j SSKJrJrJrJr SSKJrJrJrJ r J r SSK J r J r \"5(aSSKJr \"5(a SSKrSSKJrJr \(aSS KJr \ R,"\5r\"\ "S S 95"S S \ 55rg)) TYPE_CHECKINGAnyUnionoverload)add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends)Pipelinebuild_pipeline_init_args) load_imageN)(MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES)ImageT)has_image_processorc B^\rSrSrSrSrSrSrSrU4Sjr Sr \ S\ \ S 4S \S \S \\\ \44S j5r\ S\\ \S -S \S \S \\\\ \44Sj5rS \\\ \4\\\\ \4-4U4SjjrSSjrSrSSjrSSS \\ \44SjrSrU=r$)ObjectDetectionPipelinea Object detection pipeline using any `AutoModelForObjectDetection`. This pipeline predicts bounding boxes of objects and their classes. Example: ```python >>> from transformers import pipeline >>> detector = pipeline(model="facebook/detr-resnet-50") >>> detector("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png") [{'score': 0.997, 'label': 'bird', 'box': {'xmin': 69, 'ymin': 171, 'xmax': 396, 'ymax': 507}}, {'score': 0.999, 'label': 'bird', 'box': {'xmin': 398, 'ymin': 105, 'xmax': 767, 'ymax': 507}}] >>> # x, y are expressed relative to the top left hand corner. ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This object detection pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"object-detection"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=object-detection). FTNc>[TU]"U0UD6 [US5 [R"5nUR [ 5 URU5 g)Nvision)super__init__r rcopyupdatercheck_model_type)selfargskwargsmapping __class__s ڵC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\transformers/pipelines/object_detection.pyr ObjectDetectionPipeline.__init__8sH $)&)$):??ACD g&c L0nSU;aUSUS'0nSU;aUSUS'U0U4$)Ntimeout threshold)rr!preprocess_paramspostprocess_kwargss r$_sanitize_parameters,ObjectDetectionPipeline._sanitize_parameters@sL  +1)+< i ( & .4[.A { + "&888r&imagez Image.Imager r!returncgNr*rr/r r!s r$__call__ ObjectDetectionPipeline.__call__Ismpr&cgr2r*r3s r$r4r5Ls&)r&cd>SU;aSU;aURS5US'[TU]"U0UD6$)a Detect objects (bounding boxes & classes) in the image(s) passed as inputs. Args: inputs (`str`, `list[str]`, `PIL.Image` or `list[PIL.Image]`): The pipeline handles three types of images: - A string containing an HTTP(S) link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the same format: all as HTTP(S) links, all as local paths, or all as PIL images. threshold (`float`, *optional*, defaults to 0.5): The probability necessary to make a prediction. timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and the call may block forever. Return: A list of dictionaries or a list of list of dictionaries containing the result. If the input is a single image, will return a list of dictionaries, if the input is a list of several images, will return a list of list of dictionaries corresponding to each image. The dictionaries contain the following keys: - **label** (`str`) -- The class label identified by the model. - **score** (`float`) -- The score attributed by the model for that label. - **box** (`list[dict[str, int]]`) -- The bounding box of detected object in image's original size. imagesinputs)poprr4)rr r!r#s r$r4r5Qs=@ v (&"8%zz(3F8 w000r&c[XS9n[R"URUR//5nUR U/SS9nUR UR5nURbURUSUSSS9nX4S'U$)N)r(pt)r8return_tensorswordsboxes)textr?r= target_size) rtorch IntTensorheightwidthimage_processortodtype tokenizer)rr/r(rAr9s r$ preprocess"ObjectDetectionPipeline.preprocessus52oo ekk'B&CD %%eWT%J4::& >> %^^w`d^eF +} r&cURS5nUR"S0UD6nURSU0UE5nURbUSUS'U$)NrAbboxr*)r:modelr#rI)r model_inputsrAoutputs model_outputss r$_forward ObjectDetectionPipeline._forwards\"&&}5 **,|,))=+*Q*QR >> %$0$8M& !r&c H^^^USnTRGbUSR5ummUUU4SjnUSRS5RSS9R SS9upVUR5Vs/sH&nTR R RUPM( nnUSRS5V s/sH o"U 5PM n n /SQn [UR5X5V s/sH!oSU:dM [[X55PM# n n U $TRRXU5nUSnUS nUS nUS n UR5US 'UVs/sH4nTR R RUR5PM6 snUS 'U Vs/sHnTRU5PM snUS '/SQn [US US US 5V s/sHn [[X55PM n n U $s snfs sn fs sn fs snfs snfs sn f) NrArc >TR[R"TUS-S- TUS-S- TUS-S- TUS-S- /55$)Nrir r)_get_bounding_boxrBTensor)rMrDrrEs r$ unnormalize8ObjectDetectionPipeline.postprocess..unnormalizesi--LL"T!W_t3#d1g-4"T!W_t3#d1g-4   r&logits)dimrM)scorelabelboxscoreslabelsr?)rItolistsqueezesoftmaxmaxrNconfigid2labelzipdictrFpost_process_object_detectionitemrW)rrQr)rArYraclasses predictionrbrMr?keysvals annotationraw_annotationsraw_annotationr_r`rDrEs` @@r$ postprocess#ObjectDetectionPipeline.postprocesss.#M2 >> %(N113MFE ,H5==a@HHRHPTTY[T\OFOV~~O_`O_djj''00" #))"14S#X+?$tDQTVYQYNG[B\+\"1H+Z^S#Xr&r)typingrrrrutilsrr r r r baserr image_utilsrrBmodels.auto.modeling_autorrPILr get_loggerr}loggerrr*r&r$rsw66kk4(    H %,FGkhkHkr&