U diF@sddlZddlmZmZddlmZmZmZmZm Z ddl m Z m Z erbddl mZddlmZertdd lmZeeZee d d Gd d 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)Zhas_image_processorcsleZdZdZfddZdeeeededfdfdd Zdd d Z dd d Z d dZ ddZ Z 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). cs&tj||t|d|tdS)NZvision)super__init__r Zcheck_model_typer)selfargskwargs __class__K/tmp/pip-unpacked-wheel-bm_b0l5e/transformers/pipelines/depth_estimation.pyr3s z DepthEstimationPipeline.__init__Nz Image.Image)inputsc s2d|kr|d}|dkr"tdtj|f|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__)rrrrrrr8s  z DepthEstimationPipeline.__call__cKsHi}|dk r tdt||d<t|tr>d|kr>|d|d<|iifS)NzUThe `timeout` argument is deprecated and will be removed in version 5 of Transformerstimeout)warningswarn FutureWarning isinstancedict)rr parametersrZpreprocess_paramsrrr_sanitize_parameters]s z,DepthEstimationPipeline._sanitize_parameterscCsHt||}|j||jd}|jdkr0||j}|jddd|d<|S)N)rZreturn_tensorspt target_size)rimage_processorZ frameworktoZ torch_dtypesize)rimager model_inputsrrr preprocesshs    z"DepthEstimationPipeline.preprocesscCs"|d}|jf|}||d<|S)Nr))rmodel)rr.r) model_outputsrrr_forwardps  z DepthEstimationPipeline._forwardcCs|j||dg}g}|D]\}|d}||||}t|d d}| |d|dqt |dkr|dS|S)Nr)predicted_depthZuint8)r3depthr r) r*Zpost_process_depth_estimationdetachcpuZnumpyminmaxr Z fromarrayZastypeappendlen)rr1outputsZformatted_outputsoutputr5rrr postprocessvsz#DepthEstimationPipeline.postprocess)N)NN)N)__name__ __module__ __qualname____doc__rrstrrrr&r/r2r> __classcell__rrrrrs (% r)r typingrrutilsrrrrr baser r ZPILr Z image_utilsrZmodels.auto.modeling_autorZ get_loggerr?loggerrrrrrs