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DigiKam
DigiKam
DigiKam
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digiKam
DeveloperKDE
Stable release
8.8.0[1] Edit this on Wikidata / 19 October 2025
Repositoryhttps://invent.kde.org/graphics/digikam.git
Written inC++ (Qt)
Operating systemLinux, Windows, macOS[2]
Type
LicenseGPL-2.0-or-later
Websitewww.digikam.org

digiKam is a free and open-source image organizer and tag editor written in C++ using the KDE Frameworks.

Features

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digiKam runs on most known desktop environments and window managers, as long as the required libraries are installed. It supports all major image file formats, such as JPEG and PNG as well as over 200 raw image formats[3] and can organize collections of photographs in directory-based albums, or dynamic albums by date, timeline, or by tags. Users can also add captions and ratings to their images, search through them and save searches for later use. Using plug-ins, users can export albums to various online services including (among others) 23hq, Facebook, Flickr, Gallery2, Google Earth's KML files, Yandex.Fotki, MediaWiki, Rajce, SmugMug, Piwigo, Simpleviewer, Picasa Web Albums. Plug-ins are also available to enable burning photos to a CD and the creation of web galleries.

digiKam provides functions for organizing, previewing, downloading and/or deleting images from digital cameras. Basic auto-transformations can also be deployed on the fly during picture downloading. In addition, digiKam offers image enhancement tools through its KIPI (KDE Image Plugins Interface) framework and its own plugins, like red-eye removal, color management, image filters, or special effects. digiKam was the only free photo management application on Linux that could handle 16 bit/channel images, until RawTherapee version 4.0 was released in 2011, using a new 32 bits/channel engine for all internal image processing.[4] Digital Asset Management is the mainstay of digiKam.

digiKam relies on libraries such as exiv2, allowing it to edit XMP metadata embedded in images or separately as sidecar files.[5] It also supports DNG format reading and writing. Marble is also integrated for editing and viewing of geolocations in images.

digiKam also efficiently caches image thumbnails and paths in a database,[6] in the PGF format,[7] allowing for quick overviews. There are various database backends to choose from, with scalability and portability considerations taken into account.[8] This database file is independent of photo libraries, enabling remote paths, multiple roots and offline backups.

As a non-modal image editor; digiKam's UI also supports live search boxes in both sidebars and the main window.

History

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digiKam has been in development since before 2006.

As of version 0.9 features include a GPS locator[9] and synchronization,[10] iPod Photo upload support,[11] an advanced metadata editor,[12] better support for raw image formats (using dcraw included in digiKam), full color management, a light-table,[13] pan-tool in Image Editor and Preview mode, improvements in usability, and many new plugins.

digiKam 2.0 was released in July 2011, sporting a number of new features, including:

  • Face detection and recognition support
  • XMP sidecar support
  • Reverse geocoding
  • Image versioning
  • Pick Labels & Color Labels support to improve photographic workflow
  • Many new RAW decoding features

In August 2018 a beta version of DigiKam 6.0 was published. Improvements comprise support for video files used as photos, as well as new RAW and export options.[14]

Face detection and recognition

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Starting with version 2.0, digiKam has introduced face recognition allowing you to automatically identify photos of certain people and tag them. DigiKam's photo manager was the first free project to feature similar functionality, with face recognition previously implemented only in proprietary products such as Google Picasa, Apple's Photos, and Windows Live Photo Gallery.

Face recognition was implemented in version 2.0 through the libface library, and from version 3.3 it is based on OpenTLD project work. Version 7.0.0-beta1 uses the Deep Neural Network module from the OpenCV library.

Other OS

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  • Windows: Install digiKam via KDE Installer with digiKam-msvc.
  • There is an official port on macOS that can be compiled from raw or using MacPorts.
  • Most other Unix-like OSes are also supported.[15]

Awards received

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digiKam has been awarded the TUX 2005, 2008, and 2010 Readers' Choice Award in the category Favorite Digital Photo Management Tool.[16][17][18]

See also

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References

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

DigiKam

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from Grokipedia
digiKam is a free and open-source digital photo management application designed for importing, organizing, editing, and sharing photographs and videos across large collections. It runs on , Windows, and macOS, providing a comprehensive framework for photographers to manage assets without dependencies. Licensed under the version 2 or later, digiKam emphasizes user control over data and metadata, supporting formats like RAW files through integrated libraries such as LibRaw and Exiv2. Developed by the project, digiKam's first version was released on Christmas Day 2001 as an alternative to commercial photo organizers, marking the beginning of over two decades of continuous improvement. The software has undergone regular updates, with the latest stable release, version 8.8.0, arriving in October 2025, introducing enhancements in performance, color management, and image processing efficiency. As part of the ecosystem, it leverages cross-platform frameworks to ensure compatibility and extensibility, with contributions from a global open-source community. Key features include album-based organization with tags, ratings, and labels for efficient cataloging; advanced search capabilities using metadata, geolocation, duplicates detection, and dates; and AI-powered tools for automatic face recognition, , and metadata tagging to streamline workflows. Editing functionalities encompass non-destructive adjustments like , noise reduction, lens corrections via Lensfun, and for high-volume tasks, while export options support web galleries, slideshows, and integration. digiKam's database architecture handles collections exceeding 100,000 images robustly, storing thumbnails, metadata, and search indexes locally to maintain and speed.

