video.scene_detect

Operation: video.scene_detect
Category: video
Tags: video, analysis, scene

Detect scene boundaries in a video

Runtime

Type: Native (built-in)
Timeout: 300s
Retries: 3 (ExponentialWithJitter)

Inputs

Name	Type	Required	Default	Description
video_path	String	Yes	—	Path to video file

Outputs

Name	Type	Description
scenes	JSON	Array of {index, start, end} scene objects
scene_count	Number	Number of scenes detected
duration_secs	Number	Total video duration

Default Configuration

{
  "min_scene_len": 1.0,
  "threshold": 0.3
}

Usage

TypeScript

import { WorkflowBuilder } from "@fabric-platform/sdk";

const workflow = new WorkflowBuilder("my-workflow")
  .node("video-scene-detect", "tool", (n) =>
    n.config({
      operation: "video.scene_detect",
      // ... node-specific config
    })
  )
  .build();

Python

from fabric_platform import FabricClient

fabric = FabricClient(api_key="fab_xxx")

wf_id = fabric.upsert_workflow("my-workflow", nodes=[
    {
        "key": "video-scene-detect",
        "kind": "tool",
    },
])

Rust

use fabric_sdk::FabricClient;

let client = FabricClient::new("http://localhost:3001", api_key)?;

let wf_id = client.upsert_workflow("my-workflow", serde_json::json!({
    "nodes": [{
        "key": "video-scene-detect",
        "kind": "tool"
    }]
})).await?;

curl

curl -X POST http://localhost:3001/v1/workflow-definitions \
  -H "Authorization: Bearer $FABRIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my-workflow",
    "nodes": [{
      "key": "video-scene-detect",
      "kind": "tool"
    }]
  }'