o 0 i@sddlmZmZmZddlmZmZmZmZm Z ddl m Z m Z er/ddl mZddlmZer8ddlmZeeZee d d Gd d d e Zd S))AnyUnionoverload)add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends)Pipelinebuild_pipeline_init_args)Image) load_image)(MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMEST)Zhas_image_processorc seZdZdZdZdZdZdZfddZe de e dfde d e e e ffd d Ze dee e dfde d ee e e ffd d Zde e ee dedfde d e e e e fee e e ffffd d ZdddZdddZddZddZZS)DepthEstimationPipelinea Depth estimation pipeline using any `AutoModelForDepthEstimation`. This pipeline predicts the depth of an image. Example: ```python >>> from transformers import pipeline >>> depth_estimator = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-base-hf") >>> output = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg") >>> # This is a tensor with the values being the depth expressed in meters for each pixel >>> output["predicted_depth"].shape torch.Size([1, 384, 384]) ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This depth estimation pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"depth-estimation"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=depth-estimation). FTcs*tj|i|t|d|tdS)NZvision)super__init__r Zcheck_model_typer)selfargskwargs __class__s/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/transformers/pipelines/depth_estimation.pyr7s z DepthEstimationPipeline.__init__inputsz Image.ImagerreturncKdSNrrrrrrr__call__<z DepthEstimationPipeline.__call__cKrrrrrrrr ?r!c s6d|vr |d}|durtdtj|fi|S)a Predict the depth(s) of the image(s) passed as inputs. Args: inputs (`str`, `list[str]`, `PIL.Image` or `list[PIL.Image]`): The pipeline handles three types of images: - A string containing a http 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, which must then be passed as a string. Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL images. parameters (`Dict`, *optional*): A dictionary of argument names to parameter values, to control pipeline behaviour. The only parameter available right now is `timeout`, which is the length of time, in seconds, that the pipeline should wait before giving up on trying to download an image. timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and the call may block forever. Return: A dictionary or a list of dictionaries containing result. If the input is a single image, will return a dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to the images. The dictionaries contain the following keys: - **predicted_depth** (`torch.Tensor`) -- The predicted depth by the model as a `torch.Tensor`. - **depth** (`PIL.Image`) -- The predicted depth by the model as a `PIL.Image`. imagesNzECannot call the depth-estimation pipeline without an inputs argument!)pop ValueErrorrr rrrrr Bs $ NcKs<i}|dur ||d<t|trd|vr|d|d<|iifS)Ntimeout) isinstancedict)rr% parametersrZpreprocess_paramsrrr_sanitize_parametersls   z,DepthEstimationPipeline._sanitize_parameterscCsHt||}|j||jd}|jdkr||j}|jddd|d<|S)N)r"Zreturn_tensorspt target_size)rimage_processorZ frameworktoZdtypesize)rimager% model_inputsrrr preprocessts   z"DepthEstimationPipeline.preprocesscCs&|d}|jdi|}||d<|S)Nr,r)r#model)rr1r, model_outputsrrr_forward|s z DepthEstimationPipeline._forwardcCs|j||dg}g}|D].}|d}||||}t|d d}| |d|dqt |dkrG|dS|S)Nr,predicted_depthZuint8)r6depthr r) r-Zpost_process_depth_estimationdetachcpunumpyminmaxrZ fromarrayZastypeappendlen)rr4ZoutputsZformatted_outputsoutputr8rrr postprocesssz#DepthEstimationPipeline.postprocess)NNr)__name__ __module__ __qualname____doc__Z_load_processorZ_load_image_processorZ_load_feature_extractorZ_load_tokenizerrrrstrrr'r listr)r2r5rA __classcell__rrrrrs, (0 * rN)typingrrrutilsrrrr r baser r ZPILrZ image_utilsrZmodels.auto.modeling_autorZ get_loggerrBloggerrrrrrs