soft_foreground_segmenter.py

# Copied From https://github.com/microsoft/DAViD/blob/main/runtime/soft_foreground_segmenter.py
"""This module provides a SoftForegroundSegmenter which segments the foreground human subjects from the background.

Copyright (c) Microsoft Corporation.

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"""

from pathlib import Path
from typing import Optional, Union

import cv2
import numpy as np
from pixelwise_estimator import PixelwiseEstimator
from utils import composite_model_output_to_image


class SoftForegroundSegmenter(PixelwiseEstimator):
    """Estimates the soft foreground segmentation mask of human in an image."""

    def __init__(
        self,
        onnx_model: Union[str, Path],
        providers: Optional[list[str]] = None,
        binarization_threshold: Optional[float] = None,
    ):
        """Creates a soft foreground segmenter to segment the foreground human subjects in an image.

        Arguments:
            onnx_model: A path to an ONNX model.
            providers: Optional list of ONNX execution providers to use, defaults to [GPU, CPU].
            binarization_threshold: Threshold above which the mask is considered foreground. When None, the mask is returned as is.

        Raises:
            TypeError: if onnx_model is not a string or Path.
            ModelNotFoundError: if the model file does not exist.
        """
        super().__init__(
            onnx_model,
            providers=providers,
        )
        self.binarization_threshold = binarization_threshold

    def estimate_foreground_segmentation(self, image: np.ndarray) -> np.ndarray:
        """Predict the soft foreground/background segmentation given input image."""
        mask, metadata = self._estimate_dense_map(image)
        mask = mask[0][0]
        mask = np.transpose(mask, (1, 2, 0))

        # post_process to get the final segmentation mask and composite it onto the original size
        segmented_image = composite_model_output_to_image(mask, metadata, interp_mode=cv2.INTER_CUBIC)

        # clip the mask to [0, 1]
        segmented_image = np.clip(segmented_image, 0, 1)

        # Apply threshold if binarization_threshold is set
        if self.binarization_threshold:
            return ((segmented_image > self.binarization_threshold) * 1).astype(np.uint8)

        return segmented_image