o e/@sddlZddlZddlmZddlmZddlZddlZddlm Z ddl m Z gdZ ddd d d dd d d Z ddZddZGddde ZdS)N)List)urljoin)API_URL)InferenceModel)zobject-detectionclassificationzinstance-segmentationclip1zclip-embed-imagemodel_id model_versioninference_typegazezgaze-detection)rr cCstjdd}||}|dvS)NT)mime)z video/mp4z video/aviz video/webm)magicMagic from_file)filenamer file_typerD/var/www/myenv/lib/python3.10/site-packages/roboflow/models/video.py is_valid_mime s  rcCst|sdSdS)NFT)r)rrrris_valid_video&src @sfeZdZdZddZ  ddededed ed eeeff d d Z dd ed e fddZ d e fddZ dS)VideoInferenceModelzm Run inference on an object detection model hosted on Roboflow or served through Roboflow Inference. cCs ||_dS)z Create a VideoDetectionModel object through which you can run inference on videos. Args: api_key (str): Your API key (obtained via your workspace API settings page). N)_VideoInferenceModel__api_key)selfapi_keyrrr__init__3s zVideoInferenceModel.__init__N video_pathr fpsadditional_modelsreturnc Cs ttd|j}|dkrtd|D]}|tvrtd|dq|tvr,td|dt|s4tdtd|i}d d i}t j d |||d } | d } t d| ttd|j}|j |j |jdg} |D] }| t|qgt| || d}t j d |||d } | d} | |_| | fS)a Infers detections based on image from specified model and image path. Args: video_path (str): path to the video you'd like to perform prediction on inference_type (str): type of the model to run fps (int): frames per second to run inference Returns: A list of the signed url and job id Example: >>> import roboflow >>> rf = roboflow.Roboflow(api_key="") >>> project = rf.workspace().project("PROJECT_ID") >>> model = project.version("1").model >>> prediction = model.predict("video.mp4", fps=5, inference_type="object-detection") z"/video_upload_signed_url/?api_key=z%FPS must be less than or equal to 30.zModel z' is no t supported for video inference.z& is not supported for video inference.zVideo path is not valid file_name Content-Typeapplication/jsonPOST)headersdata signed_urlzUploaded video to signed url: /videoinfer/?api_key=r ) input_url infer_fpsmodelsjob_id)rrr ExceptionSUPPORTED_ADDITIONAL_MODELSSUPPORTED_ROBOFLOW_MODELSrjsondumpsrequestsrequestprint dataset_idversionr appendr.) rrr rr urlmodelpayloadr'responser)r-r.rrrpredict?s@   zVideoInferenceModel.predictr.c Cs|dur|j}ttd|jd|j}z tj|ddid}Wnty0}zt|tdd}~ww|js8td| }|dd krS|d }tj|ddid}| S|dd kr_td iStd )a Polls the Roboflow API to check if video inference is complete. Returns: Inference results as a dict Example: >>> import roboflow >>> rf = roboflow.Roboflow(api_key="") >>> project = rf.workspace().project("PROJECT_ID") >>> model = project.version("1").model >>> prediction = model.predict("video.mp4") >>> results = model.poll_for_results() Nr*z&job_id=r$r%)r'zError polling for results.successroutput_signed_urlz-Job not complete yet. Check back in a minute.z Job failed.) r.rrrr4getr/r6okr2)rr.r:r=er(r@inference_datarrrpoll_for_resultss*  z$VideoInferenceModel.poll_for_resultscCsP|dur|j}d} |}|d7}|ikr|Std|ddtdq )a Polls the Roboflow API to check if video inference is complete. When inference is complete, the results are returned. Returns: Inference results as a dict Example: >>> import roboflow >>> rf = roboflow.Roboflow(api_key="") >>> project = rf.workspace().project("PROJECT_ID") >>> model = project.version("1").model >>> prediction = model.predict("video.mp4") >>> results = model.poll_until_results() NrTrA(<z"s): Checking for inference results)r.poll_for_responser6timesleep)rr.attemptsr=rrrpoll_until_resultss z&VideoInferenceModel.poll_until_results)rN)N) __name__ __module__ __qualname____doc__rstrintlistrr>dictrFrMrrrrr.s$  R2r)r2rJtypingr urllib.parserrr4roboflow.configrroboflow.models.inferencerr1r0rrrrrrrs*