Overview

Description

digiKam is a free and open-source and , serving as a comprehensive digital photo management application for importing, organizing, tagging, and editing digital photos and videos. It enables users to efficiently handle large collections of media files, providing tools for metadata management and basic image enhancement within a unified interface. Developed in C++ using the Frameworks, digiKam leverages established open-source libraries to ensure robust performance and extensibility. The software supports a broad array of file formats, including and for compressed and lossless images, as well as over 1,000 RAW formats through integration with the LibRaw library for high-fidelity processing of camera-specific data. At its core, digiKam employs a database backend that defaults to for lightweight local operations but supports for scalable, remote-access setups suitable for extensive libraries. Thumbnails are cached in the wavelet-compressed PGF format to optimize storage and loading speeds while maintaining image quality. digiKam is cross-platform, with native support for , Windows, and macOS, allowing seamless deployment across diverse computing environments.

Licensing and Development

digiKam is released under the GNU General Public License version 2.0 or later (GPL-2.0-or-later), which permits users to freely use, modify, and distribute the software while ensuring that derivative works remain open source. The project is primarily developed by the KDE Graphics team, with contributions from an international community of volunteers who submit code, bug reports, and translations through the KDE Invent GitLab platform at invent.kde.org/graphics/digikam. The source code repository is hosted on KDE's Git infrastructure, facilitating collaborative development and version control. digiKam uses as its primary build system, enabling cross-platform compilation on , Windows, and macOS through provided bootstrap scripts that handle dependencies and configuration. Development is community-driven, relying on volunteer efforts without commercial backing, though it occasionally receives grants from e.V. to support events like coding sprints. As of November 2025, the current stable version is 8.8.0, released on October 19, 2025, which includes enhancements in performance and image processing tools. digiKam integrates with to maintain consistent user interface elements across applications.

History

Origins and Early Development

digiKam was initiated in late 2001 by developers within the project as an open-source response to the growing popularity of digital cameras, which highlighted the lack of accessible tools for photo import and organization on platforms. At the time, while command-line libraries like GPhoto2 existed for camera connectivity, there was a notable gap in graphical user interfaces to streamline workflows for amateur and professional photographers. The inaugural release, version 0.1.0, arrived on December 24, 2001, functioning primarily as a basic frontend for GPhoto2 to facilitate photo downloading and simple viewing. Early alpha and beta versions through the early prioritized core functionalities like hierarchical album creation for photo organization and seamless integration with the desktop environment, allowing users to embed photo management within their daily workflows. These releases laid the groundwork for database-driven storage of image collections, enabling efficient browsing and basic tagging without relying on filesystem structures alone. Development efforts were driven by KDE contributors aiming to fill the void in free software alternatives to proprietary photo managers, though initial implementations were constrained by the era's limited open-source libraries for metadata extraction and manipulation. Version 0.9.0, released on December 18, 2006, represented a pivotal early advancement after over a year of intensive work beginning in November 2005, introducing support for RAW image decoding to handle unprocessed files from digital SLRs. This milestone also added comprehensive GPS data integration for photos and synchronization features, alongside initial export via a dedicated KIPI plugin for sharing images to portable devices. These enhancements addressed key user needs for advanced metadata handling, despite ongoing dependencies on emerging libraries like Exiv2, which posed integration challenges in the pre-2006 landscape.

Major Version Milestones

digiKam's major version milestones from 2011 onward reflect a progression toward advanced photo capabilities, incorporating AI-driven features and enhanced support while maintaining open-source principles. Version 2.0.0, released on July 31, 2011, marked a significant upgrade by introducing and recognition powered by the , enabling users to automatically identify and tag faces in images. This release also added support for XMP files to handle metadata non-destructively and improved for tagging, marking, and versioning images, addressing 236 reported bugs. In version 3.3.0, released on August 6, 2013, digiKam implemented a new core framework for face management, completing the face recognition feature for the first time through contributions from projects. This update enhanced interoperability with tools like for face tags and retained robust while fixing numerous stability issues. Version 6.0.0, launched on February 10, 2019, expanded digiKam's scope to include full video file management, treating videos similarly to photos via FFmpeg for metadata extraction, thumbnail generation, and playback. It upgraded RAW decoding with Libraw 0.19, supporting over 1,000 camera formats, and introduced new export plugins for services like , , and , integrated across all user interfaces with authentication. The 7.0.0 release on July 19, 2020, integrated Deep Neural Networks (DNN) through for face recognition, achieving up to 97% detection accuracy for faces—including non-human ones—and supporting multi-core processing with minimal training data. This version emphasized non-destructive editing by adding HEIF format support with metadata handling via libheif and XMP sidecars, alongside Libraw 0.20 for over 40 additional RAW formats like Canon CR3. The 8.x series, spanning 2023 to 2025, has focused on refinement and AI enhancements, with version 8.8.0 released on October 19, 2025, introducing tag hierarchy import/export in format, automatic monitor color profile application across platforms, and camera preview improvements like focus point visualization for and Olympus models using . This release follows a bi-annual cycle, with the next planned for early 2026 to incorporate Qt 6.10 and further AI tools. Overall, digiKam's evolution demonstrates a shift from foundational organization tools to sophisticated AI-assisted features, such as advanced face recognition and video integration, alongside improved multi-platform stability and non-destructive workflows.

