U ,-e2@sddlmZmZddlZddlmZmZmZm Z m Z ddl m Z m Z erbddlmZddlmZer|ddlZdd lmZe eZee Gd d d e ZdS) )ListUnionN)add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends)PIPELINE_INIT_ARGSPipeline)Image) load_image)(MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMEScsjeZdZdZfddZeeeededfdfdd 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="Intel/dpt-large") >>> 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__h/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/pipelines/depth_estimation.pyr0s z DepthEstimationPipeline.__init__z Image.Image)imagesc stj|f|S)a Assign labels to the image(s) passed as inputs. Args: images (`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. top_k (`int`, *optional*, defaults to 5): The number of top labels that will be returned by the pipeline. If the provided number is higher than the number of labels available in the model configuration, it will default to the number of labels. 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: - **label** (`str`) -- The label identified by the model. - **score** (`int`) -- The score attributed by the model for that label. )r__call__)rrrrrrr5s z DepthEstimationPipeline.__call__NcKsi}|dk r||d<|iifS)Ntimeoutr)rrrZpreprocess_paramsrrr_sanitize_parametersWsz,DepthEstimationPipeline._sanitize_parameterscCs&t||}|j|_|j||jd}|S)N)rZreturn_tensors)rsize image_sizeZimage_processorZ framework)rimager model_inputsrrr preprocess]s z"DepthEstimationPipeline.preprocesscCs|jf|}|S)N)model)rr! model_outputsrrr_forwardcs z DepthEstimationPipeline._forwardcCsx|j}tjjj|d|jdddddd}| }|dt | d}t |}i}||d<||d <|S) Nr ZbicubicF)rmodeZ align_cornersZuint8predicted_depthdepth)r)torchnnZ functionalZ interpolateZ unsqueezerZsqueezecpunumpynpmaxZastyper Z fromarray)rr$r)Z predictionoutput formattedr*Z output_dictrrr postprocessgs z#DepthEstimationPipeline.postprocess)N)N)__name__ __module__ __qualname____doc__rrstrrrrr"r%r3 __classcell__rrrrrs &"  r)typingrrr.r/utilsrrrrr baser r ZPILr Z image_utilsrr+Zmodels.auto.modeling_autorZ get_loggerr4loggerrrrrrs