U 5Afl%@sddlmZmZmZmZddlmZmZmZm Z m Z ddl m Z m Z erbddlmZddlmZerddlZdd lmZdd lmZe eZee d d Gd dde ZdS))AnyDictListUnion)add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends) ChunkPipelinebuild_pipeline_init_args)Image) load_imageN)BaseModelOutput)2MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMEST)Zhas_image_processorcseZdZdZfddZdeedeeee ffeeeefdfdd Z d d Z dd d Z d dZ dddZdeeefdddZZS)ZeroShotObjectDetectionPipelinea Zero shot object detection pipeline using `OwlViTForObjectDetection`. This pipeline predicts bounding boxes of objects when you provide an image and a set of `candidate_labels`. Example: ```python >>> from transformers import pipeline >>> detector = pipeline(model="google/owlvit-base-patch32", task="zero-shot-object-detection") >>> detector( ... "http://images.cocodataset.org/val2017/000000039769.jpg", ... candidate_labels=["cat", "couch"], ... ) [{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.254, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}] >>> detector( ... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", ... candidate_labels=["head", "bird"], ... ) [{'score': 0.119, 'label': 'bird', 'box': {'xmin': 71, 'ymin': 170, 'xmax': 410, 'ymax': 508}}] ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This object detection pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"zero-shot-object-detection"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=zero-shot-object-detection). c sBtjf||jdkr*td|jdt|d|tdS)NtfzThe z is only available in PyTorch.Zvision)super__init__ framework ValueError __class__r Zcheck_model_typer)selfkwargsrU/tmp/pip-unpacked-wheel-zw5xktn0/transformers/pipelines/zero_shot_object_detection.pyr8s   z(ZeroShotObjectDetectionPipeline.__init__Nz Image.Imageimagecandidate_labelsc sFd|kr|d}t|ttjfr.||d}n|}tj|f|}|S)a| Detect objects (bounding boxes & classes) in the image(s) passed as inputs. Args: image (`str`, `PIL.Image` or `List[Dict[str, Any]]`): The pipeline handles three types of images: - A string containing an http url pointing to an image - A string containing a local path to an image - An image loaded in PIL directly You can use this parameter to send directly a list of images, or a dataset or a generator like so: ```python >>> from transformers import pipeline >>> detector = pipeline(model="google/owlvit-base-patch32", task="zero-shot-object-detection") >>> detector( ... [ ... { ... "image": "http://images.cocodataset.org/val2017/000000039769.jpg", ... "candidate_labels": ["cat", "couch"], ... }, ... { ... "image": "http://images.cocodataset.org/val2017/000000039769.jpg", ... "candidate_labels": ["cat", "couch"], ... }, ... ] ... ) [[{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.25, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}], [{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.254, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}]] ``` candidate_labels (`str` or `List[str]` or `List[List[str]]`): What the model should recognize in the image. threshold (`float`, *optional*, defaults to 0.1): The probability necessary to make a prediction. top_k (`int`, *optional*, defaults to None): The number of top predictions that will be returned by the pipeline. 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