Digikam recognize faces how to#
Digikam recognize faces code#
Digikam recognize faces plus#

The Bash command passes in the image name pic.jpg as a parameter to face-box.py the photo shows yours truly in the hellishly hot desert of Arizona ( Figure 1). The Bash command changes to that directory and calls face-box.py in the container where the face recognition tool is installed. This binds the current directory (where both the Python script and the test image are located) to the /build directory in the container. To call the script in Listing 1 for face recognition in the container, the user copies it to the examples directory of the GitHub repository's clone and calls docker run -v `pwd`:/build -it face bash -c 'cd /build python3 face-box.py pic.jpg' A coffee break would be a good thing while this is going. In the project directory loads everything off the web, compiles dlib, and also loads a 100MB-heavy neural model that is already trained about faces into the container. The docker file in the top directory handles the installation of the dependent projects. This git command creates a clone of the GitHub repository in a local directory.

Hosted on GitHub, Adam Geitgey's face_recognition project uses the popular dlib library to recognize faces in photos and videos. Instead, the network is trained with millions of different faces during the learning phase and then recognizes everything that looks remotely similar with a small remaining margin of error. They do not act on fixed pixel values, which wouldn't work, because even two photos of the same person differ enormously at the pixel level. Neural networks are excellent for this purpose. Assured After TrainingĪnything that could be a face needs to be extracted from the background noise of the image. digikam.Figure 2: The face-box.py script identified a face in the photo and framed it.Ī good face recognition program not only recognizes full-screen faces on portrait photos, but also faces that only cover a few hundred pixels due to the subjects being further away or warped because they are looking into the camera from an angle. We give execution permissions with: sudo chmod +x digikam.appimageĪnd they can run the installer by double clicking or from the terminal with.

Digikam recognize faces how to#

How to install digiKam 7.2.0 on Ubuntu and derivatives?įor those interested in being able to install this new version of digiKam 7.2.0 on your system They will be able to do it quite simply.įor this we are only going to download its installer using some of the commands that we share with you below What we are going to do is open a terminal and type: wget -O digikam.appimage The ability to save location information to metadata has been added and support for GPX files has been improved.įinally, if you are interested in knowing more about it about the new version released you can check the details In the following link. Work has been done to improve the stability and usability of the tool to rename a group of files in batch mode. The tools for database maintenance have been expanded.Improved support for integration with the semantic search engine and with MySQL / MariaDB.Improved collection scan speed during startup.

Digikam recognize faces code#

The code has been optimized to work with the database and the storage schemes used for search, metadata storage, facial recognition and operation of various tools.

Builds for macOS have been significantly improved.

A utility has been added to check for updates to binary assemblies with the ability to download and install them automatically.

Improved tool for importing photos from cameras, added support for automatic naming to albums and renaming during upload.

Digikam recognize faces plus#

Added support for CR3, RAF, and DNG formats, plus added support for new camera models, including iPhone 12 Max / Max Pro, Canon EOS R5, EOS R6, EOS 850D, EOS-1D X Mark III, FujiFilm X -S10, Nikon Z 5, Z 6 II, Z 7 II, Olympus E -M10 Mark IV, Sony ILCE-7C (A7C) and ILCE-7SM3 (A7S III). The internal engine for processing RAW images has been updated to version libraw 0.21.0. In this new version face detection engine and red-eye removal tool use new machine learning model (Yolo) to better define faces in images with complex angles.ĭata processing speed has also been increased and the possibility of parallelizing operations has been implemented, they were removed from the base distribution files with data from the machine learning model, which are now loaded at runtime and the graphical interface has been redesigned to work with faces and link tags to them, as well as related widgets.Īnother change that stands out is that the photo album management process has been improvedinformation grouping capabilities have been expanded, the mask output filtering engine has been sped up, the display of properties has been optimized, and support for removable media has been improved.

2 How to install digiKam 7.2.0 on Ubuntu and derivatives?.