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5ab4602535
- 接入 SAM2 视频传播能力：新增 /api/ai/propagate，支持用当前帧 mask/polygon/bbox 作为 seed，通过 SAM2 video predictor 向前、向后或双向传播，并可保存为真实 annotation。 - 接入 SAM3 video tracker：通过独立 Python 3.12 external worker 调用 SAM3 video predictor/tracker，使用本地 checkpoint 与 bbox seed 执行视频级跟踪，并在模型状态中标记 video_track 能力。 - 完善 SAM 模型分发：sam_registry 按 model_id 明确区分 sam2 propagation 与 sam3 video_track，避免两个模型链路混用。 - 打通前端“传播片段”：VideoWorkspace 使用当前选中 mask 和当前 AI 模型调用后端传播接口，传播结果回写并刷新工作区已保存标注。 - 增强 SAM3 本地 checkpoint 配置：新增 sam3_checkpoint_path 配置和 .env.example 示例，状态检查改为基于本地 checkpoint/独立环境/模型包可用性。 - 完善视频拆帧参数：/api/media/parse 支持 parse_fps、max_frames、target_width，后端任务保存帧时间戳、源帧号和 frame_sequence 元数据。 - 增加运行时 schema 兼容处理：启动时为旧 frames 表补充 timestamp_ms 和 source_frame_number 列，避免旧库升级后缺字段。 - 强化 Canvas 标注编辑：补齐多边形闭合、点工具、顶点拖拽、边中点插入、Delete/Backspace 删除、区域合并和重叠去除等交互。 - 增强语义分类联动：选中 mask 后可通过右侧语义分类树更新标签、颜色和 class metadata，并同步到保存/导出链路。 - 增加关键帧时间轴体验：FrameTimeline 显示具体时间信息，并支持键盘左右方向键切换关键帧。 - 完善 AI 交互分割参数：前端保留正向点、反向点、框选和 interactive prompt 的调用状态，支持 SAM2 细化候选区域与 SAM3 bbox 入口。 - 扩展后端/前端 API 类型：新增 propagateMasks、传播请求/响应 schema，并补齐 annotation、导出、模型状态和任务接口的测试覆盖。 - 更新项目文档：同步 README、AGENTS、接口契约、需求冻结、设计冻结、前端元素审计、实施计划和测试计划，标明真实功能边界与剩余风险。 - 增加测试覆盖：补充 SAM2/SAM3 传播、SAM3 状态、媒体拆帧参数、Canvas 编辑、语义标签切换、时间轴、工作区传播和 API 合约测试。 - 加强仓库安全边界：将 sam3权重/ 加入 .gitignore，避免本地模型权重被误提交。 验证：npm run test:run；pytest backend/tests；npm run lint；npm run build；python -m py_compile；git diff --check。
502 lines
19 KiB
Python
502 lines
19 KiB
Python
"""SAM 2 engine wrapper with lazy loading and explicit runtime status."""
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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 PyTorch and 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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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). SAM2 will be unavailable.", exc)
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try:
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from sam2.build_sam import build_sam2
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from sam2.build_sam import build_sam2_video_predictor
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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._video_predictor = None
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self._model_loaded = False
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self._video_model_loaded = False
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self._loaded_device: str | None = None
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self._last_error: str | None = None
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self._video_last_error: str | None = None
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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 TORCH_AVAILABLE:
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self._last_error = "PyTorch is not installed."
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logger.warning("PyTorch not available; skipping SAM2 model load.")
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self._model_loaded = True
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return
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if not SAM2_AVAILABLE:
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self._last_error = "sam2 package is not installed."
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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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self._last_error = f"SAM2 checkpoint not found: {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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device = self._best_device()
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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=device,
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)
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self._predictor = SAM2ImagePredictor(model)
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self._model_loaded = True
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self._loaded_device = device
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self._last_error = None
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logger.info("SAM 2 model loaded from %s on %s", settings.sam_model_path, device)
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except Exception as exc: # noqa: BLE001
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self._last_error = str(exc)
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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 _load_video_model(self) -> None:
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"""Load the SAM 2 video predictor on first propagation use."""