Core Features

Photo Organization and Management

digiKam's album system organizes photos and videos into hierarchical collections and subfolders, mirroring the structure on disks, , or network shares to facilitate intuitive by categories like year, event, or location. Users can sort albums by folder layout, date, or custom categories, with options to invert the order for flexible viewing. Virtual albums complement this by enabling dynamic grouping of items based on attributes such as tags or geolocation, without modifying the underlying physical folders. Tagging in digiKam supports a hierarchical , allowing users to create nested categories—for example, "Animals > Birds > Eagles"—for precise and scalable of media. Ratings employ a 0-5 star scale to assess image quality or priority, enabling quick sorting and filtering, while color labels (such as red, yellow, or green) mark stages like rejection, pending review, or acceptance. These labels and ratings can be assigned individually or in batches and combined with operators for refined organization. The application's search capabilities provide advanced filtering by date ranges, locations (via album names or GPS), tags, captions, and ratings, with support for logic and grouped conditions to handle complex queries efficiently. A timeline view offers chronological browsing through an adjustable of image counts by time units (day, week, month, or year), where users can select and save date-based ranges for repeated access. Quick search complements this with simple text-based lookups across metadata fields. Import workflows streamline acquisition from cameras or devices using USB or gPhoto2 protocols, automatically reading , maker notes, and GPS metadata to inform naming and album placement. To prevent duplicates, the tool identifies new images with star markers and logs prior downloads in its database, skipping already-imported files while allowing customizable renaming rules based on capture dates or themes. These features integrate with metadata tools to enhance subsequent search and organization. Users can also manually scan for new items added to collections outside of direct imports; for a full collection scan, select Tools > Scan for New Items from the main menu, which detects new image files in defined collections. For specific albums, right-click the album in the Albums view and select Refresh to scan only that album without regenerating thumbnails for existing files if desired. Batch operations via the Batch Queue Manager enable simultaneous renaming, rating, or tagging of multiple files, applying metadata-driven rules to process large collections efficiently. For instance, users can customize filename patterns using date, sequence numbers, or tags, then queue the actions for non-destructive execution across selected items. This supports scalable management without individual file handling. digiKam supports comprehensive metadata processing through the Exiv2 library as its default backend, enabling the reading and writing of , IPTC, and XMP data embedded within supported image formats such as , TIFF, , and RAW files. This functionality extends to handling descriptive elements like captions and titles, which can be stored across these standards, as well as keywords and tags primarily managed via IPTC and XMP schemas. For enhanced compatibility, particularly with proprietary formats, users may configure as an alternative library to perform these operations. Geotagging in digiKam leverages integration with the widget, a virtual globe component, to provide interactive map views for assigning geographic coordinates to images. Users can drag images onto maps rendered with layers or offline data, editing GPS fields such as latitude, longitude, altitude, and timestamp directly within the interface. Reverse geocoding further enriches this process by querying public services to convert coordinates into hierarchical tags, such as country, region, and city names, which are then stored in or XMP metadata. The search engine in digiKam facilitates efficient retrieval through full-text querying across metadata fields, including captions, keywords, author information, and file properties. This allows users to enter terms like "birthday" to scan the entire database for matches in textual data, with advanced filters supporting date ranges, tag hierarchies, and ratings via operators (, NAND, NOR). While exact matching predominates for tags and dates, the system incorporates fuzzy thresholds for similarity-based duplicate detection, aiding in broader organizational workflows alongside album structures. Database management ensures metadata integrity via the Metadata Synchronizer tool, which reconciles changes between embedded file and the internal or database. Automatic scanning occurs during initial collection setup and new item detection, hashing files to identify updates without full re-parsing, while maintenance scans, including manual options for scanning new items (see Photo Organization and Management), propagate metadata alterations across albums. To preserve original files, digiKam supports XMP sidecar files for non-destructive storage of edits like ratings, pick labels, and face tags, configurable to read from or write to these alongside embedded . Metadata export options prioritize flexibility for backups and sharing, allowing users to embed updates directly into image files via the "Write Metadata to Files" command or generate XMP sidecars as standalone records. The Metadata View enables saving detailed reports of , IPTC, and XMP content as text files for archival purposes, while batch tools apply templates to synchronize and export descriptive information across queues without altering originals.

