import os import random import numpy as np 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, data_size, is_train=True): # data_size is None when using dynamic_size or data_size is manually set to None (for inference in the original size). self.is_train = is_train self.data_size = data_size 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.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]) self.transform_label = transforms.Compose([ transforms.ToTensor(), ]) 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=self.data_size, color_type='rgb') _label = path_to_image(label_path, size=self.data_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=self.data_size, color_type='rgb') label = path_to_image(self.label_paths[index], size=self.data_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: if config.background_color_synthesis: image.putalpha(label) array_image = np.array(image) array_foreground = array_image[:, :, :3].astype(np.float32) array_mask = (array_image[:, :, 3:] / 255).astype(np.float32) array_background = np.zeros_like(array_foreground) choice = random.random() if choice < 0.4: # Black/Gray/White backgrounds array_background[:, :, :] = random.randint(0, 255) elif choice < 0.8: # Background color that similar to the foreground object. Hard negative samples. foreground_pixel_number = np.sum(array_mask > 0) color_foreground_mean = np.mean(array_foreground * array_mask, axis=(0, 1)) * (np.prod(array_foreground.shape[:2]) / foreground_pixel_number) color_up_or_down = random.choice((-1, 1)) # Up or down for 20% range from 255 or 0, respectively. color_foreground_mean += (255 - color_foreground_mean if color_up_or_down == 1 else color_foreground_mean) * (random.random() * 0.2) * color_up_or_down array_background[:, :, :] = color_foreground_mean else: # Any color for idx_channel in range(3): array_background[:, :, idx_channel] = random.randint(0, 255) array_foreground_background = array_foreground * array_mask + array_background * (1 - array_mask) image = Image.fromarray(array_foreground_background.astype(np.uint8)) 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) # At present, we use fixed sizes in inference, instead of consistent dynamic size with training. if self.is_train: if config.dynamic_size is None: image, label = self.transform_image(image), self.transform_label(label) else: size_div_32 = (int(image.size[0] // 32 * 32), int(image.size[1] // 32 * 32)) if image.size != size_div_32: image = image.resize(size_div_32) label = label.resize(size_div_32) 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) def custom_collate_fn(batch): if config.dynamic_size: dynamic_size = tuple(sorted(config.dynamic_size)) dynamic_size_batch = (random.randint(dynamic_size[0][0], dynamic_size[0][1]) // 32 * 32, random.randint(dynamic_size[1][0], dynamic_size[1][1]) // 32 * 32) # select a value randomly in the range of [dynamic_size[0/1][0], dynamic_size[0/1][1]]. data_size = dynamic_size_batch else: data_size = config.size new_batch = [] transform_image = transforms.Compose([ transforms.Resize(data_size[::-1]), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]) transform_label = transforms.Compose([ transforms.Resize(data_size[::-1]), transforms.ToTensor(), ]) for image, label, class_label in batch: new_batch.append((transform_image(image), transform_label(label), class_label)) return data._utils.collate.default_collate(new_batch)