jZddlZddlmZddlmZddlmZddlmZGddeZ dS)N) LetterBox) BasePredictor)Results)opsceZdZdZdZdZdS)RTDETRPredictoraHRT-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. Methods: postprocess: Postprocess raw model predictions to generate bounding boxes and confidence scores. pre_transform: Pre-transform input images before feeding them into the model for inference. 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 |t|ttfr|d}|dd\}}}t|tst j|ddddf}g}t |||||jdD]&\}} } } } t j|}| d|j j k} |j j ?| tj|j j | jkd| z} tj|| | gd| d|j j}| jdd \}}|ddd gfxx|zcc<|ddd gfxx|zcc<|t+| | |jj| (|S) auPostprocess 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 (already top-k selected by the decoder head) to Results objects containing properly scaled bounding boxes. Args: preds (list | tuple): List of [predictions, extra] from the model, where predictions have shape (bs, num_queries, 6) with format [cx, cy, w, h, score, class]. 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. r)r )dim.N)devicer )pathnamesboxes) isinstancelisttuplesplitrconvert_torch2numpy_batchzipbatch xywh2xyxysqueezeargsconfclassestorchtensorranycatmax_detshapeappendrmodelr)selfpredsimg orig_imgsbboxesscoreslabelsresultsbboxscorelabelorig_imgimg_pathidxpredohows d/var/www/html/Carbon-Document/venv/lib/python3.11/site-packages/ultralytics/models/rtdetr/predict.py postprocesszRTDETRPredictor.postprocess#s" edE] + + !HE!&YB!?!?)T** L5i@@dddKI69&&&R[]a]ghi]j6k6k a a 2D%(=&&D--##din4Cy , TY->u| T T TTYYZ[\\_bb9dE51r:::3?@S$)BS@STD^BQB'FB q!f    #    q!f    #    NN78($*BRZ^___ ` ` ` `cNt|jddfd|DS)ajPre-transform input images before feeding them into the model for inference. The input images are letterboxed to ensure a square aspect ratio and scale-filled. Args: im (list[np.ndarray]): Input images of shape [(H, W, 3) x N]. Returns: (list): List of pre-transformed images ready for model inference. FT)auto scale_fillc(g|]}|S))image).0x letterboxs r9 z1RTDETRPredictor.pre_transform..Ss&///q """///r;)rimgsz)r(imrDs @r9 pre_transformzRTDETRPredictor.pre_transformGs5djuFFF ////B////r;N)__name__ __module__ __qualname____doc__r:rHrAr;r9rr s=."""H 0 0 0 0 0r;r) r ultralytics.data.augmentrultralytics.engine.predictorrultralytics.engine.resultsrultralytics.utilsrrrAr;r9rQs ......666666......!!!!!!H0H0H0H0H0mH0H0H0H0H0r;