Fabion Kauker fhk

CUSTOM GAME ENGINES: A Small Study

A couple of weeks ago I played (and finished) A Plague Tale, a game by Asobo Studio. I was really captivated by the game, not only by the beautiful graphics but also by the story and the locations in the game. I decided to investigate a bit about the game tech and I was surprised to see it was developed with a custom engine by a relatively small studio. I know there are some companies using custom engines but it's very difficult to find a detailed market study with that kind of information curated and updated. So this article.

Nowadays lots of companies choose engines like Unreal or Unity for their games (or that's what lot of people think) because d

Apple’s bugs

I still enjoy using Apple products and prefer them over alternatives, but in recent years, an increasing number of small bugs has made using them less pleasant. No single bug is fatal, but they add up. I wanted to document them to make it more likely that they will be fixed.

There are two main topics:

Many small unnecessary bugs
Strategy tax

Small unnecessary bugs

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

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:

	import argparse
	import requests
	import json
	import geopandas as gpd
	import pandas as pd
	from shapely import affinity
	from shapely.geometry import Polygon, Point, MultiPolygon
	from matplotlib import pyplot as plt
	import numpy as np
	from osmtogeojson import osmtogeojson

	# (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):

	# 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

	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)