Editing and Enhancement Tools

Built-in Editors

digiKam includes a suite of built-in tools designed for basic adjustments and corrections, emphasizing an efficient for photographers. These tools operate within the application's image editor, which supports 16-bit per channel for high-quality processing and integrates seamlessly with the photo management system. The editor allows users to apply changes non-destructively by enabling versioning in the settings, where modifications create new image versions while preserving the original files intact. Among the core transform tools, digiKam provides options for cropping, resizing, rotating, and perspective correction to refine image composition. The tool enables manual selection of areas to trim, with auto-crop for removing uniform borders and aspect ratio presets for standard formats like 2:3 or 5:7. Resizing uses to scale images while minimizing quality loss, with adjustable detail preservation and smoothing parameters. supports 90-degree increments, flipping, and free rotation for arbitrary angles, while the Perspective Adjustment tool remaps geometry by dragging corner points to correct distortions, displaying real-time angle and dimension previews. For enhancement, digiKam offers and filters to improve image clarity. The tool employs wavelet-based filters to target and from high-ISO shots or artifacts, with sliders for threshold and softness adjustments, plus an auto-estimate feature for quick setup. is handled via the Unsharp for edge contrast enhancement and the Refocus tool using kernels (circular or Gaussian) to restore detail, both with previewable parameters like , amount, and threshold to avoid over-sharpening. Color management in digiKam relies on the Little CMS (LCMS2) engine for accurate handling, ensuring consistent color rendering across workflows. Tools for white balance adjustment include sliders (e.g., 5500K for daylight) and presets like or flash, often applied during RAW import to correct casts without clipping in 16-bit mode. Exposure corrections use auto-exposure algorithms based on histograms, while the Curves tool allows precise tonal adjustments via draggable control points on or individual RGB channels, supporting smooth or for natural results. Non-destructive editing is achieved through the versioning system, which creates new image versions with applied modifications while preserving the original files intact, allowing reversion without . For pixel-level changes, the versioning system generates duplicates with edit histories. Batch editing capabilities are integrated through the Queue Manager, where users can queue multiple images for uniform application of tools like , resize, or color , processing them in parallel while supporting custom workflows and output formats. RAW processing is handled via an integrated decoder based on LibRaw, providing a darkroom-style interface for with options for white balance, during import, and 16-bit output directly into the editor for further non-destructive development. digiKam also supports integration with external RAW processing tools such as and for more advanced non-destructive editing, provided they are installed separately. For more advanced effects, these built-in tools can be extended via plugins.

Plugins and Extensions

digiKam employs an extensible architecture through its DPlugins system, which allows users to customize and enhance the application's functionality across various components such as the album view, image editor, light table, and batch queue manager. Introduced in version 6.1.0, DPlugins replaced the earlier KIPI (KDE Image Plugin Interface) framework, which had been used since digiKam's origins to share image processing plugins among applications like Gwenview and KPhotoAlbum. The KIPI framework enabled additions such as red-eye removal, panorama stitching, and HDR merging by providing a common for third-party developers. Under DPlugins, which leverages a pure Qt for better portability, plugins are dynamically loaded and can be enabled or disabled via the Settings → Configure digiKam → Plugins page, where users can view details by double-clicking entries. The application supports over 50 plugins categorized into generic tools (shared across views), image editor tools, batch queue manager modules for processing, and specialized loaders for formats like RAW files. Generic plugins include utilities for , creation, geolocation , and metadata adjustment, while image editor plugins offer effects such as blur and emboss through integrations like G'MIC-Qt (providing over 600 filters), as well as calibration tools including lens distortion correction via Lensfun and . Batch queue manager plugins facilitate operations like watermarking multiple images in workflows. These plugins are bundled with digiKam releases and support dynamic loading without restarting the application, with compatibility for third-party contributions developed using the DPlugin interface. Examples of plugin usage include batch watermarking for protecting image copyrights during export preparations and launching external editors like for advanced manipulations beyond built-in capabilities. Plugins are maintained in tandem with core digiKam updates, with the G'MIC-Qt update to version 3.6.0 in digiKam 8.8.0, enhancing image processing options.

