Video & Foreground

Workflow

1. Open or reference input video.
2. Call remove background operation.
3. Receive foreground output and metadata.
4. Pass output to composition or download pipeline.

Node.js Example

import { RemoveBGVideoClient } from 'removebgvideo-node';

const client = new RemoveBGVideoClient(process.env.REMOVEBGVIDEO_API_KEY!);

const created = await client.createJob({
  video_url: 'https://cdn.example.com/input.mp4',
  model: 'original',
  output_format: 'webm',
  bg_type: 'transparent',
  auto_start: true,
});

const result = await client.waitForCompletion(created.id);
console.log(result.output_url);

Python Example

from removebgvideo import RemoveBGVideoClient

client = RemoveBGVideoClient(api_key='YOUR_API_KEY')

job = client.create_job(
    video_url='https://cdn.example.com/input.mp4',
    model='human',
    bg_type='transparent',
    output_format='webm',
    auto_start=True
)

result = client.wait_for_completion(job['id'])
print(result['output_url'])

Recommended SDK Usage Pattern

1. Initialize singleton client per service process.
2. Wrap SDK calls with your own timeout/retry decorators.
3. Map SDK/domain errors into internal error taxonomy.
4. Capture job_id and business context in your tracing system.

SDK Hardening Checklist

• Add per-call timeout defaults and circuit breaking.
• Track SDK version in logs for post-incident debugging.
• Test SDK upgrade in staging with archived production payloads.
• Implement fallback strategy by model and output format.

Media Input Governance

• Validate source metadata before calling remove background.
• Reject unsupported codecs early in pipeline.
• Keep canonical input URL/file references for reproducibility.