o eS-@sddlZddlZddlZddlZddlZddlmZddlmZddl Z ddl m Z ddl m Z ddlmZddlmZddlmZd gZd d d d dd dd dZGdddZdS)N)List)urljoin)Image)MultipartEncoder)API_URL)validate_image_path)PredictionGroup batch-videoclip1clip-embed-imagemodel_id model_versioninference_typegazegaze-detection)r rc @sveZdZ dddZddZdddZdgd fd ed ed ed ede ef ddZ ddede fddZ de fddZ dS)InferenceModelNcOsN||_||_|dkr%|jd}|d|_|d|_|dur in||_dSdS)z Create an InferenceModel object through which you can run inference. Args: api_key (str): private roboflow api key version_id (str): the ID of the dataset version to use for inference BASE_MODEL/N)_InferenceModel__api_keyidrsplit dataset_idversioncolors)selfapi_key version_idrargskwargs version_infor$H/var/www/myenv/lib/python3.10/site-packages/roboflow/models/inference.py__init__"s   zInferenceModel.__init__cCst|tj|jdv}|rddd}d|ii|fSt|}|j}t|dt|dd}t }|j |ddd t d d | d fid }i|d|jid|fS)ao Get parameters about an image (i.e. dimensions) for use in an inference request. Args: image_path (str): path to the image you'd like to perform prediction on Returns: Tuple containing a dict of querystring params and a dict of requests kwargs Raises: Exception: Image path is not valid )httphttps Undefined)widthheightimagerrZJPEG)qualityformatfile imageToUploadz image/jpeg)fields Content-Typedataheaders)rurllibparseurlparseschemeropensizestrioBytesIOsavergetvalue content_type)r image_path hosted_image image_dimsr, dimensionsbufferedr6r$r$r%__get_image_params;s   z!InferenceModel.__get_image_paramsc Ksv||\}}}|j|d<|jdi||jdtj|}tj|fi|}| t j | ||||j dS)a Infers detections based on image from a specified model and image path. Args: image_path (str): path to the image you'd like to perform prediction on prediction_type (str): type of prediction to perform **kwargs: Any additional kwargs will be turned into querystring params Returns: PredictionGroup Object Raises: Exception: Image path is not valid Example: >>> import roboflow >>> rf = roboflow.Roboflow(api_key="") >>> project = rf.workspace().project("PROJECT_ID") >>> model = project.version("1").model >>> prediction = model.predict("YOUR_IMAGE.jpg") r?)rDprediction_typerFrNr$)!_InferenceModel__get_image_paramsrupdateapi_urlr8r9 urlencoderequestspostraise_for_statusrcreate_prediction_groupjsonr) rrDrKr"paramsrequest_kwargsrFurlresponser$r$r%predict\s zInferenceModel.predictr video_pathfpsadditional_modelsrKreturnc Cs&d}ttd|j}|D]}|tvrtd|dq |tvr&t|d|jj}|dkr2d|_n$|dkr:d|_n|d krBd |_n|d krJd |_n |d krRd|_ntdt dt j |i} |dsddi} z tjd|| | d} Wnty} ztd| d} ~ ww| jstd| j| d} | ddddd}ddi} zt|d }|}Wdn1swYWnty} ztd| d} ~ wwz tj| || d }Wnty} ztd!| d} ~ ww|jstd!|jn|} ttd"|j}|d#vr5|d krd$}nd%}t|d&t|d't|d(d)g}n |j|j|jd)g}|D] }|t|qAt | ||d*} ddi} z tjd|| | d} Wntyx} ztd+| d} ~ ww| jstd+| j| 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 prediction_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 >>> job_id,signed_url,signed_url_expires = model.predict_video("video.mp4" ,fps=5, inference_type="object-detection") Nz!/video_upload_signed_url?api_key=zModel z& is not supported for video inference.ObjectDetectionModelzobject-detectionClassificationModelclassificationInstanceSegmentationModelzinstance-segmentation GazeModelr CLIPModelr z-Model type not supported for video inference. file_name)zhttp://zhttps://r4application/jsonPOST)r7r6zError uploading video: signed_urlz&X-Goog-Expiresr&r=zapplication/octet-streamrbzError reading video: r5z(There was an error uploading the video: /videoinfer/?api_key=)rdrcr rrrrr ) input_url infer_fpsmodelsz Error starting video inference: job_id)rrrSUPPORTED_ADDITIONAL_MODELS ExceptionSUPPORTED_ROBOFLOW_MODELS __class____name__typerTdumpsospathbasename startswithrPrequestoktextsplitstripr<readputrrappendrp)rr[r\r]rKsigned_url_expiresrWmodel model_classpayloadr7rXerhf video_dataresultrorpr$r$r% predict_videos             zInferenceModel.predict_videorpc Cs|dur|j}ttd|jd|j}z tj|ddid}Wnty2}ztd|d}~ww|js>td|j| }d|vrHiS|dd krQ|S|dd krZiS|d }tj|ddid}| S) 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_video_results() Nrlz&job_id=r4rf)r7z'Error getting video inference results: statusroutput_signed_url) rprrrrPgetrrr}r~rT)rrprWrXrr6rinference_datar$r$r%poll_for_video_resultss*z%InferenceModel.poll_for_video_resultscCs`|dur|j}d}td|d tdtd|dd|}|d 7}|ikr/|Sq) 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() Nrz-Checking for video inference results for job z every 60sT<(z"s): Checking for inference resultsr)rpprinttimesleepr)rrpattemptsrXr$r$r%poll_until_video_resultsFs z'InferenceModel.poll_until_video_results)N)ru __module__ __qualname__r&rLrYr>intlistrrdictrrr$r$r$r%r!s,  !/ .r)r?rTrxrr8typingr urllib.parserrPPILr#requests_toolbelt.multipart.encoderrroboflow.configrroboflow.util.image_utilsrroboflow.util.predictionrrsrqrr$r$r$r%s0