import os import cv2 from PIL import Image from torch.utils import data from torchvision import transforms from tqdm import tqdm from .config import Config from .image_proc import preproc from .utils import path_to_image Image.MAX_IMAGE_PIXELS = None # remove DecompressionBombWarning config = Config() _class_labels_TR_sorted = ( "Airplane, Ant, Antenna, Archery, Axe, BabyCarriage, Bag, BalanceBeam, Balcony, Balloon, Basket, BasketballHoop, Beatle, Bed, Bee, Bench, Bicycle, " "BicycleFrame, BicycleStand, Boat, Bonsai, BoomLift, Bridge, BunkBed, Butterfly, Button, Cable, CableLift, Cage, Camcorder, Cannon, Canoe, Car, " "CarParkDropArm, Carriage, Cart, Caterpillar, CeilingLamp, Centipede, Chair, Clip, Clock, Clothes, CoatHanger, Comb, ConcretePumpTruck, Crack, Crane, " "Cup, DentalChair, Desk, DeskChair, Diagram, DishRack, DoorHandle, Dragonfish, Dragonfly, Drum, Earphone, Easel, ElectricIron, Excavator, Eyeglasses, " "Fan, Fence, Fencing, FerrisWheel, FireExtinguisher, Fishing, Flag, FloorLamp, Forklift, GasStation, Gate, Gear, Goal, Golf, GymEquipment, Hammock, " "Handcart, Handcraft, Handrail, HangGlider, Harp, Harvester, Headset, Helicopter, Helmet, Hook, HorizontalBar, Hydrovalve, IroningTable, Jewelry, Key, " "KidsPlayground, Kitchenware, Kite, Knife, Ladder, LaundryRack, Lightning, Lobster, Locust, Machine, MachineGun, MagazineRack, Mantis, Medal, MemorialArchway, " "Microphone, Missile, MobileHolder, Monitor, Mosquito, Motorcycle, MovingTrolley, Mower, MusicPlayer, MusicStand, ObservationTower, Octopus, OilWell, " "OlympicLogo, OperatingTable, OutdoorFitnessEquipment, Parachute, Pavilion, Piano, Pipe, PlowHarrow, PoleVault, Punchbag, Rack, Racket, Rifle, Ring, Robot, " "RockClimbing, Rope, Sailboat, Satellite, Scaffold, Scale, Scissor, Scooter, Sculpture, Seadragon, Seahorse, Seal, SewingMachine, Ship, Shoe, ShoppingCart, " "ShoppingTrolley, Shower, Shrimp, Signboard, Skateboarding, Skeleton, Skiing, Spade, SpeedBoat, Spider, Spoon, Stair, Stand, Stationary, SteeringWheel, " "Stethoscope, Stool, Stove, StreetLamp, SweetStand, Swing, Sword, TV, Table, TableChair, TableLamp, TableTennis, Tank, Tapeline, Teapot, Telescope, Tent, " "TobaccoPipe, Toy, Tractor, TrafficLight, TrafficSign, Trampoline, TransmissionTower, Tree, Tricycle, TrimmerCover, Tripod, Trombone, Truck, Trumpet, Tuba, " "UAV, Umbrella, UnevenBars, UtilityPole, VacuumCleaner, Violin, Wakesurfing, Watch, WaterTower, WateringPot, Well, WellLid, Wheel, Wheelchair, WindTurbine, Windmill, WineGlass, WireWhisk, Yacht" ) class_labels_TR_sorted = _class_labels_TR_sorted.split(", ") class MyData(data.Dataset): def __init__(self, datasets, image_size, is_train=True): self.size_train = image_size self.size_test = image_size self.keep_size = not config.size self.data_size = config.size self.is_train = is_train self.load_all = config.load_all self.device = config.device valid_extensions = [".png", ".jpg", ".PNG", ".JPG", ".JPEG"] if self.is_train and config.auxiliary_classification: self.cls_name2id = { _name: _id for _id, _name in enumerate(class_labels_TR_sorted) } self.transform_image = transforms.Compose( [ transforms.Resize(self.data_size[::-1]), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ][self.load_all or self.keep_size :] ) self.transform_label = transforms.Compose( [ transforms.Resize(self.data_size[::-1]), transforms.ToTensor(), ][self.load_all or self.keep_size :] ) dataset_root = os.path.join(config.data_root_dir, config.task) # datasets can be a list of different datasets for training on combined sets. self.image_paths = [] for dataset in datasets.split("+"): image_root = os.path.join(dataset_root, dataset, "im") self.image_paths += [ os.path.join(image_root, p) for p in os.listdir(image_root) if any(p.endswith(ext) for ext in valid_extensions) ] self.label_paths = [] for p in self.image_paths: for ext in valid_extensions: ## 'im' and 'gt' may need modifying p_gt = p.replace("/im/", "/gt/")[: -(len(p.split(".")[-1]) + 1)] + ext file_exists = False if os.path.exists(p_gt): self.label_paths.append(p_gt) file_exists = True break if not file_exists: print("Not exists:", p_gt) if len(self.label_paths) != len(self.image_paths): set_image_paths = set( [os.path.splitext(p.split(os.sep)[-1])[0] for p in self.image_paths] ) set_label_paths = set( [os.path.splitext(p.split(os.sep)[-1])[0] for p in self.label_paths] ) print("Path diff:", set_image_paths - set_label_paths) raise ValueError( f"There are different numbers of images ({len(self.label_paths)}) and labels ({len(self.image_paths)})" ) if self.load_all: self.images_loaded, self.labels_loaded = [], [] self.class_labels_loaded = [] # for image_path, label_path in zip(self.image_paths, self.label_paths): for image_path, label_path in tqdm( zip(self.image_paths, self.label_paths), total=len(self.image_paths) ): _image = path_to_image(image_path, size=config.size, color_type="rgb") _label = path_to_image(label_path, size=config.size, color_type="gray") self.images_loaded.append(_image) self.labels_loaded.append(_label) self.class_labels_loaded.append( self.cls_name2id[label_path.split("/")[-1].split("#")[3]] if self.is_train and config.auxiliary_classification else -1 ) def __getitem__(self, index): if self.load_all: image = self.images_loaded[index] label = self.labels_loaded[index] class_label = ( self.class_labels_loaded[index] if self.is_train and config.auxiliary_classification else -1 ) else: image = path_to_image( self.image_paths[index], size=config.size, color_type="rgb" ) label = path_to_image( self.label_paths[index], size=config.size, color_type="gray" ) class_label = ( self.cls_name2id[self.label_paths[index].split("/")[-1].split("#")[3]] if self.is_train and config.auxiliary_classification else -1 ) # loading image and label if self.is_train: image, label = preproc(image, label, preproc_methods=config.preproc_methods) # else: # if _label.shape[0] > 2048 or _label.shape[1] > 2048: # _image = cv2.resize(_image, (2048, 2048), interpolation=cv2.INTER_LINEAR) # _label = cv2.resize(_label, (2048, 2048), interpolation=cv2.INTER_LINEAR) image, label = self.transform_image(image), self.transform_label(label) if self.is_train: return image, label, class_label else: return image, label, self.label_paths[index] def __len__(self): return len(self.image_paths)