A photo of the camera.

F/0.38 Camera Lens Made With Oil Immersion Microscope Objective

Over on YouTube [Applied Science] shows us how to make an f/0.38 camera lens using an oil immersion microscope objective.

The f-number of a lens indicates how well it will perform in low-light. To calculate the f-number you divide the focal length by the diameter of the aperture. A common f-number is f/1.4 which is generally considered “fast”.

We are told the fastest commercial lens ever used had f/0.7 and was used by Stanley Kubrick to shoot the film Barry Lyndon which was recorded only with candle light.

A microscope objective is a crucial lens that gathers and magnifies light to form an image. It plays a key role in determining the quality and clarity of the final magnified image produced by a microscope.

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A Trail Camera Built With Raspberry Pi

You can get all kinds of great wildlife footage if you trek out into the woods with a camera, but it can be tough to stay awake all night. However, this is a task you can readily automate, as [Luke] did with his DIY trail camera.

A Raspberry Pi Zero 2W serves as the heart of the build. It’s compact and runs on very little power, but also provides a good amount more processing power than the original Raspberry Pi Zero. It’s kitted out with the Raspberry Pi AI Camera, which uses the Sony IMX500 Intelligent Vision Sensor — providing a great platform for neural networks doing image classification and similar machine learning tasks. A Witty Pi power management module is used both for its real time clock and to schedule start-ups and shutdowns to best manage the power on offer from the batteries. All these components are wrapped up in a 3D printed housing to keep the Pi safe out in the wild.

We’ve seen some neat projects in this vein before.

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Detecting Surveillance Cameras With The ESP32

These days, surveillance cameras are all around us, and they’re smarter than ever. In particular, many of them are running advanced algorithms to recognize faces and scan license plates, compiling ever-greater databases on the movements and lives of individuals. Flock You is a project that aims to, at the very least, catalogue this part of the surveillance state, by detecting these cameras out in the wild.

The system is most specifically set up to detect surveillance cameras from Flock Safety, though it’s worth noting a wide range of companies produce plate-reading cameras and associated surveillance systems these days. The device uses an ESP32 microcontroller to detect these devices, relying on the in-built wireless hardware to do the job. The project can be built on a Oui-Spy device from Colonel Panic, or just by using a standard Xiao ESP32 S3 if so desired. By looking at Wi-Fi probe requests and beacon frames, as well as Bluetooth advertisements, it’s possible for the device to pick up telltale transmissions from a range of these cameras, with various pattern-matching techniques and MAC addresses used to filter results in this regard. When the device finds a camera, it sounds a buzzer notifying the user of this fact.

Meanwhile, if you’re interested in just how prevalent plate-reading cameras really are, you might also find deflock.me interesting. It’s a map of ALPR camera locations all over the world,  and you can submit your own findings if so desired. The techniques used by in the Flock You project are based on learnings from the DeFlock project. Meanwhile, if you want to join the surveillance state on your own terms, you can always build your own license plate reader instead!

[Thanks to Eric for the tip!]

Build Your Own 6K Camera

[Curious Scientist] has been working with some image sensors. The latest project around it is a 6K camera. Of course, the sensor gives you a lot of it, but it also requires some off-the-shelf parts and, of course, some 3D printed components.

An off-the-shelf part of a case provides a reliable C mount. There’s also an IR filter in a 3D-printed bracket.

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A man holds a license plate in front of a black pickup (F-150 Lightning) tailgate. It is a novelty Georgia plate with the designation P00-5000. There are specks of black superimposed over the plate with a transparent sticker, giving it the appearance of digital mud in black.

A Deep Dive On Creepy Cameras

George Orwell might’ve predicted the surveillance state, but it’s still surprising how many entities took 1984 as a how-to manual instead of a cautionary tale. [Benn Jordan] decided to take a closer look at the creepy cameras invading our public spaces and how to circumvent them.

