Here are just two of the panoramic images I’ve made while playing through BioShock Infinite this year. Gorgeous art!
“Ever notice how people texting at night have that eerie blue glow?
Or wake up ready to write down the Next Great Idea, and get blinded by your computer screen?
During the day, computer screens look good—they’re designed to look like the sun. But, at 9PM, 10PM, or 3AM, you probably shouldn’t be looking at the sun.
f.lux fixes this: it makes the color of your computer’s display adapt to the time of day, warm at night and like sunlight during the day.
It’s even possible that you’re staying up too late because of your computer. You could use f.lux because it makes you sleep better, or you could just use it just because it makes your computer look better.”
“The Data Visualisation Catalogue is currently an on-going project developed by Severino Ribecca.
Originally, this project was a way for me to develop my own knowledge of data visualisation and create a reference tool for me to use in the future for my own work. However, I thought it would also be useful tool to not only other designers, but also anyone in a field that requires the use of data visualisation regularly (economists, scientists, statisticians etc).
Although there have been a few attempts in the past to catalogue some of the established data visualisation methods, there is no website that is really comprehensive, detailed or helps you decide the right method for your needs.
I will be adding in new visualisation methods, bit-by-bit, as I research each method to find the best way to explain how it works and what it is best suited for.”
Source 1: http://datavizcatalogue.com/
It’s impressive what these browsers started to become these days. Here you have a quite convincing wave simulation right in your browser with some knobs to play with:
Since my wife started working as a photographer on a daily basis the daily routine of getting all the pictures off the camera after a long day filled with photo shootings got her bored quickly.
Since we got some RaspberryPis to spare I gave it a try and created a small script which when the Pi gets powered on automatically copies all contents of the attached SD card to the houses storage server. Easy as Pi(e) – so to speak.
So this is now an automated process for a couple of weeks – she comes home, get’s all batteries to their chargers, drops the sd cards into the reader and poweres on the Pi. After it copied everything successfully the Pi sends an eMail with a summary report of what has been done. So far so good – everything is on our backuped storage server then.
Now the problem was that she often does not immediately starts working on the pictures. But she wants to take a closer look without the need to sit in front of a big monitor – like taking a look at her iPad in the kitchen while drinking coffee.
So what we need was a tool that does this:
- take a folder (the automated import folder) and get all images in there, order them by day
- display an overview per day of all pictures taken
- allow to see the fullsized picture if necessary
- work on any mobile or stationary device in the household – preferably html5 responsive design gallery
- it should be fast because commonly over 200 pictures are done per day
- it should be opensource because – well opensource is great – and probably we would need to tweak things a bit
Since I did not find anything near what we had in mind I sat down this afternoon and wrote a tool myself. It’s opensourced and available for you to play with it. Here’s a short description what it does:
It’s pretty fast because it’s not actively resizing the images – instead it’s taking the thumbnail picture from the original jpg file which the camera placed there during storing the picture. It’s got some caching and can be run on any operating system where mono / .net is available – which is probably anything – even the RaspberryPi.
Source 1: http://pinterest.com/0x0/webdev/
It’s been a habbit to ID software to release the source code of their previous games and game engines as open source when time is due. That’s what happened with Doom 3 as well. Since beautiful code appears to a lot of developers it’s just a logical step to analyse the Doom 3 source code with the beauty-aspects in mind.
Now there are two very good examples of such analysis.
Source 1: http://kotaku.com/5975610/the-exceptional-beauty-of-doom-3s-source-code
Source 2: ftp://ftp.idsoftware.com/idstuff/doom3/source/CodeStyleConventions.doc
Source 3: http://fabiensanglard.net/doom3/index.php
Source 4: https://github.com/TTimo/doom3.gpl
The first signs of the upcoming camera board for the raspberry pi are showing. During the Electronica 2012 fair RS showed the board to the public for the first time.
Since it’s going to be a 25 Euro add-on for the Pi the specification is quite impressive. The OmniVision OV5647 is used as the Image Sensor – it’s bigger brother is used in iPhone 4. OmniVision says:
“The OV5647 is OmniVision’s first 5-megapixel CMOS image sensor built on proprietary 1.4-micron OmniBSI™ backside illumination pixel architecture. OmniBSI enables the OV5647 to deliver 5-megapixel photography and high frame rate 720p/60 high-definition (HD) video capture in an industry standard camera module size of 8.5 x 8.5 x ≤5 mm, making it an ideal solution for the main stream mobile phone market.
The superior pixel performance of the OV5647 enables 720p and 1080p HD video at 30 fps with complete user control over formatting and output data transfer. Additionally, the 720p/60 HD video is captured in full field of view (FOV) with 2 x 2 binning to double the sensitivity and improve SNR. The post binning re-sampling filter helps minimize spatial and aliasing artifacts to provide superior image quality.
OmniBSI technology offers significant performance benefits over front-side illumination technology, such as increased sensitivity per unit area, improved quantum efficiency, reduced crosstalk and photo response non-uniformity, which all contribute to significant improvements in image quality and color reproduction. Additionally, OmniVision CMOS image sensors use proprietary sensor technology to improve image quality by reducing or eliminating common lighting/electrical sources of image contamination, such as fixed pattern noise and smearing to produce a clean, fully stable color image.
The low power OV5647 supports a digital video parallel port or high-speed two-lane MIPI interface, and provides full frame, windowed or binned 10-bit images in RAW RGB format. It offers all required automatic image control functions, including automatic exposure control, automatic white balance, automatic band filter, automatic 50/60 Hz luminance detection, and automatic black level calibration.”
That sensor delivers RAW RGB Imagery to the RaspberryPi through the onboard camera connector interface:
And the part that impressed me the most is that that 5 Megapixel sensor delivers it’s raw data stream and it gets h264 compressed directly within the GPU of the Raspberry Pi. 30 frames per second 1080p without noticeable CPU load – how does that sound? – Not bad for a 50 Euro setup!
Back in 2006 I wrote about a new technology which the also new company Geomerics was demoeing.
Back in 2006 everything was just a demo. Now it seems that Geomerics found some very well known customers and without noticing a lot of the current generation games graphics beauty comes from the capabilities real time radiosity lighting is adding to the graphics.
“Geomerics delivers cutting-edge graphics technology to customers in the games and entertainment industries. Geomerics’ Enlighten technology is behind the lighting in best-selling titles including Battlefield 3, Need for Speed: The Run, Eve Online and Quantum Conundrum. Enlighten has been licensed by many of the top developers in the industry, including EA DICE, EA Bioware, THQ, Take 2 and Square Enix.” (Source)
There even is a more updated version of the demo video:
With todays processing power and the faults of current generation digital video cameras you can have a lot of fun – if you know how:
The above demonstrated effect is called Time Remapping. The description of the video tells us more about the effect itself:
The effect was discovered accidentally by a photographer called Jacques Henri Lartigues at the beginning of the 20th century (in 1912 to be precise). He took a picture of a race car with eliptical deformed tires – an effect caused by the characteristics of the camera he was using.