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 = [
