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功能增加: - 将视频导入和生成帧拆成两个明确动作，项目库生成帧时选择 FPS，工作区不再自动触发拆帧。 - 为工作区新增调整多边形工具，支持选中 mask、拖动顶点、边中点插点、双击边界按位置插点，并保留多 polygon 子区域编辑。 - 打通 AI 页 SAM2/SAM3 结果到工作区的联动，生成 mask 后自动选中，可在右侧分类树换标签，并推送到工作区继续编辑。 - 增强 Dashboard WebSocket 连接状态与心跳，使用真实 onopen/onclose/onerror 状态驱动前端显示。 - 完善 SAM3 external worker 适配，支持 box prompt、semantic 请求级阈值和 video tracker 路径。 bugfix: - 修复 SAM2 文本语义误走自动分割的问题，改为提示使用点提示或切换 SAM3。 - 修复 SAM2 多候选重叠显示的问题，点提示和 auto fallback 默认只采用最高分候选。 - 修复 SAM2 反向点看起来无效的问题，带负点时启用背景过滤，过滤为空时移除旧候选。 - 修复 SAM3 单个 2D mask 结果无法转 polygon、低阈值 semantic 返回被默认阈值吞掉的问题。 - 修复 AI 页 mask 未选中导致分类树无法修改 SAM2 结果标签的问题。 测试和文档: - 补充 CanvasArea、AISegmentation、ProjectLibrary、VideoWorkspace、Dashboard、websocket 和 SAM engine/API 测试。 - 新增 backend/tests/test_sam2_engine.py，覆盖 SAM2 单候选请求和 auto fallback 行为。 - 更新 README、AGENTS 和 doc 需求/设计/接口/测试矩阵，按当前实现冻结功能状态。
448 lines
17 KiB
Python
448 lines
17 KiB
Python
"""SAM 3 engine adapter and runtime status.
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The official facebookresearch/sam3 package currently targets Python 3.12+
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and CUDA-capable PyTorch. This adapter reports those requirements honestly and
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only performs inference when the local runtime can actually import and execute
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the package.
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"""
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from __future__ import annotations
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import importlib.util
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import json
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import logging
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import os
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import subprocess
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import sys
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import tempfile
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import time
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from pathlib import Path
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from typing import Any
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import numpy as np
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from PIL import Image
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from config import settings
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from services.sam2_engine import SAM2Engine
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logger = logging.getLogger(__name__)
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try:
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import torch
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TORCH_AVAILABLE = True
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except Exception as exc: # noqa: BLE001
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TORCH_AVAILABLE = False
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torch = None # type: ignore[assignment]
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logger.warning("PyTorch import failed (%s). SAM3 will be unavailable.", exc)
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SAM3_PACKAGE_AVAILABLE = importlib.util.find_spec("sam3") is not None
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class SAM3Engine:
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"""Lazy SAM 3 image inference adapter."""
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def __init__(self) -> None:
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self._model: Any | None = None
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self._processor: Any | None = None
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self._model_loaded = False
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self._last_error: str | None = None
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self._external_status_cache: dict[str, Any] | None = None
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self._external_status_checked_at = 0.0
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def _python_ok(self) -> bool:
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return sys.version_info >= (3, 12)
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def _gpu_ok(self) -> bool:
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return bool(TORCH_AVAILABLE and torch is not None and torch.cuda.is_available())
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def _checkpoint_path(self) -> str | None:
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path = settings.sam3_checkpoint_path.strip()
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return path if path else None
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def _checkpoint_exists(self) -> bool:
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path = self._checkpoint_path()
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return bool(path and os.path.isfile(path))
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def _can_load(self) -> bool:
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return bool(
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SAM3_PACKAGE_AVAILABLE
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and TORCH_AVAILABLE
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and self._python_ok()
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and self._gpu_ok()
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and self._checkpoint_exists()
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)
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def _worker_path(self) -> Path:
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return Path(__file__).with_name("sam3_external_worker.py")
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def _external_python_exists(self) -> bool:
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return bool(settings.sam3_external_enabled and os.path.isfile(settings.sam3_external_python))
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def _external_status(self, force: bool = False) -> dict[str, Any]:
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now = time.monotonic()
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if (
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not force
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and self._external_status_cache is not None
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and now - self._external_status_checked_at < settings.sam3_status_cache_seconds
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):
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return self._external_status_cache
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if not settings.sam3_external_enabled:
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status = {
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"available": False,
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"package_available": False,
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"python_ok": False,
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"torch_ok": False,
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"cuda_available": False,
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"device": "unavailable",
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"message": "SAM 3 external runtime is disabled.",
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}
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elif not self._external_python_exists():
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status = {
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"available": False,
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"package_available": False,
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"python_ok": False,
