+ QV-j^RIHtHtHt^RIHtHtHtHtH t ^RI H t H t ]!4'd ^RI Ht^RIHt]!4'd^RIHt]P&!]4t]!] !RR74!R R ] 44tR #) )AnyUnionoverload)add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends)Pipelinebuild_pipeline_init_args)Image) load_image)(MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMEST)has_image_processorcaa]tRt^toRtRtRtRtRtV3Rlt ] V3RlRl4t ] V3RlRl4t V3R lV3R llt RR lt RR lt R tRtRtVtV;t#)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). FTcj<\SV`!V/VB\VR4VP\4R#)visionN)super__init__r check_model_typer)selfargskwargs __class__s&*,x/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/transformers/pipelines/depth_estimation.pyr DepthEstimationPipeline.__init__7s. $)&)$) FGcV<V^8dQhRS[S[R3,RS[RS[S[S[3,/#inputsz Image.Imagerreturn)rstrrdict)format __classdict__s"r __annotate__$DepthEstimationPipeline.__annotate__=s-__uS-%78_C_DQTVYQYN_rc R#Nrr"rs&&,r__call__ DepthEstimationPipeline.__call__<s\_rc v<V^8dQhRS[S[S[R3,,RS[RS[S[S[S[3,,/#r )listrr$rr%)r&r's"rr(r)@s:kktE#}*<$=>k#kRVW[\_ad\dWeRfkrc R#r+r,r-s&&,rr.r/?shkrc <V^8dQhRS[S[S[S[,RS[R,3,RS[RS[S[S[3,S[S[S[S[3,,,/#r )rr$r1rr%)r&r's"rr(r)BsV(2(2CcM4 ;NNO(2[^(2 c3h$tCH~. .(2rc t<RV9dVPR4pVf \R4h\SV` !V3/VB#)a( Predict the depth(s) of the image(s) passed as inputs. Args: inputs (`str`, `list[str]`, `PIL.Image` or `list[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images, which must then be passed as a string. Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL images. parameters (`Dict`, *optional*): A dictionary of argument names to parameter values, to control pipeline behaviour. The only parameter available right now is `timeout`, which is the length of time, in seconds, that the pipeline should wait before giving up on trying to download an image. timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and the call may block forever. Return: A dictionary or a list of dictionaries containing result. If the input is a single image, will return a dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to the images. The dictionaries contain the following keys: - **predicted_depth** (`torch.Tensor`) -- The predicted depth by the model as a `torch.Tensor`. - **depth** (`PIL.Image`) -- The predicted depth by the model as a `PIL.Image`. imageszECannot call the depth-estimation pipeline without an inputs argument!)pop ValueErrorrr.)rr"rrs&&,rr.r/BsBH v ZZ)F >de ew1&11rc r/pVeWR&\V\4'dRV9d VR,VR&V//3#)Ntimeout) isinstancer%)rr9 parametersrpreprocess_paramss&&&, r_sanitize_parameters,DepthEstimationPipeline._sanitize_parameterslsG  +2i ( j$ ' 'I,C+5i+@ i ( "b((rc\W4pVPVRR7pVPVP4pVPRRR1,VR&V#)pt)r5return_tensorsN target_size)rimage_processortodtypesize)rimager9 model_inputss&&& r preprocess"DepthEstimationPipeline.preprocesstsP5*++5+N #tzz2 &+jj2&6 ]#rcTVPR4pVP!R/VBpW#R&V#)rBr,)r6model)rrIrB model_outputss&& r_forward DepthEstimationPipeline._forward{s1"&&}5  2\2 '2m$rc VPPVVR,.4p.pVFpVR,P4P4P 4pWUP 4, VP 4VP 4, , p\P!V^,PR44pVPRVR,RV/4K \V4^8Xd V^,#T#)rBpredicted_depthuint8depth) rDpost_process_depth_estimationdetachcpunumpyminmaxr fromarrayastypeappendlen)rrNoutputsformatted_outputsoutputrTs&& r postprocess#DepthEstimationPipeline.postprocesss&&DD = ) *  F,-446::<BBDEYY[(UYY[599;-FGEOOUS[$8$8$ABE  $ $&7@Q9RT[]b%c d (+7|q'8 #O>OOrr,)NNr+)__name__ __module__ __qualname____firstlineno____doc___load_processor_load_image_processor_load_feature_extractor_load_tokenizerrrr.r=rJrOrb__static_attributes____classdictcell__ __classcell__)rr's@@rrrsl0O #OH __ kk(2(2T) PPrrN)typingrrrutilsrrrr r baser r PILr image_utilsrmodels.auto.modeling_autor get_loggerrdloggerrr,rrrxst''5(T   H %,FGxPhxPHxPr