o eXx@sddlmZddlmZmZddlmZmZmZm Z m Z m Z m Z ddl ZddlmZmZmZmZmZddlmZdd dZd ddZd!ddZd"ddZd#ddZd$ddZeGdddZdS)%) annotations)astuple dataclass)AnyDictIteratorListOptionalTupleUnionN)calculate_masks_centroidsextract_ultralytics_masksnon_max_suppressionprocess_roboflow_result xywh_to_xyxy)PositionxyxyrnintreturnNonecCs*t|tjo |j|dfk}|stddS)Nz,xyxy must be 2d np.ndarray with (n, 4) shape isinstancenpndarrayshape ValueError)rris_validrI/var/www/myenv/lib/python3.10/site-packages/supervision/detection/core.py_validate_xyxysr!maskcCs@|dupt|tjot|jdko|jd|k}|stddS)Nrz/mask must be 3d np.ndarray with (n, H, W) shape)rrrlenrr)r"rrrrr _validate_masks &r%cCs.tjdtjd}||}t|tstddS)N)r&r#dtypez/Callback function must return sv.Detection type)rzerosuint8r Detectionsr)callbacktmp_imgresrrr validate_inference_callback s  r/class_idcC0|dupt|tjo|j|fk}|stddS)Nz6class_id must be None or 1d np.ndarray with (n,) shaper)r0rrrrr _validate_class_id' r2 confidencecCr1)Nz8confidence must be None or 1d np.ndarray with (n,) shaper)r4rrrrr _validate_confidence/r3r5 tracker_idcCr1)Nz8tracker_id must be None or 1d np.ndarray with (n,) shaper)r6rrrrr _validate_tracker_id7r3r7c@sfeZdZUdZded<dZded<dZded<dZded<dZded <d d Z d d Z dMddZ dNddZ e dOddZe dOddZe dOddZe dOddZe dOddZe dPd!d"Ze dOd#d$Ze dQd&d'Ze dRd*d+Ze dSdTd/d0Ze dOd1d2Ze dOd3d4Ze dUd7d8ZdVd;d<ZdWd?d@ZedXdAdBZedXdCdDZ FdYdZdKdLZ dS)[r+a Data class containing information about the detections in a video frame. Attributes: xyxy (np.ndarray): An array of shape `(n, 4)` containing the bounding boxes coordinates in format `[x1, y1, x2, y2]` mask: (Optional[np.ndarray]): An array of shape `(n, H, W)` containing the segmentation masks. confidence (Optional[np.ndarray]): An array of shape `(n,)` containing the confidence scores of the detections. class_id (Optional[np.ndarray]): An array of shape `(n,)` containing the class ids of the detections. tracker_id (Optional[np.ndarray]): An array of shape `(n,)` containing the tracker ids of the detections. np.ndarrayrNzOptional[np.ndarray]r"r4r0r6cCsTt|j}t|j|dt|j|dt|j|dt|j|dt |j |ddS)N)rr)r"r)r0r)r4r)r6r) r$rr!r%r"r2r0r5r4r7r6)selfrrrr __post_init__Ws zDetections.__post_init__cCs t|jS)zL Returns the number of detections in the Detections object. )r$rr9rrr __len___s zDetections.__len__r`Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[float], Optional[int], Optional[int]]]ccstt|jD]5}|j||jdur|j|nd|jdur#|j|nd|jdur.|j|nd|jdur9|j|ndfVqdS)z Iterates over the Detections object and yield a tuple of `(xyxy, mask, confidence, class_id, tracker_id)` for each detection. N)ranger$rr"r4r0r6)r9irrr __iter__eszDetections.__iter__otherc Cstt|j|jt|jduo|jdut|j|jgt|jduo%|jdut|j|jgt|jduo8|jdut|j|jgt|jduoK|jdut|j|jggSN) allr array_equalranyr"r0r4r6)r9rArrr __eq__}s0zDetections.__eq__cCsR|jd}||ddddf|dddf|dddftdS)a Creates a Detections instance from a [YOLOv5](https://github.com/ultralytics/yolov5) inference result. Args: yolov5_results (yolov5.models.common.Detections): The output Detections instance from YOLOv5 Returns: Detections: A new Detections object. Example: ```python >>> import cv2 >>> import torch >>> import supervision as sv >>> image = cv2.imread(SOURCE_IMAGE_PATH) >>> model = torch.hub.load('ultralytics/yolov5', 'yolov5s') >>> result = model(image) >>> detections = sv.Detections.from_yolov5(result) ``` rNrrr4r0)predcpunumpyastyper)clsyolov5_resultsyolov5_detections_predictionsrrr from_yolov5s zDetections.from_yolov5cCsd||jj|jj|jjtt||jj dur.