten +mixing bowl +mobile home, manufactured home +Model T +modem +monastery +monitor +moped +mortar +mortarboard +mosque +mosquito net +motor scooter, scooter +mountain bike, all-terrain bike, off-roader +mountain tent +mouse, computer mouse +mousetrap +moving van +muzzle +nail +neck brace +necklace +nipple +notebook, notebook computer +obelisk +oboe, hautboy, hautbois +ocarina, sweet potato +odometer, hodometer, mileometer, milometer +oil filter +organ, pipe organ +oscilloscope, scope, cathode-ray oscilloscope, CRO +overskirt +oxcart +oxygen mask +packet +paddle, boat paddle +paddlewheel, paddle wheel +padlock +paintbrush +pajama, pyjama, pj's, jammies +palace +panpipe, pandean pipe, syrinx +paper towel +parachute, chute +parallel bars, bars +park bench +parking meter +passenger car, coach, carriage +patio, terrace +pay-phone, pay-station +pedestal, plinth, footstall +pencil box, pencil case +pencil sharpener +perfume, essence +Petri dish +photocopier +pick, plectrum, plectron +pickelhaube +picket fence, paling +pickup, pickup truck +pier +piggy bank, penny bank +pill bottle +pillow +ping-pong ball +pinwheel +pirate, pirate ship +pitcher, ewer +plane, carpenter's plane, woodworking plane +planetarium +plastic bag +plate rack +plow, plough +plunger, plumber's helper +Polaroid camera, Polaroid Land camera +pole +police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +poncho +pool table, billiard table, snooker table +pop bottle, soda bottle +pot, flowerpot +potter's wheel +power drill +prayer rug, prayer mat +printer +prison, prison house +projectile, missile +projector +puck, hockey puck +punching bag, punch bag, punching ball, punchball +purse +quill, quill pen +quilt, comforter, comfort, puff +racer, race car, racing car +racket, racquet +radiator +radio, wireless +radio telescope, radio reflector +rain barrel +recreational vehicle, RV, R.V. +reel +reflex camera +refrigerator, icebox +remote control, remote +restaurant, eating house, eating place, eatery +revolver, six-gun, six-shooter +rifle +rocking chair, rocker +rotisserie +rubber eraser, rubber, pencil eraser +rugby ball +rule, ruler +running shoe +safe +safety pin +saltshaker, salt shaker +sandal +sarong +sax, saxophone +scabbard +scale, weighing machine +school bus +schooner +scoreboard +screen, CRT screen +screw +screwdriver +seat belt, seatbelt +sewing machine +shield, buckler +shoe shop, shoe-shop, shoe store +shoji +shopping basket +shopping cart +shovel +shower cap +shower curtain +ski +ski mask +sleeping bag +slide rule, slipstick +sliding door +slot, one-armed bandit +snorkel +snowmobile +snowplow, snowplough +soap dispenser +soccer ball +sock +solar dish, solar collector, solar furnace +sombrero +soup bowl +space bar +space heater +space shuttle +spatula +speedboat +spider web, spider's web +spindle +sports car, sport car +spotlight, spot +stage +steam locomotive +steel arch bridge +steel drum +stethoscope +stole +stone wall +stopwatch, stop watch +stove +strainer +streetcar, tram, tramcar, trolley, trolley car +stretcher +studio couch, day bed +stupa, tope +submarine, pigboat, sub, U-boat +suit, suit of clothes +sundial +sunglass +sunglasses, dark glasses, shades +sunscreen, sunblock, sun blocker +suspension bridge +swab, swob, mop +sweatshirt +swimming trunks, bathing trunks +swing +switch, electric switch, electrical switch +syringe +table lamp +tank, army tank, armored combat vehicle, armoured combat vehicle +tape player +teapot +teddy, teddy bear +television, television system +tennis ball +thatch, thatched roof +theater curtain, theatre curtain +thimble +thresher, thrasher, threshing machine +throne +tile roof +toaster +tobacco shop, tobacconist shop, tobacconist +toilet seat +torch +totem pole +tow truck, tow car, wrecker +toyshop +tractor +trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +tray +trench coat +tricycle, trike, velocipede +trimaran +tripod +triumphal arch +trolleybus, trolley coach, trackless trolley +trombone +tub, vat +turnstile +typewriter keyboard +umbrella +unicycle, monocycle +upright, upright piano +vacuum, vacuum cleaner +vase +vault +velvet +vending machine +vestment +viaduct +violin, fiddle +volleyball +waffle iron +wall clock +wallet, billfold, notecase, pocketbook +wardrobe, closet, press +warplane, military plane +washbasin, handbasin, washbowl, lavabo, wash-hand basin +washer, automatic washer, washing machine +water bottle +water jug +water tower +whiskey jug +whistle +wig +window screen +window shade +Windsor tie +wine bottle +wing +wok +wooden spoon +wool, woolen, woollen +worm fence, snake fence, snake-rail fence, Virginia fence +wreck +yawl +yurt +web site, website, internet site, site +comic book +crossword puzzle, crossword +street sign +traffic light, traffic signal, stoplight +book jacket, dust cover, dust jacket, dust wrapper +menu +plate +guacamole +consomme +hot pot, hotpot +trifle +ice cream, icecream +ice lolly, lolly, lollipop, popsicle +French loaf +bagel, beigel +pretzel +cheeseburger +hotdog, hot dog, red hot +mashed potato +head cabbage +broccoli +cauliflower +zucchini, courgette +spaghetti squash +acorn squash +butternut squash +cucumber, cuke +artichoke, globe artichoke +bell pepper +cardoon +mushroom +Granny Smith +strawberry +orange +lemon +fig +pineapple, ananas +banana +jackfruit, jak, jack +custard apple +pomegranate +hay +carbonara +chocolate sauce, chocolate syrup +dough +meat loaf, meatloaf +pizza, pizza pie +potpie +burrito +red wine +espresso +cup +eggnog +alp +bubble +cliff, drop, drop-off +coral reef +geyser +lakeside, lakeshore +promontory, headland, head, foreland +sandbar, sand bar +seashore, coast, seacoast, sea-coast +valley, vale +volcano +ballplayer, baseball player +groom, bridegroom +scuba diver +rapeseed +daisy +yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +corn +acorn +hip, rose hip, rosehip +buckeye, horse chestnut, conker +coral fungus +agaric +gyromitra +stinkhorn, carrion fungus +earthstar +hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +bolete +ear, spike, capitulum +toilet tissue, toilet paper, bathroom tissue diff --git a/data/dnn/enet-classes.txt b/data/dnn/enet-classes.txt new file mode 100644 index 0000000..1616563 --- /dev/null +++ b/data/dnn/enet-classes.txt @@ -0,0 +1,20 @@ +Unlabeled +Road +Sidewalk +Building +Wall +Fence +Pole +TrafficLight +TrafficSign +Vegetation +Terrain +Sky +Person +Rider +Car +Truck +Bus +Train +Motorcycle +Bicycle diff --git a/data/dnn/object_detection_classes_coco.txt b/data/dnn/object_detection_classes_coco.txt new file mode 100644 index 0000000..10ecf0b --- /dev/null +++ b/data/dnn/object_detection_classes_coco.txt @@ -0,0 +1,90 @@ +person +bicycle +car +motorcycle +airplane +bus +train +truck +boat +traffic light +fire hydrant +street sign +stop sign +parking meter +bench +bird +cat +dog +horse +sheep +cow +elephant +bear +zebra +giraffe +hat +backpack +umbrella +shoe +eye glasses +handbag +tie +suitcase +frisbee +skis +snowboard +sports ball +kite +baseball bat +baseball glove +skateboard +surfboard +tennis racket +bottle +plate +wine glass +cup +fork +knife +spoon +bowl +banana +apple +sandwich +orange +broccoli +carrot +hot dog +pizza +donut +cake +chair +couch +potted plant +bed +mirror +dining table +window +desk +toilet +door +tv +laptop +mouse +remote +keyboard +cell phone +microwave +oven +toaster +sink +refrigerator +blender +book +clock +vase +scissors +teddy bear +hair drier +toothbrush diff --git a/data/dnn/object_detection_classes_pascal_voc.txt b/data/dnn/object_detection_classes_pascal_voc.txt new file mode 100644 index 0000000..8420ab3 --- /dev/null +++ b/data/dnn/object_detection_classes_pascal_voc.txt @@ -0,0 +1,20 @@ +aeroplane +bicycle +bird +boat +bottle +bus +car +cat +chair +cow +diningtable +dog +horse +motorbike +person +pottedplant +sheep +sofa +train +tvmonitor diff --git a/data/dnn/object_detection_classes_yolov3.txt b/data/dnn/object_detection_classes_yolov3.txt new file mode 100644 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0000000..c4acf61 Binary files /dev/null and b/data/tsukuba_r.png differ diff --git a/data/vtest.avi b/data/vtest.avi new file mode 100644 index 0000000..965ab12 Binary files /dev/null and b/data/vtest.avi differ diff --git a/data/wiki.jpg b/data/wiki.jpg new file mode 100644 index 0000000..8f86b28 Binary files /dev/null and b/data/wiki.jpg differ diff --git a/data/wiki_equal.jpg b/data/wiki_equal.jpg new file mode 100644 index 0000000..a789091 Binary files /dev/null and b/data/wiki_equal.jpg differ diff --git a/grabcut.py b/grabcut.py new file mode 100644 index 0000000..286b124 --- /dev/null +++ b/grabcut.