rh ddlmZmZmZddlmZmZmZmZm Z ddl m Z m Z er ddl mZddlmZerddlmZej&eZee d Gd d e Zy ))AnyUnionoverload)add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends)Pipelinebuild_pipeline_init_args)Image) load_image)(MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMEST)has_image_processorc eZdZdZdZdZdZdZfdZe de e dfde de e e ffd Ze dee e dfde dee e e ffd Zde e ee dedfde de e e e fee e e ffffd Zdd Zdd ZdZdZxZS)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). FTcft||i|t|d|jty)Nvision)super__init__r check_model_typer)selfargskwargs __class__s z/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/pipelines/depth_estimation.pyrz DepthEstimationPipeline.__init__7s. $)&)$) FGinputsz Image.Imagerreturnc yNrr rs r__call__z DepthEstimationPipeline.__call__<s\_rc yr#r$r%s rr&z DepthEstimationPipeline.__call__?shkrc hd|vr|jd}| tdt| |fi|S)a Predict the depth(s) of the image(s) passed as inputs. Args: inputs (`str`, `list[str]`, `PIL.Image` or `list[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images, which must then be passed as a string. Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL images. parameters (`Dict`, *optional*): A dictionary of argument names to parameter values, to control pipeline behaviour. The only parameter available right now is `timeout`, which is the length of time, in seconds, that the pipeline should wait before giving up on trying to download an image. timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and the call may block forever. Return: A dictionary or a list of dictionaries containing result. If the input is a single image, will return a dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to the images. The dictionaries contain the following keys: - **predicted_depth** (`torch.Tensor`) -- The predicted depth by the model as a `torch.Tensor`. - **depth** (`PIL.Image`) -- The predicted depth by the model as a `PIL.Image`. imageszECannot call the depth-estimation pipeline without an inputs argument!)pop ValueErrorrr&)rr rrs rr&z DepthEstimationPipeline.__call__BsBH v ZZ)F >de ew1&11rc Vi}|||d<t|tr d|vr|d|d<|iifS)Ntimeout) isinstancedict)rr- parametersrpreprocess_paramss r_sanitize_parametersz,DepthEstimationPipeline._sanitize_parameterslsG  +2 i ( j$ 'I,C+5i+@ i ( "b((rct||}|j||j}|jdk(r|j|j}|j ddd|d<|S)N)r)return_tensorspt target_size)rimage_processor frameworkto torch_dtypesize)rimager- model_inputss r preprocessz"DepthEstimationPipeline.preprocesstsd5'*++5+X >>T !'??4+;+;r7 model_outputss r_forwardz DepthEstimationPipeline._forward|s5"&&}5 " 2\2 '2 m$rc|jj||dg}g}|D]}|djjj }||j z |j |j z z }tj|dzjd}|j|d|dt|dk(r|dS|S)Nr7predicted_depthuint8)rEdepthr r) r8post_process_depth_estimationdetachcpunumpyminmaxr fromarrayastypeappendlen)rrBoutputsformatted_outputsoutputrHs r postprocessz#DepthEstimationPipeline.postprocesss&&DD = ) *   eF,-446::<BBDEUYY[(UYY[599;-FGEOOUS[$8$8$ABE  $ $@Q9R]b%c d  e(+7|q'8 #O>OOr)NNr#)__name__ __module__ __qualname____doc___load_processor_load_image_processor_load_feature_extractor_load_tokenizerrrrstrrr/r&listr2r?rCrV __classcell__)rs@rrrs0O #OH _uS-%78_C_DQTVYQYN__ ktE#}*<$=>k#kRVW[\_ad\dWeRfkk(2CcM4 ;NNO(2[^(2 tCH~tDcN33 4(2T) PrrN)typingrrrutilsrrr r r baser rPILr image_utilsrmodels.auto.modeling_autor get_loggerrWloggerrr$rrrjsp''5(T   H %,FGyPhyPHyPr