OpenViSTAI · Agents · Blockchain · Crypto
Menu
Image & Video AI

Imagededup

Imagededup is an open-source Python package for finding exact and near-duplicate images in a collection using image-hashing algorithms or convolutional neural networks.

Independent OpenViST project profile, based on linked sources.

Imagededup — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a Python workflow needs to identify repeated or visually similar images in a directory and provide results for review or evaluation.

Who it is for

  • Python developers building image-processing or computer-vision workflows.
  • Teams reviewing repeated or near-duplicate product images and other collection assets.

Documented uses

  • Find exact duplicates in an image directory using hashing.
  • Identify near-duplicate images with hash or CNN methods.
  • Generate image encodings and plot matches for a selected image.
  • Evaluate deduplication effectiveness against a ground-truth mapping.
Before you choose

Requirements & limits

The repository is public and the package is distributed under the Apache 2.0 license. The README documents PyPI and source installation.

  • Choose among four hashing methods and CNN-based detection according to the image-matching task.
  • CNN methods support prepackaged models or a custom model.
  • The evaluation framework requires a ground-truth mapping to assess deduplication effectiveness.
  • The README cautions that benchmark conclusions are valid only through v0.2.2 because later releases changed the methods.
  • The documented quick start generates encodings from a directory and returns a duplicate mapping; it does not describe automatic file deletion.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Find and plot image matches with PHash

Generate perceptual-hash encodings for an image directory, identify duplicates and plot matches for a selected file.

Before you start

  • Python 3.9 or later.
  • An image directory available to the Python workflow.
  • Imagededup installed from PyPI or the GitHub source.

Steps

  1. Install the package with the command below, or clone the repository and install it with the command below.

    Documented command
    pip install imagededup
    Documented command
    pip install .
  2. Import PHash from imagededup.methods and create a hasher.

  3. Call encode_images with the image directory, then pass the resulting encoding map to find_duplicates.

  4. Use plot_duplicates with the directory, duplicate map and a filename to plot matches for that file.

Expected outcome

A duplicate mapping for the directory and a plot of matches associated with the selected image.

Guide limitations

  • This recipe uses PHash; the README also offers other hash methods and CNN-based approaches.
  • The guide describes finding and plotting matches, not deleting or otherwise changing image files.
Evidence and transparency

Sources & review

Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
idealo/imagededup · Official homepage title · retained in its source language