Extracting Lightning Strikes From HD Video

Lightning photography is a fine art. It requires a lot of patience, and until recently required some fancy gear. [Saulius Lukse] has always been fascinated by lightning storms. When he was a kid he used to shoot lightning with his dad’s old Zenit camera — It was rather challenging. Now he’s figured out a way to do it using a GoPro.

He films at 1080@60, which we admit, isn’t the greatest resolution, but we’re sure the next GoPro will be filming 4K60 next. This means you can just set up your GoPro outside during the storm, and let it do it what it does best — film video. Normally, you’d then have to edit the footage and extract each lightning frame. That could be a lot of work.

[Saulius] wrote a Python script using OpenCV instead. Basically, the OpenCV script spots the lightning and saves motion data to a CSV file by detecting fast changes in the image.

graph of lightning

The result? All the lightning frames plucked out from the footage — and it only took an i7 processor about 8 minutes to analyze 15 minutes of HD footage. Not bad.

Now if you feel like this is still cheating, you could build a fancy automatic trigger for your DSLR instead…

LED Notification Cube is a Good First Project

Two years ago, [Matt] made a move away from his software hacks and into the physical world. He was part of a pilot program to provide mentorship to children as part of the Maker Education Initiative. This program gave him access to 3D printers, CNC machines, and laser cutters within the New York Hall of Science makerspace. [Matt] chose to build an illuminated notification cube for his first physical project. The idea being that smart phones have so many alerts, many of which are unimportant. His project would help him to visualize and categorize each alert to better understand its importance.

The brain of the system is a Raspberry Pi. [Matt] found a Python library that allowed him to directly control an RGB LED strip based on the LPD8806 chip. He wired the data pins directly to the Pi and used an old 5V cell phone charger to power the LEDs. The strip was cut into smaller strands. Each face of the cube would end up with three strands of two LEDs each, or six LEDs per side. [Matt] found a mount for the Pi on Thingiverse and used a 3D printer to bring it into existence. The sides were made of frosted laser cut acrylic. The frosted look helps to diffuse the light from the LEDs.

Over time [Matt] found that the cube wasn’t as useful as he originally thought it would be. He just didn’t have enough alerts to justify the need. He ended up reprogramming the Pi to pull weather information instead, making use of the exact same hardware for another, more useful purpose.

Audio Algorithm Detects When Your Team Scores

[François] lives in Canada, and as you might expect, he loves hockey. Since his local team (the Habs) is in the playoffs, he decided to make an awesome setup for his living room that puts on a light show whenever his team scores a goal. This would be simple if there was a nice API to notify him whenever a goal is scored, but he couldn’t find anything of the sort. Instead, he designed a machine-learning algorithm that detects when his home team scores by listening to his TV’s audio feed.

goal[François] started off by listening to the audio of some recorded games. Whenever a goal is scored, the commentator yells out and the goal horn is sounded. This makes it pretty obvious to the listener that a goal has been scored, but detecting it with a computer is a bit harder. [François] also wanted to detect when his home team scored a goal, but not when the opposing team scored, making the problem even more complicated!

Since the commentator’s yell and the goal horn don’t sound exactly the same for each goal, [François] decided to write an algorithm that identifies and learns from patterns in the audio. If a home team goal is detected, he sends commands to some Phillips Hue bulbs that flash his team’s colors. His algorithm tries its best to avoid false positives when the opposing team scores, and in practice it successfully identified 75% of home team goals with 0 false positives—not bad! Be sure to check out the setup in action after the break.

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Remote Controlled Wildlife Camera with Raspberry Pi

If you are interested in local wildlife, you may want to consider this wildlife camera project (Google cache). [Arnis] has been using his to film foxes and mice. The core components of this build are a Raspberry Pi and an infrared camera module specifically made for the Pi. The system runs on a 20,000 mAh battery, which [Arnis] claims results in around 18 hours of battery life.

