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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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description

description
Detect duplicate and near-duplicate images using perceptual hashing.

Find Duplicates

Upload multiple images to detect duplicates and near-duplicates using perceptual hashing (dHash). Groups similar images together, identifies the best quality version in each group, and calculates potential space savings.

API Endpoint

POST /api/v1/tools/image/find-duplicates

Accepts multipart form data with multiple image files and an optional JSON settings field.

Parameters

Parameter	Type	Required	Default	Description
threshold	number	No	`8`	Maximum Hamming distance to consider images as duplicates (0 to 20). Lower = stricter matching

File Fields

Upload at least 2 image files in the multipart request (all using the file field name or any field name for file parts).

Example Request

curl -X POST http://localhost:1349/api/v1/tools/image/find-duplicates \
  -H "Authorization: Bearer si_your-api-key" \
  -F "file=@photo1.jpg" \
  -F "file=@photo2.jpg" \
  -F "file=@photo3.jpg" \
  -F "file=@photo4.jpg" \
  -F 'settings={"threshold": 8}'

Example Response

{
  "totalImages": 4,
  "duplicateGroups": [
    {
      "groupId": 1,
      "files": [
        {
          "filename": "photo1.jpg",
          "similarity": 100,
          "width": 4032,
          "height": 3024,
          "fileSize": 2450000,
          "format": "jpeg",
          "isBest": true,
          "thumbnail": "data:image/jpeg;base64,/9j/..."
        },
        {
          "filename": "photo2.jpg",
          "similarity": 96.88,
          "width": 1920,
          "height": 1440,
          "fileSize": 850000,
          "format": "jpeg",
          "isBest": false,
          "thumbnail": "data:image/jpeg;base64,/9j/..."
        }
      ]
    }
  ],
  "uniqueImages": 2,
  "spaceSaveable": 850000,
  "skippedFiles": []
}

Response Fields

Field	Type	Description
totalImages	number	Number of images successfully analyzed
duplicateGroups	array	Groups of duplicate images
uniqueImages	number	Number of images not part of any duplicate group
spaceSaveable	number	Total bytes that could be saved by removing non-best duplicates
skippedFiles	array	Files that could not be processed (with filename and reason)

Duplicate Group Object

Field	Type	Description
groupId	number	Group identifier
files	array	Images in this duplicate group

File Object (within a group)

Field	Type	Description
filename	string	Original filename
similarity	number	Similarity percentage to the reference image (first in group)
width	number	Image width in pixels
height	number	Image height in pixels
fileSize	number	File size in bytes
format	string	Image format
isBest	boolean	Whether this is the highest quality version (most pixels, largest file)
thumbnail	string or null	Base64 JPEG thumbnail (200px wide) for preview

Notes

Uses a 128-bit dHash (64-bit row + 64-bit column) for perceptual similarity detection. This catches duplicates even across resizes, recompression, and minor edits.
The threshold represents maximum Hamming distance between hashes. Default of 8 catches near-duplicates while avoiding false positives. Use 0 for pixel-identical only, or 15-20 for very loose matching.
The "best" image in each group is the one with the most pixels (width x height), with file size as a tiebreaker.
At least 2 images are required. Files that fail validation or decoding are reported in skippedFiles rather than causing the entire request to fail.
Thumbnails are 200px-wide JPEG previews encoded as data URIs.
All common formats are supported (HEIC, RAW, PSD, SVG decoded automatically).

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