o ॵi@sddlmZddlmZmZmZmZddlZddl Z ddl Z ddl Z ddl mZddlmZddlmZmZddlmZddlmZmZddlmZdd lmZdd lmZdd lm Z m!Z!dd l"m#Z#dd l$m%Z%e%Z&ej'e!j(ej(dGdddeZ)dS)N)AnyDictListUnion) Pipelines)Model) ScrfdDetectSCRFDPreprocessor) OutputKeys)InputPipeline) PIPELINES) LoadImage)Config) ModelFileTasks)Image) get_logger) module_namecseZdZdeffdd Zdeeeefffdd Zdede ee ffdd Z de ee fde ee ffd d Z d e ee fde ee ffd dZ ZS)FaceDetectionPipelinemodelc stjdd|i|t|tj}t|}t|dd}|dur+t dd|i|}nt |j t s5Jd|j |j}|jdurEt|_||_dS)a use `model` to create a face detection pipeline for prediction Args: model (`str` or `Model`): model_id or `ScrfdDetect` or `TinyMogDetect` model. preprocessor(`Preprocessor`, *optional*, defaults to None): `SCRFDPreprocessor`. rN model_dirz model object is not initialized.)super__init__ospjoinr CONFIGURATIONr from_filegetattrr isinstancerrtodevice preprocessorr detector)selfrkwargs config_pathcfg cfg_modelr$ __class__rc/home/ubuntu/.local/lib/python3.10/site-packages/modelscope/pipelines/cv/face_detection_pipeline.pyrs    zFaceDetectionPipeline.__init__inputc stj|fi|S)a Detect objects (bounding boxes or keypoints) in the image(s) passed as inputs. Args: input (`Image` or `List[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. Return: A dictionary of result or a list of dictionary of result. If the input is an image, a dictionary is returned. If input is a list of image, a list of dictionary is returned. The dictionary contain the following keys: - **scores** (`List[float]`) -- The detection score for each card in the image. - **boxes** (`List[float]) -- The bounding boxe [x1, y1, x2, y2] of detected objects in in image's original size. - **keypoints** (`List[Dict[str, int]]`, optional) -- The corner kepoint [x1, y1, x2, y2, x3, y3, x4, y4] of detected object in image's original size. )r__call__)r%r-r&r*rr,r.6szFaceDetectionPipeline.__call__returncCs`||}d|vr.ddlm}m}||gdd}t|jjr.||t|jjgd}|S)N img_metasr)collatescatter)samples_per_gpu) r# mmcv.parallelr1r2nextr parametersis_cudar")r%r-resultr1r2rrr, preprocessSs z FaceDetectionPipeline.preprocesscCs|jdi|S)Nr)r$)r%r-rrr,forward`szFaceDetectionPipeline.forwardinputscCs|S)Nr)r%r<rrr, postprocesscsz!FaceDetectionPipeline.postprocess)__name__ __module__ __qualname__strrrrrr.rrr:r;r= __classcell__rrr*r,rs " *r)*os.pathpathrtypingrrrrcv2numpynpPILtorchmodelscope.metainfor!modelscope.models.base.base_modelr#modelscope.models.cv.face_detectionrr modelscope.outputsr modelscope.pipelines.baser r modelscope.pipelines.builderr modelscope.preprocessorsrmodelscope.utils.configrmodelscope.utils.constantrr$modelscope.utils.input_output_typingrmodelscope.utils.loggerrloggerregister_moduleface_detectionrrrrr,s,