78 lines
3.1 KiB
Python
78 lines
3.1 KiB
Python
"""This module provides a SoftForegroundSegmenter which segments the foreground human subjects from the background.
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Copyright (c) Microsoft Corporation.
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MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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"""
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from pathlib import Path
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from typing import Optional, Union
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import cv2
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import numpy as np
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from .pixelwise_estimator import PixelwiseEstimator
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from .utils import composite_model_output_to_image
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class SoftForegroundSegmenter(PixelwiseEstimator):
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"""Estimates the soft foreground segmentation mask of human in an image."""
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def __init__(
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self,
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onnx_model: Union[str, Path],
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providers: Optional[list[str]] = None,
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binarization_threshold: Optional[float] = None,
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):
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"""Creates a soft foreground segmenter to segment the foreground human subjects in an image.
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Arguments:
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onnx_model: A path to an ONNX model.
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providers: Optional list of ONNX execution providers to use, defaults to [GPU, CPU].
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binarization_threshold: Threshold above which the mask is considered foreground. When None, the mask is returned as is.
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Raises:
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TypeError: if onnx_model is not a string or Path.
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ModelNotFoundError: if the model file does not exist.
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"""
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super().__init__(
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onnx_model,
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providers=providers,
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)
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self.binarization_threshold = binarization_threshold
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def estimate_foreground_segmentation(self, image: np.ndarray) -> np.ndarray:
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"""Predict the soft foreground/background segmentation given input image."""
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mask, metadata = self._estimate_dense_map(image)
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mask = mask[0][0]
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mask = np.transpose(mask, (1, 2, 0))
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# post_process to get the final segmentation mask and composite it onto the original size
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segmented_image = composite_model_output_to_image(mask, metadata, interp_mode=cv2.INTER_CUBIC)
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# clip the mask to [0, 1]
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segmented_image = np.clip(segmented_image, 0, 1)
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# Apply threshold if binarization_threshold is set
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if self.binarization_threshold:
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return ((segmented_image > self.binarization_threshold) * 1).astype(np.uint8)
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return segmented_image
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