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SnapOtter d24e244af4 docs: organize tool pages by modality under /tools/<modality>/

Move all 157 tool pages into image/video/audio/pdf/data subfolders so URLs read /tools/<modality>/<id> (e.g. /tools/image/crop). Nest the image sub-categories under an Image group in the sidebar so the nav reads by modality. Add public/_redirects (301, clean + .html forms) mapping every old flat /tools/<id> URL to its new path so inbound links keep working. Rewrite all internal /tools links. Verified with a clean docs build (no dead links).

2026-06-18 14:12:28 +08:00

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Red Eye Removal

AI-powered detection and correction of red eye caused by camera flash.

API Endpoint

POST /api/v1/tools/image/red-eye-removal

Processing: Asynchronous (returns 202, poll /api/v1/jobs/{jobId}/progress for status via SSE)

Model bundle: face-detection (200-300 MB)

Parameters

Parameter	Type	Required	Default	Description
file	file	Yes	-	Image file (multipart)
sensitivity	number	No	`50`	Red eye detection sensitivity (0-100). Higher values detect more subtle red-eye
strength	number	No	`70`	Correction strength (0-100). How aggressively to neutralize red
format	string	No	-	Output format (optional override)
quality	number	No	`90`	Output quality (1-100)

Example Request

curl -X POST http://localhost:13490/api/v1/tools/image/red-eye-removal \
  -F "file=@flash-photo.jpg" \
  -F 'settings={"sensitivity":60,"strength":80}'

Response

Initial Response (202 Accepted)

{
  "jobId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "async": true
}

Progress (SSE at `/api/v1/jobs/{jobId}/progress`)

event: progress
data: {"phase":"processing","stage":"Detecting red eyes...","percent":40}

Final Result (via SSE)

{
  "phase": "complete",
  "percent": 100,
  "result": {
    "jobId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
    "downloadUrl": "/api/v1/download/{jobId}/flash-photo_redeye_fixed.png",
    "originalSize": 280000,
    "processedSize": 290000,
    "facesDetected": 2,
    "eyesCorrected": 4
  }
}

Notes

Requires the face-detection model bundle to be installed (200-300 MB).
First detects faces, then locates eye regions within each face, and finally identifies and corrects red-eye pixels.
The facesDetected count indicates how many faces were found; eyesCorrected is the total number of individual eyes that had red-eye corrected.
Output is always PNG for maximum quality preservation.
Supports HEIC/HEIF, RAW, TGA, PSD, EXR, and HDR input formats via automatic decoding.

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