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689a9ba283
- 打通工作区真实标注闭环：支持手工多边形、矩形、圆形、点区域和线段生成 mask，并可保存、回显、更新和删除后端 annotation。 - 增强 polygon 编辑器：支持顶点拖动、顶点删除、边中点插入、多 polygon 子区域选择编辑，以及区域合并和区域去除。 - 接入 GT mask 导入：后端支持二值/多类别 mask 拆分、contour 转 polygon、distance transform seed point，前端支持导入、回显和 seed point 拖动编辑。 - 完善导出能力：COCO JSON 导出对齐前端，PNG mask ZIP 同时包含单标注 mask、按 zIndex 融合的 semantic_frame 和 semantic_classes.json。 - 打通异步任务管理：新增任务取消、重试、失败详情接口与 Dashboard 控件，worker 支持取消状态检查并通过 Redis/WebSocket 推送 cancelled 事件。 - 对接 Dashboard 后端数据：概览统计、解析队列和实时流转记录从 FastAPI 聚合接口与 WebSocket 更新。 - 增强 AI 推理参数：前端发送 crop_to_prompt、auto_filter_background 和 min_score，后端支持点/框 prompt 局部裁剪推理、结果回映射和负向点/低分过滤。 - 接入 SAM3 基础设施：新增独立 Python 3.12 sam3 环境安装脚本、外部 worker helper、后端桥接和真实 Python/CUDA/包/HF checkpoint access 状态检测。 - 保留 SAM3 授权边界：当前官方 facebook/sam3 gated 权重未授权时状态接口会返回不可用，不伪装成可推理。 - 增强前端状态管理：新增 mask undo/redo 历史栈、AI 模型选择状态、保存状态 dirty/draft/saved 流转和项目状态归一化。 - 更新前端 API 封装：补充 annotation CRUD、GT mask import、mask ZIP export、task cancel/retry/detail、AI runtime status 和 prediction options。 - 更新 UI 控件：ToolsPalette、AISegmentation、VideoWorkspace 和 CanvasArea 接入真实操作、导入导出、撤销重做、任务控制和模型状态。 - 新增 polygon-clipping 依赖，用于前端区域 union/difference 几何运算。 - 完善后端 schemas/status/progress：补充 AI 模型外部状态字段、任务 cancelled 状态和进度事件 payload。 - 补充测试覆盖：新增后端任务控制、SAM3 桥接、GT mask、导出融合、AI options 测试；补充前端 Canvas、Dashboard、VideoWorkspace、ToolsPalette、API 和 store 测试。 - 更新 README、AGENTS 和 doc 文档：冻结当前需求/设计/测试计划，标注真实功能、剩余 Mock、SAM3 授权边界和后续实施顺序。
490 lines
18 KiB
Python
490 lines
18 KiB
Python
"""AI inference endpoints using selectable SAM runtimes."""
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import logging
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from typing import Any, List
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import cv2
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import numpy as np
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from fastapi import APIRouter, Depends, File, Form, HTTPException, Response, UploadFile, status
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from sqlalchemy.orm import Session
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from database import get_db
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from minio_client import download_file
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from models import Project, Frame, Template, Annotation
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from schemas import (
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AiRuntimeStatus,
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PredictRequest,
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PredictResponse,
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AnnotationOut,
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AnnotationCreate,
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AnnotationUpdate,
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)
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from services.sam_registry import ModelUnavailableError, sam_registry
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/ai", tags=["AI"])
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def _load_frame_image(frame: Frame) -> np.ndarray:
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"""Download a frame from MinIO and decode it to an RGB numpy array."""
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try:
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data = download_file(frame.image_url)
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arr = np.frombuffer(data, dtype=np.uint8)
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img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
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if img is None:
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raise ValueError("OpenCV could not decode image")
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return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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except Exception as exc: # noqa: BLE001
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logger.error("Failed to load frame image: %s", exc)
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raise HTTPException(status_code=500, detail="Failed to load frame image") from exc
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def _normalized_contour(contour: np.ndarray, width: int, height: int) -> list[list[float]]:
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"""Approximate a contour and convert it to normalized polygon coordinates."""
