Run pymatting foreground estimation on GPU via CuPy when available
The multi-level foreground colour estimation (pymatting estimate_foreground_ml) is the pipeline's CPU bottleneck (~7 s/image). pymatting ships a CuPy backend (estimate_foreground_ml_cupy) that runs the same multi-level solve on the GPU ~250x faster; verified visually identical to the float64 CPU result (opaque colours bit-close, the float32 edge delta is alpha-weighted away). Add ForegroundSettings.use_gpu (default True): with method "ml", try the CuPy backend when cupy + a CUDA device are present, otherwise transparently fall back to the CPU solver. pymatting only re-exports the cupy function when cupy is importable, so CPU-only deployments (no cupy) are unaffected. Requires cupy-cudaXXx matching the CUDA build on GPU boxes. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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@@ -1,11 +1,15 @@
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from __future__ import annotations
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import logging
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import numpy as np
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from .chroma import BackgroundModel
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from .deps import require_cv2
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from .settings import ForegroundSettings
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logger = logging.getLogger(__name__)
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def estimate_foreground_rgb(
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rgb: np.ndarray,
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@@ -43,6 +47,10 @@ def _estimate_ml(
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high-alpha neighbours instead of being divided out pixel-wise. That removes
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background-colour spill on thin hair where a direct unmix (divide by small
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alpha) is too noisy to trust.
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Runs on the GPU via pymatting's CuPy backend when ``settings.use_gpu`` is set
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and cupy + a CUDA device are available (~250x faster; float32, visually
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identical to the float64 CPU result); otherwise falls back to the CPU solver.
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"""
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try:
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from pymatting import estimate_foreground_ml
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@@ -53,10 +61,42 @@ def _estimate_ml(
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image = rgb.astype(np.float64) / 255.0
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a = np.clip(alpha.astype(np.float64), 0.0, 1.0)
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if settings.use_gpu:
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foreground = _estimate_ml_gpu(image, a, settings.ml_regularization)
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if foreground is not None:
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return np.clip(foreground * 255.0, 0, 255).astype(np.uint8)
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foreground = estimate_foreground_ml(image, a, regularization=settings.ml_regularization)
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return np.clip(foreground * 255.0, 0, 255).astype(np.uint8)
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def _estimate_ml_gpu(
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image: np.ndarray, alpha: np.ndarray, regularization: float
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) -> np.ndarray | None:
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"""Multi-level foreground via pymatting's CuPy backend.
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Returns the foreground as float ``[0, 1]``, or ``None`` when the GPU path is
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unavailable (cupy not installed, or no CUDA device) so the caller falls back
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to the CPU solver. pymatting only re-exports ``estimate_foreground_ml_cupy``
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when cupy is importable, so the import itself is the availability probe.
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"""
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try:
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from pymatting import estimate_foreground_ml_cupy
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except ImportError:
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return None
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try:
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import cupy
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if cupy.cuda.runtime.getDeviceCount() < 1:
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return None
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foreground = estimate_foreground_ml_cupy(image, alpha, regularization=regularization)
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return np.asarray(foreground)
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except Exception as exc: # noqa: BLE001 -- any GPU failure falls back to CPU
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logger.warning("GPU foreground estimation failed (%s); using CPU", exc)
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return None
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def _weighted_blur(values: np.ndarray, weights: np.ndarray, radius: int) -> np.ndarray:
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if radius <= 0:
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return values
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@@ -105,6 +105,11 @@ class CrossCheckSettings:
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class ForegroundSettings:
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enabled: bool = True
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method: str = "ml" # "ml" (pymatting) or "unmix" (legacy heuristic)
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# With method "ml", use pymatting's CuPy GPU backend when it is available
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# (cupy installed + a CUDA device) — ~250x faster, float32 output visually
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# identical to the float64 CPU solver. Transparently falls back to CPU when
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# cupy/CUDA are absent, so it is safe to leave on for CPU-only deployments.
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use_gpu: bool = True
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fallback_to_unmix: bool = True
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ml_regularization: float = 1e-5
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# Legacy "unmix" method parameters (used only when method == "unmix").
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