Tag Archives: machinelearning

HuskyLens Test

I’ve bought a Husky lens recently. It was very cheap for what you get.
(50 Euro’s)
The first tests are promising.

Nice little, but powerful gadget.

Cables to connect Rpi or Arduino, mounts, Huskylens and Protectioncover (sold separately)
  • face recognition
  • object tracking
  • object recognition
  • line tracking
  • color recognition
  • tag recognition
  • object classification

Communication can be done via I2C and Uart.
Uses a sdcard to store learning data.
Has white leds for object lighting.

Build-in objects which are recognised out of the box. (Others can be learned by the device)

aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining-table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, TV

Photo manager addition using ML!

A few years ago i wrote a photo manager .. again .. ( see post about my first previous photo manager )
It is a web gui to find photos in my huge photo archive.
I manually added 190k tags to 120k photos in 20+ years.

I thought wouldn’t it be nice if i can generate additional metadata using Machine Learning. A few years ago i did some testing and followed a podcast and free course about machine learning.

So today i started to implement a addition to my gui. Machine recognition tags!

It already kinda works.

Things to do :

  • Make it a background job, my fileserver doesn’t run Tensorflow on a GPU, so it is slooow
  • Embed in existing GUI and stats
  • Design a editor to remove wrong tags

Below a part of ML images

Command to get a thumbnail sheet with only directory names:

montage -verbose -units PixelsPerInch -density 300 -tile 7x6 -label "%d" -font Arial -pointsize 6 -background "#FFFFFF" -fill "black" -define jpeg:size=253x154 -geometry 253x154+2+2 -auto-orient */*.JPG -title "ML Thumbs" thumbsheet.jpg

Maybe, i can use debug output like below.

['lakeside, lakeshore (score = 0.47934)', 'seashore, coast, seacoast, sea-coast (score = 0.11385)', 'sandbar, sand bar (score = 0.08822)', 'breakwater, groin, groyne, mole, bulwark, seawall, jetty (score = 0.06281)', 'valley, vale (score = 0.01790)', '']

Machine Learning

Today i started with Coursera’s Machine Learning course.

My friend aloha is doing interesting stuff with ML, but recently i’ve been interested in a work related ML project.

Besides this course i’m following a spotify Podcast called “Machine Learning Guide”, i listen to this on my way to work and back.

I’ve been playing with a lot of code after that. Luckily there are many ebooks about this subject.

  • One of the first was a python program wich used the length of a person and shoesize to determine if it was a man or a woman
  • Another fun one was a program with could determine if a wine was red or white only based by a description
  • There are several graphic based programs i’ve tried. Deepfake, 8mm film enhancers, image classifiers, openface
  • For sound there was voice cloner to test. And audio to text (which i used to transcribe old cassette tapes and VHS tapes.

UPDATE: In 2022 i used what i have learned to enrich my photo metadata.