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python +''' +=============================================================================== +Interactive Image Segmentation using GrabCut algorithm. +This sample shows interactive image segmentation using grabcut algorithm. +USAGE: + python grabcut.py +README FIRST: + Two windows will show up, one for input and one for output. + At first, in input window, draw a rectangle around the object using the +right mouse button. Then press 'n' to segment the object (once or a few times) +For any finer touch-ups, you can press any of the keys below and draw lines on +the areas you want. Then again press 'n' to update the output. +Key '0' - To select areas of sure background +Key '1' - To select areas of sure foreground +Key '2' - To select areas of probable background +Key '3' - To select areas of probable foreground +Key 'n' - To update the segmentation +Key 'r' - To reset the setup +Key 's' - To save the results +=============================================================================== +''' + +# Python 2/3 compatibility +from __future__ import print_function + +import numpy as np +import cv2 as cv + +import sys + +class App(): + BLUE = [255,0,0] # rectangle color + RED = [0,0,255] # PR BG + GREEN = [0,255,0] # PR FG + BLACK = [0,0,0] # sure BG + WHITE = [255,255,255] # sure FG + + DRAW_BG = {'color' : BLACK, 'val' : 0} + DRAW_FG = {'color' : WHITE, 'val' : 1} + DRAW_PR_BG = {'color' : RED, 'val' : 2} + DRAW_PR_FG = {'color' : GREEN, 'val' : 3} + + # setting up flags + rect = (0,0,1,1) + drawing = False # flag for drawing curves + rectangle = False # flag for drawing rect + rect_over = False # flag to check if rect drawn + rect_or_mask = 100 # flag for selecting rect or mask mode + value = DRAW_FG # drawing initialized to FG + thickness = 3 # brush thickness + + def onmouse(self, event, x, y, flags, param): + # Draw Rectangle + if event == cv.EVENT_RBUTTONDOWN: + self.rectangle = True + self.ix, self.iy = x,y + + elif event == cv.EVENT_MOUSEMOVE: + if self.rectangle == True: + self.img = self.img2.copy() + cv.rectangle(self.img, (self.ix, self.iy), (x, y), self.BLUE, 2) + self.rect = (min(self.ix, x), min(self.iy, y), abs(self.ix - x), abs(self.iy - y)) + self.rect_or_mask = 0 + + elif event == cv.EVENT_RBUTTONUP: + self.rectangle = False + self.rect_over = True + cv.rectangle(self.img, (self.ix, self.iy), (x, y), self.BLUE, 2) + self.rect = (min(self.ix, x), min(self.iy, y), abs(self.ix - x), abs(self.iy - y)) + self.rect_or_mask = 0 + print(" Now press the key 'n' a few times until no further change \n") + + # draw touchup curves + + if event == cv.EVENT_LBUTTONDOWN: + if self.rect_over == False: + print("first draw rectangle \n") + else: + self.drawing = True + cv.circle(self.img, (x,y), self.thickness, self.value['color'], -1) + cv.circle(self.mask, (x,y), self.thickness, self.value['val'], -1) + + elif event == cv.EVENT_MOUSEMOVE: + if self.drawing == True: + cv.circle(self.img, (x, y), self.thickness, self.value['color'], -1) + cv.circle(self.mask, (x, y), self.thickness, self.value['val'], -1) + + elif event == cv.EVENT_LBUTTONUP: + if self.drawing == True: + self.drawing = False + cv.circle(self.img, (x, y), self.thickness, self.value['color'], -1) + cv.circle(self.mask, (x, y), self.thickness, self.value['val'], -1) + + def run(self): + # Loading images + if len(sys.argv) == 2: + filename = sys.argv[1] # for drawing purposes + else: + print("No input image given, so loading default image, lena.jpg \n") + print("Correct Usage: python grabcut.py \n") + filename = 'lena.jpg' + + self.img = cv.imread(cv.samples.findFile(filename)) + self.img2 = self.img.copy() # a copy of original image + self.mask = np.zeros(self.img.shape[:2], dtype = np.uint8) # mask initialized to PR_BG + self.output = np.zeros(self.img.shape, np.uint8) # output image to be shown + + # input and output windows + cv.namedWindow('output') + cv.namedWindow('input') + cv.setMouseCallback('input', self.onmouse) + cv.moveWindow('input', self.img.shape[1]+10,90) + + print(" Instructions: \n") + print(" Draw a rectangle around the object using right mouse button \n") + + while(1): + + cv.imshow('output', self.output) + cv.imshow('input', self.img) + k = cv.waitKey(1) + + # key bindings + if k == 27: # esc to exit + break + elif k == ord('0'): # BG drawing + print(" mark background regions with left mouse button \n") + self.value = self.DRAW_BG + elif k == ord('1'): # FG drawing + print(" mark foreground regions with left mouse button \n") + self.value = self.DRAW_FG + elif k == ord('2'): # PR_BG drawing + self.value = self.DRAW_PR_BG + elif k == ord('3'): # PR_FG drawing + self.value = self.DRAW_PR_FG + elif k == ord('s'): # save image + bar = np.zeros((self.img.shape[0], 5, 3), np.uint8) + res = np.hstack((self.img2, bar, self.img, bar, self.output)) + cv.imwrite('grabcut_output.png', res) + print(" Result saved as image \n") + elif k == ord('r'): # reset everything + print("resetting \n") + self.rect = (0,0,1,1) + self.drawing = False + self.rectangle = False + self.rect_or_mask = 100 + self.rect_over = False + self.value = self.DRAW_FG + self.img = self.img2.copy() + self.mask = np.zeros(self.img.shape[:2], dtype = np.uint8) # mask initialized to PR_BG + self.output = np.zeros(self.img.shape, np.uint8) # output image to be shown + elif k == ord('n'): # segment the image + print(""" For finer touchups, mark foreground and background after pressing keys 0-3 + and again press 'n' \n""") + try: + bgdmodel = np.zeros((1, 65), np.float64) + fgdmodel = np.zeros((1, 65), np.float64) + if (self.rect_or_mask == 0): # grabcut with rect + cv.grabCut(self.img2, self.mask, self.rect, bgdmodel, fgdmodel, 1, cv.GC_INIT_WITH_RECT) + self.rect_or_mask = 1 + elif (self.rect_or_mask == 1): # grabcut with mask + cv.grabCut(self.img2, self.mask, self.rect, bgdmodel, fgdmodel, 1, cv.GC_INIT_WITH_MASK) + except: + import traceback + traceback.print_exc() + + mask2 = np.where((self.mask==1) + (self.mask==3), 255, 0).astype('uint8') + self.output = cv.bitwise_and(self.img2, self.img2, mask=mask2) + + print('Done') + + +if __name__ == '__main__': + print(__doc__) + App().run() + cv.destroyAllWindows() \ No newline at end of file diff --git a/opencv-python-tutroals.pdf b/opencv-python-tutroals.pdf new file mode 100644 index 0000000..9f74b83 Binary files /dev/null and b/opencv-python-tutroals.pdf differ diff --git a/opencv.py b/opencv.py new file mode 100644 index 0000000..4524c14 --- /dev/null +++ b/opencv.py @@ -0,0 +1,1193 @@ +import numpy as np +import cv2 +from matplotlib import pyplot as plt + +img = cv2.imread("/home/wym/Downloads/google.png", 0) + +cv2.imshow("google", img) +k = cv2.waitKey(0) +#for 64-bit machine, k = cv2.waitKey(0) & 0xFF +if k == 27: + cv2.destroyAllWindows() +elif k == ord('s'): + cv2.imwrite('googlegray.png', img) + cv2.destroyAllWindows() + + +img = cv2.imread("/home/wym/Downloads/google.png", 0) +plt.imshow(img, cmap='gray', interpolation='bicubic') +plt.xticks([]), plt.yticks([]) +plt.show() + +#Color image loaded by OpenCV is in BGR mode. But Matplotlib displays in RGB mode. So color +#images will not be displayed correctly in Matplotlib if image is read with Opencv2. +# +#Solution: +#b,g,r = cv2.split(img) +#img = cv2.merge([r, g, b]) + +#Capture Video from Camera +cap = cv2.VideoCapture(0) +#To capture a video, you need to create a VideoCapture object. Its argument can be either the device index or the name +#of a video file. Device index is just the number to specify which camera. + +while True: + # Capture frame-by-frame + ret, frame = cap.read() + + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + cv2.imshow('frame', gray) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + +cap.release() +cv2.destroyAllWindows() + +#Playing Video from file +cap = cv2.VideoCapture('vtest.avi') +while(cap.isOpened()): + ret, frame = cap.read() + + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + cv2.imshow('frame',gray) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + +cap.release() +cv2.destroyAllWindows() +#Make sure proper versions of ffmpeg or gstreamer is installed. Sometimes, it is a headache to work with Video +#Capture mostly due to wrong installation of ffmpeg/gstreamer. + +#Saving a Video +cap = cv2.VideoCapture(0) + +# Define the codec and create VideoWriter object +fourcc = cv2.VideoWriter_fourcc(*'XVID') +#or fourcc = cv2.VideoWriter_fourcc('X', 'V', 'I', 'D') +out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480)) + +while(cap.isOpened()): + ret, frame = cap.read() + if ret==True: + frame = cv2.flip(frame,0) + + # write the flipped frame + out.write(frame) + + cv2.imshow('frame',frame) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + else: + break + +# Release everything if job is finished +cap.release() +out.release() +cv2.destroyAllWindows() + +#1.2.3 Drawing Functions in OpenCV +#functions:cv2.line(), cv2.circle() , cv2.rectangle(), cv2.ellipse(), cv2.putText() etc. + +#Drawing Line +#lineType; +#– LINE_8 (or omitted) - 8-connected line. +#– LINE_4 - 4-connected line. +#– LINE_AA - antialiased line. + +# Create a black image +img = np.zeros((512,512,3), np.uint8) + +# Draw a diagonal blue line with thickness of 5 px +img = cv2.line(img,(0,0),(511,511),(255,0,0),5) + +#Drawing Rectangle +img = cv2.rectangle(img,(384,0),(510,128),(0,255,0),3) + +#Drawing Circle +img = cv2.circle(img,(447,63), 63, (0,0,255), -1) + +#Drawing Ellipse +img = cv2.ellipse(img,(256,256),(100,50),0,0,180,255,-1) + +#Drawing Polygon +pts = np.array([[10,5],[20,30],[70,20],[50,10]], np.int32) +pts = pts.reshape((-1,1,2)) +img = cv2.polylines(img,[pts],True,(0,255,255)) +#cv2.polylines() can