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ultralytics 8.0.235 YOLOv8 OBB train, val, predict and export (#4499)

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Glenn Jocher 2024-01-05 03:00:26 +01:00 • committed by GitHub
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9
ultralytics/data/augment.py
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@ -13,7 +13,7 @@ from ultralytics.utils import LOGGER, colorstr
from ultralytics.utils.checks import check_version
from ultralytics.utils.instance import Instances
from ultralytics.utils.metrics import bbox_ioa
from ultralytics.utils.ops import segment2box
from ultralytics.utils.ops import segment2box, xyxyxyxy2xywhr
from ultralytics.utils.torch_utils import TORCHVISION_0_10, TORCHVISION_0_11, TORCHVISION_0_13
from .utils import polygons2masks, polygons2masks_overlap
@ -485,6 +485,8 @@ class RandomPerspective:
xy = xy[:, :2] / xy[:, 2:3]
segments = xy.reshape(n, -1, 2)
bboxes = np.stack([segment2box(xy, self.size[0], self.size[1]) for xy in segments], 0)
segments[..., 0] = segments[..., 0].clip(bboxes[:, 0:1], bboxes[:, 2:3])
segments[..., 1] = segments[..., 1].clip(bboxes[:, 1:2], bboxes[:, 3:4])
return bboxes, segments
def apply_keypoints(self, keypoints, M):
@ -891,6 +893,7 @@ class Format:
normalize=True,
return_mask=False,
return_keypoint=False,
return_obb=False,
mask_ratio=4,
mask_overlap=True,
batch_idx=True):
@ -899,6 +902,7 @@ class Format:
self.normalize = normalize
self.return_mask = return_mask # set False when training detection only
self.return_keypoint = return_keypoint
self.return_obb = return_obb
self.mask_ratio = mask_ratio
self.mask_overlap = mask_overlap
self.batch_idx = batch_idx # keep the batch indexes
@ -928,6 +932,9 @@ class Format:
labels['bboxes'] = torch.from_numpy(instances.bboxes) if nl else torch.zeros((nl, 4))
if self.return_keypoint:
labels['keypoints'] = torch.from_numpy(instances.keypoints)
if self.return_obb:
labels['bboxes'] = xyxyxyxy2xywhr(torch.from_numpy(instances.segments)) if len(
instances.segments) else torch.zeros((0, 5))
# Then we can use collate_fn
if self.batch_idx:
labels['batch_idx'] = torch.zeros(nl)