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