be used to draw multiple lines. Just create a list of all the lines you want to draw +#and pass it to the function. All lines will be drawn individually. It is more better and faster way to draw a group of +#lines than calling cv2.line() for each line. + +#Adding Text to Images +font = cv2.FONT_HERSHEY_SIMPLEX +cv2.putText(img,'OpenCV',(10,500), font, 4,(255,255,255),2,cv2.LINE_AA) + +#1.2.4 Mouse as a Paint-Brush + +#Simple Demo + +events = [i for i in dir(cv2) if 'EVENT' in i] +print(events) + +# mouse callback function +def draw_circle(event,x,y,flags,param): + if event == cv2.EVENT_LBUTTONDBLCLK: + cv2.circle(img,(x,y),100,(255,0,0),-1) + +# Create a black image, a window and bind the function to window +img = np.zeros((512,512,3), np.uint8) +cv2.namedWindow('image') +cv2.setMouseCallback('image',draw_circle) + +while(1): + cv2.imshow('image',img) + if cv2.waitKey(20) & 0xFF == 27: + break + +cv2.destroyAllWindows() + +#More Advanced Demo + +drawing = False +# true if mouse is pressed + +mode = True +# if True, draw rectangle. Press 'm' to toggle to curve + +ix,iy = -1,-1 + +# mouse callback function +def draw_circle(event,x,y,flags,param): + global ix,iy,drawing,mode + + if event == cv2.EVENT_LBUTTONDOWN: + drawing = True + ix,iy = x,y + + elif event == cv2.EVENT_MOUSEMOVE: + if drawing == True: + if mode == True: + cv2.rectangle(img,(ix,iy),(x,y),(0,255,0),-1) + else: + cv2.circle(img,(x,y),5,(0,0,255),-1) + + elif event == cv2.EVENT_LBUTTONUP: + drawing = False + if mode == True: + cv2.rectangle(img,(ix,iy),(x,y),(0,255,0),-1) + else: + cv2.circle(img,(x,y),5,(0,0,255),-1) + +img = np.zeros((512,512,3), np.uint8) +cv2.namedWindow('image') +cv2.setMouseCallback('image',draw_circle) + +while(1): + cv2.imshow('image',img) + k = cv2.waitKey(1) & 0xFF + if k == ord('m'): + mode = not mode + elif k == 27: + break + +cv2.destroyAllWindows() + +#Trackbar as the Color Palette + +def nothing(x): + pass + +# Create a black image, a window +img = np.zeros((300,512,3), np.uint8) +cv2.namedWindow('image') + +# create trackbars for color change +cv2.createTrackbar('R','image',0,255,nothing) +cv2.createTrackbar('G','image',0,255,nothing) +cv2.createTrackbar('B','image',0,255,nothing) + +# create switch for ON/OFF functionality +switch = '0 : OFF \n1 : ON' +cv2.createTrackbar(switch, 'image',0,1,nothing) + +while(1): + cv2.imshow('image',img) + k = cv2.waitKey(1) & 0xFF + if k == 27: + break + # get current positions of four trackbars + r = cv2.getTrackbarPos('R','image') + g = cv2.getTrackbarPos('G','image') + b = cv2.getTrackbarPos('B','image') + s = cv2.getTrackbarPos(switch,'image') + if s == 0: + img[:] = 0 + else: + img[:] = [b,g,r] + +cv2.destroyAllWindows() + +#Basic Operations on Images +import cv2 +import numpy as np + +img = cv2.imread("C:\\Users\\cole\\Desktop\\logo.png") + +shape = img.shape + +img.item(10, 10, 2) +img.itemset((10, 10, 2), 100) + +print(img.size) +print(img.dtype) + +#Image ROI +ball = img[280:340, 330:390] +img[273:333, 100:160] = ball + +#Splitting and Merging Image Channels +b, g, r = cv2.split(img) +img = cv2.merge((b, g, r)) + +#or +b = img[:, :, 0] +img[:, :, 2] = 0 +# cv2.split() is a costly operation (in terms of time), so only use it if necessary. Numpy indexing is much more efficient and should be used if possible. + +#Making Borders for Images (Padding) +import cv2 +import numpy as np +from matplotlib import pyplot as plt + +BLUE = [255,0,0] + +img1 = cv2.imread('C:\\Users\\cole\\Desktop\\logo.png') + +replicate = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REPLICATE) +reflect = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT) +reflect101 = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT_101) +wrap = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_WRAP) +constant= cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_CONSTANT,value=BLUE) + +plt.subplot(231),plt.imshow(img1,'gray'),plt.title('ORIGINAL') +plt.subplot(232),plt.imshow(replicate,'gray'),plt.title('REPLICATE') +plt.subplot(233),plt.imshow(reflect,'gray'),plt.title('REFLECT') +plt.subplot(234),plt.imshow(reflect101,'gray'),plt.title('REFLECT_101') +plt.subplot(235),plt.imshow(wrap,'gray'),plt.title('WRAP') +plt.subplot(236),plt.imshow(constant,'gray'),plt.title('CONSTANT') + +plt.show() + +#Arithmetic Operations on Images +# +#Image Addition +#There is a difference between OpenCV addition and Numpy addition. OpenCV addition is a saturated operation +#while Numpy addition is a modulo operation. +x, y = np.uint8([250]), np.uint8([10]) + +print(cv2.add(x, y)) +print(x+y) + +#Image Blending +#𝑑𝑠𝑡 = 𝛼 · 𝑖𝑚𝑔1 + 𝛽 · 𝑖𝑚𝑔2 + 𝛾 +#dst(I) = saturate(src1(I) * alpha + src2(I) * beta + gamma) +img1 = cv2.imread('ml.png') +img2 = cv2.imread('opencv_logo.jpg') +dst = cv2.addWeighted(img1,0.7,img2,0.3,0) +cv2.imshow('dst',dst) +cv2.waitKey(0) +cv2.destroyAllWindows() + +#Bitwise Operations +# Load two images +img1 = cv2.imread('messi5.jpg') +img2 = cv2.imread('opencv_logo.png') + +# I want to put logo on top-left corner, So I create a ROI +rows,cols,channels = img2.shape +roi = img1[0:rows, 0:cols ] + +# Now create a mask of logo and create its inverse mask also +img2gray = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY) +ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY) +mask_inv = cv2.bitwise_not(mask) + +# Now black-out the area of logo in ROI +img1_bg = cv2.bitwise_and(roi,roi,mask = mask_inv) + +# Take only region of logo from logo image. +img2_fg = cv2.bitwise_and(img2,img2,mask = mask) + +# Put logo in ROI and modify the main image +dst = cv2.add(img1_bg,img2_fg) +img1[0:rows, 0:cols ] = dst +cv2.imshow('res',img1) +cv2.waitKey(0) +cv2.destroyAllWindows() + +#Performance Measurement and Improvement Techniques +img1 = cv2.imread('messi5.jpg') + +e1 = cv2.getTickCount() +for i in range(5,49,2): + img1 = cv2.medianBlur(img1,i) +e2 = cv2.getTickCount() +t = (e2 - e1)/cv2.getTickFrequency() +print(t) + +#Default Optimization in OpenCV +cv2.useOptimized() +%timeit res = cv2.medianBlur(img1,49) + +cv2.setUseOptimized(False) +cv2.useOptimized() +%timeit res = cv2.medianBlur(img1,49) + +cv2.setUseOptimized(True) + +img = cv2.imread('opencv_logo.png') +#Measuring Performance in IPython +x = 5 +%timeit y = x**2 + +%timeit y = x*x + +z = np.uint8([5]) +%timeit y = z*z + +%timeit y = np.square(z) +#Python scalar operations are faster than Numpy scalar operations. So for operations including one or two +#elements, Python scalar is better than Numpy arrays. Numpy takes advantage when size of array is a little bit bigger. + +%timeit z = cv2.countNonZero(img) + +%timeit z = np.count_nonzero(img) +#Normally, OpenCV functions are faster than Numpy functions. So for same operation, OpenCV functions are +#preferred. But, there can be exceptions, especially when Numpy works with views instead of copies. + +#There are several techniques and coding methods to exploit maximum performance of Python and Numpy. Only +#relevant ones are noted here and links are given to important sources. The main thing to be noted here is that, first try +#to implement the algorithm in a simple manner. Once it is working, profile it, find the bottlenecks and optimize them. +# +#1. Avoid using loops in Python as far as possible, especially double/triple loops etc. They are inherently slow. +#2. Vectorize the algorithm/code to the maximum possible extent because Numpy and OpenCV are optimized for +# vector operations. +#3. Exploit the cache coherence. +#4. Never make copies of array unless it is needed. Try to use views instead. Array copying is a costly operation. +# +#Even after doing all these operations, if your code is still slow, or use of large loops are inevitable, use additional +#libraries like Cython to make it faster. + + +#Image Processing in OpenCV +#Changing Colorspaces + +import cv2 +flags = [i for i in dir(cv2) if i.startswith('COLOR_')] +print(flags) + +#Object Tracking +import cv2 +import numpy as np + +cap = cv2.VideoCapture(0) + +while(1): + # Take each frame + _, frame = cap.read() + + # Convert BGR to HSV + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + + # define range of blue color in HSV + lower_blue = np.array([110,50,50]) + upper_blue = np.array([130,255,255]) + + # Threshold the HSV image to get only blue colors + mask = cv2.inRange(hsv, lower_blue, upper_blue) + + # Bitwise-AND mask and original image + res = cv2.bitwise_and(frame,frame, mask= mask) + cv2.imshow('frame',frame) + cv2.imshow('mask',mask) + cv2.imshow('res',res) + + k = cv2.waitKey(5) & 0xFF + if k == 27: + break + +cv2.destroyAllWindows() + + +#How to find HSV values to track? +green = np.uint8([[[0,255,0]]]) +hsv_green = cv2.cvtColor(green,cv2.COLOR_BGR2HSV) +print(hsv_green) +#Now you take [H-10, 100,100] and [H+10, 255, 255] as lower bound and upper bound respectively. Apart from this +#method, you can use any image editing tools like GIMP or any online converters to find these values, but don’t forget +#to adjust the HSV ranges. + +#Image Thresholding +#Simple Thresholding +import cv2 +import numpy as np +from matplotlib import pyplot as plt + +img = cv2.imread('gradient.png',0) + +ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) +ret,thresh2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV) +ret,thresh3 = cv2.threshold(img,127,255,cv2.THRESH_TRUNC) +ret,thresh4 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO) +ret,thresh5 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV) + +titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV'] +images = [img, thresh1, thresh2, thresh3, thresh4, thresh5] + +for i in range(6): + plt.subplot(2,3,i+1),plt.imshow(images[i],'gray') + plt.title(titles[i]) + plt.xticks([]),plt.yticks([]) + +plt.show() + + +#Adaptive Thresholding +import cv2 +import numpy as np +from matplotlib import pyplot as plt + +img = cv2.imread('dave.jpg',0) +img = cv2.medianBlur(img,5) + +ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) +th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2) +th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2) + +titles = ['Original Image', 'Global Thresholding (v = 127)','Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding'] + +images = [img, th1, th2, th3] + +for i in range(4): + plt.subplot(2,2,i+1),plt.imshow(images[i],'gray') + plt.title(titles[i]) + plt.xticks([]),plt.yticks([]) + +plt.show() + +#Otsu’s Binarization +import cv2 +import numpy as np +from matplotlib import pyplot as plt + +img = cv2.imread('noisy2.png',0) + +# global thresholding +ret1,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) + +# Otsu's thresholding +ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) + +# Otsu's thresholding after Gaussian filtering +blur = cv2.GaussianBlur(img,(5,5),0) +ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) + +# plot all the images and their histograms +images = [img, 0, th1, img, 0, th2, blur, 0, th3] +titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)', 'Original Noisy Image','Histogram',"Otsu's Thresholding", 'Gaussian filtered Image','Histogram',"Otsu's Thresholding"] + +for i in range(3): + plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray') + plt.title(titles[i*3]), plt.xticks([]), plt.yticks([]) + plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256) + plt.title(titles[i*3+1]), plt.xticks([]), plt.yticks([]) + plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray') + plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([]) + +plt.show() + + +#How Otsu’s Binarization Works? +img = cv2.imread('noisy2.png',0) +blur = cv2.GaussianBlur(img,(5,5),0) + +# find normalized_histogram, and its cumulative distribution function +hist = cv2.calcHist([blur],[0],None,[256],[0,256]) +hist_norm = hist.ravel()/hist.max() +Q = hist_norm.cumsum() + +bins = np.arange(256) + +fn_min = np.inf +thresh = -1 + +for i in range(1,256): + p1,p2 = np.hsplit(hist_norm,[i]) # probabilities + q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes + b1,b2 = np.hsplit(bins,[i]) # weights + + # finding means and variances + m1,m2 = np.sum(p1*b1)/q1, np.sum(p2*b2)/q2 + v1,v2 = np.sum(((b1-m1)**2)*p1)/q1,np.sum(((b2-m2)**2)*p2)/q2 + + # calculates the minimization function + fn = v1*q1 + v2*q2 + if fn < fn_min: + fn_min = fn + thresh = i + +# find otsu's threshold value with OpenCV function +ret, otsu = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) +print(thresh,ret) + +#Geometric Transformations of Images + +# Histograms +import cv2 +import numpy as np +from matplotlib import pyplot as plt +img = cv2.imread('data/home.jpg') +color = ('b','g','r') +for i,col in enumerate(color): + histr = cv2.calcHist([img],[i],None,[256],[0,256]) + plt.plot(histr,color = col) + plt.xlim([0,256]) + +plt.show() + +import cv2 +import numpy as np +from matplotlib import pyplot as plt +img = cv2.imread('data/wiki.jpg',0) +hist,bins = np.histogram(img.flatten(),256,[0,256]) +cdf = hist.cumsum() +cdf_normalized = cdf * hist.max()/ cdf.max() +plt.plot(cdf_normalized, color = 'b') +plt.hist(img.flatten(),256,[0,256], color = 'r') +plt.xlim([0,256]) +plt.legend(('cdf','histogram'), loc = 'upper left') +plt.show() + +cdf_m = np.ma.masked_equal(cdf,0) +# not cdf_m*255/cdf_m.max(), avoid the situation that if input is zero, and the output is also zero +cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min()) +cdf = np.ma.filled(cdf_m,0).astype('uint8') +img2 = cdf[img] +plt.imshow(img2, cmap='gray') + +img = cv2.imread('data/wiki.jpg',0) +equ = cv2.equalizeHist(img) +res = np.hstack((img,equ)) #stacking images side-by-side +cv2.imwrite('data/wiki_equal.jpg',res) + +import numpy as np +import cv2 +img = cv2.imread('data/tsukuba_l.png',0) +# create a CLAHE object (Arguments are optional). +clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) +cl1 = clahe.apply(img) +cv2.imwrite('data/clahe_2.jpg',cl1) + +import cv2 +import numpy as np +img = cv2.imread('data/home.jpg') +hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) +hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256]) + +import cv2 +import numpy as np +from matplotlib import pyplot as plt +img = cv2.imread('data/home.jpg') +hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) +hist = cv2.calcHist( [hsv], [0, 1], None, [180, 256], [0, 180, 0, 256] ) +plt.imshow(hist,interpolation = 'nearest') +plt.show() + +import cv2 +import numpy as np +from matplotlib import pyplot as plt +#roi is the object or region of object we need to find +roi = cv2.imread('data/rose_red.png') +hsv = cv2.cvtColor(roi,cv2.COLOR_BGR2HSV) +#target is the image we search in +target = cv2.imread('data/rose.png') +hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV) +# Find the histograms using calcHist. Can be done with np.histogram2d also +M = cv2.calcHist([hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] ) +I = cv2.calcHist([hsvt],[0, 1], None, [180, 256], [0, 180, 0, 256] ) + +R = M/I + +h,s,v = cv2.split(hsvt) +B = R[h.ravel(),s.ravel()] +B = np.minimum(B,1) +B = B.reshape(hsvt.shape[:2]) + +disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)) +cv2.filter2D(B,-1,disc,B) +B = np.uint8(B) +cv2.normalize(B,B,0,255,cv2.NORM_MINMAX) + +ret,thresh = cv2.threshold(B,50,255,0) + +import cv2 +import numpy as np +roi = cv2.imread('data/rose_red.png') +hsv = cv2.cvtColor(roi,cv2.COLOR_BGR2HSV) +target = cv2.imread('data/rose.png') +hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV) +# calculating object histogram +roihist = cv2.calcHist([hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] ) +# normalize histogram and apply backprojection +cv2.normalize(roihist,roihist,0,255,cv2.NORM_MINMAX) +dst = cv2.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256],1) +# Now convolute with circular disc +disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)) +cv2.filter2D(dst,-1,disc,dst) +# threshold and binary AND +ret,thresh = cv2.threshold(dst,50,255,0) +thresh = cv2.merge((thresh,thresh,thresh)) +res = cv2.bitwise_and(target,thresh) +res = np.vstack((target,thresh,res)) +cv2.imwrite('data/ros_res.jpg',res) + +# add with closing operation + +kernel = np.ones((5,5),np.uint8) + +import cv2 +import numpy as np +roi = cv2.imread('data/rose_red.png') +hsv = cv2.cvtColor(roi,cv2.COLOR_BGR2HSV) +target = cv2.imread('data/rose.png') +hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV) +# calculating object histogram +roihist = cv2.calcHist([hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] ) +# normalize histogram and apply backprojection +cv2.normalize(roihist,roihist,0,255,cv2.NORM_MINMAX) +dst = cv2.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256],1) +# Now convolute with circular disc +disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)) +cv2.filter2D(dst,-1,disc,dst) +# threshold and binary AND +ret,thresh = cv2.threshold(dst,50,255,0) + +thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel) +thresh = cv2.merge((thresh,thresh,thresh)) +res = cv2.bitwise_and(target,thresh) +res = np.vstack((target,thresh,res)) +cv2.imwrite('data/ros_res.jpg',res) + +# FFT +import cv2 +import numpy as np +from matplotlib import pyplot as plt +img = cv2.imread('data/messi5.jpg',0) +f = np.fft.fft2(img) +fshift = np.fft.fftshift(f) +magnitude_spectrum = 20*np.log(np.abs(fshift)) + +plt.subplot(121),plt.imshow(img, cmap = 'gray') +plt.title('Input Image'), plt.xticks([]), plt.yticks([]) +plt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray') +plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([]) +plt.show() + +rows, cols = img.shape +crow,ccol = int(rows/2) , int(cols/2) +fshift[crow-30:crow+30, ccol-30:ccol+30] = 0 +f_ishift = np.fft.ifftshift(fshift) +img_back = np.fft.ifft2(f_ishift) +img_back = np.abs(img_back) +plt.subplot(131),plt.imshow(img, cmap = 'gray') +plt.title('Input Image'), plt.xticks([]), plt.yticks([]) +plt.subplot(132),plt.imshow(img_back, cmap = 'gray') +plt.title('Image after HPF'), plt.xticks([]), plt.yticks([]) +plt.subplot(133),plt.imshow(img_back) +plt.title('Result in JET'), plt.xticks([]), plt.yticks([]) +plt.show() + +import numpy as np +import cv2 +from matplotlib import pyplot as plt +img = cv2.imread('data/messi5.jpg',0) +dft = cv2.dft(np.float32(img),flags = cv2.DFT_COMPLEX_OUTPUT) +dft_shift = np.fft.fftshift(dft) +magnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0],dft_shift[:,:,1])) +plt.subplot(121),plt.imshow(img, cmap = 'gray') +plt.title('Input Image'), plt.xticks([]), plt.yticks([]) +plt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray') +plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([]) +plt.show() + +import cv2 +import numpy as np +from matplotlib import pyplot as plt +# simple averaging filter without scaling parameter +mean_filter = np.ones((3,3)) +# creating a guassian filter +x = cv2.getGaussianKernel(5,10) +gaussian = x*x.T +# different edge detecting filters +# scharr in x-direction +scharr = np.array([[-3, 0, 3], +[-10,0,10], +[-3, 0, 3]]) +# sobel in x direction +sobel_x= np.array([[-1, 0, 1], +[-2, 0, 2], +[-1, 0, 1]]) + +# sobel in y direction +sobel_y= np.array([[-1,-2,-1], +[0, 0, 0], +[1, 2, 1]]) +# laplacian +laplacian=np.array([[0, 1, 0], +[1,-4, 1], +[0, 1, 0]]) +filters = [mean_filter, gaussian, laplacian, sobel_x, sobel_y, scharr] +filter_name = ['mean_filter', 'gaussian','laplacian', 'sobel_x', \ +'sobel_y', 'scharr_x'] +fft_filters = [np.fft.fft2(x) for x in filters] +fft_shift = [np.fft.fftshift(y) for y in fft_filters] +mag_spectrum = [np.log(np.abs(z)+1) for z in fft_shift] +for i in range(6): + plt.subplot(2,3,i+1),plt.imshow(mag_spectrum[i],cmap = 'gray') + plt.title(filter_name[i]), plt.xticks([]), plt.yticks([]) +plt.show() + +import cv2 +import numpy as np +from matplotlib import pyplot as plt +img = cv2.imread('data/messi5.jpg',0) +img2 = img.copy() +template = cv2.imread('data/messi_face.jpg',0) + +w, h = template.shape[::-1] +# All the 6 methods for comparison in a list +methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR', +'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED'] +for meth in methods: + img = img2.copy() + method = eval(meth) + # Apply template Matching + res = cv2.matchTemplate(img,template,method) + min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) + # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum + if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]: + top_left = min_loc + else: + top_left = max_loc + bottom_right = (top_left[0] + w, top_left[1] + h) + cv2.rectangle(img,top_left, bottom_right, 255, 2) + plt.subplot(121),plt.imshow(res,cmap = 'gray') + plt.title('Matching Result'), plt.xticks([]), plt.yticks([]) + plt.subplot(122),plt.imshow(img,cmap = 'gray') + plt.title('Detected Point'), plt.xticks([]), plt.yticks([]) + plt.suptitle(meth) + plt.show() + + +import cv2 +import numpy as np +from matplotlib import pyplot as plt +img_rgb = cv2.imread('data/mario.png') +img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) +template = cv2.imread('data/mario_coin.png',0) +w, h = template.shape[::-1] +res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED) +threshold = 0.8 + +loc = np.where( res >= threshold) +for pt in zip(*loc[::-1]): + cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2) +cv2.imwrite('data/mario_res.png',img_rgb) + + +import cv2 +import numpy as np + +img = cv2.imread('data/dave.jpg') +gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) +edges = cv2.Canny(gray,50,150,apertureSize = 3) +lines = cv2.HoughLines(edges,1,np.pi/180,110) +for rho,theta in lines[:, 0, :]: + a = np.cos(theta) + b = np.sin(theta) + x0 = a*rho + y0 = b*rho + x1 = int(x0 + 1000*(-b)) + y1 = int(y0 + 1000*(a)) + x2 = int(x0 - 1000*(-b)) + y2 = int(y0 - 1000*(a)) + cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2) + +cv2.imwrite('data/houghlines3.jpg',img) + +import cv2 +import numpy as np +img = cv2.imread('data/dave.jpg') +gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) +edges = cv2.Canny(gray,50,150,apertureSize = 3) +minLineLength = 100 +maxLineGap = 10 +lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength,maxLineGap) +for x1,y1,x2,y2 in lines[:, 0, :]: + cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2) +cv2.imwrite('data/houghlines5.jpg',img) + +import cv2 +import numpy as np +img = cv2.imread('data/opencv_logo.png',0) +img = cv2.medianBlur(img,5) +cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR) +circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20, +param1=50,param2=30,minRadius=0,maxRadius=0) +circles = np.uint16(np.around(circles)) +for i in circles[0,:]: + # draw the outer circle + cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2) + # draw the center of the circle + cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3) +cv2.imshow('detected circles',cimg) +cv2.waitKey(0) +cv2.destroyAllWindows() + +import numpy as np +import cv2 +from matplotlib import pyplot as plt +img = cv2.imread('data/coins.jpg') +gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) +ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) + +# noise removal +kernel = np.ones((3,3),np.uint8) +opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2) +# sure background area +sure_bg = cv2.dilate(opening,kernel,iterations=3) +# Finding sure foreground area +dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5) +ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0) +# Finding unknown region +sure_fg = np.uint8(sure_fg) +unknown = cv2.subtract(sure_bg,sure_fg) + +# Marker labelling +ret, markers = cv2.connectedComponents(sure_fg) +# Add one to all labels so that sure background is not 0, but 1 +markers = markers+1 +# Now, mark the region of unknown with zero +markers[unknown==255] = 0 + +markers = cv2.watershed(img,markers) +img[markers == -1] = [255,0,0] + +import numpy as np +import cv2 +from matplotlib import pyplot as plt +img = cv2.imread('data/messi5.jpg') +mask = np.zeros(img.shape[:2],np.uint8) +bgdModel = np.zeros((1,65),np.float64) +fgdModel = np.zeros((1,65),np.float64) +rect = (50,50,450,290) +cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT) +mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8') +img = img*mask2[:,:,np.newaxis] +plt.imshow(img),plt.colorbar(),plt.show() + +# newmask is the mask image I manually labelled +newmask = cv2.imread('data/newmask.png',0) +# whereever it is marked white (sure foreground), change mask=1 +# whereever it is marked black (sure background), change mask=0 +mask[newmask == 0] = 0 +mask[newmask == 255] = 1 +mask, bgdModel, fgdModel = cv2.grabCut(img,mask,None,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_MASK) +mask = np.where((mask==2)|(mask==0),0,1).astype('uint8') +img = img*mask[:,:,np.newaxis] +plt.imshow(img),plt.colorbar(),plt.show() + +# feature detection +import cv2 +import numpy as np +filename = 'data/chessboard.jpg' +img = cv2.imread(filename) + +gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) +gray = np.float32(gray) +dst = cv2.cornerHarris(gray,2,3,0.04) +#result is dilated for marking the corners, not important +dst = cv2.dilate(dst,None) +# Threshold for an optimal value, it may vary depending on the image. +img[dst>0.01*dst.max()]=[0,0,255] +cv2.imshow('dst',img) +if cv2.waitKey(0) & 0xff == 27: + cv2.destroyAllWindows() + +import cv2 +import numpy as np +filename = 'data/chessboard.jpg' +img = cv2.imread(filename) +gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) +# find Harris corners +gray = np.float32(gray) +dst = cv2.cornerHarris(gray,2,3,0.04) +dst = cv2.dilate(dst,None) +ret, dst = cv2.threshold(dst,0.01*dst.max(),255,0) +dst = np.uint8(dst) +# find centroids +ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst) +# define the criteria to stop and refine the corners +criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001) +corners = cv2.cornerSubPix(gray,np.float32(centroids),(5,5),(-1,-1),criteria) +# Now draw them +res = np.hstack((centroids,corners)) +res = np.int0(res) +img[res[:,1],res[:,0]]=[0,0,255] +img[res[:,3],res[:,2]] = [0,255,0] +cv2.imwrite('data/subpixel5.png',img) + +import numpy as np +import cv2 +from matplotlib import pyplot as plt +img = cv2.imread('data/simple.jpg') +gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) +corners = cv2.goodFeaturesToTrack(gray,25,0.01,10) +corners = np.int0(corners) +for i in corners: + x,y = i.ravel() + cv2.circle(img,(x,y),3,255,-1) + +plt.imshow(img),plt.show() + +# sift +import cv2 +import numpy as np +img = cv2.imread('data/home.jpg') + +gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) +sift = cv2.xfeatures2d.SIFT_create() +kp = sift.detect(gray,None) +cv2.drawKeypoints(gray, kp, img, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) +cv2.imwrite('data/sift_keypoints.jpg',img) + +kp, des = sift.detectAndCompute(gray,None) + +import numpy as np +import cv2 +from matplotlib import pyplot as plt +img = cv2.imread('data/simple.jpg',0) +# Initiate FAST object with default values +fast = cv2.FastFeatureDetector_create() +# find and draw the keypoints +kp = fast.detect(img,None) +img2 = cv2.drawKeypoints(img, kp, None, color=(255,0,0)) +# Print all default params +print("Threshold: ", fast.getThreshold()) +print("nonmaxSuppression: ", fast.getNonmaxSuppression()) +print("neighborhood: ", fast.getType()) +print("Total Keypoints with nonmaxSuppression: ", len(kp)) +cv2.imwrite('data/fast_true.png',img2) +# Disable nonmaxSuppression +fast.setNonmaxSuppression(0) +kp = fast.detect(img,None) +print("Total Keypoints without nonmaxSuppression: ", len(kp)) +img3 = cv2.drawKeypoints(img, kp, None, color=(255,0,0)) +cv2.imwrite('data/fast_false.png',img3) + +import numpy as np +import cv2 as cv +from matplotlib import pyplot as plt +img = cv.imread('data/simple.jpg',0) +# Initiate FAST detector +star = cv.xfeatures2d.StarDetector_create() +# Initiate BRIEF extractor +brief = cv.xfeatures2d.BriefDescriptorExtractor_create() +# find the keypoints with STAR +kp = star.detect(img,None) +# compute the descriptors with BRIEF +kp, des = brief.compute(img, kp) +print( brief.descriptorSize() ) +print( des.shape ) + +# ORB +import numpy as np +import cv2 +from matplotlib import pyplot as plt +img = cv2.imread('data/simple.jpg',0) +# Initiate STAR detector +orb = cv2.ORB_create() +# find the keypoints with ORB +kp = orb.detect(img,None) +# compute the descriptors with ORB +kp, des = orb.compute(img, kp) +# draw only keypoints location,not size and orientation +img2 = cv2.drawKeypoints(img,kp,None, color=(0,255,0), flags=0) +plt.imshow(img2),plt.show() + +import numpy as np +import cv2 +from matplotlib import pyplot as plt +img1 = cv2.imread('data/book.png',0) # queryImage +img2 = cv2.imread('data/box_in_scene.png',0) # trainImage +# Initiate SIFT detector +orb = cv2.ORB_create() +# find the keypoints and descriptors with SIFT +kp1, des1 = orb.detectAndCompute(img1,None) +kp2, des2 = orb.detectAndCompute(img2,None) + +# create BFMatcher object +bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) +# Match descriptors. +matches = bf.match(des1,des2) +# Sort them in the order of their distance. +matches = sorted(matches, key = lambda x:x.distance) + +# Draw first 10 matches. +img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:10], None, flags=2) +plt.imshow(img3),plt.show() + +import numpy as np +import cv2 as cv +import matplotlib.pyplot as plt +img1 = cv.imread('data/book.png',cv.IMREAD_GRAYSCALE) # queryImage +img2 = cv.imread('data/box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage +# Initiate SIFT detector +sift = cv.xfeatures2d.SIFT_create() +# find the keypoints and descriptors with SIFT +kp1, des1 = sift.detectAndCompute(img1,None) +kp2, des2 = sift.detectAndCompute(img2,None) +# BFMatcher with default params +bf = cv.BFMatcher() +matches = bf.knnMatch(des1,des2,k=2) +# Apply ratio test +good = [] +for m,n in matches: + if m.distance < 0.75*n.distance: + good.append([m]) +# cv.drawMatchesKnn expects list of lists as matches. +img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS) +plt.imshow(img3),plt.show() + +import numpy as np +import cv2 as cv +import matplotlib.pyplot as plt +img1 = cv.imread('data/book.png',cv.IMREAD_GRAYSCALE) # queryImage +img2 = cv.imread('data/box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage +# Initiate SIFT detector +sift = cv.xfeatures2d.SIFT_create() +# find the keypoints and descriptors with SIFT +kp1, des1 = sift.detectAndCompute(img1,None) +kp2, des2 = sift.detectAndCompute(img2,None) +# FLANN parameters +FLANN_INDEX_KDTREE = 1 +index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) +search_params = dict(checks=50) # or pass empty dictionary +flann = cv.FlannBasedMatcher(index_params,search_params) +matches = flann.knnMatch(des1,des2,k=2) +# Need to draw only good matches, so create a mask +matchesMask = [[0,0] for i in range(len(matches))] +# ratio test as per Lowe's paper +for i,(m,n) in enumerate(matches): + if m.distance < 0.7*n.distance: + matchesMask[i]=[1,0] +draw_params = dict(matchColor = (0,255,0), + singlePointColor = (255,0,0), + matchesMask = matchesMask, + flags = cv.DrawMatchesFlags_DEFAULT) +img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params) +plt.imshow(img3,),plt.show() + + +import numpy as np +import cv2 as cv +from matplotlib import pyplot as plt +MIN_MATCH_COUNT = 10 +img1 = cv.imread('data/book.png',0) # queryImage +img2 = cv.imread('data/box_in_scene.png',0) # trainImage +# Initiate SIFT detector +sift = cv.xfeatures2d.SIFT_create() +# find the keypoints and descriptors with SIFT +kp1, des1 = sift.detectAndCompute(img1,None) +kp2, des2 = sift.detectAndCompute(img2,None) +FLANN_INDEX_KDTREE = 1 +index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) +search_params = dict(checks = 50) +flann = cv.FlannBasedMatcher(index_params, search_params) +matches = flann.knnMatch(des1,des2,k=2) +# store all the good matches as per Lowe's ratio test. +good = [] +for m,n in matches: + if m.distance < 0.7*n.distance: + good.append(m) + +if len(good)>MIN_MATCH_COUNT: + src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2) + dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2) + M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0) + matchesMask = mask.ravel().tolist() + h,w = img1.shape + pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2) + dst = cv.perspectiveTransform(pts,M) + img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA) +else: + print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) ) + matchesMask = None + +draw_params = dict(matchColor = (0,255,0), # draw matches in green color + singlePointColor = None, + matchesMask = matchesMask, # draw only inliers + flags = 2) +img3 = cv.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params) +plt.imshow(img3, 'gray'),plt.show() + +import numpy as np +import cv2 as cv +import argparse + +parser = argparse.ArgumentParser(description='This sample demonstrates the meanshift algorithm. \ + The example file can be downloaded from: \ + https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4') +parser.add_argument('image', type=str, help='path to image file') +args = parser.parse_args() + +cap = cv.VideoCapture(args.image) + +# take first frame of the video +ret,frame = cap.read() + +# setup initial location of window +x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values +track_window = (x, y, w, h) + +# set up the ROI for tracking +roi = frame[y:y+h, x:x+w] +hsv_roi = cv.cvtColor(roi, cv.COLOR_BGR2HSV) +mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.))) +roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180]) +cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX) + +# Setup the termination criteria, either 10 iteration or move by atleast 1 pt +term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 ) + +while(1): + ret, frame = cap.read() + + if ret == True: + hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV) + dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1) + + # apply meanshift to get the new location + ret, track_window = cv.meanShift(dst, track_window, term_crit) + + # Draw it on image + x,y,w,h = track_window + img2 = cv.rectangle(frame, (x,y), (x+w,y+h), 255,2) + cv.imshow('img2',img2) + + k = cv.waitKey(30) & 0xff + if k == 27: + break + else: + break diff --git a/opencv2refman.pdf b/opencv2refman.pdf new file mode 100644 index 0000000..a181c48 Binary files /dev/null and b/opencv2refman.pdf differ diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt new file mode 100644 index 0000000..31c2bd1 --- /dev/null +++ b/python/CMakeLists.txt @@ -0,0 +1,6 @@ +if(INSTALL_PYTHON_EXAMPLES) + file(GLOB install_list *.py ) + install(FILES ${install_list} + DESTINATION ${OPENCV_SAMPLES_SRC_INSTALL_PATH}/python + PERMISSIONS OWNER_READ GROUP_READ WORLD_READ COMPONENT samples) +endif() diff --git a/python/_coverage.py b/python/_coverage.py new file mode 100644 index 0000000..62dd7aa --- /dev/null +++ b/python/_coverage.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python + +''' +Utility for measuring python opencv API coverage by samples. +''' + +# Python 2/3 compatibility +from __future__ import print_function + +from glob import glob +import cv2 as cv +import re + +if __name__ == '__main__': + cv2_callable = set(['cv.'+name for name in dir(cv) if callable( getattr(cv, name) )]) + + found = set() + for fn in glob('*.py'): + print(' --- ', fn) + code = open(fn).read() + found |= set(re.findall('cv2?\.\w+', code)) + + cv2_used = found & cv2_callable + cv2_unused = cv2_callable - cv2_used + with open('unused_api.txt', 'w') as f: + f.write('\n'.join(sorted(cv2_unused))) + + r = 1.0 * len(cv2_used) / len(cv2_callable) + print('\ncv api coverage: %d / %d (%.1f%%)' % ( len(cv2_used), len(cv2_callable), r*100 )) diff --git a/python/_doc.py b/python/_doc.py new file mode 100644 index 0000000..02c733c --- /dev/null +++ b/python/_doc.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python + +''' +Scans current directory for *.py files and reports +ones with missing __doc__ string. +''' + +# Python 2/3 compatibility +from __future__ import print_function + +from glob import glob + +if __name__ == '__main__': + print('--- undocumented files:') + for fn in glob('*.py'): + loc = {} + try: + try: + execfile(fn, loc) # Python 2 + except NameError: + exec(open(fn).read(), loc) # Python 3 + except Exception: + pass + if '__doc__' not in loc: + print(fn) diff --git a/python/_run_winpack_demo.cmd b/python/_run_winpack_demo.cmd new file mode 100644 index 0000000..afc314b --- /dev/null +++ b/python/_run_winpack_demo.cmd @@ -0,0 +1,3 @@ +@echo off +call ..\_winpack_run_python_sample.cmd %* +exit /B diff --git a/python/asift.py b/python/asift.py new file mode 100644 index 0000000..e21ef5b --- /dev/null +++ b/python/asift.