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if self._video_model_loaded:
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return
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if not TORCH_AVAILABLE:
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self._video_last_error = "PyTorch is not installed."
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self._video_model_loaded = True
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return
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if not SAM2_AVAILABLE:
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self._video_last_error = "sam2 package is not installed."
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self._video_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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self._video_last_error = f"SAM2 checkpoint not found: {settings.sam_model_path}"
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self._video_model_loaded = True
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return
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try:
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device = self._best_device()
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self._video_predictor = build_sam2_video_predictor(
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settings.sam_model_config,
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settings.sam_model_path,
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device=device,
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)
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self._video_model_loaded = True
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self._loaded_device = device
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self._video_last_error = None
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logger.info("SAM 2 video predictor loaded from %s on %s", settings.sam_model_path, device)
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except Exception as exc: # noqa: BLE001
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self._video_last_error = str(exc)
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self._video_model_loaded = True
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logger.error("Failed to load SAM 2 video predictor: %s", exc)
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def _best_device(self) -> str:
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if TORCH_AVAILABLE and torch is not None and torch.cuda.is_available():
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return "cuda"
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return "cpu"
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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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def _ensure_video_ready(self) -> bool:
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"""Ensure the video predictor is loaded; return whether it is usable."""
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self._load_video_model()
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return SAM2_AVAILABLE and self._video_predictor is not None
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def status(self) -> dict:
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"""Return lightweight, real runtime status without forcing model load."""
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checkpoint_exists = os.path.isfile(settings.sam_model_path)
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device = self._loaded_device or self._best_device()
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available = bool(TORCH_AVAILABLE and SAM2_AVAILABLE and checkpoint_exists)
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if self._predictor is not None:
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message = "SAM 2 model loaded and ready."
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elif available:
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message = "SAM 2 dependencies and checkpoint are present; model will load on first inference."
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else:
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missing = []
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if not TORCH_AVAILABLE:
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missing.append("PyTorch")
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if not SAM2_AVAILABLE:
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missing.append("sam2 package")
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if not checkpoint_exists:
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missing.append("checkpoint")
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message = f"SAM 2 unavailable: missing {', '.join(missing)}."
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if self._last_error and not self._predictor:
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message = self._last_error
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return {
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"id": "sam2",
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"label": "SAM 2",
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"available": available,
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"loaded": self._predictor is not None,
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"device": device,
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"supports": ["point", "box", "interactive", "auto", "propagate"],
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"message": message,
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"package_available": SAM2_AVAILABLE,
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"checkpoint_exists": checkpoint_exists,
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"checkpoint_path": settings.sam_model_path,
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"python_ok": True,
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"torch_ok": TORCH_AVAILABLE,
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"cuda_required": False,
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}
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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_interactive(
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self,
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image: np.ndarray,
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box: list[float] | None,
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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 combined box and point prompt segmentation for refinement."""
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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 = None
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if box:
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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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pts = None
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lbls = None
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if points:
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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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box=bbox,
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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 interactive 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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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,
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direction: str = "forward",
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max_frames: int | None = None,
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) -> list[dict]:
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"""Propagate one seed mask across a prepared frame directory with SAM 2 video."""
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if not self._ensure_video_ready():
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raise RuntimeError(self._video_last_error or self.status()["message"])
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if not frame_paths:
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return []
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if source_frame_index < 0 or source_frame_index >= len(frame_paths):
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raise ValueError("source_frame_index is outside the frame sequence.")
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import cv2
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source_image = cv2.imread(frame_paths[source_frame_index])
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if source_image is None:
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raise RuntimeError("Failed to decode source frame for SAM 2 propagation.")
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height, width = source_image.shape[:2]
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seed_mask = self._polygons_to_mask(seed.get("polygons") or [], width, height)
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if not seed_mask.any():
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bbox = seed.get("bbox")
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if isinstance(bbox, list) and len(bbox) == 4:
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seed_mask = self._bbox_to_mask(bbox, width, height)
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if not seed_mask.any():
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raise ValueError("SAM 2 propagation requires a non-empty seed polygon or bbox.")
