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SnapOtter
8532f3227b
Reconcile every tool page's parameters, defaults, and response shape against the tool's Zod settings schema and executionHint in code. Notable fixes: color-palette (add count + format params, hex output, median-cut algorithm), favicon (add 5 params, was documented as having none), qr-generate (add logoDataUri), convert (add ppm/eps/tga formats), video-loudnorm (-16 LUFS not -14), smart-crop (async 202 not sync 200), images-to-video (1080x1080 square), and several output-filename and behavior-note corrections. Also normalize API endpoint paths to /api/v1/tools/<id> (no modality segment) and standardize curl examples on the Docker API port 1349. Verified with a clean docs build.
77 lines
2.2 KiB
Markdown
77 lines
2.2 KiB
Markdown
---
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description: AI-powered detection and correction of red eye caused by camera flash.
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---
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# Red Eye Removal
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AI-powered detection and correction of red eye caused by camera flash.
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## API Endpoint
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`POST /api/v1/tools/red-eye-removal`
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**Processing:** Asynchronous (returns 202, poll `/api/v1/jobs/{jobId}/progress` for status via SSE)
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**Model bundle:** `face-detection` (200-300 MB)
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## Parameters
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| Parameter | Type | Required | Default | Description |
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|-----------|------|----------|---------|-------------|
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| file | file | Yes | - | Image file (multipart) |
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| sensitivity | number | No | `50` | Red eye detection sensitivity (0-100). Higher values detect more subtle red-eye |
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| strength | number | No | `70` | Correction strength (0-100). How aggressively to neutralize red |
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| format | string | No | - | Output format (optional override) |
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| quality | number | No | `90` | Output quality (1-100) |
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## Example Request
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```bash
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curl -X POST http://localhost:1349/api/v1/tools/red-eye-removal \
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-F "file=@flash-photo.jpg" \
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-F 'settings={"sensitivity":60,"strength":80}'
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```
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## Response
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### Initial Response (202 Accepted)
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```json
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{
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"jobId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
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"async": true
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}
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```
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### Progress (SSE at `/api/v1/jobs/{jobId}/progress`)
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```
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event: progress
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data: {"phase":"processing","stage":"Detecting red eyes...","percent":40}
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```
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### Final Result (via SSE)
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```json
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{
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"phase": "complete",
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"percent": 100,
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"result": {
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"jobId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
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"downloadUrl": "/api/v1/download/{jobId}/flash-photo_redeye_fixed.png",
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"originalSize": 280000,
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"processedSize": 290000,
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"facesDetected": 2,
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"eyesCorrected": 4
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}
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}
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```
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## Notes
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- Requires the `face-detection` model bundle to be installed (200-300 MB).
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- First detects faces, then locates eye regions within each face, and finally identifies and corrects red-eye pixels.
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- The `facesDetected` count indicates how many faces were found; `eyesCorrected` is the total number of individual eyes that had red-eye corrected.
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- Output is always PNG for maximum quality preservation.
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- Supports HEIC/HEIF, RAW, TGA, PSD, EXR, and HDR input formats via automatic decoding.
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