[Jordan] starts us off with an overview of how machine learning “AI” is used Automated License Plate Reader (ALPR) cameras and some of the history behind their usage in the United States. Basically, when you drive by one of these cameras, an ” image segmentation model or something similar” detects the license plate and then runs optical character recognition (OCR) on the plate contents. It will also catalog any bumper stickers with the make and model of the car for a pretty good guess of it being your vehicle, even if the OCR isn’t 100% on the exact plate sequence.

Where the video gets really interesting is when [Jordan] starts disassembling, building, and designing countermeasures to these systems. We get a teardown of a Motorola ALPR for in-vehicle use that is better at being closed hardware than it is at reading license plates, and [Jordan] uses a Raspberry Pi 5, a Halo AI board, and You Only Look Once (YOLO) recognition software to build a “computer vision system that’s much more accurate than anything on the market for law enforcement” for $250.

[Jordan] was able to develop a transparent sticker that renders a license plate unreadable to the ALPR but still plainly visible to a human observer. What’s interesting is that depending on the pattern, the system could read it as either an incorrect alphanumeric sequence or miss detecting the license plate entirely. It turns out, filtering all the rectangles in the world to find just license plates is a tricky problem if you’re a computer. You can find the code on his Github, if you want to take a gander.

You’ve probably heard about using IR LEDs to confuse security cameras, but what about yarn? If you’re looking for more artistic uses for AI image processing, how about this camera that only takes nudes or this one that generates a picture based on geographic data?

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How To Have A Medium Format Camera Without Breaking The Bank

For most people, experimentation with film photography comes in the form of the 35 mm format. Its ubiquity in snapshot photography means cameras are readily available at all levels, and the film offers a decent compromise between resolution and number of shots per dollar spent.

For those who wish to take their film photography further there’s the so-called medium format 120 roll film, but here opting for a higher-end camera can become expensive. Fortunately [Javier Doroteo] is here with a 3D printed medium format camera designed to use lenses intended for the Mamiya Press cameras, and from where we’re sitting it looks very nicely designed indeed.

All the files can be found on Printables along with a list of the other parts required. It’s made simple by the Mamiya lenses incorporating the shutter, but there’s still a lot of attention that has been paid to the back of the camera. This is the third version of the design and it shows, details such as the film holder and light proofing are well thought out.

Photography is so often a world in which collecting the latest kit is seen as more important than the photographs themselves, so we like and encourage camera hackers as a reaction to all that. If you’d like to see another medium format camera, this certainly isn’t the first we’ve brought you.

Camera And ChArUco Keep The Skew Out Of Your 3D Prints

Do you or a loved one suffer from distorted 3D prints? Does your laser cutter produce parallelograms instead of rectangles? If so, you might be suffering from CNC skew miscalibration, and you could be entitled to significant compensation for your pain and suffering. Or, in the reality-based world, you could simply fix the problem yourself with this machine-vision skew correction system and get back to work.

If you want to put [Marius Wachtler]’s solution to work for you, it’s probably best to review his earlier work on pressure-advance correction. The tool-mounted endoscopic camera he used in that project is key to this one, but rather than monitoring a test print for optimum pressure settings, he’s using it to detect minor differences in the X-Y feed rates, which can turn what’s supposed to be a 90-degree angle into something else.

The key to detecting these problems is the so-called ChArUco board, which is a hybrid of a standard chess board pattern with ArUco markers added to the white squares. ArUco markers are a little like 2D barcodes in that they encode an identifier in an array of black and white pixels. [Marius] provides a PDF of a ChArUco that can be printed and pasted to a board, along with a skew correction program that analyzes the ChArUco pattern and produces Klipper commands to adjust for any skew detected in the X-Y plane. The video below goes over the basics.

For as clever and useful as ChArUco patterns seem to be, we’re surprised we haven’t seen them used for more than this CNC toolpath visualization project (although we do see the occasional appearance of ArUco). We wonder what other applications there might be for these boards. OpenCV supports it, so let us know what you come up with.

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