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Add OBB save_txt
feature (#7450)
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@ -285,7 +285,8 @@ class Results(SimpleClass):
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txt_file (str): txt file path.
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save_conf (bool): save confidence score or not.
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"""
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boxes = self.boxes
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is_obb = self.obb is not None
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boxes = self.obb if is_obb else self.boxes
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masks = self.masks
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probs = self.probs
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kpts = self.keypoints
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@ -297,7 +298,7 @@ class Results(SimpleClass):
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# Detect/segment/pose
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for j, d in enumerate(boxes):
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c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
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line = (c, *d.xywhn.view(-1))
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line = (c, *(d.xyxyxyxyn.view(-1) if is_obb else d.xywhn.view(-1)))
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if masks:
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seg = masks[j].xyn[0].copy().reshape(-1) # reversed mask.xyn, (n,2) to (n*2)
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line = (c, *seg)
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@ -640,6 +641,15 @@ class OBB(BaseTensor):
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"""Return the boxes in xyxyxyxy format, (N, 4, 2)."""
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return ops.xywhr2xyxyxyxy(self.xywhr)
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@property
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@lru_cache(maxsize=2)
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def xyxyxyxyn(self):
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"""Return the boxes in xyxyxyxy format, (N, 4, 2)."""
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xyxyxyxyn = self.xyxyxyxy.clone() if isinstance(self.xyxyxyxy, torch.Tensor) else np.copy(self.xyxyxyxy)
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xyxyxyxyn[..., 0] /= self.orig_shape[1]
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xyxyxyxyn[..., 1] /= self.orig_shape[1]
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return xyxyxyxyn
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@property
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@lru_cache(maxsize=2)
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def xyxy(self):
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