U 9%e@sddlmZmZmZmZddlmZmZmZm Z m Z ddl m Z m Z erVddlmZertddlZddlmZmZe eZeeefZeeZee Gd d d e ZdS) )AnyDictListUnion)add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends)PIPELINE_INIT_ARGSPipeline) load_imageN)(MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMEScszeZdZdZfddZddZeeee fdfdd Z dd d Z d d Z dddZ deeefdddZZS)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). csTtj|||jdkr*td|jdt|dt}|t | |dS)NtfzThe z is only available in PyTorch.Zvision) super__init__ framework ValueError __class__r rcopyupdaterZcheck_model_type)selfargskwargsmappingrf/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/pipelines/object_detection.pyr4s   z ObjectDetectionPipeline.__init__cKs:i}d|kr|d|d<i}d|kr0|d|d<|i|fS)Ntimeout thresholdr )rrZpreprocess_paramsZpostprocess_kwargsr r r!_sanitize_parameters?s  z,ObjectDetectionPipeline._sanitize_parameters)returncstj||S)ai Detect objects (bounding boxes & classes) in the image(s) passed as inputs. 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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. )r__call__)rrrrr r!r&Hs z ObjectDetectionPipeline.__call__NcCs^t||d}t|j|jgg}|j|gdd}|jdk rR|j|d|ddd}||d<|S)N)r"pt)Zimagesreturn_tensorswordsboxes)textr*r( target_size)rtorchZ IntTensorheightwidthimage_processor tokenizer)rimager"r,inputsr r r! preprocessjs  z"ObjectDetectionPipeline.preprocesscCsB|d}|jf|}|d|i|}|jdk r>|d|d<|S)Nr,bbox)popmodelrr1)rZ model_inputsr,outputs model_outputsr r r!_forwardss     z ObjectDetectionPipeline._forward?c sN|d}jdk r|d\fdd|ddjddjdd\}}fdd |D}fd d |d dD}d d dgfdd t|||D}nj||} | d} | d}| d}| d}|| d<fdd |D| d<fdd |D| d<d d dgfdd t| d| d| dD}|S)Nr,rc sHt|dd|dd|dd|ddgS)Nrir r)_get_bounding_boxr-ZTensor)r5)r.rr/r r! unnormalizesz8ObjectDetectionPipeline.postprocess..unnormalizeZlogits)dimcsg|]}jjj|qSr )r7configid2label).0Z predictionrr r! sz7ObjectDetectionPipeline.postprocess..csg|] }|qSr r )rCr5)r>r r!rEsr5Zscorelabelboxcs&g|]}|dkrtt|qS)rdictziprCvals)keysr#r r!rEs scoreslabelsr*csg|]}jjj|qSr )r7rArBitem)rCrFrDr r!rEscsg|]}|qSr )r=)rCrGrDr r!rEscsg|]}tt|qSr rHrK)rMr r!rEs)r1tolistZsqueezeZsoftmaxmaxrJr0Zpost_process_object_detection) rr9r#r,rNclassesrOr* annotationZraw_annotationsZraw_annotationr )r.rMrr#r>r/r! postprocess{s,  " "   z#ObjectDetectionPipeline.postprocessz torch.Tensor)rGr%cCs8|jdkrtd|\}}}}||||d}|S)a% Turns list [xmin, xmax, ymin, ymax] into dict { "xmin": xmin, ... } Args: box (`torch.Tensor`): Tensor containing the coordinates in corners format. Returns: bbox (`Dict[str, int]`): Dict containing the coordinates in corners format. r'z9The ObjectDetectionPipeline is only available in PyTorch.)xminyminxmaxymax)rrintrQ)rrGrVrWrXrYr5r r r!r=s z)ObjectDetectionPipeline._get_bounding_box)N)r;)__name__ __module__ __qualname____doc__rr$r Predictionsr Predictionr&r4r:rUrstrrZr= __classcell__r r rr!rs  "  -r)typingrrrrutilsrrr r r baser rZ image_utilsrr-Zmodels.auto.modeling_autorrZ get_loggerr[loggerrar`r_rr r r r!s