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.
https://wiki.dfrobot.com/HUSKYLENS_V1.0_SKU_SEN0305_SEN0336

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.
https://www.henriaanstoot.nl/2022/05/29/photo-manager-addition-using-ml/