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"torch_ok": False,
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"cuda_available": False,
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"device": "unavailable",
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"message": f"SAM 3 external Python not found: {settings.sam3_external_python}",
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}
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else:
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try:
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env = os.environ.copy()
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env["SAM3_MODEL_VERSION"] = settings.sam3_model_version
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if self._checkpoint_path():
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env["SAM3_CHECKPOINT_PATH"] = self._checkpoint_path() or ""
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completed = subprocess.run(
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[settings.sam3_external_python, str(self._worker_path()), "--status"],
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capture_output=True,
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text=True,
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timeout=min(settings.sam3_timeout_seconds, 30),
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check=False,
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env=env,
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)
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if completed.returncode != 0:
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detail = completed.stderr.strip() or completed.stdout.strip()
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status = {
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"available": False,
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"package_available": False,
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"python_ok": False,
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"torch_ok": False,
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"cuda_available": False,
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"device": "unavailable",
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"message": f"SAM 3 external status failed: {detail}",
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}
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else:
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status = json.loads(completed.stdout)
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except Exception as exc: # noqa: BLE001
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status = {
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"available": False,
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"package_available": False,
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"python_ok": False,
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"torch_ok": False,
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"cuda_available": False,
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"device": "unavailable",
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"message": f"SAM 3 external status failed: {exc}",
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}
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self._external_status_cache = status
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self._external_status_checked_at = now
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return status
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def _load_model(self) -> None:
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if self._model_loaded:
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return
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if not self._can_load():
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self._last_error = self._status_message()
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self._model_loaded = True
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return
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try:
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from sam3.model.sam3_image_processor import Sam3Processor
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from sam3.model_builder import build_sam3_image_model
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self._model = build_sam3_image_model(
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checkpoint_path=self._checkpoint_path(),
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load_from_HF=False,
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)
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self._processor = Sam3Processor(self._model)
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self._model_loaded = True
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self._last_error = None
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logger.info("SAM 3 image model loaded with version setting %s", settings.sam3_model_version)
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except Exception as exc: # noqa: BLE001
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self._last_error = str(exc)
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self._model_loaded = True
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logger.error("Failed to load SAM 3 model: %s", exc)
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def _ensure_ready(self) -> bool:
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self._load_model()
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return self._processor is not None
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def _status_message(self) -> str:
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missing = []
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if not SAM3_PACKAGE_AVAILABLE:
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missing.append("sam3 package")
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if not self._python_ok():
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missing.append("Python 3.12+ runtime")
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if not TORCH_AVAILABLE:
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missing.append("PyTorch")
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if not self._gpu_ok():
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missing.append("CUDA GPU")
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if not self._checkpoint_exists():
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missing.append(f"local checkpoint ({settings.sam3_checkpoint_path})")
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if missing:
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return f"SAM 3 unavailable: missing {', '.join(missing)}."
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return "SAM 3 dependencies are present; model will load on first inference."
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def status(self) -> dict:
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external_status = self._external_status()
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available = bool(self._can_load() or external_status.get("available"))
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external_ready = bool(external_status.get("available"))
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message = self._last_error or self._status_message()
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if self._processor is not None:
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message = "SAM 3 model loaded and ready."
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elif external_ready:
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message = "SAM 3 external runtime is ready; local checkpoint will load in the helper process on inference."
