o e@s^ddlmZddlmZddlmZmZmZddlZ dd d Z dddZ eGdddZ dS)) annotations) dataclass)AnyOptionalTupleNclass_idrnintreturnNonecCs(t|tjo |j|fk}|stddS)zC Ensure that class_id is a 1d np.ndarray with (n, ) shape. z/class_id must be 1d np.ndarray with (n, ) shapeN isinstancenpndarrayshape ValueError)rris_validrN/var/www/myenv/lib/python3.10/site-packages/supervision/classification/core.py_validate_class_ids sr confidencecCs4|durt|tjo|j|fk}|stddSdS)zE Ensure that confidence is a 1d np.ndarray with (n, ) shape. Nz1confidence must be 1d np.ndarray with (n, ) shaper )rrrrrr_validate_confidences rc@sjeZdZUded<dZded<ddd Zdd d Zedd dZedddZ edddZ dddZ dS)Classificationsz np.ndarrayrNzOptional[np.ndarray]rr r cCs&t|j}t|j|t|j|dS)z5 Validate the classification inputs. N)lenrrrr)selfrrrr __post_init__!s  zClassifications.__post_init__r cCs t|jS)z8 Returns the number of classifications. )rr)rrrr__len__*s zClassifications.__len__cCsZ|jddd}t|dkr |tgtgdStt|}|||dS)a. Creates a Classifications instance from a [clip](https://github.com/openai/clip) inference result. Args: clip_results (np.ndarray): The inference result from clip model. Returns: Classifications: A new Classifications object. Example: ```python >>> from PIL import Image >>> import clip >>> import supervision as sv >>> model, preprocess = clip.load('ViT-B/32') >>> image = cv2.imread(SOURCE_IMAGE_PATH) >>> image = preprocess(image).unsqueeze(0) >>> text = clip.tokenize(["a diagram", "a dog", "a cat"]) >>> output, _ = model(image, text) >>> classifications = sv.Classifications.from_clip(output) ``` )dimrrr)softmaxcpudetachnumpyrrarrayarange)cls clip_resultsr class_idsrrr from_clip0s   zClassifications.from_clipcCs(|jj}|t|jd|dS)a Creates a Classifications instance from a [ultralytics](https://github.com/ultralytics/ultralytics) inference result. Args: ultralytics_results (ultralytics.engine.results.Results): The inference result from ultralytics model. Returns: Classifications: A new Classifications object. Example: ```python >>> import cv2 >>> from ultralytics import YOLO >>> import supervision as sv >>> image = cv2.imread(SOURCE_IMAGE_PATH) >>> model = YOLO('yolov8n-cls.pt') >>> output = model(image)[0] >>> classifications = sv.Classifications.from_ultralytics(output) ``` rr)probsdatar!r#rr%r)r&ultralytics_resultsrrrrfrom_ultralyticsUsz Classifications.from_ultralyticscCsR|d}t|dkr|tgtgdStt|}|||dS)a Creates a Classifications instance from a [timm](https://huggingface.co/docs/hub/timm) inference result. Args: timm_results: The inference result from timm model. Returns: Classifications: A new Classifications object. Example: ```python >>> from PIL import Image >>> import timm >>> from timm.data import resolve_data_config, create_transform >>> import supervision as sv >>> model = timm.create_model( ... model_name='hf-hub:nateraw/resnet50-oxford-iiit-pet', ... pretrained=True ... ).eval() >>> config = resolve_data_config({}, model=model) >>> transform = create_transform(**config) >>> image = Image.open(SOURCE_IMAGE_PATH).convert('RGB') >>> x = transform(image).unsqueeze(0) >>> output = model(x) >>> classifications = sv.Classifications.from_timm(output) ``` rr)r!r"r#rrr$r%)r& timm_resultsrrrrr from_timmrs #  zClassifications.from_timmkTuple[np.ndarray, np.ndarray]cCsP|jdur tdt|jddd}|d|}|j|}|j|}||fS)aJ Retrieve the top k class IDs and confidences, ordered in descending order by confidence. Args: k (int): The number of top class IDs and confidences to retrieve. Returns: Tuple[np.ndarray, np.ndarray]: A tuple containing the top k class IDs and confidences. Example: ```python >>> import supervision as sv >>> classifications = sv.Classifications(...) >>> classifications.get_top_k(1) (array([1]), array([0.9])) ``` Nz1top_k could not be calculated, confidence is Noner)rrrargsortr)rr0order top_k_ordertop_k_class_idtop_k_confidencerrr get_top_ks    zClassifications.get_top_k)r r )r r )r r)r0r r r1) __name__ __module__ __qualname____annotations__rrr classmethodr)r-r/r7rrrrrs     $  *r)rrrr r r )rrrr r r ) __future__r dataclassesrtypingrrrr#rrrrrrrrs