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arc_length = cv2.arcLength(contour, True)
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epsilon = max(1.0, arc_length * 0.01)
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approx = cv2.approxPolyDP(contour, epsilon, True)
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points = approx.reshape(-1, 2)
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if len(points) < 3:
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points = contour.reshape(-1, 2)
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return [
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[
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min(max(float(x) / max(width, 1), 0.0), 1.0),
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min(max(float(y) / max(height, 1), 0.0), 1.0),
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]
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for x, y in points
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]
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def _contour_bbox(contour: np.ndarray, width: int, height: int) -> list[float]:
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x, y, w, h = cv2.boundingRect(contour)
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return [
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min(max(float(x) / max(width, 1), 0.0), 1.0),
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min(max(float(y) / max(height, 1), 0.0), 1.0),
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min(max(float(w) / max(width, 1), 0.0), 1.0),
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min(max(float(h) / max(height, 1), 0.0), 1.0),
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]
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def _component_seed_point(component_mask: np.ndarray, width: int, height: int) -> list[float]:
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"""Reduce a binary component to one positive prompt point using distance transform."""
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dist = cv2.distanceTransform(component_mask.astype(np.uint8), cv2.DIST_L2, 5)
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_, _, _, max_loc = cv2.minMaxLoc(dist)
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x, y = max_loc
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return [
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min(max(float(x) / max(width, 1), 0.0), 1.0),
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min(max(float(y) / max(height, 1), 0.0), 1.0),
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]
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def _clamp01(value: float) -> float:
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return min(max(float(value), 0.0), 1.0)
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def _point_in_polygon(point: list[float], polygon: list[list[float]]) -> bool:
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"""Return whether a normalized point is inside a normalized polygon."""
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if len(polygon) < 3:
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return False
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x, y = point
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inside = False
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j = len(polygon) - 1
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for i, current in enumerate(polygon):
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xi, yi = current
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xj, yj = polygon[j]
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intersects = ((yi > y) != (yj > y)) and (
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x < (xj - xi) * (y - yi) / ((yj - yi) or 1e-9) + xi
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)
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if intersects:
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inside = not inside
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j = i
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return inside
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def _crop_bounds_from_points(points: list[list[float]], margin: float) -> tuple[float, float, float, float]:
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xs = [_clamp01(point[0]) for point in points]
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ys = [_clamp01(point[1]) for point in points]
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x1 = max(0.0, min(xs) - margin)
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y1 = max(0.0, min(ys) - margin)
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x2 = min(1.0, max(xs) + margin)
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y2 = min(1.0, max(ys) + margin)
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if x2 - x1 < 0.05:
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center = (x1 + x2) / 2
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x1 = max(0.0, center - 0.025)
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x2 = min(1.0, center + 0.025)
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if y2 - y1 < 0.05:
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center = (y1 + y2) / 2
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y1 = max(0.0, center - 0.025)
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y2 = min(1.0, center + 0.025)
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return x1, y1, x2, y2
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def _crop_image(image: np.ndarray, bounds: tuple[float, float, float, float]) -> np.ndarray:
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height, width = image.shape[:2]
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x1, y1, x2, y2 = bounds
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left = int(round(x1 * width))
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top = int(round(y1 * height))
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right = max(left + 1, int(round(x2 * width)))
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bottom = max(top + 1, int(round(y2 * height)))
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return image[top:bottom, left:right]
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def _to_crop_point(point: list[float], bounds: tuple[float, float, float, float]) -> list[float]:
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x1, y1, x2, y2 = bounds
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return [
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_clamp01((float(point[0]) - x1) / max(x2 - x1, 1e-9)),
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_clamp01((float(point[1]) - y1) / max(y2 - y1, 1e-9)),
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]
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def _from_crop_polygon(
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polygon: list[list[float]],
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bounds: tuple[float, float, float, float],
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) -> list[list[float]]:
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x1, y1, x2, y2 = bounds
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return [
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[
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_clamp01(x1 + float(point[0]) * (x2 - x1)),
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_clamp01(y1 + float(point[1]) * (y2 - y1)),
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]
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for point in polygon
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]
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def _filter_predictions(
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polygons: list[list[list[float]]],
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scores: list[float],
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options: dict[str, Any],
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negative_points: list[list[float]] | None = None,
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) -> tuple[list[list[list[float]]], list[float]]:
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if not options.get("auto_filter_background"):
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return polygons, scores
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min_score = float(options.get("min_score", 0.0) or 0.0)
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next_polygons: list[list[list[float]]] = []
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next_scores: list[float] = []
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for index, polygon in enumerate(polygons):
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score = scores[index] if index < len(scores) else 0.0
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if score < min_score:
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continue
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if negative_points and any(_point_in_polygon(point, polygon) for point in negative_points):
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continue
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next_polygons.append(polygon)
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next_scores.append(score)
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return next_polygons, next_scores
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@router.post(
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"/predict",
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response_model=PredictResponse,
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summary="Run SAM inference with a prompt",
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)
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def predict(payload: PredictRequest, db: Session = Depends(get_db)) -> dict:
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"""Execute selected SAM segmentation given an image and a prompt.
