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from typing import Any, Dict, List, Union |
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import numpy as np |
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from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends |
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from .base import PIPELINE_INIT_ARGS, Pipeline |
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if is_vision_available(): |
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from PIL import Image |
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from ..image_utils import load_image |
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if is_torch_available(): |
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from ..models.auto.modeling_auto import ( |
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MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES, |
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MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES, |
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MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, |
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MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES, |
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) |
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logger = logging.get_logger(__name__) |
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Prediction = Dict[str, Any] |
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Predictions = List[Prediction] |
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@add_end_docstrings(PIPELINE_INIT_ARGS) |
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class ImageSegmentationPipeline(Pipeline): |
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""" |
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Image segmentation pipeline using any `AutoModelForXXXSegmentation`. This pipeline predicts masks of objects and |
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their classes. |
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Example: |
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```python |
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>>> from transformers import pipeline |
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>>> segmenter = pipeline(model="facebook/detr-resnet-50-panoptic") |
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>>> segments = segmenter("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png") |
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>>> len(segments) |
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2 |
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>>> segments[0]["label"] |
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'bird' |
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>>> segments[1]["label"] |
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'bird' |
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>>> type(segments[0]["mask"]) # This is a black and white mask showing where is the bird on the original image. |
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<class 'PIL.Image.Image'> |
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>>> segments[0]["mask"].size |
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(768, 512) |
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``` |
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This image segmentation pipeline can currently be loaded from [`pipeline`] using the following task identifier: |
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`"image-segmentation"`. |
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See the list of available models on |
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[huggingface.co/models](https://huggingface.co/models?filter=image-segmentation). |
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""" |
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def __init__(self, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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if self.framework == "tf": |
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raise ValueError(f"The {self.__class__} is only available in PyTorch.") |
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requires_backends(self, "vision") |
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mapping = MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES.copy() |
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mapping.update(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES) |
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mapping.update(MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES) |
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mapping.update(MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES) |
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self.check_model_type(mapping) |
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def _sanitize_parameters(self, **kwargs): |
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preprocess_kwargs = {} |
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postprocess_kwargs = {} |
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if "subtask" in kwargs: |
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postprocess_kwargs["subtask"] = kwargs["subtask"] |
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preprocess_kwargs["subtask"] = kwargs["subtask"] |
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if "threshold" in kwargs: |
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postprocess_kwargs["threshold"] = kwargs["threshold"] |
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if "mask_threshold" in kwargs: |
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postprocess_kwargs["mask_threshold"] = kwargs["mask_threshold"] |
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if "overlap_mask_area_threshold" in kwargs: |
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postprocess_kwargs["overlap_mask_area_threshold"] = kwargs["overlap_mask_area_threshold"] |
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if "timeout" in kwargs: |
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preprocess_kwargs["timeout"] = kwargs["timeout"] |
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return preprocess_kwargs, {}, postprocess_kwargs |
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def __call__(self, images, **kwargs) -> Union[Predictions, List[Prediction]]: |
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""" |
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Perform segmentation (detect masks & classes) in the image(s) passed as inputs. |
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Args: |
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images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`): |
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The pipeline handles three types of images: |
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- A string containing an HTTP(S) link pointing to an image |
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- A string containing a local path to an image |
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- An image loaded in PIL directly |
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The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the |
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same format: all as HTTP(S) links, all as local paths, or all as PIL images. |
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subtask (`str`, *optional*): |
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Segmentation task to be performed, choose [`semantic`, `instance` and `panoptic`] depending on model |
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capabilities. If not set, the pipeline will attempt tp resolve in the following order: |
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`panoptic`, `instance`, `semantic`. |
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threshold (`float`, *optional*, defaults to 0.9): |
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Probability threshold to filter out predicted masks. |
