o V hf@sLddlZddlmZddlmZddlmZddlmZGdddeZ dS)N) LetterBox) BasePredictor)Results)opsc@s eZdZdZddZddZdS)RTDETRPredictorar RT-DETR (Real-Time Detection Transformer) Predictor extending the BasePredictor class for making predictions. This class leverages Vision Transformers to provide real-time object detection while maintaining high accuracy. It supports key features like efficient hybrid encoding and IoU-aware query selection. Attributes: imgsz (int): Image size for inference (must be square and scale-filled). args (dict): Argument overrides for the predictor. model (torch.nn.Module): The loaded RT-DETR model. batch (list): Current batch of processed inputs. Examples: >>> from ultralytics.utils import ASSETS >>> from ultralytics.models.rtdetr import RTDETRPredictor >>> args = dict(model="rtdetr-l.pt", source=ASSETS) >>> predictor = RTDETRPredictor(overrides=args) >>> predictor.predict_cli() c CsPt|ttfs |dg}|djd}|djd|dfdd\}}t|ts+t|}g}t||||jdD]n\}} } } t |}| j ddd\} } | d|j j k}|j jdurk| tj|j j| jdkd |@}tj|| | gdd|}| jdd \}}|d dd gf|9<|d d d gf|9<|t| | |jj|d q7|S)a" Postprocess the raw predictions from the model to generate bounding boxes and confidence scores. The method filters detections based on confidence and class if specified in `self.args`. It converts model predictions to Results objects containing properly scaled bounding boxes. Args: preds (List | Tuple): List of [predictions, extra] from the model, where predictions contain bounding boxes and scores. img (torch.Tensor): Processed input images with shape (N, 3, H, W). orig_imgs (List | torch.Tensor): Original, unprocessed images. Returns: (List[Results]): A list of Results objects containing the post-processed bounding boxes, confidence scores, and class labels. Nr)dimT)keepdim)device.)pathnamesboxes) isinstancelisttupleshapesplitrconvert_torch2numpy_batchzipbatch xywh2xyxymaxsqueezeargsconfclassestorchtensorr anycatappendrmodelr)selfpredsimg orig_imgsndbboxesscoresresultsbboxscoreorig_imgimg_path max_scoreclsidxpredohowr8U/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/models/rtdetr/predict.py postprocess s&     "zRTDETRPredictor.postprocesscs"t|jdddfdd|DS)a Pre-transforms the input images before feeding them into the model for inference. The input images are letterboxed to ensure a square aspect ratio and scale-filled. The size must be square(640) and scale_filled. Args: im (list[np.ndarray] |torch.Tensor): Input images of shape (N,3,h,w) for tensor, [(h,w,3) x N] for list. Returns: (list): List of pre-transformed images ready for model inference. FT)auto scale_fillcsg|]}|dqS))imager8).0x letterboxr8r9 Tsz1RTDETRPredictor.pre_transform..)rimgsz)r&imr8r@r9 pre_transformHs zRTDETRPredictor.pre_transformN)__name__ __module__ __qualname____doc__r:rEr8r8r8r9r s (r) r ultralytics.data.augmentrultralytics.engine.predictorrultralytics.engine.resultsrultralytics.utilsrrr8r8r8r9s