2026-04-29-21-51-19 - 全栈系统改造：FastAPI后端+SAM2+PostgreSQL+Redis+MinIO+前端Zustand重构
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234
backend/services/sam2_engine.py
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234
backend/services/sam2_engine.py
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"""SAM 2 engine wrapper with lazy loading and fallback stubs."""
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import logging
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import os
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from typing import Optional
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import numpy as np
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from config import settings
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Attempt to import SAM 2; fall back to stubs if unavailable.
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# ---------------------------------------------------------------------------
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try:
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import torch
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from sam2.build_sam import build_sam2
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from sam2.sam2_image_predictor import SAM2ImagePredictor
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SAM2_AVAILABLE = True
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logger.info("SAM2 library imported successfully.")
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except Exception as exc: # noqa: BLE001
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SAM2_AVAILABLE = False
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logger.warning("SAM2 import failed (%s). Using stub engine.", exc)
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class SAM2Engine:
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"""Lazy-loaded SAM 2 inference engine."""
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def __init__(self) -> None:
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self._predictor: Optional[SAM2ImagePredictor] = None
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self._model_loaded = False
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# -----------------------------------------------------------------------
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# Internal helpers
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# -----------------------------------------------------------------------
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def _load_model(self) -> None:
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"""Load the SAM 2 model and predictor on first use."""
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if self._model_loaded:
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return
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if not SAM2_AVAILABLE:
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logger.warning("SAM2 not available; skipping model load.")
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self._model_loaded = True
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return
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if not os.path.isfile(settings.sam_model_path):
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logger.error("SAM checkpoint not found at %s", settings.sam_model_path)
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self._model_loaded = True
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return
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try:
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model = build_sam2(
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settings.sam_model_config,
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settings.sam_model_path,
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device="cuda",
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)
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self._predictor = SAM2ImagePredictor(model)
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self._model_loaded = True
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logger.info("SAM 2 model loaded from %s", settings.sam_model_path)
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except Exception as exc: # noqa: BLE001
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logger.error("Failed to load SAM 2 model: %s", exc)
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self._model_loaded = True # Prevent repeated load attempts
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def _ensure_ready(self) -> bool:
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"""Ensure the model is loaded; return whether it is usable."""
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self._load_model()
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return SAM2_AVAILABLE and self._predictor is not None
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# -----------------------------------------------------------------------
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# Public API
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# -----------------------------------------------------------------------
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def predict_points(
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self,
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image: np.ndarray,
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points: list[list[float]],
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labels: list[int],
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) -> tuple[list[list[list[float]]], list[float]]:
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"""Run point-prompt segmentation.
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Args:
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image: HWC numpy array (uint8).
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points: List of [x, y] normalized coordinates (0-1).
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labels: 1 for foreground, 0 for background.
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Returns:
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Tuple of (polygons, scores).
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"""
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if not self._ensure_ready():
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logger.warning("SAM2 not ready; returning dummy masks.")
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return self._dummy_polygons(image.shape[1], image.shape[0]), [0.5]
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try:
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h, w = image.shape[:2]
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pts = np.array([[p[0] * w, p[1] * h] for p in points], dtype=np.float32)
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lbls = np.array(labels, dtype=np.int32)
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with torch.inference_mode(): # type: ignore[name-defined]
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self._predictor.set_image(image)
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masks, scores, _ = self._predictor.predict(
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point_coords=pts,
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point_labels=lbls,
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multimask_output=True,
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)
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polygons = []
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for m in masks:
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poly = self._mask_to_polygon(m)
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if poly:
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polygons.append(poly)
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return polygons, scores.tolist()
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except Exception as exc: # noqa: BLE001
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logger.error("SAM2 point prediction failed: %s", exc)
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return self._dummy_polygons(image.shape[1], image.shape[0]), [0.5]
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def predict_box(
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self,
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image: np.ndarray,
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box: list[float],
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) -> tuple[list[list[list[float]]], list[float]]:
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"""Run box-prompt segmentation.
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Args:
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image: HWC numpy array (uint8).
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box: [x1, y1, x2, y2] normalized coordinates.
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Returns:
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Tuple of (polygons, scores).
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"""
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if not self._ensure_ready():
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logger.warning("SAM2 not ready; returning dummy masks.")
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return self._dummy_polygons(image.shape[1], image.shape[0]), [0.5]
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try:
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h, w = image.shape[:2]
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bbox = np.array(
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[box[0] * w, box[1] * h, box[2] * w, box[3] * h],
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dtype=np.float32,
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)
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with torch.inference_mode(): # type: ignore[name-defined]
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self._predictor.set_image(image)
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masks, scores, _ = self._predictor.predict(
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box=bbox[None, :],
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multimask_output=False,
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)
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polygons = []
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for m in masks:
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poly = self._mask_to_polygon(m)
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if poly:
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polygons.append(poly)
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return polygons, scores.tolist()
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except Exception as exc: # noqa: BLE001
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logger.error("SAM2 box prediction failed: %s", exc)
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return self._dummy_polygons(image.shape[1], image.shape[0]), [0.5]
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def predict_auto(self, image: np.ndarray) -> tuple[list[list[list[float]]], list[float]]:
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"""Run automatic mask generation (grid of points).
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Args:
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image: HWC numpy array (uint8).
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Returns:
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Tuple of (polygons, scores).
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"""
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if not self._ensure_ready():
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logger.warning("SAM2 not ready; returning dummy masks.")
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return self._dummy_polygons(image.shape[1], image.shape[0]), [0.5]
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try:
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with torch.inference_mode(): # type: ignore[name-defined]
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self._predictor.set_image(image)
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# Generate a uniform 16x16 grid of point prompts
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h, w = image.shape[:2]
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grid = np.mgrid[0:1:17j, 0:1:17j].reshape(2, -1).T
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pts = grid * np.array([w, h])
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lbls = np.ones(pts.shape[0], dtype=np.int32)
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masks, scores, _ = self._predictor.predict(
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point_coords=pts,
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point_labels=lbls,
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multimask_output=True,
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)
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polygons = []
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for m in masks[:3]: # Limit to top 3 masks
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poly = self._mask_to_polygon(m)
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if poly:
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polygons.append(poly)
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return polygons, scores[:3].tolist()
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except Exception as exc: # noqa: BLE001
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logger.error("SAM2 auto prediction failed: %s", exc)
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return self._dummy_polygons(image.shape[1], image.shape[0]), [0.5]
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# -----------------------------------------------------------------------
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# Helpers
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# -----------------------------------------------------------------------
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@staticmethod
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def _mask_to_polygon(mask: np.ndarray) -> list[list[float]]:
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"""Convert a binary mask to a normalized polygon."""
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import cv2
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if mask.dtype != np.uint8:
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mask = (mask > 0).astype(np.uint8)
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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h, w = mask.shape[:2]
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largest = []
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for cnt in contours:
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if len(cnt) > len(largest):
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largest = cnt
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if len(largest) < 3:
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return []
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return [[float(pt[0][0]) / w, float(pt[0][1]) / h] for pt in largest]
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@staticmethod
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def _dummy_polygons(w: int, h: int) -> list[list[list[float]]]:
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"""Return a dummy rectangle polygon for fallback mode."""
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return [
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[
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[0.25, 0.25],
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[0.75, 0.25],
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[0.75, 0.75],
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[0.25, 0.75],
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]
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]
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# Singleton instance
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sam_engine = SAM2Engine()
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