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mask_threshold (`float`, *optional*, defaults to 0.5): |
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Threshold to use when turning the predicted masks into binary values. |
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overlap_mask_area_threshold (`float`, *optional*, defaults to 0.5): |
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Mask overlap threshold to eliminate small, disconnected segments. |
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timeout (`float`, *optional*, defaults to None): |
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The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and |
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the call may block forever. |
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Return: |
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A dictionary or a list of dictionaries containing the result. If the input is a single image, will return a |
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list of dictionaries, if the input is a list of several images, will return a list of list of dictionaries |
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corresponding to each image. |
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The dictionaries contain the mask, label and score (where applicable) of each detected object and contains |
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the following keys: |
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- **label** (`str`) -- The class label identified by the model. |
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- **mask** (`PIL.Image`) -- A binary mask of the detected object as a Pil Image of shape (width, height) of |
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the original image. Returns a mask filled with zeros if no object is found. |
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- **score** (*optional* `float`) -- Optionally, when the model is capable of estimating a confidence of the |
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"object" described by the label and the mask. |
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""" |
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return super().__call__(images, **kwargs) |
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def preprocess(self, image, subtask=None, timeout=None): |
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image = load_image(image, timeout=timeout) |
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target_size = [(image.height, image.width)] |
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if self.model.config.__class__.__name__ == "OneFormerConfig": |
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if subtask is None: |
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kwargs = {} |
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else: |
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kwargs = {"task_inputs": [subtask]} |
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inputs = self.image_processor(images=[image], return_tensors="pt", **kwargs) |
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inputs["task_inputs"] = self.tokenizer( |
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inputs["task_inputs"], |
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padding="max_length", |
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max_length=self.model.config.task_seq_len, |
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return_tensors=self.framework, |
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)["input_ids"] |
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else: |
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inputs = self.image_processor(images=[image], return_tensors="pt") |
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inputs["target_size"] = target_size |
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return inputs |
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def _forward(self, model_inputs): |
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target_size = model_inputs.pop("target_size") |
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model_outputs = self.model(**model_inputs) |
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model_outputs["target_size"] = target_size |
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return model_outputs |
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def postprocess( |
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self, model_outputs, subtask=None, threshold=0.9, mask_threshold=0.5, overlap_mask_area_threshold=0.5 |
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): |
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fn = None |
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if subtask in {"panoptic", None} and hasattr(self.image_processor, "post_process_panoptic_segmentation"): |
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fn = self.image_processor.post_process_panoptic_segmentation |
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elif subtask in {"instance", None} and hasattr(self.image_processor, "post_process_instance_segmentation"): |
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fn = self.image_processor.post_process_instance_segmentation |
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if fn is not None: |
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outputs = fn( |
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model_outputs, |
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threshold=threshold, |
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mask_threshold=mask_threshold, |
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overlap_mask_area_threshold=overlap_mask_area_threshold, |
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target_sizes=model_outputs["target_size"], |
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)[0] |
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annotation = [] |
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segmentation = outputs["segmentation"] |
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for segment in outputs["segments_info"]: |
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mask = (segmentation == segment["id"]) * 255 |
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mask = Image.fromarray(mask.numpy().astype(np.uint8), mode="L") |
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label = self.model.config.id2label[segment["label_id"]] |
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score = segment["score"] |
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annotation.append({"score": score, "label": label, "mask": mask}) |
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elif subtask in {"semantic", None} and hasattr(self.image_processor, "post_process_semantic_segmentation"): |
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outputs = self.image_processor.post_process_semantic_segmentation( |
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model_outputs, target_sizes=model_outputs["target_size"] |
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)[0] |
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annotation = [] |
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segmentation = outputs.numpy() |
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labels = np.unique(segmentation) |
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for label in labels: |
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mask = (segmentation == label) * 255 |
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mask = Image.fromarray(mask.astype(np.uint8), mode="L") |
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label = self.model.config.id2label[label] |
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annotation.append({"score": None, "label": label, "mask": mask}) |
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else: |
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raise ValueError(f"Subtask {subtask} is not supported for model {type(self.model)}") |
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return annotation |
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