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• Well, we have v2 now...

  Eugene • 11/09/2020 at 11:33 • 0 comments

  Thanks to your support our previous board, the StereoPi v1, was brought to life. Today we are thrilled to introduce an updated version!

  Thanks to the newest Compute Module 4, we have a bunch of new features to introduce.

  Here is our StereoPi v2 project page on Hackaday.

• How to Synchronize Multiple StereoPi?

  Eugene • 10/12/2020 at 20:11 • 0 comments

  👉 WHY?
  You may ask, "Why do I need to use multiple StereoPi at once?"
  Well, we got a set of requests from some customers who needed 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 case is a 3D reconstruction, using a set of cameras. For example, through the use of Multi-View 3D Digital Image Correlation (Multi-DIC), a technique recently published by Dr Dana Solav and Dr Kevin Moerman.

  
  👉 HOW?
  If you are interested in this synchronization approach, you can find our detailed how-to guide here on our blog.

• StereoPi used as a USB device

  Eugene • 03/24/2020 at 10:58 • 0 comments

  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.

  You can read full guide here:

  https://stereopi.com/blog/stereopi-used-usb-device
  

• OpenCV: comparing the speed of C++ and Python code

  Eugene • 02/15/2020 at 12:13 • 0 comments

  You often hear that Python is too slow for computer vision, especially when it comes to single-board computers like Raspberry Pi. In this article, we decided to measure the actual speed difference between C++ and Python code, and find the performance 'bottleneck'.

  You can find full article here in our blog: https://stereopi.com/blog/opencv-comparing-speed-c-and-python-code-raspberry-pi-stereo-vision

• AnyCase Kit

  Eugene • 12/19/2019 at 16:03 • 0 comments

  We've developed this accessories kit for the very quick case prototyping. You can assemble your setup in 10 minutes! 

  5 mounting plates for your cameras and your StereoPi

  You can do a stereoscopic setup with 25 mm, 65 mm, 120 mm or 200 mm stereobase. Also 360 degree photo/video setup is available!

  M2 nylon spacers kit

  With this M2 kit and a screwdriver you can assemble your setup for just 10 minutes! JFYI, both StereoPi and Raspberry Pi cameras have M2 mounting holes!

  3 types of Raspberry Pi camera ribbons included

  Sometimes you need to bend a camera ribbons for your custom setup. Don't limit yourself with a fear to damage your ribbon. We've added 12 camera ribbons:

  • 4 pcs of 5 cm camera ribbons
  • 4 pcs of 10 cm camera ribbons
  • 4 pcs of 15 cm camera ribbons

  With the AnyCase kit your StereoPi quick start will be really quick!

  You can find more details on Tindie or on our site.

• DIY VR headset as a StereoPi Companion

  Eugene • 11/12/2019 at 16:55 • 0 comments

  One of our friends does psychological research in the field of human perception. He was impressed by our experiment with Oculus Go, and wanted to use this solution in his research. But he had one very strict requirement: the video latency should not exceed 10 ms.

  Here is our TL;DR step-by-step guide for creating this solution.

• A robot on StereoPi, part 1: fisheye cameras

  Eugene • 08/04/2019 at 20:03 • 0 comments

  The goal of this series of articles is to create a compact indoor robot that can navigate using stereo vision. As a platform for it, we’ll use a small Dagu RP5 platform on tracks that we have. Here’s how it looks like next to the StereoPi.

  Detailed TL;DR for fisheye cameras calibration can be found here in our blog.

• 1.44 TFT Raspberry Pi HAT screen test

  Eugene • 06/21/2019 at 13:07 • 0 comments

  I've got one of these tiny funny screens.
  So following this manual with this fix I got this result:

  It just works! :-)

• 3 more DIY guides for the StereoPi

  Eugene • 06/20/2019 at 13:58 • 0 comments

  We have 3 more guides now:

  1. The Art Of Stereoscopic Photo, part 1 (basics)

  2. The Art Of Stereoscopic Photo, part 2 (assembling a camera)

  3. Hacking Skybox on Oculus Go for StereoPi live streaming (just a hack)

• OpenCV and Depth Map on StereoPi tutorial

  Eugene • 04/09/2019 at 08:33 • 0 comments

  Today we’re pleased to share with you a series of Python examples for OpenCV development. This code works with either the StereoPi or the Raspberry Pi Development Board, as both support using two cameras simultaneously. Our ready-to-use code (and also Raspbian image) will help you every step of the way, from the first image capture to the Depth Map created via real-time video capture.

  Introduction

  We would like to emphasize that all of these examples are for those new to OpenCV and are not intended for production use. If you are an advanced OpenCV user and have worked with the Raspberry Pi before, you’ll know it’s better to use C/C++ (instead of Python) and to utilize the GPU for better performance. At the end of this article we’ll provide some notes regarding the various bottlenecks we experienced using Python.

  Hardware setup

  Here is our hardware setup:

  We used the StereoPi board with Raspberry Pi Compute Module 3+. Also two Raspberry Pi cameras V1 connected (based on ov5647 sensor).

  Software used:

  • Raspbian Stretch (kernel 4.14.98-v7+)
  • Python 3.5.3
  • OpenCV 3.4.4 (pre-compiled, ‘pip’ from Python Wheels)
  • Picamera 1.13
  • StereoVision lib 1.0.3 (https://github.com/erget/StereoVision)

  The software installation process is beyond the scope of this article but we have prepared a Raspbian image with all software installed. Here is a link to our GitHub stereopi-tutorial repository.

  Notice

  All scripts support key stroke processing, and you can press ‘Q’ key to stop them. If you use Ctrl+C to stop the script, it may break the Python interaction with the cameras. In this case, you will need to reboot StereoPi.

  Step 1: Image Capture

  
  We use 1_test.py script for this purpose. Open the console and go to our examples folder:

  cd stereopi-tutorial

  Console Command: 

  python 1_test.py

  After starting the script you can see a preview window with the stereoscopic video. Pressing ‘Q’ will stop the process and save the last captured image. This image will be used in the next scripts for Depth Map parameters tuning.

  This script allows you to check if your hardware is operational and helps you obtain your first stereoscopic picture.

  The following video shows how the first script works:

  Step 2: Collecting Images For calibration

  
  In an ideal world, a perfect depth map needs to use two identical cameras with their optical, vertical and horizontal axis all in parallel. In the real world, however, cameras are different and it’s impossible to align them perfectly. Thus, a software calibration method is used. Using two cameras you take multiple photos of an object. In our case, we used a printed chessboard. A special algorithm will then analyze these photos and find parameters for correction. This script begins this process by capturing a series of chessboard photos for calibration. Before each photo, the script starts a five (5) second countdown. Five seconds is generally enough time to reposition the chessboard. Make sure it can be seen by both cameras and ensure it’s stable to avoid “blurred” photos. The default number of photos captured per series is 30.

  
  Console Command:

  python 2_chess_cycle.py

  The process:

  At the end, we have 30 stereoscopic photos, saved in /scenes folder.

  Step 3: Image Separation

  
  The third script 3_pairs_cut.py separates the captured photos into “left” and “right” images and saves them in /pairs folder. These separations could be done on-the-fly, without saving, but this step is helpful for your next experiments. You can save image-pairs from different capture series. Use your own code to work with this images, or use another stereoscopic camera’s images by putting them in this folder.

  This script will show you every stereo pair before it’s separated (and waiting for key press). This lets you find bad photos and remove them before the next...

  Read more »

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