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New YOLOv8 Results() class for prediction outputs (#314)

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This commit is contained in:
Ayush Chaurasia 2023-01-17 19:02:34 +05:30 • committed by GitHub
parent 0cb87f7dd3
commit c6985da9de
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32 changed files with 813 additions and 259 deletions
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23
ultralytics/yolo/engine/model.py
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@ -54,8 +54,8 @@ class YOLO:
# Load or create new YOLO model
{'.pt': self._load, '.yaml': self._new}[Path(model).suffix](model)
def __call__(self, source, **kwargs):
return self.predict(source, **kwargs)
def __call__(self, source=None, stream=False, verbose=False, **kwargs):
return self.predict(source, stream, verbose, **kwargs)
def _new(self, cfg: str, verbose=True):
"""
@ -111,13 +111,20 @@ class YOLO:
self.model.fuse()
@smart_inference_mode()
def predict(self, source, return_outputs=False, **kwargs):
def predict(self, source=None, stream=False, verbose=False, **kwargs):
"""
Visualize prediction.
Perform prediction using the YOLO model.
Args:
source (str): Accepts all source types accepted by yolo
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
source (str | int | PIL | np.ndarray): The source of the image to make predictions on.
Accepts all source types accepted by the YOLO model.
stream (bool): Whether to stream the predictions or not. Defaults to False.
verbose (bool): Whether to print verbose information or not. Defaults to False.
**kwargs : Additional keyword arguments passed to the predictor.
Check the 'configuration' section in the documentation for all available options.
Returns:
(dict): The prediction results.
"""
overrides = self.overrides.copy()
overrides["conf"] = 0.25
@ -127,8 +134,8 @@ class YOLO:
predictor = self.PredictorClass(overrides=overrides)
predictor.args.imgsz = check_imgsz(predictor.args.imgsz, min_dim=2) # check image size
predictor.setup(model=self.model, source=source, return_outputs=return_outputs)
return predictor() if return_outputs else predictor.predict_cli()
predictor.setup(model=self.model, source=source)
return predictor(stream=stream, verbose=verbose)
@smart_inference_mode()
def val(self, data=None, **kwargs):