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| import os | |
| import cv2 | |
| from tqdm import tqdm | |
| from PIL import Image | |
| from torch.utils import data | |
| from torchvision import transforms | |
| from .image_proc import preproc | |
| from .config import Config | |
| 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, 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), | |
| 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), | |
| 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): | |
| 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, config.size), color_type='rgb') | |
| _label = path_to_image(label_path, size=(config.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, config.size), color_type='rgb') | |
| label = path_to_image(self.label_paths[index], size=(config.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) | |