YOLO and Redbol

YOLO (You Only Look Once) is a wonderful tool for object detection and image segmentation (https://docs.ultralytics.com/).

Of course, this works very well with Python.  You'll find here a very clear documentation (https://pyimagesearch.com/2025/01/13/getting-started-with-yolo11/). 

But YOLO also offers a CLI mode that can be used with Rebol3 or Red.

With Rebol3 we use the opencv module made by Oldes.  The result with articulations detection in around 1 sec!

And for Red: just Red language functions.

#! /usr/local/bin/red-view

Red [

    Needs: View

    Author: "ldci"

]

appDir: system/options/path
change-dir appDir
imageFile: ""
iSize: 320x240
margins: 5x5
isFile?: false
tasks: ["segment" "pose" "detect"] ;--("obb" "classify" not yet)
modes: ["predict" "track" ];--("benchmark"  "train"  "export" "val" not yet)
models: ["yolo11n-seg.pt" "yolo11n-pose.pt" "yolo11n.pt" "yolov9c-seg.pt" "yolov8n-seg.pt"]
source: ""
task: tasks/1
mode: modes/1
model: rejoin ["models/" models/1]
loadImage:  does [
isFile?: false
tmpFile: request-file
unless none? tmpFile [
canvas1/image: load to-red-file tmpFile
canvas2/image: none
s: split-path tmpFile
imageFile: s/2
source: rejoin ["images/" imageFile]
sb/text: source
isFile?: true
]
]
runYOLO: does [
if isFile? [
canvas2/image: none
clear retStr/text
results: %results.txt
if exists? results [delete results]
prog: rejoin ["yolo " task " " mode " model=" model" " "source=" source]
sb/text: prog
do-events/no-wait
tt:  dt [ret: call/wait/shell/output prog results]
if ret = 0 [
retStr/text: f: read results
f: find f "runs" ;--get directory
s: split f "" ;--get complete directory
destination: rejoin [s/1 "/" imageFile]
canvas2/image: load to-red-file destination]
sb/text: rejoin ["Result: " destination "  in " round/to (tt/3) 0.01 " sec"]
]
]
mainWin: layout [
title "Red and YOLO"
origin margins space margins
base 40x22 snow "Model" 
dp1: drop-down 120 data models select 1
on-change [model: rejoin ["models/" pick face/data face/selected]]
base 40x22 snow "Tasks" 
dp2: drop-down 80 data tasks select 1
on-change [task: pick face/data face/selected]
base 40x22 snow "Mode"  
dp3: drop-down 80 data modes select 1 
on-change [mode: pick face/data face/selected]
button "Load Image" [loadImage]
button "Run YOLO" [runYOLO]
pad 280x0 button 50 "Quit" [quit]
return
canvas1: base iSize
canvas2: base iSize
retStr: area iSize wrap
return
sb: field 645
]
view mainWin

Nice job YOLO.

Septimus: another real-world application with Red

My medical colleagues in the R2P2 unit are not all experienced developers. They've got other things to do, like saving lives. And they want immediate answers to their clinical questions. And they need easy-to-use tools.

That's why I like to use Red (or Rebol3) to develop tailor-made applications for my colleagues. No complexity (like Python), just Redbol simplicity!

That's what Septimus was designed for. We want to be able to follow the evolution of bacterial infections in our young patients according to the treatment applied. The big idea was to use an infra-red camera to detect points not visible to the naked eye.I've adapted some of redCV's functions to make it an independent module (see flir.red code). Actually, we use Septimus for following patients with acute tibial osteomyelitis.

Septimus is very simple. Once the IR image has been loaded, you can use a rectangle (of variable size or colour) to select the relevant body part in IR image. Then with a single button, you get the hottest point in that area.

Septimus is also a salute to the Blake and Mortimer comic strip, of which I've long been a fan. I think I own every album in the series.

The code is here: https://github.com/ldci/Septimus