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elif external_status.get("message") and not self._can_load():
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message = str(external_status["message"])
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return {
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"id": "sam3",
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"label": "SAM 3",
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"available": available,
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"loaded": self._processor is not None,
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"device": "cuda" if self._gpu_ok() else str(external_status.get("device", "unavailable")),
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"supports": ["semantic", "box", "video_track"],
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"message": message,
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"package_available": bool(SAM3_PACKAGE_AVAILABLE or external_status.get("package_available")),
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"checkpoint_exists": bool(self._checkpoint_exists() or external_status.get("checkpoint_access")),
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"checkpoint_path": self._checkpoint_path() or f"official/HuggingFace ({settings.sam3_model_version})",
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"python_ok": bool(self._python_ok() or external_status.get("python_ok")),
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"torch_ok": bool(TORCH_AVAILABLE or external_status.get("torch_ok")),
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"cuda_required": True,
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"external_available": external_ready,
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"external_python": settings.sam3_external_python if settings.sam3_external_enabled else None,
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}
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def _xyxy_to_cxcywh(self, box: list[float]) -> list[float]:
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if len(box) != 4:
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raise ValueError("SAM 3 box prompt requires [x1, y1, x2, y2].")
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x1, y1, x2, y2 = [min(max(float(value), 0.0), 1.0) for value in box]
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left, right = sorted([x1, x2])
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top, bottom = sorted([y1, y2])
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width = max(right - left, 1e-6)
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height = max(bottom - top, 1e-6)
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return [left + width / 2, top + height / 2, width, height]
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def _prediction_to_polygons(self, output: Any) -> tuple[list[list[list[float]]], list[float]]:
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masks = output.get("masks", [])
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scores = output.get("scores", [])
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polygons = []
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for mask in masks:
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if hasattr(mask, "detach"):
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mask = mask.detach().cpu().numpy()
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if mask.ndim == 3:
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mask = mask[0]
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poly = SAM2Engine._mask_to_polygon(mask)
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if poly:
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polygons.append(poly)
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if hasattr(scores, "detach"):
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scores = scores.detach().cpu().tolist()
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elif hasattr(scores, "tolist"):
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scores = scores.tolist()
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return polygons, list(scores)
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def _predict_external(
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self,
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image: np.ndarray,
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prompt_type: str,
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*,
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text: str = "",
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box: list[float] | None = None,
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confidence_threshold: float | None = None,
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) -> tuple[list[list[list[float]]], list[float]]:
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status = self._external_status(force=True)
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if not status.get("available"):
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raise RuntimeError(status.get("message") or "SAM 3 external runtime is unavailable.")
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with tempfile.TemporaryDirectory(prefix="sam3_") as tmpdir:
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tmp_path = Path(tmpdir)
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image_path = tmp_path / "image.png"
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request_path = tmp_path / "request.json"
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Image.fromarray(image).save(image_path)
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request_path.write_text(
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json.dumps(
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{
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"image_path": str(image_path),
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"prompt_type": prompt_type,
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"text": text.strip(),
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"box": box,
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"model_version": settings.sam3_model_version,
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"checkpoint_path": self._checkpoint_path(),
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"confidence_threshold": (
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confidence_threshold
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if confidence_threshold is not None
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else settings.sam3_confidence_threshold
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),
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},
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ensure_ascii=False,
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),
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encoding="utf-8",
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)
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env = os.environ.copy()
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env["SAM3_MODEL_VERSION"] = settings.sam3_model_version
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if self._checkpoint_path():
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env["SAM3_CHECKPOINT_PATH"] = self._checkpoint_path() or ""
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completed = subprocess.run(
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[settings.sam3_external_python, str(self._worker_path()), "--request", str(request_path)],
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capture_output=True,
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text=True,
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timeout=settings.sam3_timeout_seconds,
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check=False,
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env=env,
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)
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if completed.returncode != 0:
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detail = completed.stderr.strip() or completed.stdout.strip()
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try:
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parsed = json.loads(detail)
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detail = parsed.get("error", detail)
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except Exception: # noqa: BLE001
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pass
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raise RuntimeError(f"SAM 3 external inference failed: {detail}")
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payload = json.loads(completed.stdout)
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if payload.get("error"):
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raise RuntimeError(str(payload["error"]))
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return payload.get("polygons", []), payload.get("scores", [])
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def _predict_semantic_external(
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self,
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image: np.ndarray,
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text: str,
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confidence_threshold: float | None = None,
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) -> tuple[list[list[list[float]]], list[float]]:
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return self._predict_external(
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image,
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"semantic",
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text=text,
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confidence_threshold=confidence_threshold,
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)
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def _predict_box_external(self, image: np.ndarray, box: list[float]) -> tuple[list[list[list[float]]], list[float]]:
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return self._predict_external(image, "box", box=box)
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def _propagate_video_external(
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self,
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frame_paths: list[str],
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source_frame_index: int,
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seed: dict[str, Any],
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direction: str,
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max_frames: int | None,
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) -> list[dict[str, Any]]:
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status = self._external_status(force=True)
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if not status.get("available"):
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raise RuntimeError(status.get("message") or "SAM 3 external runtime is unavailable.")
