#2LabsToGoEco: #OpenSource combination of an #analytical (high-performance thin-layer #chromatography) & #bioassay #laboratory
-#solar-powered
-#autosampler, #nebulizer, #incubator, #imager, ...
Paper: https://doi.org/10.1016/j.aca.2025.344103
GitHub: https://github.com/OfficeChromatography/2LabsToGo-Eco
#DIYbio #DIYchem #analytics #lab #instruments #TLC #imaging #PiCam #sustainable
Welcome to KAMPi! My Self-Built Digital Camera
Hello everyone! I have always wanted to build my own digital camera. But not just any digital camera, one I would actually use like a regular point-and-shoot camera. I wanted something that would give me traditional feel, but still shoot in 4K. A tall order for sure, and to top it all off, I wanted to build everything myself. Well the time has come. . . Please join me in welcoming KAMPi! Check out my build below.
Why KAMPi?
KAMPi is short for “Kampay” which is Tagalog slang for “Kanpai” the Japanese word for cheers. It also sounds like CAMPi another way of saying Pi Cam, which is exactly what it is – a digital camera built using a Raspberry Pi mini computer.
What’s Inside?
I built KAMPi from the ground up: selected, wired, and soldered the hardware electronics, 3D printed the parts, and prepared the software.
For the computer internals, I chose to use a Raspberry Pi Compute Module 5 which rides on a Waveshare Nano A. The camera imager and lens are from Raspberry Pi as well, the Raspberry Pi High Quality Camera with the 16mm Telephoto Lens. For the display I used a Pimoroni HyperPixel 4.0″ Hi-Res Display. The display is connected to the Waveshare Nano A via GPIO pins (the header of the Nano I soldered myself). Everything is packed together in a tight package shown above.
For the power electronics I chose to use a Pimoroni LiPo SHIM I purchased from Adafruit. I wired it up with a on/off switch and connected it to a USB-C plug.
For the trigger mechanism I chose to use Adafruit’s KB 2040 electronic board due to it’s small form factor, and also that it works over USB-C / USB serial. I also chose to use the KB 2040 because the HyperPixel 4.0 uses all the GPIO pins of the Waveshare Nano A and USB-C / USB serial seemed like a more straightforward alternative. Alternatively I could have used I2C, via the HyperPixel 4.0, but I didn’t have enough time to go that direction before the convention. The trigger itself is an illuminated pushbutton switch also from Adafruit.
I’m no expert in 3D printing and I originally wanted the form factor to be smaller. But since the camera cable stuck out from the top, I needed to make extend the base.
So I designed and 3D printed a cap portion to hold it all together.
I even added a hinge and latch lock to for easy access.
For the software, I wanted something simple. I’m running python script to take the photos. One thing to note is that the camera does not have any autofocus (which is exactly how I wanted it). That meant I needed to see what I’m shooting before taking the photo. I added a preview in the software, so I could focus the lens, and then take the shot.
The desktop above shows the camera python script and the folder where the photos are saved. You can also see the Circuitpython mounted “disk” of the KB 2040 on the desktop. I’m also a space nerd so I chose a James Webb galaxy image as a backdrop to show off the beautiful Pimironi display. I included a fun logo and added a nice rectangle so I could easily see the program icons on the desktop too.
KAMPi in Action
KAMPi is so new, I haven’t been able to test it in the wild yet. But here are some raw unprocessed photos from my home test shots.
https://www.instagram.com/reel/DMR6OegOWSn/?utm_source=ig_web_copy_link&igsh=MzRlODBiNWFlZA==
More to come from Opensaucelive!*
Tomorrow is Opensaucelive 2025 and I thought what better place to test and share my build there. Wow, I’m so excited to share KAMPi with everyone at Opensauce. If you see me, please do say hello. I’ll also upload some photos from KAMPi at Opensauce below:
[*UPDATED July 20, 2025] See above sample photos I took at Opensaucelive 2025 using KAMPi. I chose the sharpest in focus images to share. Since it was my first time shooting with it, many of the photos came out blurry – which was exactly what I was expecting! I wanted KAMPi to emulate the feel of a film camera, capturing the moment. And KAMPi did just that. I’m also sure I’ll get better at taking photos with KAMPi with a litter more practice 🙂
[*UPDATED Sept 20, 2025] Updated the description of the KB 2040 to provide additional info on why I decided to use it over I2C.
Kampay (Cheers) for now!
Did you like my build? Would you like to learn more about it? Let me know at the comments below!
If you enjoyed reading this post please be sure to like, and follow us here at SKKAW.BLOG (IG: @skkaw) for more geek and pop-culture goodness.
#Adafruit #camera #CamPi #ComputeModule5 #digitalCamera #DIY #DIYCamera #DIYDigitalCamera #KAMPi #Opensauce #OpenSauceLive #PiCam #RaspberryPi #RaspberryPiCamera #RaspberryPiComputeModule5 #RaspberryPiComputeModule5Camera #SelfMade #selfMadeDigitalCamera #selfBuilt #SelfMade #Waveshare
#Microscope Upcycling: Transforming legacy microscopes into #automated #cloud-integrated #imaging systems:
-#OpenSource automation: X-Y positioning, focus stacking, image acquisition/storage
- cost: US $300
https://doi.org/10.1016/j.ohx.2025.e00637
#DIYbio #lab #instruments #microscopy #Raspi #PiCam #Python #OpenCV #StreamDeck #GUI
#openµView: A portable #RaspberryPi-based #OpenSource desktop #microscope:
-brightfield/oblique/dark-field & #Rheinberg illumination
-touchscreen display
-#LED ring
-#PiCam
https://doi.org/10.1016/j.ohx.2024.e00593
#DIYbio #lab #instruments #imaging #optics #microscopy #MINT #STEM #education
#OpenSource #strobe-enhanced #microscopy stage:
-workstation for sharp #imaging of fast processes
-developed for #microfluidic #droplet generation
-#PiCam lens: FoV ~1mm
-#LED strobe #illumination
Web: https://wenzel-lab.github.io/strobe-enhanced-microscopy-stage/
#DIYbio #lab #instruments #microscope #microfluidics
#PiRamid: A compact #3Dprinted #OpenSource #RaspberryPi #imaging box to #automate small-scale time-lapse digital analysis, suitable for #laboratory & field use:
-multiple units fit into a #microbiological incubator
https://doi.org/10.1016/j.ohx.2022.e00377
#DIYbio #lab #instruments #LED #PiCam
#Robotic #microscopy for everyone: The #OpenFlexure #OpenSource #microscope:
-#3Dprinted
-fully #automated using #RaspberryPi & #PiCam
https://doi.org/10.1364/BOE.385729
#DIYbio #lab #instruments #imaging #OFM #fluorescence #MINT #STEM #education
#Schistoscope: An #automated #OpenSource #microscope with #AI for detection of #Schistosoma haematobium #eggs in resource-limited settings:
-based on #RaspberryPi 4, #PiCam & #Arduino
https://doi.org/10.3390/mi13050643
#DIYbio #lab #instruments #NTDs #parasites #imaging #PoC #diagnostics
For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.
Wie Sie eine Radarfalle mit Pi-Cam selber bauen
Bauen Sie einen Bewegungsrekorder, der Videos analysiert, um zu schnell fahrende Fahrzeuge in Ihrer Straße zu erfassen.
#EchtzeitErkennung #Erkennungsalgorithmen #Kamera #PiCam #RaspberryPi
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