FifddlZddlZddlmZddlmZddlmZGddeZ Gdde eZ y) N)LoadVisualPrompt)DetectionPredictor)SegmentationPredictorcVeZdZdZd deffd ZdZfdZd fd ZfdZ dZ xZ S) YOLOEVPDetectPredictoraaA class extending DetectionPredictor for YOLO-EVP (Enhanced Visual Prompting) predictions. This class provides common functionality for YOLO models that use visual prompting, including model setup, prompt handling, and preprocessing transformations. Attributes: model (torch.nn.Module): The YOLO model for inference. device (torch.device): Device to run the model on (CPU or CUDA). prompts (dict | torch.Tensor): Visual prompts containing class indices and bounding boxes or masks. Methods: setup_model: Initialize the YOLO model and set it to evaluation mode. set_prompts: Set the visual prompts for the model. pre_transform: Preprocess images and prompts before inference. inference: Run inference with visual prompts. get_vpe: Process source to get visual prompt embeddings. verbosec6t|||d|_y)zSet up the model for prediction. Args: model (torch.nn.Module): Model to load or use. verbose (bool, optional): If True, provides detailed logging. )rTN)super setup_model done_warmup)selfmodelr __class__s g/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/models/yolo/yoloe/predict.pyr z"YOLOEVPDetectPredictor.setup_models E73c||_y)zSet the visual prompts for the model. Args: prompts (dict): Dictionary containing class indices and bounding boxes or masks. Must include a 'cls' key with class indices. N)prompts)r rs r set_promptsz"YOLOEVPDetectPredictor.set_prompts(s  rc t ||}|jjdd}|jjdd}|jd}t |dk(ra|j |dj dd|dj dd|||}|jdj|j}nb| Jd|d t|trtd |Ds Jd |d t|trtd |Ds Jd |d t |t |cxk(rt |k(s.nJd t |dt |dt |d tt |Dcgc]<}|j ||j dd||j dd||||>}}tjj j"j%|dj|j}|j&j(r|j+|_|S|j-|_|Scc}w)aPreprocess images and prompts before inference. This method applies letterboxing to the input image and transforms the visual prompts (bounding boxes or masks) accordingly. Args: im (list): List of input images. Returns: (list): Preprocessed images ready for model inference. Raises: ValueError: If neither valid bounding boxes nor masks are provided in the prompts. bboxesNmasksclsrzExpected bboxes, but got !c3PK|]}t|tj ywN isinstancenpndarray.0bs r z7YOLOEVPDetectPredictor.pre_transform..Ks3^RSJq"**4M3^$&z#Expected list[np.ndarray], but got c3PK|]}t|tj ywrrr"s rr%z7YOLOEVPDetectPredictor.pre_transform..Ns5bTUjBJJ6O5br&z-Expected same length for all inputs, but got vsT) batch_first)r pre_transformrpoplen_process_single_imageshape unsqueezetodevicerlistallrangetorchnnutilsrnn pad_sequencerfp16halffloat) r imimgrrcategoryvisualsrirs rr*z$YOLOEVPDetectPredictor.pre_transform1sRg#B'!!(D1   $/<<& s8q=00Qbq1A2a5;;rPQ?T\^dfklG''*--dkk:G% L)B6(!'L L%fd+3^W]3^0^ 5fXQ? ^h-#5bYa5b2b 5hZqA br7c(m:s6{: ?By3x=/Y[\_`f\g[hhij : s3x**3q6<<+;RU[[!_hWXk[abc[deGhhnn((55g45PSSTXT_T_`G)-w||~  ?Fmmo  sAI-c d|t|rtj|tj}|jdk(r |dddf}t |d|dz |d|dz }||z}|ddddfxxt |dt |d|zz dz dz z cc<|ddddfxxt |dt |d|zz dz dz z cc<n:|-t|!|}tj|}d||dk(<n td tj||||S) aProcess a single image by resizing bounding boxes or masks and generating visuals. Args: dst_shape (tuple): The target shape (height, width) of the image. src_shape (tuple): The original shape (height, width) of the image. category (list | np.ndarray): The category indices for visual prompts. bboxes (list | np.ndarray, optional): A list of bounding boxes in the format [x1, y1, x2, y2]. masks (np.ndarray, optional): A list of masks corresponding to the image. Returns: (torch.Tensor): The processed visuals for the image. Raises: ValueError: If neither `bboxes` nor `masks` are provided. N)dtyperr.rg?rz$Please provide valid bboxes or masks) r,r arrayfloat32ndimminroundr r*stack ValueErrorr get_visuals) r dst_shape src_shaper?rrgain resized_masksrs rr-z,YOLOEVPDetectPredictor._process_single_image\sJ  #f+XXfBJJ7F{{aay|il2IaL9Q<4OPD dNF 319  ! uYq\D=P7Q(QUV'VY\'\!] ]  319  ! uYq\D=P7Q(QUV'VY\'\!] ]   !G1%8MHH]+E"#E%3, CD D !--h 65QQrcBt||g|d|ji|S)a&Run inference with visual prompts. Args: im (torch.Tensor): Input image tensor. *args (Any): Variable length argument list. **kwargs (Any): Arbitrary keyword arguments. Returns: (torch.Tensor): Model prediction results. vpe)r inferencer)r r=argskwargsrs rrSz YOLOEVPDetectPredictor.inferences(w GGGGGrc|j|t|jdk(sJd|jD]6\}}}|j|}|j ||j dcSy)aProcess the source to get the visual prompt embeddings (VPE). Args: source (str | Path | int | PIL.Image | np.ndarray | torch.Tensor | list | tuple): The source of the image to make predictions on. Accepts various types including file paths, URLs, PIL images, numpy arrays, and torch tensors. Returns: (torch.Tensor): The visual prompt embeddings (VPE) from the model. rz get_vpe only supports one image!T)rR return_vpeN) setup_sourcer,dataset preprocessrr)r source_im0sr=s rget_vpezYOLOEVPDetectPredictor.get_vpesq &!4<< A%I'II%,, EJAtQ&B::bdllt:D D Er)T)NN) __name__ __module__ __qualname____doc__boolr rr*r-rSr^ __classcell__)rs@rrr s2$ $ )V"RH HErrceZdZdZy)YOLOEVPSegPredictorz\Predictor for YOLO-EVP segmentation tasks combining detection and segmentation capabilities.N)r_r`rarbrrrfrfsfrrf) numpyr r5ultralytics.data.augmentrultralytics.models.yolo.detectrultralytics.models.yolo.segmentrrrfrgrrrls8 5=AQE/QEh 02G r