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inference_state = self._video_predictor.init_state(
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video_path=os.path.dirname(frame_paths[0]),
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offload_video_to_cpu=True,
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offload_state_to_cpu=True,
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)
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self._video_predictor.add_new_mask(
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inference_state,
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frame_idx=source_frame_index,
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obj_id=1,
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mask=seed_mask,
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)
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results: dict[int, dict] = {}
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def collect(reverse: bool) -> None:
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for out_frame_idx, out_obj_ids, out_mask_logits in self._video_predictor.propagate_in_video(
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inference_state,
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start_frame_idx=source_frame_index,
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max_frame_num_to_track=max_frames,
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reverse=reverse,
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):
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masks = out_mask_logits
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if hasattr(masks, "detach"):
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masks = masks.detach().cpu().numpy()
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masks = np.asarray(masks)
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if masks.ndim == 4:
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masks = masks[:, 0]
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polygons = []
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scores = []
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for mask in masks:
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polygon = self._mask_to_polygon(mask > 0)
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if polygon:
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polygons.append(polygon)
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scores.append(1.0)
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results[int(out_frame_idx)] = {
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"frame_index": int(out_frame_idx),
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"polygons": polygons,
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"scores": scores,
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"object_ids": [int(obj_id) for obj_id in list(out_obj_ids)],
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}
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normalized_direction = direction.lower()
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if normalized_direction in {"forward", "both"}:
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collect(reverse=False)
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if normalized_direction in {"backward", "both"}:
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collect(reverse=True)
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try:
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self._video_predictor.reset_state(inference_state)
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except Exception: # noqa: BLE001
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pass
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return [results[index] for index in sorted(results)]
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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]]:
|
|
"""Convert a binary mask to a normalized polygon."""
|
|
import cv2
|
|
|
|
if mask.dtype != np.uint8:
|
|
mask = (mask > 0).astype(np.uint8)
|
|
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
|
h, w = mask.shape[:2]
|
|
largest = []
|
|
for cnt in contours:
|
|
if len(cnt) > len(largest):
|
|
largest = cnt
|
|
if len(largest) < 3:
|
|
return []
|
|
return [[float(pt[0][0]) / w, float(pt[0][1]) / h] for pt in largest]
|
|
|
|
@staticmethod
|
|
def _dummy_polygons(w: int, h: int) -> list[list[list[float]]]:
|
|
"""Return a dummy rectangle polygon for fallback mode."""
|
|
return [
|
|
[
|
|
[0.25, 0.25],
|
|
[0.75, 0.25],
|
|
[0.75, 0.75],
|
|
[0.25, 0.75],
|
|
]
|
|
]
|
|
|
|
@staticmethod
|
|
def _polygons_to_mask(polygons: list[list[list[float]]], width: int, height: int) -> np.ndarray:
|
|
import cv2
|
|
|
|
mask = np.zeros((height, width), dtype=np.uint8)
|
|
for polygon in polygons:
|
|
if len(polygon) < 3:
|
|
continue
|
|
pts = np.array(
|
|
[
|
|
[
|
|
int(round(min(max(float(x), 0.0), 1.0) * max(width - 1, 1))),
|
|
int(round(min(max(float(y), 0.0), 1.0) * max(height - 1, 1))),
|
|
]
|
|
for x, y in polygon
|
|
],
|
|
dtype=np.int32,
|
|
)
|
|
cv2.fillPoly(mask, [pts], 1)
|
|
return mask.astype(bool)
|
|
|
|
@staticmethod
|
|
def _bbox_to_mask(bbox: list[float], width: int, height: int) -> np.ndarray:
|
|
x, y, w, h = [min(max(float(value), 0.0), 1.0) for value in bbox]
|
|
left = int(round(x * max(width - 1, 1)))
|
|
top = int(round(y * max(height - 1, 1)))
|
|
right = int(round(min(x + w, 1.0) * max(width - 1, 1)))
|
|
bottom = int(round(min(y + h, 1.0) * max(height - 1, 1)))
|
|
mask = np.zeros((height, width), dtype=bool)
|
|
mask[top:max(bottom + 1, top + 1), left:max(right + 1, left + 1)] = True
|
|
return mask
|
|
|
|
|
|
# Singleton instance
|
|
sam_engine = SAM2Engine()
|