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if not frame_paths:
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return []
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with tempfile.TemporaryDirectory(prefix="sam3_video_") as tmpdir:
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request_path = Path(tmpdir) / "request.json"
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request_path.write_text(
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json.dumps(
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{
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"prompt_type": "video_track",
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"frame_dir": str(Path(frame_paths[0]).parent),
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"source_frame_index": source_frame_index,
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"seed": seed,
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"direction": direction,
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"max_frames": max_frames,
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"model_version": settings.sam3_model_version,
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"checkpoint_path": self._checkpoint_path(),
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"confidence_threshold": settings.sam3_confidence_threshold,
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},
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ensure_ascii=False,
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),
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encoding="utf-8",
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)
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env = os.environ.copy()
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env["SAM3_MODEL_VERSION"] = settings.sam3_model_version
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if self._checkpoint_path():
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env["SAM3_CHECKPOINT_PATH"] = self._checkpoint_path() or ""
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completed = subprocess.run(
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[settings.sam3_external_python, str(self._worker_path()), "--request", str(request_path)],
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capture_output=True,
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text=True,
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timeout=settings.sam3_timeout_seconds,
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check=False,
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env=env,
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)
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if completed.returncode != 0:
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detail = completed.stderr.strip() or completed.stdout.strip()
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try:
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parsed = json.loads(detail)
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detail = parsed.get("error", detail)
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except Exception: # noqa: BLE001
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pass
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raise RuntimeError(f"SAM 3 external video tracking failed: {detail}")
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payload = json.loads(completed.stdout)
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if payload.get("error"):
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raise RuntimeError(str(payload["error"]))
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return payload.get("frames", [])
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def predict_semantic(
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self,
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image: np.ndarray,
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text: str,
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confidence_threshold: float | None = None,
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) -> tuple[list[list[list[float]]], list[float]]:
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if not text.strip():
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raise ValueError("SAM 3 semantic prompt requires non-empty text.")
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if not self._can_load() and self._external_status().get("available"):
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return self._predict_semantic_external(image, text, confidence_threshold=confidence_threshold)
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if not self._ensure_ready():
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raise RuntimeError(self.status()["message"])
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pil_image = Image.fromarray(image)
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with torch.inference_mode(): # type: ignore[union-attr]
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state = self._processor.set_image(pil_image)
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output = self._processor.set_text_prompt(state=state, prompt=text.strip())
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return self._prediction_to_polygons(output)
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def predict_points(self, *_args: Any, **_kwargs: Any) -> tuple[list[list[list[float]]], list[float]]:
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raise NotImplementedError("This backend currently exposes SAM 3 semantic text inference; use SAM 2 for point prompts.")
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def predict_box(self, image: np.ndarray, box: list[float]) -> tuple[list[list[list[float]]], list[float]]:
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if not self._can_load() and self._external_status().get("available"):
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return self._predict_box_external(image, box)
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if not self._ensure_ready():
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raise RuntimeError(self.status()["message"])
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pil_image = Image.fromarray(image)
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with torch.inference_mode(): # type: ignore[union-attr]
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state = self._processor.set_image(pil_image)
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output = self._processor.add_geometric_prompt(
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state=state,
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box=self._xyxy_to_cxcywh(box),
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label=True,
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)
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return self._prediction_to_polygons(output)
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def propagate_video(
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self,
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frame_paths: list[str],
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source_frame_index: int,
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seed: dict[str, Any],
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direction: str = "forward",
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max_frames: int | None = None,
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) -> list[dict[str, Any]]:
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return self._propagate_video_external(frame_paths, source_frame_index, seed, direction, max_frames)
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sam3_engine = SAM3Engine()
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