[Arnis] appears to be using a passive infrared (PIR) sensor to detect motion. These sensors work by detecting sudden changes in the amount of ambient infrared radiation. Mammals are good sources of infrared radiation, so the sensor would work well to detect animals in the vicinity. The Pi is also hooked up to a secondary circuit consisting of a relay, a battery, and an infrared light. When it’s dark outside, [Arnis] can enable “night mode” which will turn on the infrared light. This provides some level of night vision for recording the furry critters in low light conditions.

[Arnis] is also using a Bluetooth dongle with the Pi in order to communicate with an Android phone. Using a custom Android app, he is able to connect back to the Pi and start the camera recording script. He can also use the app to sync the time on the Pi or download an updated image from the camera to ensure it is pointed in the right direction. Be sure to check out the demo video below.

If you like these wildlife cameras, you might want to check out some older projects that serve a similar purpose. Continue reading “Remote Controlled Wildlife Camera with Raspberry Pi”

The Pi 2 Means Faster GPIO

The Raspberry Pi is a great machine to learn the ins and outs of blinking pins, but for doing anything that requires blinking pins fast, you’re better off going with a BeagleBone. This has been the conventional wisdom for years now, and now that the updated Raspberry Pi 2 is out, there’s the expectation that you’ll be able to blink a pin faster. The data are here, and yes, you can.

The method of testing was connecting a PicoScope 5444B to a pin on the GPIO pin and toggling between zero and one as fast as possible. The original test wasn’t very encouraging; Python maxed out at around 70 kHz, Ruby was terrible, and only C with the native library was useful for interesting stuff – 22MHz.

Using the same experimental setup, the Raspberry Pi 2 is about 2 to three times faster. The fastest is still the C native library, topping out at just under 42 MHz. Other languages and libraries are much slower, but the RPi.GPIO Python library stukk sees a 2.5x increase.

Laser-Cut Clock Kicks Your CAD Tools to the Curb and Opts for Python

In a world deprived of stock hardware other than #6-32 bolts and sheets and sheets of acrylic, [Lawrence Kesteloot] took it upon himself to design and build a laser-cut pendulum clock. No Pricey CAD programs? No Problem. In a world where many fancy CAD tools can auto-generate gear models, [Lawrence] went back to first principles and wrote scripts to autogenerate the gear profiles. Furthermore, not only can these scripts export SVG files for the entire model for easy laser cutting, they can also render a 3D model within the browser using Javascript.

Given the small selection of materials, the entire project is a labor of love. Even the video (after the break) glosses over the careful selection of bearings, bolt-hole spacing, and time-sensitive gear ratios, each of which may be an easy macro in other CAD programs that [Lawrence], in this case, needed to add himself.

Finally, the entire project is open source and up for download on the Githubs. It’s not every day we can build ourselves a pendulum clock with a simple command-line-incantation to

make cut

Thanks for the tip, [Bartgrantham]!

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IoT Chameleon Lamp Does It with Python

If this Internet of Things thing is gonna leave the launchpad, it will need the help of practical and semi-practical project ideas for smartifying everyday items. Part of getting those projects off the ground is overcoming the language barrier between humans that want to easily prototype complex ideas and hardware that wants specific instructions. A company called Things on Internet [TOI] has created a system called VIPER to easily program any Spark Core, UDOO or Arduino Due with Python by creating a virtual machine on the board.

The suite includes a shield, an IDE, and the app. By modifying a simple goose neck IKEA lamp, [TOI] demonstrates VIPER (Viper Is Python Embedded in Realtime). They opened the lamp and added an 24-LED Adafruit NeoPixel ring, which can be controlled remotely by smartphone using the VIPER app. To demonstrate the capacitive sensing capabilities of the VIPER shield, they lined the head of the lamp with foil. This code example will change the NeoPixels to a random color each time the button is pressed in the app.

Check out the lamp demonstration after the break and stay for the RC car.

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