|jj dSddS)a Creates a Detections instance from a [YOLOv8](https://github.com/ultralytics/ultralytics) inference result. Args: ultralytics_results (ultralytics.yolo.engine.results.Results): The output Results instance from YOLOv8 Returns: Detections: A new Detections object. Example: ```python >>> import cv2 >>> from ultralytics import YOLO, FastSAM, SAM, RTDETR >>> import supervision as sv >>> image = cv2.imread(SOURCE_IMAGE_PATH) >>> model = YOLO('yolov8s.pt') >>> model = SAM('sam_b.pt') >>> model = SAM('mobile_sam.pt') >>> model = FastSAM('FastSAM-s.pt') >>> model = RTDETR('rtdetr-l.pt') >>> # model inferences >>> result = model(image)[0] >>> # if tracker is enabled >>> result = model.track(image)[0] >>> detections = sv.Detections.from_ultralytics(result) ``` Nrr4r0r"r6) boxesrrJrKconfrMrLrr id)rMultralytics_resultsrrr from_ultralyticss! zDetections.from_ultralyticscCs@t|jjjddkr|S||jj|jj|jjt dS)a Creates a Detections instance from a [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) inference result. Args: yolo_nas_results (ImageDetectionPrediction): The output Results instance from YOLO-NAS ImageDetectionPrediction is coming from 'super_gradients.training.models.prediction_results' Returns: Detections: A new Detections object. Example: ```python >>> import cv2 >>> from super_gradients.training import models >>> import supervision as sv >>> image = cv2.imread(SOURCE_IMAGE_PATH) >>> model = models.get('yolo_nas_l', pretrained_weights="coco") >>> result = list(model.predict(image, conf=0.35))[0] >>> detections = sv.Detections.from_yolo_nas(result) ``` rrH) rasarray prediction bboxes_xyxyremptyr4labelsrLr)rMyolo_nas_resultsrrr from_yolo_nass zDetections.from_yolo_nascCs`t|jdjddkr|S|t|jdt|jdt|jdt t dS)a Creates a Detections instance from a [DeepSparse](https://github.com/neuralmagic/deepsparse) inference result. Args: deepsparse_results (deepsparse.yolo.schemas.YOLOOutput): The output Results instance from DeepSparse. Returns: Detections: A new Detections object. Example: ```python >>> from deepsparse import Pipeline >>> import supervision as sv >>> yolo_pipeline = Pipeline.create( ... task="yolo", ... model_path = "zoo:cv/detection/yolov5-l/pytorch/" ... "ultralytics/coco/pruned80_quant-none" >>> pipeline_outputs = yolo_pipeline(SOURCE_IMAGE_PATH, ... iou_thres=0.6, conf_thres=0.001) >>> detections = sv.Detections.from_deepsparse(result) ``` rrH) rrWrRrrZarrayscoresr[rLfloatr)rMdeepsparse_resultsrrr from_deepsparse szDetections.from_deepsparsecCs8||jj|jj|jjtdS)a Creates a Detections instance from a [mmdetection](https://github.com/open-mmlab/mmdetection) inference result. Also supported for [mmyolo](https://github.com/open-mmlab/mmyolo) Args: mmdet_results (mmdet.structures.DetDataSample): The output Results instance from MMDetection. Returns: Detections: A new Detections object. Example: ```python >>> import cv2 >>> import supervision as sv >>> from mmdet.apis import DetInferencer >>> inferencer = DetInferencer(model_name, checkpoint, device) >>> mmdet_result = inferencer(SOURCE_IMAGE_PATH, out_dir='./output', ... return_datasamples=True)["predictions"][0] >>> detections = sv.Detections.from_mmdet(mmdet_result) ``` rH)pred_instancesbboxesrJrKr_r[rLr)rM mmdet_resultsrrr from_mmdetection2s zDetections.from_mmdetectiontransformers_resultsdictcCs8||d|d|dtdS)z Creates a Detections instance from object detection [transformer](https://github.com/huggingface/transformers) inference result. Returns: Detections: A new Detections object. rRr_r[rH)rJrKrLr)rMrgrrr from_transformersSs  zDetections.from_transformerscCs@||djj|dj|djtdS)a Create a Detections object