Here is the first version of a simple droplet for converting and publishing 360 panoramic videos. It is intended to be used for the processed output file from a Ricoh Theta S that has the standard 1920x960 resolution.
It conveniently includes 32-bit and 64-bit versions of FFMPEG for performing video conversion.
Extract to your KRPano folder.
Drag your MP4 video file to the ‘MAKE PANO (VIDEO)’.
Be patient while your video is encoded to various formats.
Rename the finished ‘video’ folder to a name of your choice.
You can download the first version of my droplet here:
You will likely find that some users have to wait for the video to buffer or playback may stutter. It would be optimal to reduce the quality, but, as a KRPano user you are likely willing to sacrifice speed for quality.
I listened to an interesting podcast today, exploring the game Rock, Paper, Scissors, prediction patterns and game theory.
Being clever isn’t an effective way of winning, as people are rarely perfect decision makers. The Nash equilibrium is only relevant to domains with perfect decision makers. So it is important to consider the choices of others.
In the podcast, it was mentioned that from large datasets:
“The biggest chunk of people will think one step ahead, the next biggest chunk of people will think totally randomly, the next biggest chunk of people will think two steps ahead, and a small number of people will go beyond two steps.”
It also considers crowds and groups influencing decisions.
This project entitled “Large-Scale, Real-Time, Visual-Inertial Localization” is interesting, using Google’s experimental ‘Tango’ hardware to improve real-time tracking of location and position.
The hardware is a tablet computer with a motion tracking camera, a 4 megapixel 2µp pixel camera, integrated depth sensing and a high-performance processor. This equipment aids in tasks like scanning rooms. A limited number of kits were produced and given or sold to professional developers with the intent of making technological developments.
One day we may see more accurate and interesting augmented reality. I’ve often thought overlaying information onto our current reality would be interesting. Walking down a street and seeing for-sale signs could be interesting. It may just being overloaded in advertising, making a virtual eyesore though.
Today I used an Arduino reprogrammable electronics board and a GSM/GPRS modem to send data to the Internet. This will be useful for the sensors I intend to build.
You can see a video of it operating below. One window shows the output from the serial interface as it makes a request to my web server and outputs the response, and the other window displays the access logs on my web server.
I used a software UART (parallel and serial data converter), as using the Arduino’s built-in serial interface caused conflicts. Learning to use a software UART is going to be very useful for the next step in the project.
I did consider making a Cat Facts for Arduino, but I resisted the distraction.
I didn’t have any issues with power spikes causing a reset (the GSM board uses a lot of power) as this is a version 2 board with soft start circuitry:
The only stumbling point I had was when the GSM modem was set to a different baud rate than my software serial interface. To change this, I sent the AT command AT+IPR=9600 to reconfigure the modem.
I learnt that AT commands are also used to send and receive via TCP/UDP. This made it much more straightforward than some kind of low level system I had imagined. All that is required from the Arduino is to send AT commands (e.g. AT+CIPSEND=) and listen for incoming responses.
Explanation for non-techies:
I made an electronic circuit that can send information to the Internet. This will be useful for making a sensor e.g. a temperature sensor for an office that is accessible on any computer/smartphone.
Interesting work by a team led by Nottingham Trent University on making a mobile scanner that can detect early signs of potholes. It uses conventional cameras, 3D scanners and computer vision to detect ‘ravelling’. Moving this around at traffic speed mounted to a vehicle, and combining it with GPS and a suitable logging system, it could be used to improve road conditions.
Researchers are developing smart scanning technology using existing cameras to detect the early signs of potholes and determine their severity.
The technology, developed by a team led by Nottingham Trent University research fellow Dr Senthan Mathavan, scans roads for ravelling — the loss of aggregates from the asphalt which leads to potholes and cracks.
Combined with 2D and 3D scanners on a pavement monitoring vehicle, a computer vision algorithm can examine the road with accuracy at traffic speed during day or night.
The system works by detecting different textures of the road to identify ravelling and distinguishes it from shadows and blemishes such as tire marks, oil spills and recent pothole repairs.
“It’s imperative for authorities across the world to be able to monitor road conditions efficiently and safely,” said Dr Mathavan, a research fellow of the School of Architecture, Design and the Built Environment.
“For the first time, academic research has addressed the issue of detecting ravelling in an automated way, which has led to the development of this novel software which can be used across the industry.”
The research was published today in Transportation Research Record, a leading academic journal for transportation infrastructure research. It also involves Dr Mujib Rahman of Brunel University, Martyn Stonecliffe-Jones of Dynatest UK Ltd, and Dr Khurram Kamal of the National University of Sciences and Technology in Pakistan.
During the research, the team found that the technology detected road surfaces correctly in all 900 images tested. It took approximately 0.65 seconds to 3D process the ravelling measurements, but it is believed that this could be reduced further.
Dr Rahman added: “Potholes, in their worst potential form, can create dangerous driving conditions and cause costly damage to vehicles.
“What this technology allows us to do is capture better quality information on road conditions, without disrupting the flow of traffic or incurring unnecessary costs.
“This could be a significant step forward in the way that potholes are managed, helping improve the timeliness and efficiency of repairs.”