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- **point**: `prompt_data` is either a list of `[[x, y], ...]` normalized
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coordinates or `{ "points": [[x, y], ...], "labels": [1, 0, ...] }`.
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- **box**: `prompt_data` is `[x1, y1, x2, y2]` normalized coordinates.
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- **semantic**: SAM 3 text prompt when model=`sam3`; SAM 2 falls back to auto.
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"""
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frame = db.query(Frame).filter(Frame.id == payload.image_id).first()
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if not frame:
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raise HTTPException(status_code=404, detail="Frame not found")
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image = _load_frame_image(frame)
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prompt_type = payload.prompt_type.lower()
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options = payload.options or {}
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polygons: List[List[List[float]]] = []
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scores: List[float] = []
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negative_points: list[list[float]] = []
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try:
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if prompt_type == "point":
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point_payload = payload.prompt_data
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if isinstance(point_payload, dict):
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points = point_payload.get("points")
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labels = point_payload.get("labels")
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else:
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points = point_payload
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labels = None
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if not isinstance(points, list) or len(points) == 0:
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raise HTTPException(status_code=400, detail="Invalid point prompt data")
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if not isinstance(labels, list) or len(labels) != len(points):
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labels = [1] * len(points)
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negative_points = [
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point for point, label in zip(points, labels) if label == 0
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]
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inference_image = image
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inference_points = points
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crop_bounds = None
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if options.get("crop_to_prompt"):
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margin = float(options.get("crop_margin", 0.25) or 0.25)
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crop_bounds = _crop_bounds_from_points(points, margin)
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inference_image = _crop_image(image, crop_bounds)
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inference_points = [_to_crop_point(point, crop_bounds) for point in points]
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polygons, scores = sam_registry.predict_points(payload.model, inference_image, inference_points, labels)
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if crop_bounds:
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polygons = [_from_crop_polygon(polygon, crop_bounds) for polygon in polygons]
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elif prompt_type == "box":
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box = payload.prompt_data
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if not isinstance(box, list) or len(box) != 4:
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raise HTTPException(status_code=400, detail="Invalid box prompt data")
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inference_image = image
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inference_box = box
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crop_bounds = None
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if options.get("crop_to_prompt"):
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margin = float(options.get("crop_margin", 0.05) or 0.05)
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crop_bounds = _crop_bounds_from_points([[box[0], box[1]], [box[2], box[3]]], margin)
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inference_image = _crop_image(image, crop_bounds)
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inference_box = [
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*_to_crop_point([box[0], box[1]], crop_bounds),
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*_to_crop_point([box[2], box[3]], crop_bounds),
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]
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polygons, scores = sam_registry.predict_box(payload.model, inference_image, inference_box)
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if crop_bounds:
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polygons = [_from_crop_polygon(polygon, crop_bounds) for polygon in polygons]
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elif prompt_type == "semantic":
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text = payload.prompt_data if isinstance(payload.prompt_data, str) else ""
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polygons, scores = sam_registry.predict_semantic(payload.model, image, text)
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else:
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raise HTTPException(status_code=400, detail=f"Unsupported prompt_type: {prompt_type}")
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except ModelUnavailableError as exc:
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raise HTTPException(status_code=503, detail=str(exc)) from exc
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except NotImplementedError as exc:
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raise HTTPException(status_code=400, detail=str(exc)) from exc
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except ValueError as exc:
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raise HTTPException(status_code=400, detail=str(exc)) from exc
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polygons, scores = _filter_predictions(polygons, scores, options, negative_points)
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return {"polygons": polygons, "scores": scores}
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@router.get(
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"/models/status",
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response_model=AiRuntimeStatus,
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summary="Get SAM model and GPU runtime status",
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)
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def model_status(selected_model: str | None = None) -> dict:
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"""Return real runtime availability for GPU, SAM 2, and SAM 3."""
