raftheunis87

Saafke

Google Summer of Code 2019 with OpenCV

Learning-based Super Resolution

Student: Xavier Weber
Mentors: Vladimir Tyan & Yida Wang
Student on the same project: Fanny Monori

Link to accomplished work:

• PR in the opencv_contrib repository: opencv_contrib/pull/2231

lopespm

# (Variant #4 for exercise 16.2 on EPI (Elements of Programming Interviews)) (September 2018 edition)
# The core idea is calculate the levenshtein distance, while taking into account the special cases of the regex expression
# *, +, ? and . were taken into account for the regex expression. Expression blocks are not supported
# This algorithm uses recursion with memoization (could be transposed to a DP solution), yielding a O(mn) time complexity, O(mn) auxiliary space for cache and O(max(m,n)) function call stack
# (m and n are the lengths of regex and target strings respectively)
#
# Version using dynamic programming: https://gist.github.com/lopespm/2362a77e7bd230a4622a43709c195826

def regex_dist(regex: str, target: str):
    def regex_dist_aux(r_i, t_i):

bmaupin

# Cache sudo password
sudo -v

# Enable installation of 32-bit packages
sudo dpkg --add-architecture i386

# Enable the Ubuntu partner repository (this doesn't seem to be necessary for 20.04)
if [ $(lsb_release -r | awk '{print $2}') == '18.04' ]; then
    sudo sed -i 's/^# deb http:\/\/archive.canonical.com\/ubuntu/deb http:\/\/archive.canonical.com\/ubuntu/' /etc/apt/sources.list
    sudo apt update

RickStrahl

Mermaid

Mermaid is a JavaScript based parsing engine that allows you to embed blocks of graph markup into a page. Markdown Monster supports two different approaches using either code block or HTML tag syntax to embed the mermaid blocks.

@icon-warning Internal Preview Limitations

Note the MM internal preview can't properly display or refresh all chart types, but you can preview in your system browser via Shift-F12 to see proper representation.

Sequence Diagram

You can use a mermaid code block:

russss

import cv2
import numpy as np

def deskew(im, max_skew=10):
    height, width = im.shape

    # Create a grayscale image and denoise it
    im_gs = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    im_gs = cv2.fastNlMeansDenoising(im_gs, h=3)

matt-bailey

How to set up DNS records on gandi.net to use a custom domain on Github Pages

You would think it would be easy to find this information, but none of the Github or Gandi documentation is clear so I have recorded the required steps here.

Create the following A records:

@ 1800 IN A 185.199.108.153
@ 1800 IN A 185.199.109.153
@ 1800 IN A 185.199.110.153

andyrbell

# use ImageMagick convert
# the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf
convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf

waterpump.ino

#include "InternetButton.h"
#include "math.h"

/**
 * Sensor calibration data:
 * Wet = 1520
 * Dry = 3120
 * (Calibrate this for yourself!)
 */

Piasy

brew options ffmpeg

brew install ffmpeg \
--with-chromaprint \
--with-fdk-aac \
--with-fontconfig \
--with-freetype \
--with-frei0r \
--with-game-music-emu \
--with-libass \