hBSSKJr SSKJr SSKJrJr "SS\5rg)) BasePredictor)Results)nmsopsc0\rSrSrSrSrSrSrSrSr g) DetectionPredictora A class extending the BasePredictor class for prediction based on a detection model. This predictor specializes in object detection tasks, processing model outputs into meaningful detection results with bounding boxes and class predictions. Attributes: args (namespace): Configuration arguments for the predictor. model (nn.Module): The detection model used for inference. batch (list): Batch of images and metadata for processing. Methods: postprocess: Process raw model predictions into detection results. construct_results: Build Results objects from processed predictions. construct_result: Create a single Result object from a prediction. get_obj_feats: Extract object features from the feature maps. Examples: >>> from ultralytics.utils import ASSETS >>> from ultralytics.models.yolo.detect import DetectionPredictor >>> args = dict(model="yolo11n.pt", source=ASSETS) >>> predictor = DetectionPredictor(overrides=args) >>> predictor.predict_cli() c [USS5SLn[R"UURRURR URR URRURRURRS:XaSO[URR5[URSS5URRS:HUS9 n[U[5(d[R "U5nU(a$UR#UR$US 5nUSnUR&"XU40UD6nU(a[)UW5H upXlM U$) a Post-process predictions and return a list of Results objects. This method applies non-maximum suppression to raw model predictions and prepares them for visualization and further analysis. Args: preds (torch.Tensor): Raw predictions from the model. img (torch.Tensor): Processed input image tensor in model input format. orig_imgs (torch.Tensor | list): Original input images before preprocessing. **kwargs (Any): Additional keyword arguments. Returns: (list): List of Results objects containing the post-processed predictions. Examples: >>> predictor = DetectionPredictor(overrides=dict(model="yolo11n.pt")) >>> results = predictor.predict("path/to/image.jpg") >>> processed_results = predictor.postprocess(preds, img, orig_imgs) _featsNdetectrend2endFobb)max_detncr rotated return_idxs)getattrrnon_max_suppressionargsconfiouclasses agnostic_nmsrtasklenmodelnames isinstancelistrconvert_torch2numpy_batch get_obj_featsr construct_resultszipfeats) selfpredsimg orig_imgskwargs save_feats obj_featsresultsrfs `/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/models/yolo/detect/predict.py postprocessDetectionPredictor.postprocess"s+*T8T2$> ''  IINN IIMM II   II " "II%%IINNh.qC 8H8H4IDJJ 59IINNe+"  )T**55i@I **4;;aAI!HE((YI&I GY/0c SSKn[SU55nURUVs/sHQoURSSSS5R UR SSXER SU-5R SS9PMS snSS9n[Xb5VVs/sHupxUR S(aXxO/PM! snn$s snfs snnf) z.Extract object features from the feature maps.rNc3># UHoRSv M g7f)rN)shape).0xs r0 3DetectionPredictor.get_obj_feats..Xs.Iq Isr)dim)torchmincatpermutereshaper6meanr$) r& feat_mapsidxsr?sr8r,r%idxs r0r" DetectionPredictor.get_obj_featsTs .I. .IIen oen`aYYq!Q " * *1771:r1ggajAo N S SXZ S [en ouv FIEYZEYzuciil 2EYZZ pZs AB<&Cc [XURS5VVVs/sHupEnURXBXV5PM snnn$s snnnf)a Construct a list of Results objects from model predictions. Args: preds (list[torch.Tensor]): List of predicted bounding boxes and scores for each image. img (torch.Tensor): Batch of preprocessed images used for inference. orig_imgs (list[np.ndarray]): List of original images before preprocessing. Returns: (list[Results]): List of Results objects containing detection information for each image. r)r$batchconstruct_result)r&r'r(r)predorig_imgimg_paths r0r#$DetectionPredictor.construct_results^sJ-0$**Q-,P ,P(  ! !$X @,P   sAc [R"URSSUSS2SS24UR5USS2SS24'[X4URR USS2SS24S9$)a Construct a single Results object from one image prediction. Args: pred (torch.Tensor): Predicted boxes and scores with shape (N, 6) where N is the number of detections. img (torch.Tensor): Preprocessed image tensor used for inference. orig_img (np.ndarray): Original image before preprocessing. img_path (str): Path to the original image file. Returns: (Results): Results object containing the original image, image path, class names, and scaled bounding boxes. r;N)pathrboxes)r scale_boxesr6rrr)r&rMr(rNrOs r0rL#DetectionPredictor.construct_resultosfoociimT!RaR%[(..QQU xdjj6F6FdSTVXWXVXSXkZZr3N) __name__ __module__ __qualname____firstlineno____doc__r1r"r#rL__static_attributes__rXr3r0rrs20d[ "[r3rN)ultralytics.engine.predictorrultralytics.engine.resultsrultralytics.utilsrrrrXr3r0rbs 7.&u[u[r3