Braille Translator with Rebol and Red

I've always been impressed to see how blind children and adults are able to read Braille. It requires unparalleled tactile sensitivity and cognitive skills. In the early days, the braille cell consisted of 6 dots in a 2x3 matrix, representing 64 characters.  Later, this matrix became 2x4 with 8 dots, enabling 256 characters to be represented. 

[dots order 
1 4 
2 5 
3 6 
7 8
]

All these dots characters are now accessible in Unicode with values ranging from 10240 to 10495 (integer values). I've written a little ANSI->Braille->ANSI translator. The code is written in Rebol 3.19.0, but can be easily adapted to Red 0.6.6. There are some differences about the map! datatype.

The idea is simple. We build 2 dictionaries, one for ANSI->Braille coding and the second for Braille->ANSI coding. Maps are high performance dictionaries that associate keys with values and are very fast.

Classically, the first 32 ANSI codes do not represent characters, but escape codes used for communication with a terminal or printer. On the other hand, these 32 codes are used in Braille to facilitate document layout. 

This is the code:

#!/usr/local/bin/r3

Rebol [

]

;--generate ANSI and Braille codes

generateCodes: does [

i: 0 ;--we use all chars

codesA: #[] ;--a map object ANSI->Braille

codesB: #[] ;--a map object Braille->ANSI

while [i <= 255] [

idx: i + 10240 ;--for Braille code value

key: form to-char i ;--map key is ANSI value

value: form to-char idx ;--map value is Braille code

append codesA reduce [key value];--update map as string values

append codesB reduce [value key];--idem but reverse order key value

++ i

]

]

processString: func [

"Processes ANSI string or Braille string"

string [string!]

/ansi /braille

][

str: copy ""

;--for ansi use select/case, characters are case-sensitive

if ansi [foreach c string [append str select/case codesA form c]] 

if braille [foreach c string [append str select codesB form c]]

str

] 

generateCodes

print-horizontal-line

print a: "Hello Fantastic Red and Rebol Worlds!"  

print-horizontal-line

print b: processString/ansi a

print-horizontal-line

print c: processString/braille b

print-horizontal-line

And the result: 

-------------------------------------------------------------------------------

Hello Fantastic Red and Rebol Worlds!

-------------------------------------------------------------------------------

⡈⡥⡬⡬⡯⠠⡆⡡⡮⡴⡡⡳⡴⡩⡣⠠⡒⡥⡤⠠⡡⡮⡤⠠⡒⡥⡢⡯⡬⠠⡗⡯⡲⡬⡤⡳⠡

-------------------------------------------------------------------------------

Hello Fantastic Red and Rebol Worlds!

-------------------------------------------------------------------------------

With Red: https://github.com/ldci/Red_KIS/blob/main/Map/braille4.red

What tools are available for image processing with Red and Rebol?

 For Rebol 2 we have: https://github.com/ldci/OpenCV3-rebol. 

This version is old (2015) but still operational. There are around 600 basic OpenCV functions available with Rebol 2.

For Rebol 3, there's the fabulous module created by Oldes: 

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

Although incomplete, this module is fanatstic as it allows you to use the latest versions of OpenCV on different X86 or ARM64 platforms. 

You'll find a lot of samples here: https://github.com/ldci/R3_OpenCV_Samples

For Red, we have https://github.com/ldci/OpenCV3-red, which is still active. Although written more than 10 years ago, the code is compatible with the latest versions of Red (0.6.6).

And of course for Red, we have RedCV: https://github.com/ldci/redCV. Most of the code is written in Red/System and offers over 600 basic functions or routines for image processing with Red. 

With the exception of the Oldes code, I'm the only one to maintain all this, and I'm not sure that many people other than me use these codes. In any case, it has enabled me to write some very nice professional applications used at R2P2 (https://uniter2p2.fr).

Motiongrams

A few years ago, I discovered the work of Alexander Refsum Jensenius (https://www.uio.no/ritmo/english/people/management/alexanje/) and really appreciated his work on motiongrams. In my memory, the code was written with Processing (https://processing.org). 

As you know, at R2P2 we make extensive use of video motion analysis to create algorithms for screening babies for motor disorders, using sophisticated neural networks.

But sometimes a simple actimetric analysis is all that's needed, and that's where motiongrams come into their own, because they're so easy to use. 

A few days ago, I resumed the analysis of films of premature babies that we had collected in various Parisian university hospitals (thanks to them). The videos were acquired with a GoPro camera with an FPS of 120 frames by second.

The code is very simple and can be used with Red and redCV or Rebol 3 and OpenCV.

The first step is to define a ROI in the first image. This prevents the movement of the caregivers from adding noise to the image. 

Once this has been done, we proceed to analyze the video. The simple idea is to have two images at T and T+1. Then, a simple difference between the two images lets us know if there has been any movement.

As a precaution, I add a binary filter to remove the background noise present in the image. Then simply average the binary image to obtain a direct assessment of the rate of movement.