A simple GUI for Rebol3

Rebol3, developed by Oldes (https://github.com/Oldes/Rebol3), is improving day by day. Unfortunately, Rebol3 does not yet have a VID (except for Windows as a proof of concept), which means that attractive interfaces cannot yet be created. 

Fortunately, Oldes added a nice modules management to Rebol3, which allows this problem to be overcome. 

In this code, we use two modules:

Blend2D (https://github.com/Siskin-framework/Rebol-Blend2D) 

OpenCV (https://github.com/Oldes/Rebol-OpenCV) 

The idea is as follows: we use the Blend2D module to create the interface for our application and the OpenCV module to display the result. 

With Blend2D module, I created five buttons (simple blend2D boxes). Each button is associated with a Rebol3 function that will execute when the button is clicked. Button 1 calls the loadImage function, which allows you to select an image and display it. Buttons 2 and 3 call the convertTo function, which displays the image in grayscale or in HSV color space. Button 4 displays the original image and button 5 exits the application.

Now, the question is how to associate mouse movements with different buttons. Blend2D does not have a mouse manager, which is where the OpenCV module comes in.

Oldes has introduced a mouse event handler into the OpenCV module for these events:

• MOUSEMOVE
• LBUTTONDOWN
• RBUTTONDOWN
• MBUTTONDOWN
• LBUTTONUP
• RBUTTONUP
• MBUTTONUP
• LBUTTONDBLCLK
• RBUTTONDBLCLK
• MBUTTONDBLCLK
• MOUSEWHEEL
• MOUSEHWHEEL

With the cv/setMouseCallback function, we can retrieve the event type, the x and y position of the mouse, and the event flag. We also obtain the mouse position as a pair! x y.

;--OpenCV mouse callback in context

ctx: context [

    on-mouse-click: func [

        type  [integer!]

        x     [integer!]

        y     [integer!]

        flags [integer!]

    ][

    pos: mcb/pos ;--mouse position as a pair!

        if type == cv/EVENT_LBUTTONDOWN [

        if pos/y < 20 [

        case [

        all [pos > 0x0 pos < 80x20]   [loadImage]         ;--button 1

        all [pos > 80x0 pos < 160x20] [if isLoaded? [convertTo/GS fileName]]        ;--button 2

        all [pos > 160x0 pos < 240x20][if isLoaded? [convertTo/HSV fileName]]    ;--button 3

        all [pos > 240x0 pos < 320x20][showSource]    ;--button 4

        all [pos > 320x0 pos < 400x20][quit] ;--button 5

        ]

        ]

        ]

    ]

]

In the on-mouse-click function, we first check that the mouse is within the button area. Then we check that the mouse is on the selected button. If so, we execute the function associated with the button. In this example button 3 is clicked and convertTo function is executed.

Added new faces for Rebol3 GUI

CheckBox

RadioBox

Horizontal Scroller

Toggle On

Progress

The code is here: https://github.com/ldci/R3_tests/tree/main/GUI

Virginia Project: A first paper from R2P2 Lab

https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2025.1636667/full

First, the Virginia software, written with Red language, isolated neonatal anatomy through PyTorch-based PointRend segmentation combined with morphological filtering.

Second, radiometric decoding via ExifTool and ImageMagick extracted pixel-level temperature values mapped to anatomical regions of interest (chest, extremities). Finally, quantitative thermal metrics were derived, including median body surface, temperature and spatial thermal variability (interquartile range).

A key advantage of this automated pipeline is its low operator dependence; once the image is acquired, the entire segmentation and feature extraction process is software-driven, minimizing human interpretation bias.

BlurHash with r3

 

What is BlurHash? (from https://uploadcare.com/blog/blurhash-images/ and https://github.com/woltapp/blurhash )

BlurHash is a lightweight way to represent a blurred version of an image that was invented by the Wolt team. It is represented by a short piece of text that when decoded can produce a low quality version of an image which can be shown to the user while the actual image is being loaded.

The idea behind BlurHash is to design a placeholder that is close to the original image but has a smaller size. This makes it possible to send the placeholder to the client side of your application, which reduces the time that the user spends waiting for the page to load.

Oldes wrote an extension for Rebol 3 that makes it very easy to use BlurHash.

See https://github.com/Siskin-framework/Rebol-BlurHash

#!/usr/local/bin/r3
Rebol [
    title: "Rebol/BlurHash test"
]
blurhash: import 'blurhash
cv:         import 'opencv

image: load %../pictures/test1.tiff ;--use your own image
print ["Encoding image of size" as-yellow image/size]
hash: blurhash/encode image
print ["String: " hash]
print ["Decoding hash into image"]
blured: resize blurhash/decode hash 32x32 image/size

;--use OpenCV extension for visualisation
with cv [
print "Source Image"
imshow/name image "Source"
waitkey 0
print "Blured Image"
imshow/name blured "Blured"
waitkey 0
print "Any key to close"
]

Thanks to Oldes!😀

SEDFormer

Exciting news! Our paper "SEDformer: Path Signatures and Transformers to Predict Newborns Movement Symmetry" has been accepted at the International Joint Workshop of Artificial Intelligence for Healthcare (HC@AIxIA) and HYbrid Models for Coupling Deductive and Inductive ReAsoning (HYDRA), ECAI 2025.

This research introduces SEDformer, a FEDformer variant that integrates path signatures to enhance newborn movement symmetry prediction - a crucial step toward automated early screening tools for motor development disorders.