Platform Support

Linux Integration

digiKam is primarily developed as a application, offering seamless integration within the KDE Plasma desktop environment. It leverages for its and functionality, ensuring native support for Plasma's workflows, such as metadata synchronization via the file indexing system. This allows digiKam's managed photo collections to appear in Plasma's search results and integrate with system-wide tagging. The application provides tight coupling with KDE's Dolphin file manager, enabling users to browse and interact with photo albums directly from while utilizing digiKam's database for enhanced metadata views and searches. Similarly, it shares compatibility with Gwenview, KDE's default , through common plugin architectures like KIPI for extended import and export operations. On non-KDE desktops like , digiKam maintains a minimal footprint by relying on core Qt libraries, though it requires installation of select KDE dependencies for full feature parity. digiKam is readily available through official repositories across major distributions, including Ubuntu's universe repository, Fedora's main packages, and openSUSE's software collection, facilitating straightforward installation via package managers like apt, dnf, and zypper. For enhanced portability across distributions, the project offers a universal bundle, which bundles all dependencies into a single executable file requiring no system-wide installation. In terms of hardware support, digiKam employs the LibRaw library for native decoding of RAW files from major camera manufacturers, including Canon's CR2 and CR3 formats as well as Nikon's NEF files, enabling direct import and non-destructive editing without proprietary software. GPU acceleration is supported through for compute-intensive tasks such as , image quality analysis, and auto-tagging, with fallback to software rendering for compatibility on varied graphics hardware. Performance optimizations make digiKam suitable for managing extensive photo libraries exceeding 100,000 images, with multi-threaded processing for generation, database scanning, and batch operations to leverage modern multi-core CPUs effectively. Background scanning at startup ensures efficient updates to large collections without blocking the , though performance may degrade on traditional HDDs compared to SSDs for very large datasets.

Windows Port

The Windows port of digiKam provides a native application experience on platforms, leveraging pre-compiled binaries and build tools adapted for the ecosystem. Installation is primarily achieved through official self-contained installers available from the digiKam page, which include all necessary components without requiring additional infrastructure. These installers, built using the KDE Craft system, support both installable (system-wide, requiring admin rights) and standalone (portable, user-specific) modes, ensuring compatibility with and later 64-bit versions. Alternative installation methods include using Windows Initiative binaries, such as the digiKam-msvc packages compiled with Microsoft Visual C++ (MSVC) for optimal performance on Windows. For users preferring custom builds, MSYS2 can be employed to compile digiKam using toolchains, or manual compilation can be performed directly with MSVC via scripts and for dependency management. These approaches allow integration with the latest development snapshots but require familiarity with Windows development environments. digiKam has offered full feature support on Windows since version 5.x, including core photo management, editing tools, and metadata handling, with adaptations for the file system to manage permissions and support large image libraries efficiently. The application handles NTFS-specific attributes, such as long path names (enabled in Windows 10 version 1607 and later), enabling seamless operation with extensive collections without the native Unix optimizations found on . Dependencies, including Qt 6 and ported KDE Frameworks, are bundled via the build system, which facilitates cross-platform consistency while addressing Windows-specific runtime requirements like and support. Known issues in the Windows port may include occasional crashes related to plugins, particularly during face recognition or third-party extensions, often resolvable by updating to the latest release or disabling problematic modules. Initial database setup can be slower compared to Linux due to NTFS file traversal and indexing overhead, though this improves with subsequent scans and hardware acceleration via OpenCL. Updates remain aligned with Linux releases, with version 8.8.0 available through the official installer as of October 2025, incorporating Qt 6.10 enhancements for better Windows 11 compatibility.

macOS Compatibility

digiKam provides robust support for macOS, offering multiple installation options tailored to user preferences. Users can install the application via the official self-contained DMG package available from the digiKam website, which includes all necessary dependencies and supports macOS 11.3 (Big Sur) or later for Apple Silicon and macOS 10.15 (Catalina) or later for Intel-based systems. Alternatively, it can be installed through package managers such as MacPorts using the command sudo port install digikam, or via Homebrew with brew install --cask digikam, though the latter is marked for deprecation in future updates. These methods ensure compatibility with both Intel and Apple Silicon (ARM) architectures, with native ARM support introduced in version 8.5.0, while earlier versions from 7.x onward run on Apple Silicon via Rosetta 2 translation. The application integrates seamlessly with macOS ecosystem features, including direct imports from the Apple .app library once permissions are granted through > Privacy & Security > Photos. digiKam handles metadata storage efficiently on the APFS file system, embedding , IPTC, and XMP data directly into image files without compatibility issues, leveraging its cross-platform database for non-destructive organization. This allows users to manage photos from devices or macOS Photos alongside local collections, preserving geolocation and edit histories. Key features on macOS include comprehensive RAW processing for formats from Apple-compatible cameras, such as DNG files from iPhone ProRAW and HEIF/HEIC from recent iOS models, powered by the libraw library for accurate decoding. digiKam supports macOS Dark Mode through Qt framework integration, automatically adapting the interface theme to system preferences for a native look (available since version 7.2.0). Additionally, it requires user approval for Automation, Accessibility, and Full Disk Access in Privacy & Security settings to enable advanced functionalities like batch processing and external drive scanning. Despite its strengths, digiKam on macOS has some limitations, particularly in older versions where certain dependencies may necessitate installing for X11 graphics support, though modern bundles from 8.x onward are self-contained and avoid this requirement. Occasional UI scaling challenges occur on displays, addressable via the application's high DPI scaling options in Miscellaneous Settings, which adjust coordinates based on display factors to prevent blurring or misalignment. Recent updates have enhanced macOS-specific stability; for instance, the 8.8.0 release in October 2025 includes fixes for color profile handling, ensuring accurate monitor calibration and application during editing workflows.