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try:
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return sam_registry.runtime_status(selected_model)
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except ValueError as exc:
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raise HTTPException(status_code=400, detail=str(exc)) from exc
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@router.post(
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"/auto",
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response_model=PredictResponse,
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summary="Run automatic segmentation",
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)
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def auto_segment(image_id: int, db: Session = Depends(get_db)) -> dict:
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"""Run automatic mask generation on a frame using a grid of point prompts."""
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frame = db.query(Frame).filter(Frame.id == image_id).first()
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if not frame:
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raise HTTPException(status_code=404, detail="Frame not found")
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image = _load_frame_image(frame)
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try:
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polygons, scores = sam_registry.predict_auto(None, image)
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except ModelUnavailableError as exc:
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raise HTTPException(status_code=503, detail=str(exc)) from exc
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return {"polygons": polygons, "scores": scores}
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@router.post(
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"/annotate",
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response_model=AnnotationOut,
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status_code=status.HTTP_201_CREATED,
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summary="Save an AI-generated annotation",
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)
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def save_annotation(
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payload: AnnotationCreate,
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db: Session = Depends(get_db),
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) -> Annotation:
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"""Persist an annotation (mask, points, bbox) into the database."""
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project = db.query(Project).filter(Project.id == payload.project_id).first()
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if not project:
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raise HTTPException(status_code=404, detail="Project not found")
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if payload.frame_id:
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frame = db.query(Frame).filter(Frame.id == payload.frame_id).first()
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if not frame:
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raise HTTPException(status_code=404, detail="Frame not found")
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annotation = Annotation(**payload.model_dump())
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db.add(annotation)
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db.commit()
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db.refresh(annotation)
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logger.info("Saved annotation id=%s project_id=%s", annotation.id, annotation.project_id)
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return annotation
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@router.post(
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"/import-gt-mask",
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response_model=List[AnnotationOut],
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status_code=status.HTTP_201_CREATED,
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summary="Import a GT mask and reduce components to editable point regions",
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)
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async def import_gt_mask(
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project_id: int = Form(...),
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frame_id: int = Form(...),
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template_id: int | None = Form(None),
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label: str = Form("GT Mask"),
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color: str = Form("#22c55e"),
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file: UploadFile = File(...),
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db: Session = Depends(get_db),
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) -> List[Annotation]:
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"""Convert a binary/label mask image into persisted polygon annotations.
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Each connected component becomes one annotation. The `points` field stores a
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positive seed point at the component's distance-transform center, which gives
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the frontend an editable point-region representation instead of a static
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bitmap layer.
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"""
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project = db.query(Project).filter(Project.id == project_id).first()
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if not project:
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raise HTTPException(status_code=404, detail="Project not found")
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frame = db.query(Frame).filter(Frame.id == frame_id, Frame.project_id == project_id).first()
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if not frame:
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raise HTTPException(status_code=404, detail="Frame not found")
