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All 181 docs markdown files translated into 20 languages (apps/docs/<locale>/**). Companion to the i18n code PR; admin-merged because the file count exceeds GitHub's per-PR CI trigger limit. Validated by pnpm i18n:check (all surfaces, 0 stale/missing) and a clean all-locale docs build.
80 lines
2.5 KiB
Markdown
80 lines
2.5 KiB
Markdown
---
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description: "Deteksi dan koreksi mata merah bertenaga AI yang disebabkan oleh flash kamera."
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i18n_source_hash: 647c6ff1ef7c
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i18n_provenance: human
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i18n_output_hash: ca509f56b766
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---
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# Red Eye Removal {#red-eye-removal}
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Deteksi dan koreksi mata merah bertenaga AI yang disebabkan oleh flash kamera.
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## API Endpoint {#api-endpoint}
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`POST /api/v1/tools/image/red-eye-removal`
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**Processing:** Asinkron (mengembalikan 202, polling `/api/v1/jobs/{jobId}/progress` untuk status via SSE)
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**Model bundle:** `face-detection` (200-300 MB)
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## Parameters {#parameters}
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| Parameter | Type | Required | Default | Description |
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|-----------|------|----------|---------|-------------|
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| file | file | Yes | - | Berkas gambar (multipart) |
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| sensitivity | number | No | `50` | Sensitivitas deteksi mata merah (0-100). Nilai lebih tinggi mendeteksi mata merah yang lebih halus |
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| strength | number | No | `70` | Kekuatan koreksi (0-100). Seberapa agresif menetralkan warna merah |
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| format | string | No | - | Format output (penimpaan opsional) |
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| quality | number | No | `90` | Kualitas output (1-100) |
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## Example Request {#example-request}
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```bash
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curl -X POST http://localhost:1349/api/v1/tools/image/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 {#response}
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### Initial Response (202 Accepted) {#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`) {#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) {#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 {#notes}
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- Membutuhkan model bundle `face-detection` terpasang (200-300 MB).
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- Pertama mendeteksi wajah, lalu menemukan area mata dalam setiap wajah, dan akhirnya mengidentifikasi serta mengoreksi piksel mata merah.
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- Jumlah `facesDetected` menunjukkan berapa banyak wajah yang ditemukan; `eyesCorrected` adalah jumlah total mata individu yang mata merahnya dikoreksi.
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- Output selalu PNG untuk pelestarian kualitas maksimum.
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- Mendukung format input HEIC/HEIF, RAW, TGA, PSD, EXR, dan HDR via dekode otomatis.
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