Advanced Capabilities

Face Detection and Recognition

digiKam's and recognition capabilities have evolved significantly since their introduction. In released in 2011, basic was implemented using the libface library, enabling users to locate and tag faces in photographs for the first time. By version 3.3 in 2013, the system incorporated OpenTLD for improved face tracking across images, enhancing the ability to follow faces in sequences or varied poses. The major advancement came with version 7.0 in 2020, which integrated deep neural networks (DNN) via the library, achieving recognition accuracy up to 95% through advanced classifiers that outperform earlier methods like Eigenfaces and LBPH. Subsequent versions have further improved these features. Version 8.5.0 (November 2024) introduced the YuNet DNN model for and the SFace model for recognition, enhancing accuracy and speed. In version 8.6.0 (March 2025), the face management framework was completely rewritten, incorporating face image quality assessment (FIQA) and achieving 25-50% better performance through optimized classifiers and full CPU utilization. Version 8.7.0 (June 2025) added background face recognition triggered by new tag assignments. The latest version 8.8.0 (October 2025) includes bug fixes to improve stability in face recognition tasks. The workflow begins with automatic scanning of photo albums to detect faces, initiated through the Maintenance Tool or the People view sidebar. Detected faces are presented as thumbnails for manual tagging by users, who assign names to build a personalized database of face profiles. This database stores fingerprints and training data, allowing the system to match and suggest identities for newly detected faces in future scans, with options to rebuild fingerprints if tags are modified. Key features include automatic grouping of similar faces into clusters for easier review and identification, reducing manual effort in large collections. Privacy controls allow users to exclude specific albums or collections from scanning, preventing processing of sensitive images, while confirmed tags can be ignored or rejected without affecting the overall model. Performance is optimized through multi-core processing during detection and recognition tasks, enabling efficient handling of large libraries on modern hardware. The models are trainable, adapting to user corrections over time to improve accuracy for specific individuals, though initial training requires sufficient tagged examples. Limitations include the necessity for adequate training data to achieve reliable results, as undertrained models may produce false positives or misses. Recognition is less effective on low-quality images, such as those with poor , heavy occlusion, or extreme angles, where detection rates drop significantly. These features integrate with metadata handling to enable searches by person tags across albums.

Geolocation and Export Options

digiKam provides robust geolocation tools integrated with the widget, enabling users to visualize and edit GPS coordinates on interactive s. The Geolocation Editor, accessible via Image → Geolocation (Ctrl+Shift+G), displays a using data or offline maps in projections such as Spherical or Mercator, allowing precise assignment of coordinates to image metadata stored as tags. Users can manually adjust locations via drag-and-drop markers or search for addresses using integrated services like . The application's timeline view integrates with these maps by overlaying geolocated images chronologically on a , facilitating review of photo journeys over time. For batch processing, the GPS Correlator tool imports GPX tracks from GPS devices, correlating timestamps between tracks and multiple images to automatically assign coordinates. Reverse geocoding further enhances this by retrieving place names from coordinates for tagging, using public APIs. Export options in digiKam emphasize seamless sharing while preserving metadata integrity. Plugins support batch uploads to platforms including , Piwigo, and , where users authenticate once and select albums or create new ones, with options to apply digiKam keywords as tags and resize images prior to transfer. Metadata such as , IPTC, and XMP is retained during these exports unless explicitly stripped, ensuring comprehensive data transfer. For web sharing, the HTML Gallery tool generates static, responsive web pages from albums, embedding thumbnails, full-size views, and metadata like captions and locations in HTML5-compliant formats. Advanced export capabilities extend to generation via the Panorama Creator tool, which stitches horizontal, vertical, or matrix shots into high-resolution composites exportable as new images. KML export further allows sharing geolocated thumbnails and positions with tools like . Privacy is addressed through configurable options to strip sensitive data before export; for instance, the Export to Local Storage tool includes a checkbox to remove all , IPTC, and XMP metadata, including GPS coordinates, from copies while leaving originals intact. Similar metadata controls are available in social media plugins to prevent unintended location disclosure.