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if template_id is not None:
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template = db.query(Template).filter(Template.id == template_id).first()
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if not template:
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raise HTTPException(status_code=404, detail="Template not found")
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data = await file.read()
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image = cv2.imdecode(np.frombuffer(data, dtype=np.uint8), cv2.IMREAD_GRAYSCALE)
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if image is None:
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raise HTTPException(status_code=400, detail="Invalid mask image")
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width = int(frame.width or image.shape[1])
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height = int(frame.height or image.shape[0])
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label_values = [int(value) for value in np.unique(image) if int(value) > 0]
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if not label_values:
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raise HTTPException(status_code=400, detail="No foreground mask regions found")
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has_multiple_labels = len(label_values) > 1
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annotations: list[Annotation] = []
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for label_value in label_values:
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binary = np.where(image == label_value, 255, 0).astype(np.uint8)
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contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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annotation_label = f"{label} {label_value}" if has_multiple_labels else label
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for contour in contours:
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if cv2.contourArea(contour) < 1:
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continue
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polygon = _normalized_contour(contour, image.shape[1], image.shape[0])
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if len(polygon) < 3:
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continue
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component = np.zeros_like(binary, dtype=np.uint8)
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cv2.drawContours(component, [contour], -1, 1, thickness=-1)
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seed_point = _component_seed_point(component, image.shape[1], image.shape[0])
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bbox = _contour_bbox(contour, image.shape[1], image.shape[0])
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annotation = Annotation(
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project_id=project_id,
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frame_id=frame_id,
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template_id=template_id,
|
|
mask_data={
|
|
"polygons": [polygon],
|
|
"label": annotation_label,
|
|
"color": color,
|
|
"source": "gt_mask",
|
|
"gt_label_value": label_value,
|
|
"image_size": {"width": width, "height": height},
|
|
},
|
|
points=[seed_point],
|
|
bbox=bbox,
|
|
)
|
|
db.add(annotation)
|
|
annotations.append(annotation)
|
|
|
|
if not annotations:
|
|
raise HTTPException(status_code=400, detail="No foreground mask regions found")
|
|
|
|
db.commit()
|
|
for annotation in annotations:
|
|
db.refresh(annotation)
|
|
logger.info("Imported %s GT mask annotations for project_id=%s frame_id=%s", len(annotations), project_id, frame_id)
|
|
return annotations
|
|
|
|
|
|
@router.get(
|
|
"/annotations",
|
|
response_model=List[AnnotationOut],
|
|
summary="List saved annotations for a project",
|
|
)
|
|
def list_annotations(
|
|
project_id: int,
|
|
frame_id: int | None = None,
|
|
db: Session = Depends(get_db),
|
|
) -> List[Annotation]:
|
|
"""Return persisted annotations for a project, optionally scoped to one frame."""
|
|
project = db.query(Project).filter(Project.id == project_id).first()
|
|
if not project:
|
|
raise HTTPException(status_code=404, detail="Project not found")
|
|
|
|
query = db.query(Annotation).filter(Annotation.project_id == project_id)
|
|
if frame_id is not None:
|
|
query = query.filter(Annotation.frame_id == frame_id)
|
|
return query.order_by(Annotation.id).all()
|
|
|
|
|
|
@router.patch(
|
|
"/annotations/{annotation_id}",
|
|
response_model=AnnotationOut,
|
|
summary="Update a saved annotation",
|
|
)
|
|
def update_annotation(
|
|
annotation_id: int,
|
|
payload: AnnotationUpdate,
|
|
db: Session = Depends(get_db),
|
|
) -> Annotation:
|
|
"""Update mutable annotation fields persisted in the database."""
|
|
annotation = db.query(Annotation).filter(Annotation.id == annotation_id).first()
|
|
if not annotation:
|
|
raise HTTPException(status_code=404, detail="Annotation not found")
|
|
|
|
updates = payload.model_dump(exclude_unset=True)
|
|
if "template_id" in updates and updates["template_id"] is not None:
|
|
template = db.query(Template).filter(Template.id == updates["template_id"]).first()
|
|
if not template:
|
|
raise HTTPException(status_code=404, detail="Template not found")
|
|
|
|
for field, value in updates.items():
|
|
setattr(annotation, field, value)
|
|
|
|
db.commit()
|
|
db.refresh(annotation)
|
|
logger.info("Updated annotation id=%s", annotation.id)
|
|
return annotation
|
|
|
|
|
|
@router.delete(
|
|
"/annotations/{annotation_id}",
|
|
status_code=status.HTTP_204_NO_CONTENT,
|
|
summary="Delete a saved annotation",
|
|
)
|
|
def delete_annotation(
|
|
annotation_id: int,
|
|
db: Session = Depends(get_db),
|
|
) -> Response:
|
|
"""Delete an annotation and its derived mask rows through ORM cascade."""
|
|
annotation = db.query(Annotation).filter(Annotation.id == annotation_id).first()
|
|
if not annotation:
|
|
raise HTTPException(status_code=404, detail="Annotation not found")
|
|
|
|
db.delete(annotation)
|
|
db.commit()
|
|
logger.info("Deleted annotation id=%s", annotation_id)
|
|
return Response(status_code=status.HTTP_204_NO_CONTENT)
|