tjddlmZmZmZddlmZmZmZmZm Z ddl m Z m Z er ddl mZddlmZerddlmZejeZee d Gd 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)has_image_processorc deZdZdZdZdZdZdZfdZe de e dfde de e e ffd Ze dee e dfde dee e e ffd Zde e ee dedfde de e e fee e e fzffd Zdd ZddZdZdZxZS)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). FTctj|i|t|d|tdS)Nvision)super__init__r check_model_typer)selfargskwargs __class__s e/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/pipelines/depth_estimation.pyrz DepthEstimationPipeline.__init__7sJ$)&)))$))) FGGGGGinputsz Image.Imagerreturnc dSNrr rs r__call__z DepthEstimationPipeline.__call__<s\_\_rc dSr#r$r%s rr&z DepthEstimationPipeline.__call__?shkhkrc d|vr|d}|tdtj|fi|S)a Predict the depth(s) of the image(s) passed as inputs. Args: inputs (`str`, `list[str]`, `PIL.Image` or `list[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images, which must then be passed as a string. Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL images. parameters (`Dict`, *optional*): A dictionary of argument names to parameter values, to control pipeline behaviour. The only parameter available right now is `timeout`, which is the length of time, in seconds, that the pipeline should wait before giving up on trying to download an image. timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and the call may block forever. Return: A dictionary or a list of dictionaries containing result. If the input is a single image, will return a dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to the images. The dictionaries contain the following keys: - **predicted_depth** (`torch.Tensor`) -- The predicted depth by the model as a `torch.Tensor`. - **depth** (`PIL.Image`) -- The predicted depth by the model as a `PIL.Image`. imagesNzECannot call the depth-estimation pipeline without an inputs argument!)pop ValueErrorrr&)rr rrs rr&z DepthEstimationPipeline.__call__BsUH v  ZZ))F >dee euww11&111rNc fi}|||d<t|trd|vr |d|d<|iifS)Ntimeout) isinstancedict)rr- parametersrpreprocess_paramss r_sanitize_parametersz,DepthEstimationPipeline._sanitize_parameterslsS  +2 i ( j$ ' ' AI,C,C+5i+@ i ( "b((rct||}||d}||j}|jddd|d<|S)Npt)r)return_tensors target_size)rimage_processortodtypesize)rimager- model_inputss r preprocessz"DepthEstimationPipeline.preprocesstsY5'**++5+NN #tz22 &+j2&6 ]#rcT|d}|jdi|}||d<|S)Nr7r$)r*model)rr=r7 model_outputss r_forwardz DepthEstimationPipeline._forward{s<"&&}55 " 22\22 '2 m$rc|j||dg}g}|D]}|d}||z ||z z }tj|dz d}| |d|dt|dkr|dn|S)Nr7predicted_depthuint8)rDdepthr r) r8post_process_depth_estimationdetachcpunumpyminmaxr fromarrayastypeappendlen)rrAoutputsformatted_outputsoutputrGs r postprocessz#DepthEstimationPipeline.postprocesss&DD = ) *    e eF,-4466::<<BBDDEUYY[[(UYY[[599;;-FGEOUS[$8$8$A$ABBE  $ $@Q9R]b%c%c d d d d'*7||q'8'8 ##>OOr)NNr#)__name__ __module__ __qualname____doc___load_processor_load_image_processor_load_feature_extractor_load_tokenizerrrrstrrr/r&listr2r>rBrU __classcell__)rs@rrrs0O #OHHHHH _uS-%78_C_DQTVYQYN___X_ ktE#}*<$=>k#kRVW[\_ad\dWeRfkkkXk(2CcM4 ;NNO(2[^(2 c3h$tCH~. .(2(2(2(2(2(2T)))) PPPPPPPrrN)typingrrrutilsrrr r r baser rPILr image_utilsrmodels.auto.modeling_autor get_loggerrVloggerrr$rrris`''''''''''54444444)((((((UTTTTTT  H % %,,FFFGGxPxPxPxPxPhxPxPHGxPxPxPr