Thank you to the entire team for this interdisciplinary collaboration between applied mathematics, AI, and pediatric medicine.

 Rambaud, P., Rimmel, A., Trabelsi, I., Zini, J., Wodecki, A., Motte Signoret, E., Jouen, F., Tomasik, J., Bergounioux, J. (2025). SEDformer: Path Signatures and Transformers to Predict Newborns Movement Symmetry. International Joint Workshop of Artificial Intelligence for Healthcare (HC@AIxIA) and HYbrid Models for Coupling Deductive and Inductive ReAsoning (HYDRA)" ECAI 2025.

Nonlinear acoustic phenomena tune the adults’ facial thermal response to baby cries with the cry amplitude envelope

A new article on the use of thermography: https://royalsocietypublishing.org/doi/10.1098/rsif.2025.0150.

I am very proud of this innovative article. The first thermal image processing codes used Red and Rebol-3.

Yolo CDD

Our latest article using YOLO is here: https://ieeexplore.ieee.org/document/11078264.

Using FLIR cameras for research

The IR cameras from FLIR (https://www.flir.fr) are little marvels of technology that can acquire quality IR images. What I like about FLIR is that the data format remains the same regardless of the camera used. For my work in neonatal medicine, I use either the C3 model (basic) or the 650SC model (much more expensive and more powerful).

FLIR generates four types of image. The first is the IR image, whose resolution varies from 320x240 to 640x480 pixels, depending on the camera model. The second is an RGB image, up to six times larger than the IR image. The third image, which can be the same size as the IR image or smaller (80x60 pixels).  It contains temperatures in degrees Celsius. Finally, the last image is the color palette of the IR image. So you can imagine all the calculations that have to be made to obtain comparable images. You'll find various toolkits in Python, MatLab, R... that allow you to process these different images. Unfortunately, these libraries are not universal and often depend on other libraries that are not easy to install.

That's why, as part of the Virginia project (https://uniter2p2.fr/projets/), I designed an easy-to-use FLIR image processing module for the Red and Rebol 3 languages.

THE FLIR MODULE

This module has been tested with various FLIR cameras. Its main function is to decode the metadata contained in a radiometric file and extract the visible image (RGB), the infrared image (IR), the color palette associated with the IR image and the temperatures associated with each pixel.

This module calls on two external programs that are installed by default on macOS and Linux.

ExifTool (https://exiftool.org), written and maintained by Phil Harvey, is a fabulous program written in Perl that lets you read and write the metadata of a large number of computer files. ExifTool supports FLIR files. It runs on all MacOs, Linux and Windows platforms.

ImageMagick (https://imagemagick.org/index.php) is an open-source software package comprising a library and a set of command-line utilities for creating, converting, modifying and displaying images in a wide range of formats. The FLIR module essentially uses the magick utility for MacOs and Linux versions. For Windows, use a portable version that supports 16-bit images (https://imagemagick.org/archive/binaries/ImageMagick-7.1.0-60-portable-Q16-x64.zip) and the magick command.

The module calls for:

rcvGetFlirMetaData: This function takes the name of the FLIR file as a parameter (in the form of a character string). It returns all the information in the patient's irtmp/exif.red file in a format that can be directly processed by Red or Rebol 3.

rcvGetVisibleImage: This function extracts the RGB image from the FLIR file and saves it in the irtmp/rgb.jpg file.

rcvGetFlirPalette: Extracts the color palette contained in the FLIR file and samples it for a linear range of values [0..255]. The extracted image is saved as irtmp/palette.png.

rcvMakeRedPalette: Exports the color palette as a block for fast processing with Red or Rebol 3.

rcvGetFlirRawData: Extracts raw temperature data (in 16-bit format) into the irtmp/rawimg.png file.

rcvGetPlanckValues: Retrieves all constants required for accurate temperature calculations.

rcvGetImageTemperatures: This function uses the previous two functions to calculate the temperature of each image pixel as an integer value. It creates the image tmp/celsius.pgm. This is a 16-bit image with a maximum value of 65535. It's a simple text file containing the image size and the 16-bit values of each pixel.

rcvGetTemperatureAsBlock : The temperatures contained in the irtmp/celsius.pgm image are returned as a real value (e.g. 37.2) in the block passed as a parameter to the function. This is a dynamic calculation.

WHY IMAGES ALIGNMENT IS FUNDAMENTAL?

The neural networks we use to identify babies' bodies have not been trained on thermal images, which are difficult to process, but work very well with RGB images. Once the baby's body is correctly identified in the RGB image, we can use the resulting body mask to retrieve the temperatures in the thermal image.  Obviously, we can't use the RGB image directly, but the RGB image aligned with the thermal image. 

In previous versions of Virginia, I wrote a rather complicated algorithm for aligning thermal and IR images. Studying the code, I found that it was possible to make it simpler. There are three values that will help us: Real2IR, offsetX and offsetY, which come from the rcvGetFlirMetaData function. Real2IR allows us to calculate the ratio between the RGB image and the thermal image.  OffsetX and offsetY are the X and Y offset coordinates to be applied to find the origins of the ROI in the RGB image. If these values are equal to 0, alignment is not required.

The result is perfect!

The code for Rebol 3 is here:

https://github.com/ldci/FLIR

The code for Red is here:

https://github.com/ldci/redCV/blob/master/samples/image_thermal/Flir/align.red