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from ultralytics.models.yolo.detect import DetectionPredictor
import torch
from ultralytics.utils import ops
from ultralytics.engine.results import Results
class YOLOv10DetectionPredictor(DetectionPredictor):
def postprocess(self, preds, img, orig_imgs):
if isinstance(preds, dict):
preds = preds["one2one"]
if isinstance(preds, (list, tuple)):
preds = preds[0]
if preds.shape[-1] == 6:
pass
else:
preds = preds.transpose(-1, -2)
bboxes, scores, labels = ops.v10postprocess(preds, self.args.max_det, preds.shape[-1]-4)
bboxes = ops.xywh2xyxy(bboxes)
preds = torch.cat([bboxes, scores.unsqueeze(-1), labels.unsqueeze(-1)], dim=-1)
mask = preds[..., 4] > self.args.conf
if self.args.classes is not None:
mask = mask & (preds[..., 5:6] == torch.tensor(self.args.classes, device=preds.device).unsqueeze(0)).any(2)
preds = [p[mask[idx]] for idx, p in enumerate(preds)]
if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
results = []
for i, pred in enumerate(preds):
orig_img = orig_imgs[i]
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
img_path = self.batch[0][i]
results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
return results
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