Life plus Linux: October 2016

Posts

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Magic Cauldron: Harry Potter Themed Gender Reveal Party - #Aurdino
Earlier this year, we had a very fun filled Harry Potter themed gender reveal party. For the reveal, I built a Magic Cauldron which would reveal the gender. Check it out for yourself! For this I needed: A Cauldron. WS2812B LED array. Aurdino UNO. Bread board and jumper wires. Dry ice. Kasa Smart bulbs I will go over in the following sections The Mist.
Kakashi: The Copycat Robot - #Aurdino #image processing
In this post, I want to share about "Kakashi: The Copycat Robot"—a fun side project I built a few years ago. The name is inspired by the famous character from Naruto, Kakashi Hatake, also known as the Copycat Ninja.The goal of this robot was to mimic Kakashi's ability to copy movements—though, of course, in a more limited way. Check it out for yourself!Here are the things I used to build this:
Neural network inference pipeline for videos in Tensorflow - #Deeplearning #Tensorflow
Just as we saw a huge influx of images in the past decade or so, we are now seeing a lot of videos being produced on social media. The need to understand and moderate videos using machine learning has never been greater. In this post, I will show you how to build an efficient pipeline to processes videos in Tensorflow.  For simplicity, let us consider a Resnet50 model pre-trained on
Finding Where's Waldo using Mask R-CNN - #Deeplearning #ML
When I was a kid, I really loved solving Where's Waldo. There were few books (it used to be called Where's Wally) in our school library on which I spent hours finding Waldo. For people who do not know what it is, basically Waldo - a unique character is hidden among hundreds of other characters and you have to find him in all the chaos in the image. Now that I am too old to be solving it and
Higher level ops for building neural network layers with deeplearn.js - #Deeplearning #javascript #ML
I have been meddling with google's deeplearn.js lately for fun. It is surprisingly good given how new the project is and it seems to have a sold roadmap. However it still lacks something like tf.layers and tf.contrib.layers which have many higher level functions that has made using tensorflow so easy. It looks like they will be added to Graphlayers in future but their priorities as of now is to

Tuesday, 18 October 2016

How to download large folders on dropbox

Recently, someone shared a large folder with me and when I tried to download it, I was getting an error; "There was an error downloading your file".

This error seemed very vague and after a quick search online, I figured out that it is not possible to download folders which are bigger than 1 GB. And according to dropbox's help article, I will be able to download it only if I add it to my dropbox. With dropbox's puny 2GB free storage it was not possible and I was not ready to spend $$$ just for this.

So, I wrote a simple script in javascript that I can run it on browser console to download all files in a folder!

How to do it?

Step 1. Navigate to the dropbox folder on the browser and open your developer console. Press cmd + j on mac or ctrl + shift + j on linux and windows.

Step 2. Paste the following code in the console.

Step 3. The browser will try to block the windows trying to download it. Select the option to Always allow pop-ups from https://www.dropbox.com . For instance this is how it will look on Google Chrome (you have to click on right most icon in the search bar).

Step 4. Your files will be downloaded one by one!

NOTE: If any folder inside the folder is greater than 1GB in size, then you may have to do the same process after navigating to that folder in the browser.

Update: Make sure that you use the list view to see files by clicking on this: