Fi BddlmZddlmZddlmZmZGddeZy)) BasePredictor)Results)nmsopsc2eZdZdZdZedZdZdZy)DetectionPredictora5A 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="yolo26n.pt", source=ASSETS) >>> predictor = DetectionPredictor(overrides=args) >>> predictor.predict_cli() c t|dddu}tj||jj|jj |jj |jj|jj|jjdk(rdnt|jjt|jdd|jjdk(| }t|tstj |d ddd f}|r$|j#|j$|d }|d}|j&|||fi|}|rt)|D] \}} | |_|S) apPost-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="yolo26n.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 h/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/models/yolo/detect/predict.py postprocesszDetectionPredictor.postprocess!sB(T8T2$> ''  IINN IIMM II   II " "II%%IINNh.qC 8H8H4IDJJ 59IINNe+"  )T*55i@ddKI **4;;aAI!HE($((YI&I GY/ 1 c ddl}td|D}|j|Dcgc]U}|jddddj |j dd||j d|zj dWc}d}t||Dcgc]\}}|j dr||ngc}}Scc}wcc}}w) z.Extract object features from the feature maps.rNc3:K|]}|jdyw)rN)shape).0xs r0 z3DetectionPredictor.get_obj_feats..Ws.q .srr)dim)torchmincatpermutereshaper5meanr$) feat_mapsidxsr<sr7r,r%idxs r0r"z DetectionPredictor.get_obj_featsRs  .I. .IIen o`aQYYq!Q " * *1771:r1aggajAo N S SXZ S [ ouv FITXEYZzucciilc 2ZZ pZs AB< Cc t|||jdDcgc]\}}}|j||||c}}}Scc}}}w)aConstruct a list of Results objects from model predictions. Args: preds (list[torch.Tensor]): List of predicted bounding boxes and scores for each image. img (torch.Tensor): Batch of preprocessed images used for inference. orig_imgs (list[np.ndarray]): List of original images before preprocessing. Returns: (list[Results]): List of Results objects containing detection information for each image. r)r$batchconstruct_result)r&r'r(r)predorig_imgimg_paths r0r#z$DetectionPredictor.construct_results]sQ-0y$**Q-,P  (h  ! !$Xx @   sAc tj|jdd|ddddf|j|ddddf<t|||jj |ddddfS)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. r9N)pathrboxes)r scale_boxesr5rrr)r&rIr(rJrKs r0rHz#DetectionPredictor.construct_resultmshoociimT!RaR%[(..QQU xhdjj6F6FdSTVXWXVXSXkZZr2N) __name__ __module__ __qualname____doc__r1 staticmethodr"r#rHr2r0rrs.0/b[[ [r2rN)ultralytics.engine.predictorrultralytics.engine.resultsrultralytics.utilsrrrrWr2r0r[s 7.&r[r[r2