o 0j!@sddlmZmZmZmZmZddlZddlm Z ddl m Z ddl m Z ddlmZdd lmZmZdd lmZd d lmZd d lmZd dlmZddlmZejGdddeZe dddGdddeZdS))AnyDictListOptionalUnionN)logging)pipeline_requires_extra)ImageBatchSampler) ReadImage) HPIConfigPaddlePredictorOption) benchmark)(AutoParallelImageSimpleInferencePipeline) BasePipeline) rotate_image)DocPreprocessorResultcseZdZdZddddddddedeedeedeeeefd eed e d ee eeefe fd dffd dZ ded e fddZ d efddZ  dde eeeejeejfdee dee d efddZZS)_DocPreprocessorPipelinezDoc Preprocessor PipelineNFdeviceengine engine_config pp_optionuse_hpip hpi_configconfigrrrrrrreturnc stjd||||||d||dd|_|jr,|didddi} || |_|dd|_|jrH|did dd i} || |_t|d d d |_ t dd|_ dS)aInitializes the doc preprocessor pipeline. Args: config (Dict): Configuration dictionary containing various settings. device (Optional[str], optional): The device to use for prediction. Defaults to `None`. engine (Optional[str], optional): Inference engine. Defaults to `None`. engine_config (Optional[Dict[str, Any]], optional): Engine-specific config. Defaults to `None`. pp_option (Optional[PaddlePredictorOption], optional): Paddle predictor options. Defaults to `None`. use_hpip (bool, optional): Whether to use HPIP. Defaults to `False`. hpi_config (Optional[Union[Dict[str, Any], HPIConfig]], optional): HPIP configuration. Defaults to `None`. ruse_doc_orientation_classifyTZ SubModulesZDocOrientationClassifyZmodel_config_errorz(config error for doc_ori_classify_model!use_doc_unwarpingZ DocUnwarpingz%config error for doc_unwarping_model! batch_sizer)r"ZBGR)formatN) super__init__getr Z create_modeldoc_ori_classify_modelr!doc_unwarping_modelr batch_samplerr img_reader) selfrrrrrrrkwargsZdoc_ori_classify_configZdoc_unwarping_config __class__r$v/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddlex/inference/pipelines/doc_preprocessor/pipeline.pyr&#s8     z!_DocPreprocessorPipeline.__init__model_settingscCs<|dr|jstddS|dr|jstddSdS)aI Check if the the input params for model settings are valid based on the initialized models. Args: model_settings (Dict): A dictionary containing model settings. Returns: bool: True if all required models are initialized according to the model settings, False otherwise. r z`Set use_doc_orientation_classify, but the model for doc orientation classify is not initialized.Fr!zJSet use_doc_unwarping, but the model for doc unwarping is not initialized.T)r rerrorr!)r,r1r$r$r0check_model_settings_valid[s z3_DocPreprocessorPipeline.check_model_settings_validcCs*|dur|j}|dur|j}||d}|S)at Retrieve the model settings dictionary based on input parameters. Args: use_doc_orientation_classify (bool, optional): Whether to use document orientation classification. use_doc_unwarping (bool, optional): Whether to use document unwarping. Returns: dict: A dictionary containing the model settings. Nr r!r4)r,r r!r1r$r$r0get_model_settingsws z+_DocPreprocessorPipeline.get_model_settingsinputr r!c cs.|||}||sddiVt||D]|\}}||j}|drSt||}g} g} t||D]\} } t | dd} | | t | | }| |q6n ddt t |D} |} |drod d|| D}n| }t|j|j|| | |D]\}}}} }}||||| ||d }t|Vq|qd S) a! Predict the preprocessing result for the input image or images. Args: input (Union[str, list[str], np.ndarray, list[np.ndarray]]): The input image(s) or path(s) to the images or pdfs. use_doc_orientation_classify (bool): Whether to use document orientation classification. use_doc_unwarping (bool): Whether to use document unwarping. **kwargs: Additional keyword arguments. Returns: DocPreprocessorResult: A generator yielding preprocessing results. r2z0the input params for model settings are invalid!r Z label_namesrcSsg|]}dqS)r$).0_r$r$r0 sz4_DocPreprocessorPipeline.predict..r!cSs,g|]}|ddddddddfqS)Z doctr_imgNr7r$)r8itemr$r$r0r:s) input_path page_indexZ input_imgr1anglerot_img output_imgN)r5r3 enumerater*r+Z instanceslistr(zipintappendrrangelenr)Z input_pathsZ page_indexesr)r,r6r r!r1r9Z batch_dataZ image_arrayspredsZanglesZrot_imgsZimgpredr>r?Z output_imgsr<r=Z image_arrayr@Zsingle_img_resr$r$r0predictsX        z _DocPreprocessorPipeline.predict)NN)__name__ __module__ __qualname____doc__rrstrrrboolrr r&r3dictr5rnpZndarrayrrJ __classcell__r$r$r.r0rsP  8 rZocrzocr-core)Zaltc@s$eZdZdZeddZddZdS)DocPreprocessorPipelineZdoc_preprocessorcCstS)N)r)r,r$r$r0 _pipeline_clssz%DocPreprocessorPipeline._pipeline_clscCs |ddS)Nr"r)r')r,rr$r$r0_get_batch_sizes z'DocPreprocessorPipeline._get_batch_sizeN)rKrLrMentitiespropertyrUrVr$r$r$r0rTs   rT) typingrrrrrnumpyrRutilsrZ utils.depsr Zcommon.batch_samplerr Z common.readerr modelsr rZutils.benchmarkrZ _parallelrbaser componentsrresultrZ time_methodsrrTr$r$r$r0s"          4