def build_train_loader(cls, cfg): """ Returns: iterable It calls :func:`detectron2.data.build_detection_train_loader` with a customized DatasetMapper, which adds categorical labels as a semantic mask. """ mapper = DatasetMapperWithBasis(cfg, True) return build_detection_train_loader(cfg, mapper=mapper)
def build_train_loader(cls, cfg): """ Returns: iterable It calls :func:`detectron2.data.build_detection_train_loader` with a customized DatasetMapper, which adds categorical labels as a semantic mask. """ register_coco_instances("cam-cv-1.0_Train", {}, '../Images/annotations/noncamo_train.json', '') register_coco_instances("cam-cv-1.0_Test", {}, '../Images/annotations/noncamo_test.json', '') mapper = DatasetMapperWithBasis(cfg, True) # DatasetCatalog.register('cam-cv-1.0_Train', mapper) # DatasetCatalog.register('cam-cv-1.0_Test', mapper) return build_detection_train_loader(cfg, mapper)
os.makedirs(dirname, exist_ok=True) metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0]) def output(vis, fname): if args.show: print(fname) cv2.imshow("window", vis.get_image()[:, :, ::-1]) cv2.waitKey() else: filepath = os.path.join(dirname, fname) print("Saving to {} ...".format(filepath)) vis.save(filepath) scale = 2.0 if args.show else 1.0 if args.source == "dataloader": mapper = DatasetMapperWithBasis(cfg, True) train_data_loader = build_detection_train_loader(cfg, mapper) for batch in train_data_loader: for per_image in batch: # Pytorch tensor is in (C, H, W) format img = per_image["image"].permute(1, 2, 0) if cfg.INPUT.FORMAT == "BGR": img = img[:, :, [2, 1, 0]] else: img = np.asarray( Image.fromarray(img, mode=cfg.INPUT.FORMAT).convert("RGB")) visualizer = Visualizer(img, metadata=metadata, scale=scale) target_fields = per_image["instances"].get_fields() labels = [