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python + +''' +Affine invariant feature-based image matching sample. + +This sample is similar to find_obj.py, but uses the affine transformation +space sampling technique, called ASIFT [1]. While the original implementation +is based on SIFT, you can try to use SURF or ORB detectors instead. Homography RANSAC +is used to reject outliers. Threading is used for faster affine sampling. + +[1] http://www.ipol.im/pub/algo/my_affine_sift/ + +USAGE + asift.py [--feature=[-flann]] [ ] + + --feature - Feature to use. Can be sift, surf, orb or brisk. Append '-flann' + to feature name to use Flann-based matcher instead bruteforce. + + Press left mouse button on a feature point to see its matching point. +''' + +# Python 2/3 compatibility +from __future__ import print_function + +import numpy as np +import cv2 as cv + +# built-in modules +import itertools as it +from multiprocessing.pool import ThreadPool + +# local modules +from common import Timer +from find_obj import init_feature, filter_matches, explore_match + + +def affine_skew(tilt, phi, img, mask=None): + ''' + affine_skew(tilt, phi, img, mask=None) -> skew_img, skew_mask, Ai + + Ai - is an affine transform matrix from skew_img to img + ''' + h, w = img.shape[:2] + if mask is None: + mask = np.zeros((h, w), np.uint8) + mask[:] = 255 + A = np.float32([[1, 0, 0], [0, 1, 0]]) + if phi != 0.0: + phi = np.deg2rad(phi) + s, c = np.sin(phi), np.cos(phi) + A = np.float32([[c,-s], [ s, c]]) + corners = [[0, 0], [w, 0], [w, h], [0, h]] + tcorners = np.int32( np.dot(corners, A.T) ) + x, y, w, h = cv.boundingRect(tcorners.reshape(1,-1,2)) + A = np.hstack([A, [[-x], [-y]]]) + img = cv.warpAffine(img, A, (w, h), flags=cv.INTER_LINEAR, borderMode=cv.BORDER_REPLICATE) + if tilt != 1.0: + s = 0.8*np.sqrt(tilt*tilt-1) + img = cv.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01) + img = cv.resize(img, (0, 0), fx=1.0/tilt, fy=1.0, interpolation=cv.INTER_NEAREST) + A[0] /= tilt + if phi != 0.0 or tilt != 1.0: + h, w = img.shape[:2] + mask = cv.warpAffine(mask, A, (w, h), flags=cv.INTER_NEAREST) + Ai = cv.invertAffineTransform(A) + return img, mask, Ai + + +def affine_detect(detector, img, mask=None, pool=None): + ''' + affine_detect(detector, img, mask=None, pool=None) -> keypoints, descrs + + Apply a set of affine transformations to the image, detect keypoints and + reproject them into initial image coordinates. + See http://www.ipol.im/pub/algo/my_affine_sift/ for the details. + + ThreadPool object may be passed to speedup the computation. + ''' + params = [(1.0, 0.0)] + for t in 2**(0.5*np.arange(1,6)): + for phi in np.arange(0, 180, 72.0 / t): + params.append((t, phi)) + + def f(p): + t, phi = p + timg, tmask, Ai = affine_skew(t, phi, img) + keypoints, descrs = detector.detectAndCompute(timg, tmask) + for kp in keypoints: + x, y = kp.pt + kp.pt = tuple( np.dot(Ai, (x, y, 1)) ) + if descrs is None: + descrs = [] + return keypoints, descrs + + keypoints, descrs = [], [] + if pool is None: + ires = it.imap(f, params) + else: + ires = pool.imap(f, params) + + for i, (k, d) in enumerate(ires): + print('affine sampling: %d / %d\r' % (i+1, len(params)), end='') + keypoints.extend(k) + descrs.extend(d) + + print() + return keypoints, np.array(descrs) + + +def main(): + import sys, getopt + opts, args = getopt.getopt(sys.argv[1:], '', ['feature=']) + opts = dict(opts) + feature_name = opts.get('--feature', 'brisk-flann') + try: + fn1, fn2 = args + except: + fn1 = 'aero1.jpg' + fn2 = 'aero3.jpg' + + img1 = cv.imread(cv.samples.findFile(fn1), cv.IMREAD_GRAYSCALE) + img2 = cv.imread(cv.samples.findFile(fn2), cv.IMREAD_GRAYSCALE) + detector, matcher = init_feature(feature_name) + + if img1 is None: + print('Failed to load fn1:', fn1) + sys.exit(1) + + if img2 is None: + print('Failed to load fn2:', fn2) + sys.exit(1) + + if detector is None: + print('unknown feature:', feature_name) + sys.exit(1) + + print('using', feature_name) + + pool=ThreadPool(processes = cv.getNumberOfCPUs()) + kp1, desc1 = affine_detect(detector, img1, pool=pool) + kp2, desc2 = affine_detect(detector, img2, pool=pool) + print('img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))) + + def match_and_draw(win): + with Timer('matching'): + raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2 + p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches) + if len(p1) >= 4: + H, status = cv.findHomography(p1, p2, cv.RANSAC, 5.0) + print('%d / %d inliers/matched' % (np.sum(status), len(status))) + # do not draw outliers (there will be a lot of them) + kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag] + else: + H, status = None, None + print('%d matches found, not enough for homography estimation' % len(p1)) + + explore_match(win, img1, img2, kp_pairs, None, H) + + + match_and_draw('affine find_obj') + cv.waitKey() + print('Done') + + +if __name__ == '__main__': + print(__doc__) + main() + cv.destroyAllWindows() diff --git a/python/browse.py b/python/browse.py new file mode 100644 index 0000000..edc791f --- /dev/null +++ b/python/browse.py @@ -0,0 +1,67 @@ +#!/usr/bin/env python + +''' +browse.py +========= + +Sample shows how to implement a simple hi resolution image navigation + +Usage +----- +browse.py [image filename] + +''' + +# Python 2/3 compatibility +from __future__ import print_function +import sys +PY3 = sys.version_info[0] == 3 + +if PY3: + xrange = range + +import numpy as np +import cv2 as cv + +# built-in modules +import sys + +def main(): + if len(sys.argv) > 1: + fn = cv.samples.findFile(sys.argv[1]) + print('loading %s ...' % fn) + img = cv.imread(fn) + if img is None: + print('Failed to load fn:', fn) + sys.exit(1) + + else: + sz = 4096 + print('generating %dx%d procedural image ...' % (sz, sz)) + img = np.zeros((sz, sz), np.uint8) + track = np.cumsum(np.random.rand(500000, 2)-0.5, axis=0) + track = np.int32(track*10 + (sz/2, sz/2)) + cv.polylines(img, [track], 0, 255, 1, cv.LINE_AA) + + + small = img + for _i in xrange(3): + small = cv.pyrDown(small) + + def onmouse(event, x, y, flags, param): + h, _w = img.shape[:2] + h1, _w1 = small.shape[:2] + x, y = 1.0*x*h/h1, 1.0*y*h/h1 + zoom = cv.getRectSubPix(img, (800, 600), (x+0.5, y+0.5)) + cv.imshow('zoom', zoom) + + cv.imshow('preview', small) + cv.setMouseCallback('preview', onmouse) + cv.waitKey() + print('Done') + + +if __name__ == '__main__': + print(__doc__) + main() + cv.destroyAllWindows() diff --git a/python/calibrate.py b/python/calibrate.py new file mode 100644 index 0000000..bca430b --- /dev/null +++ b/python/calibrate.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python + +''' +camera calibration for distorted images with chess board samples +reads distorted images, calculates the calibration and write undistorted images + +usage: + calibrate.py [--debug ] [--square_size] [] + +default values: + --debug: ./output/ + --square_size: 1.0 + defaults to ../data/left*.jpg +''' + +# Python 2/3 compatibility +from __future__ import print_function + +import numpy as np +import cv2 as cv + +# local modules +from common import splitfn + +# built-in modules +import os + +def main(): + import sys + import getopt + from glob import glob + + args, img_mask = getopt.getopt(sys.argv[1:], '', ['debug=', 'square_size=', 'threads=']) + args = dict(args) + args.setdefault('--debug', './output/') + args.setdefault('--square_size', 1.0) + args.setdefault('--threads', 4) + if not img_mask: + img_mask = '../data/left??.jpg' # default + else: + img_mask = img_mask[0] + + img_names = glob(img_mask) + debug_dir = args.get('--debug') + if debug_dir and not os.path.isdir(debug_dir): + os.mkdir(debug_dir) + square_size = float(args.get('--square_size')) + + pattern_size = (9, 6) + pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32) + pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2) + pattern_points *= square_size + + obj_points = [] + img_points = [] + h, w = cv.imread(img_names[0], cv.IMREAD_GRAYSCALE).shape[:2] # TODO: use imquery call to retrieve results + + def processImage(fn): + print('processing %s... ' % fn) + img = cv.imread(fn, 0) + if img is None: + print("Failed to load", fn) + return None + + assert w == img.shape[1] and h == img.shape[0], ("size: %d x %d ... " % (img.shape[1], img.shape[0])) + found, corners = cv.findChessboardCorners(img, pattern_size) + if found: + term = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1) + cv.cornerSubPix(img, corners, (5, 5), (-1, -1), term) + + if debug_dir: + vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR) + cv.drawChessboardCorners(vis, pattern_size, corners, found) + _path, name, _ext = splitfn(fn) + outfile = os.path.join(debug_dir, name + '_chess.png') + cv.imwrite(outfile, vis) + + if not found: + print('chessboard not found') + return None + + print(' %s... OK' % fn) + return (corners.reshape(-1, 2), pattern_points) + + threads_num = int(args.get('--threads')) + if threads_num <= 1: + chessboards = [processImage(fn) for fn in img_names] + else: + print("Run with %d threads..." % threads_num) + from multiprocessing.dummy import Pool as ThreadPool + pool = ThreadPool(threads_num) + chessboards = pool.map(processImage, img_names) + + chessboards = [x for x in chessboards if x is not None] + for (corners, pattern_points) in chessboards: + img_points.append(corners) + obj_points.append(pattern_points) + + # calculate camera distortion + rms, camera_matrix, dist_coefs, _rvecs, _tvecs = cv.calibrateCamera(obj_points, img_points, (w, h), None, None) + + print("\nRMS:", rms) + print("camera matrix:\n", camera_matrix) + print("distortion coefficients: ", dist_coefs.ravel()) + + # undistort the image with the calibration + print('') + for fn in img_names if debug_dir else []: + _path, name, _ext = splitfn(fn) + img_found = os.path.join(debug_dir, name + '_chess.png') + outfile = os.path.join(debug_dir, name + '_undistorted.png') + + img = cv.imread(img_found) + if img is None: + continue + + h, w = img.shape[:2] + newcameramtx, roi = cv.getOptimalNewCameraMatrix(camera_matrix, dist_coefs, (w, h), 1, (w, h)) + + dst = cv.undistort(img, camera_matrix, dist_coefs, None, newcameramtx) + + # crop and save the image + x, y, w, h = roi + dst = dst[y:y+h, x:x+w] + + print('Undistorted image written to: %s' % outfile) + cv.imwrite(outfile, dst) + + print('Done') + + +if __name__ == '__main__': + print(__doc__) + main() + cv.destroyAllWindows() diff --git a/python/camera_calibration_show_extrinsics.py b/python/camera_calibration_show_extrinsics.py new file mode 100644 index 0000000..0118b5b --- /dev/null +++ b/python/camera_calibration_show_extrinsics.py @@ -0,0 +1,227 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Python 2/3 compatibility +from __future__ import print_function + +import numpy as np +import cv2 as cv + +from numpy import linspace + +def inverse_homogeneoux_matrix(M): + R = M[0:3, 0:3] + T = M[0:3, 3] + M_inv = np.identity(4) + M_inv[0:3, 0:3] = R.T + M_inv[0:3, 3] = -(R.T).dot(T) + + return M_inv + +def transform_to_matplotlib_frame(cMo, X, inverse=False): + M = np.identity(4) + M[1,1] = 0 + M[1,2] = 1 + M[2,1] = -1 + M[2,2] = 0 + + if inverse: + return M.dot(inverse_homogeneoux_matrix(cMo).dot(X)) + else: + return M.dot(cMo.dot(X)) + +def create_camera_model(camera_matrix, width, height, scale_focal, draw_frame_axis=False): + fx = camera_matrix[0,0] + fy = camera_matrix[1,1] + focal = 2 / (fx + fy) + f_scale = scale_focal * focal + + # draw image plane + X_img_plane = np.ones((4,5)) + X_img_plane[0:3,0] = [-width, height, f_scale] + X_img_plane[0:3,1] = [width, height, f_scale] + X_img_plane[0:3,2] = [width, -height, f_scale] + X_img_plane[0:3,3] = [-width, -height, f_scale] + X_img_plane[0:3,4] = [-width, height, f_scale] + + # draw triangle above the image plane + X_triangle = np.ones((4,3)) + X_triangle[0:3,0] = [-width, -height, f_scale] + X_triangle[0:3,1] = [0, -2*height, f_scale] + X_triangle[0:3,2] = [width, -height, f_scale] + + # draw camera + X_center1 = np.ones((4,2)) + X_center1[0:3,0] = [0, 0, 0] + X_center1[0:3,1] = [-width, height, f_scale] + + X_center2 = np.ones((4,2)) + X_center2[0:3,0] = [0, 0, 0] + X_center2[0:3,1] = [width, height, f_scale] + + X_center3 = np.ones((4,2)) + X_center3[0:3,0] = [0, 0, 0] + X_center3[0:3,1] = [width, -height, f_scale] + + X_center4 = np.ones((4,2)) + X_center4[0:3,0] = [0, 0, 0] + X_center4[0:3,1] = [-width, -height, f_scale] + + # draw camera frame axis + X_frame1 = np.ones((4,2)) + X_frame1[0:3,0] = [0, 0, 0] + X_frame1[0:3,1] = [f_scale/2, 0, 0] + + X_frame2 = np.ones((4,2)) + X_frame2[0:3,0] = [0, 0, 0] + X_frame2[0:3,1] = [0, f_scale/2, 0] + + X_frame3 = np.ones((4,2)) + X_frame3[0:3,0] = [0, 0, 0] + X_frame3[0:3,1] = [0, 0, f_scale/2] + + if draw_frame_axis: + return [X_img_plane, X_triangle, X_center1, X_center2, X_center3, X_center4, X_frame1, X_frame2, X_frame3] + else: + return [X_img_plane, X_triangle, X_center1, X_center2, X_center3, X_center4] + +def create_board_model(extrinsics, board_width, board_height, square_size, draw_frame_axis=False): + width = board_width*square_size + height = board_height*square_size + + # draw calibration board + X_board = np.ones((4,5)) + #X_board_cam = np.ones((extrinsics.shape[0],4,5)) + X_board[0:3,0] = [0,0,0] + X_board[0:3,1] = [width,0,0] + X_board[0:3,2] = [width,height,0] + X_board[0:3,3] = [0,height,0] + X_board[0:3,4] = [0,0,0] + + # draw board frame axis + X_frame1 = np.ones((4,2)) + X_frame1[0:3,0] = [0, 0, 0] + X_frame1[0:3,1] = [height/2, 0, 0] + + X_frame2 = np.ones((4,2)) + X_frame2[0:3,0] = [0, 0, 0] + X_frame2[0:3,1] = [0, height/2, 0] + + X_frame3 = np.ones((4,2)) + X_frame3[0:3,0] = [0, 0, 0] + X_frame3[0:3,1] = [0, 0, height/2] + + if draw_frame_axis: + return [X_board, X_frame1, X_frame2, X_frame3] + else: + return [X_board] + +def draw_camera_boards(ax, camera_matrix, cam_width, cam_height, scale_focal, + extrinsics, board_width, board_height, square_size, + patternCentric): + from matplotlib import cm + + min_values = np.zeros((3,1)) + min_values = np.inf + max_values = np.zeros((3,1)) + max_values = -np.inf + + if patternCentric: + X_moving = create_camera_model(camera_matrix, cam_width, cam_height, scale_focal) + X_static = create_board_model(extrinsics, board_width, board_height, square_size) + else: + X_static = create_camera_model(camera_matrix, cam_width, cam_height, scale_focal, True) + X_moving = create_board_model(extrinsics, board_width, board_height, square_size) + + cm_subsection = linspace(0.0, 1.0, extrinsics.shape[0]) + colors = [ cm.jet(x) for x in cm_subsection ] + + for i in range(len(X_static)): + X = np.zeros(X_static[i].shape) + for j in range(X_static[i].shape[1]): + X[:,j] = transform_to_matplotlib_frame(np.eye(4), X_static[i][:,j]) + ax.plot3D(X[0,:], X[1,:], X[2,:], color='r') + min_values = np.minimum(min_values, X[0:3,:].min(1)) + max_values = np.maximum(max_values, X[0:3,:].max(1)) + + for idx in range(extrinsics.shape[0]): + R, _ = cv.Rodrigues(extrinsics[idx,0:3]) + cMo = np.eye(4,4) + cMo[0:3,0:3] = R + cMo[0:3,3] = extrinsics[idx,3:6] + for i in range(len(X_moving)): + X = np.zeros(X_moving[i].shape) + for j in range(X_moving[i].shape[1]): + X[0:4,j] = transform_to_matplotlib_frame(cMo, X_moving[i][0:4,j], patternCentric) + ax.plot3D(X[0,:], X[1,:], X[2,:], color=colors[idx]) + min_values = np.minimum(min_values, X[0:3,:].min(1)) + max_values = np.maximum(max_values, X[0:3,:].max(1)) + + return min_values, max_values + +def main(): + import argparse + + parser = argparse.ArgumentParser(description='Plot camera calibration extrinsics.', + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument('--calibration', type=str, default='left_intrinsics.yml', + help='YAML camera calibration file.') + parser.add_argument('--cam_width', type=float, default=0.064/2, + help='Width/2 of the displayed camera.') + parser.add_argument('--cam_height', type=float, default=0.048/2, + help='Height/2 of the displayed camera.') + parser.add_argument('--scale_focal', type=float, default=40, + help='Value to scale the focal length.') + parser.add_argument('--patternCentric', action='store_true', + help='The calibration board is static and the camera is moving.') + args = parser.parse_args() + + fs = cv.FileStorage(cv.samples.findFile(args.calibration), cv.FILE_STORAGE_READ) + board_width = int(fs.getNode('board_width').real()) + board_height = int(fs.getNode('board_height').real()) + square_size = fs.getNode('square_size').real() + camera_matrix = fs.getNode('camera_matrix').mat() + extrinsics = fs.getNode('extrinsic_parameters').mat() + + import matplotlib.pyplot as plt + from mpl_toolkits.mplot3d import Axes3D # pylint: disable=unused-variable + + fig = plt.figure() + ax = fig.gca(projection='3d') + ax.set_aspect("equal") + + cam_width = args.cam_width + cam_height = args.cam_height + scale_focal = args.scale_focal + min_values, max_values = draw_camera_boards(ax, camera_matrix, cam_width, cam_height, + scale_focal, extrinsics, board_width, + board_height, square_size, args.patternCentric) + + X_min = min_values[0] + X_max = max_values[0] + Y_min = min_values[1] + Y_max = max_values[1] + Z_min = min_values[2] + Z_max = max_values[2] + max_range = np.array([X_max-X_min, Y_max-Y_min, Z_max-Z_min]).max() / 2.0 + + mid_x = (X_max+X_min) * 0.5 + mid_y = (Y_max+Y_min) * 0.5 + mid_z = (Z_max+Z_min) * 0.5 + ax.set_xlim(mid_x - max_range, mid_x + max_range) + ax.set_ylim(mid_y - max_range, mid_y + max_range) + ax.set_zlim(mid_z - max_range, mid_z + max_range) + + ax.set_xlabel('x') + ax.set_ylabel('z') + ax.set_zlabel('-y') + ax.set_title('Extrinsic Parameters Visualization') + + plt.show() + print('Done') + + +if __name__ == '__main__': + print(__doc__) + main() + cv.destroyAllWindows() diff --git a/python/camshift.py b/python/camshift.py new file mode 100644 index 0000000..0fcab72 --- /dev/null +++ b/python/camshift.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python + +''' +Camshift tracker +================ + +This is a demo that shows mean-shift based tracking +You select a color objects such as your face and it tracks it. +This reads from video camera (0 by default, or the camera number the user enters) + +[1] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.7673 + +Usage: +------ + camshift.py [