o ॵi#@sddlZddlZddlmZmZddlZddlZddlZddl m Z m Z m Z m Z ddlmZddlmZddlmZmZddlmZddlmZmZmZdd lmZdd lmZdd lm Z d e _!e Z"d gZ#ej$ej%ej&dGdd d eZ'dS)N)AnyDict)Image ImageDraw ImageFile ImageFont) Pipelines) OutputKeys)InputPipeline) PIPELINES) _get_anchorsgenerate post_process) LoadImage)Tasks) get_loggerTTBSDetectionPipeline) module_namecseZdZdZdgddddZeddZd effd d Zd e d e ee ffddZ d e ee fd e ee ffddZ d e ee fd e ee ffddZZS)ra TBS Detection Pipeline. Example: ```python >>> from modelscope.pipelines import pipeline >>> tbs_detect = pipeline(Tasks.image_object_detection, model='landingAI/LD_CytoBrainCerv') >>> tbs_detect(input='data/test/images/tbs_detection.jpg') { "boxes": [ [ 446.9007568359375, 36.374977111816406, 907.0919189453125, 337.439208984375 ], [ 454.3310241699219, 336.08477783203125, 921.26904296875, 641.7871704101562 ] ], "labels": [ ["Positive"] ], "scores": [ 0.9296008944511414, 0.9260380268096924 ] } >>> # ``` positive)rg?g333333?) class_namesmodel_image_size confidenceioucCs ||jvr |j|Sd|dS)NzUnrecognized attribute name '') _defaults)clsnr b/home/ubuntu/.local/lib/python3.10/site-packages/modelscope/pipelines/cv/tbs_detection_pipeline.py get_defaultsKs   z!TBSDetectionPipeline.get_defaultsmodelc s<tjd|dd||j|jt||_t|dS)z8 model: model id on modelscope hub. F)r# auto_collateNr )super__init____dict__updaterr anchorsr)selfr#kwargs __class__r r!r&Rs  zTBSDetectionPipeline.__init__inputreturncCs"t|}|t}||d}|S)a Detect objects (bounding boxes) 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 or opencv 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. - **labels** (`List[str]`, optional) -- The boxes's class_names of detected object in image. )imgimg_path)rconvert_to_ndarrayastypefloat)r*r.r0resultr r r! preprocess[s   zTBSDetectionPipeline.preprocesscCs|dtj}t|d}|tj}t|dd}t| tj }tj |dd}t j |jd}t|}||}||dd }|S) Nr0)rrgo@)rr)dimzpytorch_yolov4.ptr1)datar1)r3npuint8cv2resizefloat32 transposetorch from_numpytype FloatTensor unsqueezeospathjoinr#loadcuda)r*r.r0tmp_inp model_pathr#outputsr5r r r!forward{s    zTBSDetectionPipeline.forwardcOsTt||d|d\}}|durtjgtjgi}|Stj|tjdgtj|i}|S)Nr:r1Positive)rr SCORESBOXEStolistLABELS)r*r.argsr+bboxesscoresrMr r r! postprocesss z TBSDetectionPipeline.postprocess)__name__ __module__ __qualname____doc__r classmethodr"strr&r rrr6rNrW __classcell__r r r,r!rs$  "  )(colorsysrFtypingrrr=numpyr;rAPILrrrrmodelscope.metainformodelscope.outputsr modelscope.pipelines.baser r modelscope.pipelines.builderr 1modelscope.pipelines.cv.tbs_detection_utils.utilsr rrmodelscope.preprocessorsrmodelscope.utils.constantrmodelscope.utils.loggerrLOAD_TRUNCATED_IMAGESlogger__all__register_moduleimage_object_detection tbs_detectionrr r r r!s,