Sounds like fun, right? Today, we’ll share with you a couple of projects that are using this set of technologies to solve real problems.
Laser weed control
As a human, you can easily distinguish and then neutralize weeds from cultivated plants, like tomatoes. A hardware solution to this problem needs to perform a bunch of complex tasks:
We are glad to announce that our StereoPi v2 campaign is live!
In this next generation of the StereoPi, we took into account a lot of requests from our customers. That’s why we added a lot of new features, and dramatically reviewed our kits content. Now you can assemble a ready-to-use stereoscopic camera with the Camera Kit. If you want to get the highest quality from the couple of HQ cameras — our metal HQ housing is here! The Camera Kit now includes both OpenCV and SLP Raspberry OS microSD cards, so you can start your experiments right after unpacking. We also have the new Compute Module 4 onboard, Wi-Fi, Bluetooth, and… we have too many to say!
Just take a look at the full list of features on our campaign page!
The new Compute Module 4 was recently announced. It just so happens that the Raspberry Pi Foundation has provided us with early access to technical documentation and a restricted developer forum long before its official announcement.
After gaining access to the forum, the first thing we did was read the history. You know, there’s a big difference between developing on a serial production module and early access to a device that has not even been fully formed yet. It was amazingly interesting to read through the evolution of the changes that were made to Compute Module 4. …
You may ask “Why do I need to use a bunch of the StereoPi?”
Well, we got a set of requests from our customers, who need to take a lot of images at once. One of the most popular use cases is the creation of animated “3D” GIFs. These images are created using a set of images (usually 4). In our article we are describing all processes, starting from capturing files and up to creating MP4 video files. So you can post it as a video, or use a video->gif converter to get an animated image.
The second popular use…
We’ve found fantastic research made by a group of oceanologists. They suggest affordable solution based on the Raspberry Pi computer, 3D printed cases and several engineering tricks to obtain results, previously accessible for expensive professional systems only. Now this system (called DEEPi) is based on a Raspberry Pi Zero, but the next generation is planned to be equipped with the two cameras and based on the StereoPi. In this article we’ll cite two related paperworks.
Bio-logging is a data acquisition approach to collect challenging information from direct observation of long-distance traveling animals or animals that are out of boundary of…
The Raspberry Pi Compute Module powering the StereoPi has the ability to operate as an USB device thanks to an USB-OTG hardware within the processor. This means that we can connect it to the PC and make it appear as an USB stick, a serial interface, or — as exposed in this article — as an external network interface. It is very powerful but it comes with a few limitations due to the architecture of the Raspberry ecosystem followed by the StereoPi. Let’s see how it works.
Appearing as a device requires a special hardware. Fortunately, in the case of…
You often hear that Python is too slow for computer vision, especially when it comes to single-board computers like Raspberry Pi. Python is very simple and easy to learn, and it’s currently one of the most popular programming languages for a good reason.
So how good Python’s performance in computer vision tasks actually is? Where exactly is it slower than C++, and how much? The answer to this question is not so clear. For example, when constructing depth maps on Raspberry Pi using Python code, it uses binary libraries written in C++ ‘under the hood’. …