gFddlZddlmZmZddlmZmZmZmZm Z ddl m Z m Z er ddl mZddlmZerdd lmZejeZee d Gd d e ZdS)N)ListUnion)add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends)Pipelinebuild_pipeline_init_args)Image) load_image)(MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMEST)has_image_processorc|eZdZdZfdZd deeeededfffd Zd dZ d dZ d Z d Z xZ S) 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). ctj|i|t|d|tdS)Nvision)super__init__r check_model_typer)selfargskwargs __class__s c/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/pipelines/depth_estimation.pyrz DepthEstimationPipeline.__init__3sJ$)&)))$))) FGGGGGNinputsz Image.Imagec d|vr|d}|tdtj|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. 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 ValueErrorr__call__)rrrrs rr$z DepthEstimationPipeline.__call__8sT> v  ZZ))F >dee euww11&111rc i}|tjdt||d<t|trd|vr |d|d<|iifS)NzUThe `timeout` argument is deprecated and will be removed in version 5 of Transformerstimeout)warningswarn FutureWarning isinstancedict)rr& parametersrpreprocess_paramss r_sanitize_parametersz,DepthEstimationPipeline._sanitize_parameters]so   Mgiv   ,3 i ( j$ ' ' AI,C,C+5i+@ i ( "b((rct||}|||j}|jdkr||j}|jddd|d<|S)N)r!return_tensorspt target_size)rimage_processor frameworkto torch_dtypesize)rimager& model_inputss r preprocessz"DepthEstimationPipeline.preprocesshsi5'**++5+XX >T ! !'??4+;<OOr)N)NN)__name__ __module__ __qualname____doc__rrstrrr$r.r;r@rS __classcell__)rs@rrrs0HHHHH #2#2uS$s)]DDW%WX#2#2#2#2#2#2J ) ) ) ) PPPPPPPrr)r'typingrrutilsrrrr r baser r PILr image_utilsrmodels.auto.modeling_autor get_loggerrTloggerrr=rrrbsc54444444)((((((UTTTTTT  H % %,,FFFGGlPlPlPlPlPhlPlPHGlPlPlPr