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| """AI Annotation — 声明式模块:标记工作流启用 AI 辅助标注。 | |
| 卡片本身**不参与自动执行**(透传上游,秒级完成);它的作用是: | |
| 1. 声明该项目的工作流启用了 AI 辅助标注 → Review 页显示 | |
| "✨ AI Annotate" 按钮(与"手套显示由工作流驱动"同哲学); | |
| 2. 批次处理完成后后台异步跑一次 AI 标注(不阻塞管线,失败可见); | |
| 3. 节点配置(mode/min_confidence)作为该项目的 AI 标注默认参数。 | |
| 真正的切段 + VLM 标注逻辑在 app/ai_annotation.py(独立服务模块, | |
| 不进 DAG),产物写入 state/annotations/<批次>.json 的已确认段 | |
| (status="confirmed"),直接进入导出。 | |
| """ | |
| from app.processing import ProcessingModule, JobContext, ArtifactRef, field | |
| from app.processing.registry import register | |
| from app.processing.theme import ANNOTATION_COLOR | |
| class AIAnnotationModule(ProcessingModule): | |
| slug = "ai_annotation" | |
| version = "1.0" | |
| category = "process" | |
| label = "AI Annotation" | |
| icon = "ant-design:robot-outlined" | |
| color = ANNOTATION_COLOR | |
| # Keep the persisted ``data`` handle for old workflows, but make the | |
| # semantic contract explicit: AI annotation consumes RGB video. | |
| inputs = ({"key": "data", "label": "RGB Video"},) | |
| outputs = ({"key": "annotation", "label": "Annotation"},) | |
| default_config = {"mode": "signal_vlm", | |
| "min_confidence": 0.7, "prompt_language": "zh", | |
| "max_segments": 50, | |
| # VLM 供应商(local = 本地 vLLM;api = 云端厂商接口。 | |
| # 两模式严格分离互不回退,api 字段见下) | |
| "vlm_provider": "local", | |
| "api_vendor": "kimi", | |
| "api_model": "kimi-k3", | |
| "api_key": "", | |
| "api_base_url": ""} | |
| config_schema = ( | |
| field("mode", "select", "Mode", "signal_vlm", | |
| options=["signal_only", "signal_vlm", "vlm_only"]), | |
| field("min_confidence", "number", "Min confidence", 0.7, | |
| min=0, max=1, step=0.05), | |
| field("prompt_language", "select", "Label language", "zh", | |
| options=["zh", "en"]), | |
| # VLM 供应商(provider=api 时由卡片头部 ⚙ 设置弹窗编辑) | |
| field("vlm_provider", "select", "VLM provider", "local", | |
| options=["local", "api"]), | |
| field("api_vendor", "select", "API vendor", "kimi", | |
| options=["kimi", "qwen", "siliconflow"]), | |
| field("api_model", "string", "API model", "kimi-k3"), | |
| field("api_key", "string", | |
| "API key (enter in the frontend node settings)", ""), | |
| field("api_base_url", "string", | |
| "API base URL (blank = vendor default)", ""), | |
| # P2 切段参数(信号去抖 / 最小段长 / 段数上限) | |
| field("debounce_sec", "number", "Debounce (s)", 2.0, | |
| min=0.5, max=10, step=0.5), | |
| field("min_seg_sec", "number", "Min segment (s)", 0.8, | |
| min=0.2, max=30, step=0.2), | |
| field("max_segments", "number", "Max segments (long video)", 50, | |
| min=1, max=50, step=1), | |
| ) | |
| execution_target = "server" | |
| capabilities = ("ai_annotation",) | |
| def run(self, ctx: JobContext) -> dict[str, ArtifactRef] | None: | |
| # The actual VLM job is scheduled separately; this node only marks | |
| # the video input and passes the artifact through. Sensor/keypoint | |
| # artifacts must not be accepted as the AI annotation source. | |
| if not any(ref.kind == "video" for ref in ctx.incoming.values()): | |
| ctx.skip("No RGB video input — skipped") | |
| return dict(ctx.incoming) | |