from the [Detectron2](https://github.com/facebookresearch/detectron2) inference result. Args: detectron2_results: The output of a Detectron2 model containing instances with prediction data. Returns: (Detections): A Detections object containing the bounding boxes, class IDs, and confidences of the predictions. Example: ```python >>> import cv2 >>> from detectron2.engine import DefaultPredictor >>> from detectron2.config import get_cfg >>> import supervision as sv >>> image = cv2.imread(SOURCE_IMAGE_PATH) >>> cfg = get_cfg() >>> cfg.merge_from_file("path/to/config.yaml") >>> cfg.MODEL.WEIGHTS = "path/to/model_weights.pth" >>> predictor = DefaultPredictor(cfg) >>> result = predictor(image) >>> detections = sv.Detections.from_detectron2(result) ``` instancesrH) pred_boxestensorrJrKr_ pred_classesrLr)rMdetectron2_resultsrrr from_detectron2cszDetections.from_detectron2roboflow_resultcCsBt|d\}}}}}t|jddkr|S||||||dS)aB Create a Detections object from the [Roboflow](https://roboflow.com/) API inference result. Args: roboflow_result (dict): The result from the Roboflow API containing predictions. Returns: (Detections): A Detections object containing the bounding boxes, class IDs, and confidences of the predictions. Example: ```python >>> import supervision as sv >>> roboflow_result = { ... "predictions": [ ... { ... "x": 0.5, ... "y": 0.5, ... "width": 0.2, ... "height": 0.3, ... "class_id": 0, ... "class": "person", ... "confidence": 0.9 ... }, ... # ... more predictions ... ... ] ... } >>> detections = sv.Detections.from_roboflow(roboflow_result) ``` )rprrQ)rrrWrrZ)rMrprr4r0maskstrackersrrr from_roboflows$zDetections.from_roboflow sam_result List[dict]cCslt|dddd}tdd|D}tdd|D}t|jddkr+|St|d }|||d S) a Creates a Detections instance from [Segment Anything Model](https://github.com/facebookresearch/segment-anything) inference result. Args: sam_result (List[dict]): The output Results instance from SAM Returns: Detections: A new Detections object. Example: ```python >>> import supervision as sv >>> from segment_anything import ( ... sam_model_registry, ... SamAutomaticMaskGenerator ... ) >>> sam_model_reg = sam_model_registry[MODEL_TYPE] >>> sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE) >>> mask_generator = SamAutomaticMaskGenerator(sam) >>> sam_result = mask_generator.generate(IMAGE) >>> detections = sv.Detections.from_sam(sam_result=sam_result) ``` cSs|dS)Narear)xrrr sz%Detections.from_sam..T)keyreversecSg|]}|dqS)bboxr.0r"rrr z'Detections.from_sam..cSr{) segmentationrr}rrr rrr) boxes_xywh)rr")sortedrr^rWrrZr)rMrtsorted_generated_masksxywhr"rrrr from_sams   zDetections.from_sam azure_result class_mapOptional[Dict[int, str]]cCs>d|vrtd|ddggg}}}|du}|ri}dd|D}|ddD]V}|d }|d } |d } |d } | |d } | |d} | D]5}|d}|d}||d}|rl|durlt|}|||<|dur|| | | | g||||qNq.t|dkrtS|t|t|t|dS)a  Creates a Detections instance from [Azure Image Analysis 4.0]( https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/ concept-object-detection-40). Args: azure_result (dict): The result from Azure Image Analysis. It should contain detected objects and their bounding box coordinates. class_map (Optional[Dict[int, str]]): A mapping ofclass IDs (int) to class names (str). If None, a new mapping is created dynamically. Returns: Detections: A new Detections object. Example: ```python >>> import requests >>> import supervision as sv >>> image = open(input, "rb").read() >>> endpoint = "https://.cognitiveservices.azure.com/" >>> subscription_key = "..." >>> headers = { ... "Content-Type": "application/octet-stream", ... "Ocp-Apim-Subscription-Key": subscription_key ... } >>> response = requests.post(endpoint, ... headers=self.headers, ... data=image ... ).json() >>> detections = sv.Detections.from_azure_analyze_image(response) ``` errorzAzure API returned an error messageNcSsi|]\}}||qSrr)r~ryvaluerrr z7Detections.from_azure_analyze_image.. objectsResultvalues boundingBoxtagsrwywhr4namer)rr0r4) ritemsgetr$appendr+rZrr^)rMrrr confidences class_idsis_dynamic_mapping detectionr|rx0y0x1y1tagr4 class_namer0rrr from_azure_analyze_imagesH)       z#Detections.from_azure_analyze_imagecCsxt|dddddfjddkr|S||dddddf|ddddf|ddddftdS)a Creates a Detections instance from [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) inference result. Args: paddledet_result (List[dict]): The output Results instance from PaddleDet Returns: Detections: A new Detections object. Example: ```python >>> import supervision as sv >>> import paddle >>> from ppdet.engine import Trainer >>> from ppdet.core.workspace import load_config >>> weights = (...) >>> config = (...) >>> cfg = load_config(config) >>> trainer = Trainer(cfg, mode='test') >>> trainer.load_weights(weights) >>> paddledet_result = trainer.predict([images])[0] >>> detections = sv.Detections.from_paddledet(paddledet_result) ``` r|NrrH)rrWrrZrLr)rMpaddledet_resultrrr from_paddledet?s(!zDetections.from_paddledetcCs0|tjdtjdtjgtjdtjgtddS)a\ Create an empty Detections object with no bounding boxes, confidences, or class IDs. Returns: (Detections): An empty Detections object. Example: ```python >>> from supervision import Detections >>> empty_detections = Detections.empty() ``` )rrr'rH)rrZfloat32r^r)rMrrr rZis  zDetections.emptydetections_listList[Detections]c Cst|dkr tSdd|D}ddt|D\}}}}}d dd}t|}||r2t|nd }||r=t|nd }||rHt|nd }||rSt|nd }||||||d S) a Merge a list of Detections objects into a single Detections object. This method takes a list of Detections objects and combines their respective fields (`xyxy`, `mask`, `confidence`, `class_id`, and `tracker_id`) into a single Detections object. If all elements in a field are not `None`, the corresponding field will be stacked. Otherwise, the field will be set to `None`. Args: detections_list (List[Detections]): A list of Detections objects to merge. Returns: (Detections): A single Detections object containing the merged data from the input list. Example: ```python >>> from supervision import Detections >>> detections_1 = Detections(...) >>> detections_2 = Detections(...) >>> merged_detections = Detections.merge([detections_1, detections_2]) ``` rcSg|]}t|qSr)r)r~rrrr rrz$Detections.merge..cSrr)list)r~fieldrrr rs item_list List[Any]cSstdd|DS)Ncss|]}|duVqdSrBr)r~rwrrr sz;Detections.merge..__all_not_none..)rC)rrrr __all_not_nonesz(Detections.merge..__all_not_noneNrr"r4r0r6)rr)r$r+rZziprvstackhstack) rMrdetections_tuples_listrr"r4r0r6_Detections__all_not_nonerrr merges&   zDetections.mergeanchorrcCs|tjkr2t|jdddf|jdddfd|jdddf|jdddfdgS|tjkrF|jdur@tdt |jdS|tj krmt|jdddf|jdddf|jdddfdgS|tj krt|jdddf|jdddf|jdddfdgS|tj krt|jdddf|jdddfd|jdddfgS|tj krt|jdddf|jdddfgS|tjkrt|jdddf|jdddfgS|tjkrt|jdddf|jdddfd|jdddfgS|tjkr8t|jdddf|jdddfgS|tjkrUt|jdddf|jdddfgSt|d) aQ Calculates and returns the coordinates of a specific anchor point within the bounding boxes defined by the `xyxy` attribute. The anchor point can be any of the predefined positions in the `Position` enum, such as `CENTER`, `CENTER_LEFT`, `BOTTOM_RIGHT`, etc. Args: anchor (Position): An enum specifying the position of the anchor point within the bounding box. Supported