Recognition

Awards

digiKam has received several notable awards from Linux and open-source communities, particularly in the mid-2000s and early , recognizing its excellence as a digital photo management tool. In 2005, it won the TUX Readers' Choice Award for Favorite Digital Photo Management Tool, selected by readers of TUX Magazine. This accolade highlighted digiKam's early strengths in organizing and editing photos within the ecosystem. The software continued to earn recognition in subsequent years. In 2008, digiKam secured the TUX Readers' Choice Award again in the same category, with 24.9% of votes from Journal readers, edging out competitors like . By 2010, it claimed the Journal Readers' Choice Award for Best Digital Photo Management Tool, maintaining its lead in a competitive field that included ongoing rivalry with . These victories underscored digiKam's growing reputation for robust features like metadata handling and among users. In 2015, digiKam received the Readers' Choice Award for Best Photo Management Application at the Media Awards during , winning by a significant margin over entries like GIMP and based on votes from thousands of Computec magazine and website readers. This honor celebrated its 14th anniversary and affirmed its enduring value in open-source workflows. These awards significantly boosted digiKam's visibility and adoption within early communities, encouraging contributions and user growth during a period when open-source photo tools were gaining traction. No major formal awards have been documented since , aligning with digiKam's evolution toward specialized professional applications in rather than broad consumer recognition.

Community and Impact

DigiKam's development is driven by a global community within the project, where volunteers contribute through code, documentation, translations, and testing. The core team, coordinated via the KDE infrastructure, encourages participation via the official bug tracker on bugs.kde.org for reporting issues and feature requests, as well as the KDE Invent platform for submitting pull requests to the source code repository. Users can also provide sample image files to improve metadata handling and RAW support, while translators localize the application into over 60 languages using tools like Weblate. Support is facilitated through a dedicated for discussions and the KDE Community Forums, fostering ongoing user-developer interaction despite the forums' archival status in recent years. The application's impact lies in its role as a leading free alternative to proprietary photo management software, enabling local, privacy-focused organization of large image libraries without cloud dependencies or subscriptions. Adopted by photographers and enthusiasts worldwide, digiKam handles advanced tasks like , face recognition, and geolocation tagging across , Windows, and macOS, influencing the broader open-source ecosystem by integrating with tools such as LibRaw for RAW decoding and Exiv2 for metadata. Its regular updates—such as version 8.6 in March 2025, which enhanced face management and color labeling—demonstrate sustained maintenance, with over 360 bug fixes in some releases alone, ensuring reliability for professional workflows. DigiKam's contributions extend to educational and archival contexts, where its open standards support, including DNG and files, promotes long-term data preservation in the open-source community. By providing extensible plugins and APIs, it has inspired integrations in projects like PhotoPrism for facial data import, amplifying its reach in self-hosted photo solutions. It underscores its reputation for robustness and accessibility in democratizing advanced photo management.

References

  1. https://www.digikam.org/about/
  2. https://snapcraft.io/digikam
  3. https://www.digikam.org/news/2021-12-11_digikam_20_anniversary/
  4. https://www.digikam.org/news/2025-10-19-8.8.0_release_announcement/
  5. https://invent.kde.org/graphics/digikam
  6. https://www.digikam.org/about/overview/
  7. https://docs.digikam.org/en/getting_started/database_intro.html
  8. https://apps.kde.org/digikam/
  9. https://docs.digikam.org/en/supported_materials/image_formats.html
  10. https://docs.digikam.org/en/batch_queue/raw_converter.html
  11. https://docs.digikam.org/en/setup_application/database_settings.html
  12. https://docs.digikam.org/en/maintenance_tools/maintenance_thumbnails.html
  13. https://www.digikam.org/download/
  14. https://packages.fedoraproject.org/pkgs/digikam/digikam/
  15. https://repology.org/project/digikam/packages
  16. https://www.digikam.org/download/git/
  17. https://ev.kde.org/reports/ev-2023/
  18. https://www.digikam.org/news/2010-05-31_kde-imaging_coding_sprint_2010/
  19. https://www.digikam.org/documentation/changelog/
  20. https://www.digikam.org/news/2006-12-18_it_s_a_masterpiece_digikam_digikamimageplugins_0.9.0_major_release/
  21. https://www.digikam.org/news/2006-12-04_kipi-plugins_0.1.3-beta1_released/
  22. https://www.digikam.org/news/2011-07-31_digikam_software_collection_2.0.0_is_out/
  23. https://www.digikam.org/news/2013-08-06_digikam_software_collection_3.3.0_released/
  24. https://www.digikam.org/news/2019-02-10-6.0.0_release_announcement/
  25. https://www.digikam.org/news/2020-07-19-7.0.0_release_announcement/
  26. https://docs.digikam.org/en/left_sidebar/albums_view.html
  27. https://docs.digikam.org/en/left_sidebar/tags_view.html
  28. https://docs.digikam.org/en/left_sidebar/labels_view.html
  29. https://docs.digikam.org/en/left_sidebar/search_view.html
  30. https://docs.digikam.org/en/left_sidebar/timeline_view.html
  31. https://docs.digikam.org/en/import_tools/camera_import.html
  32. https://docs.digikam.org/en/asset_management/organize_find.html
  33. https://docs.digikam.org/en/maintenance_tools/maintenance_newitems.html
  34. https://docs.digikam.org/en/batch_queue/metadata_tools.html
  35. https://docs.digikam.org/en/main_window/image_view.html
  36. https://docs.digikam.org/en/batch_queue/batchqueue_overview.html
  37. https://docs.digikam.org/en/setup_application/metadata_settings.html
  38. https://docs.digikam.org/en/right_sidebar/metadata_view.html
  39. https://docs.digikam.org/en/geolocation_editor/geoeditor_map.html
  40. https://docs.digikam.org/en/setup_application/miscs_settings.html
  41. https://docs.digikam.org/en/maintenance_tools/maintenance_metadata.html
  42. https://docs.digikam.org/en/setup_application/editor_settings.html
  43. https://www.digikam.org/news/2010-04-30_gsoc_non-destructive_image_editing_for_digikam/
  44. https://docs.digikam.org/en/image_editor/transform_tools.html
  45. https://docs.digikam.org/en/image_editor/enhancement_tools.html
  46. https://docs.digikam.org/en/image_editor/colors_tools.html
  47. https://www.digikam.org/news/2005-11-21_dimg_framework_supports_icc_profiles/
  48. https://www.digikam.org/news/2010-06-17_gsoc_explaining_digikam_s_non-destructive_editing_concept/
  49. https://docs.digikam.org/en/batch_queue.html
  50. https://userbase.kde.org/Digikam/Basic_RAW_Processing/en
  51. https://docs.digikam.org/en/image_editor.html
  52. https://www.digikam.org/news/2019-04-14-6.1.0_release_announcement/
  53. https://www.digikam.org/news/2011-01-25_kipi-plugins_1.8.0_released/
  54. https://docs.digikam.org/en/setup_application/plugins_settings.html
  55. https://www.digikam.org/about/features/
  56. https://github.com/cgilles/digikam-plugins-demo
  57. https://docs.kde.org/stable5/en/digikam/digikam/
  58. https://software.opensuse.org/package/digikam
  59. https://www.digikam.org/download/binary/
  60. https://docs.digikam.org/en/color_management/camera_profiles.html
  61. https://www.digikam.org/news/2009-01-23_digikam_0.10.0-rc1_release_for_kde4/
  62. https://www.digikam.org/news/2024-11-16-8.5.0_release_announcement/
  63. https://docs.digikam.org/en/getting_started/installation.html
  64. https://github.com/cgilles/digikam-vcpkg-windows
  65. https://userbase.kde.org/Digikam/Windows
  66. https://www.digikam.org/news/2023-12-03-8.2.0_release_announcement/
  67. https://develop.kde.org/docs/getting-started/building/craft/
  68. https://ports.macports.org/port/digikam/
  69. https://formulae.brew.sh/cask/digikam
  70. https://www.digikam.org/news/2021-03-22-7.2.0_release_announcement/
  71. https://www.digikam.org/news/2011-04-11_new_features_in_digikam_2.0_face_recognition/
  72. https://libface.sourceforge.net/
  73. https://www.digikam.org/news/2019-12-22-7.0.0-beta1_release_announcement/
  74. https://www.digikam.org/news/2025-03-15-8.6.0_release_announcement/
  75. https://www.digikam.org/news/2025-06-30-8.7.0_release_announcement/
  76. https://docs.digikam.org/en/maintenance_tools/maintenance_faces.html
  77. https://docs.digikam.org/en/left_sidebar/people_view.html
  78. https://community.kde.org/Digikam/GSoC2019/AIFaceRecognition
  79. https://docs.digikam.org/en/right_sidebar/maps_view.html
  80. https://userbase.kde.org/Digikam/Geotagging
  81. https://docs.digikam.org/en/export_tools/flickr_export.html
  82. https://docs.digikam.org/en/export_tools/piwigo_export.html
  83. https://docs.digikam.org/en/export_tools/smugmug_export.html
  84. https://docs.digikam.org/en/post_processing/html_gallery.html
  85. https://docs.digikam.org/en/post_processing/pano_creator.html
  86. https://docs.digikam.org/en/export_tools/local_export.html
  87. https://www.digikam.org/news/2005-09-01_digikam_wins_tux_2005_readers_choice_award/
  88. https://kde.org/community/awards/
  89. https://www.linuxjournal.com/article/10065
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  93. https://www.digikam.org/
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  95. https://www.digikam.org/support/
  96. https://www.dpreview.com/news/3788652829/digikam-free-open-source-raw-photo-management-macos-windows-and-linux-updated
  97. https://www.xda-developers.com/digikam-best-free-open-source-local-photos-manager/
  98. https://9to5linux.com/digikam-8-6-released-with-many-face-management-updates-and-improvements
  99. https://docs.digikam.org/en/asset_management/data_protection.html
  100. https://github.com/photoprism/photoprism/issues/747
  101. https://slashdot.org/software/p/digiKam/
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