positions are defined in the `Position` enum. Returns: np.ndarray: An array of shape `(n, 2)`, where `n` is the number of bounding boxes. Each row contains the `[x, y]` coordinates of the specified anchor point for the corresponding bounding box. Raises: ValueError: If the provided `anchor` is not supported. Nrrrr#z>Cannot use `Position.CENTER_OF_MASS` without a detection mask.)rqz is not supported.)rCENTERrr^r transposeCENTER_OF_MASSr"rr CENTER_LEFT CENTER_RIGHT BOTTOM_CENTER BOTTOM_LEFT BOTTOM_RIGHT TOP_CENTERTOP_LEFT TOP_RIGHT)r9rrrr get_anchors_coordinatessf &&    & & 8 . . 8 . .z"Detections.get_anchors_coordinatesindex(Union[int, slice, List[int], np.ndarray]cCs|t|tr|g}t|j||jdur|j|nd|jdur"|j|nd|jdur-|j|nd|jdur:|j|dSddS)a Get a subset of the Detections object. Args: index (Union[int, slice, List[int], np.ndarray]): The index or indices of the subset of the Detections Returns: (Detections): A subset of the Detections object. Example: ```python >>> import supervision as sv >>> detections = sv.Detections(...) >>> first_detection = detections[0] >>> first_10_detections = detections[0:10] >>> some_detections = detections[[0, 2, 4]] >>> class_0_detections = detections[detections.class_id == 0] >>> high_confidence_detections = detections[detections.confidence > 0.5] ``` Nr)rrr+rr"r4r0r6)r9rrrr __getitem__s zDetections.__getitem__cCs&|jdurtdd|jDS|jS)a Calculate the area of each detection in the set of object detections. If masks field is defined property returns are of each mask. If only box is given property return area of each box. Returns: np.ndarray: An array of floats containing the area of each detection in the format of `(area_1, area_2, ..., area_n)`, where n is the number of detections. NcSsg|]}t|qSr)rsumr}rrr r+rz#Detections.area..)r"rr^box_arear;rrr rvs zDetections.areacCsH|jdddf|jdddf|jdddf|jdddfS)a7 Calculate the area of each bounding box in the set of object detections. Returns: np.ndarray: An array of floats containing the area of each bounding box in the format of `(area_1, area_2, ..., area_n)`, where n is the number of detections. Nr#rrr)rr;rrr r/sH zDetections.box_area?F thresholdr`class_agnosticboolcCst|dkr|S|jdusJd|r*t|j|jddf}t||d}||S|jdus3Jdt|j|jdd|jddf}t||d}||S)a  Perform non-maximum suppression on the current set of object detections. Args: threshold (float, optional): The intersection-over-union threshold to use for non-maximum suppression. Defaults to 0.5. class_agnostic (bool, optional): Whether to perform class-agnostic non-maximum suppression. If True, the class_id of each detection will be ignored. Defaults to False. Returns: Detections: A new Detections object containing the subset of detections after non-maximum suppression. Raises: AssertionError: If `confidence` is None and class_agnostic is False. If `class_id` is None and class_agnostic is False. rNz;Detections confidence must be given for NMS to be executed.r) predictions iou_thresholdzDetections class_id must be given for NMS to be executed. If you intended to perform class agnostic NMS set class_agnostic=True.)r$r4rrrreshaperr0)r9rrrindicesrrr with_nms;s&    zDetections.with_nms)rr=)rAr+)rr+)rgrhrr+)rprhrr+)rtrurr+rB)rrhrrrr+)rrrr+)rrrr8)rrrr+)rr8)rF)rr`rrrr+)!__name__ __module__ __qualname____doc____annotations__r"r4r0r6r:r<r@rF classmethodrPrVr]rbrfrirorsrrrrZrrrpropertyrvrrrrrr r+?s\         * $ $   ' 2 )V )   4 B(   r+)rrrrrr)r"rrrrr)rr)r0rrrrr)r4rrrrr)r6rrrrr) __future__r dataclassesrrtypingrrrrr r r rKrsupervision.detection.utilsr r rrrsupervision.geometry.corerr!r%r/r2r5r7r+rrrr s $