The OpenMV Cam is a small, low power, microcontroller board featuring an STM32H743II Arm® Cortex® M7 processor running at 480MHz, which allows you to easily implement applications using machine vision in the real world. It is equipped with 32MB of SDRAM, 1MB of RAM and 2MB of flash.
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The OpenMV Cam is a small, low power, microcontroller board featuring an STM32H743II Arm® Cortex® M7 processor running at 480MHz, which allows you to easily implement applications using machine vision in the real world. It is equipped with 32MB of SDRAM, 1MB of RAM and 2MB of flash. You program the OpenMV Cam in high-level Python scripts (courtesy of the MicroPython Operating System) instead of C/C++. This makes it easier to deal with the complex outputs of machine vision algorithms and working with high-level data structures. But you still have total control over your OpenMV Cam and its I/O pins in Python. You can easily trigger taking pictures and videos on external events or execute machine vision algorithms to figure out how to control your I/O pins.
All this programming can be done on the OpenMV IDE which makes it easy to program your OpenMV Cam. OpenMV IDE features a modern Python multi-file text editor, a slick serial terminal, and a frame buffer to visualize what your OpenMV Cam sees in real-time.
The OpenMV Cam comes with an OV5640 image sensor is capable of taking 2592x1944 (5MP) images. Most simple algorithms will run at above 30 FPS on QVGA (320x240) resolutions and below. Your image sensor comes with a 2.8mm lens on a standard M12 lens mount.
Furthermore, it has a removable camera module system allowing it to interface with different sensors. Different camera modules such as FLIR Lepton Adapter Module and Global Shutter Camera Module can be used for more advanced projects. Also, if you want to use more specialized lenses with your image sensor you can easily attach them yourself.This also supports the addition of different shields such as Proto Shield, LCD Shield, Wi-Fi Shield, Servo Shield and Motor Shield to further expand your projects.
We have released two different OpenMV Cam products in the past and the table below illustrates the differences between them along with the newly released OpenMV Cam H7 Plus.
Don't forget to check our blog What is OpenMV Cam? The Arduino of Machine vision to explore more about OpenMV Cam!
You can use Frame Differencing on your OpenMV Cam to detect motion in a scene by looking at what's changed. Frame Differencing allows you to use your OpenMV Cam for security applications. Check out the video of the feature here.
You can use your OpenMV Cam to detect up to 16 colors at a time in an image (realistically you'd never want to find more than 4) and each color can have any number of distinct blobs. Your OpenMV Cam will then tell you the position, size, centroid, and orientation of each blob. Using color tracking your OpenMV Cam can be programmed to do things like tracking the sun, line following, target tracking, and much, much, more. Video demo here.
You can use your OpenMV Cam to detect groups of colors instead of independent colors. This allows you to create color makers (2 or more color tags) which can be put on objects allowing your OpenMV Cam to understand what the tagged objects are. Video demo here.
You can detect Faces with your OpenMV Cam (or any generic object). Your OpenMV Cam can process Haar Cascades to do generic object detection and comes with a built-in Frontal Face Cascade and Eye Haar Cascade to detect faces and eyes. Video demo here.
You can use Eye Tracking with your OpenMV Cam to detect someone's gaze. You can then, for example, use that to control a robot. Eye Tracking detects where the pupil is looking versus detecting if there's an eye in the image.
You can detect if there's a person in the field of view using our built-in person detector TensorFlow Lite model. Video demo here.
You can use Optical Flow to detect translation of what your OpenMV Cam is looking at. For example, you can use Optical Flow on a quad-copter to determine how stable it is in the air. See the video of the feature here.
You can use the OpenMV Cam to read QR Codes in it's field of view. With QR Code Detection/Decoding you can make smart robots which can read labels in the environment. You can see our video on this feature here.
The OpenMV Cam H7 Plus can also detect and decode data matrix 2D barcodes too. You can see our video on this feature here.
The OpenMV Cam H7 Plus can also decode 1D linear bar codes. In particular, it can decode EAN2, EAN5, EAN8, UPCE, ISBN10, UPCA, EAN13, ISBN13, I25, DATABAR, DARABAR_EXP, CODABAR, CODE39, CODE93, and CODE128 barcodes. You can see our video on this feature here.
Even better than QR Codes above, the OpenMV Cam H7 Plus can also track AprilTags. AprilTags are rotation, scale, shear, and lighting invariant state-of-the-art fiducial markers. We have a video on this feature here.
Infinite line detection can be done speedily on your OpenMV Cam at near max FPS. And, you can also find non-infinite length line segments too. You can see our video of this feature here. Additionally, we support running linear regressions on the image for use in line following applications like this DIY Robocar.
You can use the OpenMV Cam H7 Plus to easily detect circles in the image. See for yourself in this video.
The OpenMV Cam H7 Plus can also detect rectangles using our AprilTag library's quad detector code. Checkout the video here.
You can use template matching with your OpenMV Cam to detect when a translated pre-saved image is in view. For example, template matching can be used to find fiducials on a PCB or read known digits on a display.
You can use the OpenMV Cam to capture Grayscale/RGB565 BMP/JPG/PPM/PGM images. You directly control how images are captured in your Python script. Best of all, you can preform machine vision functions and/or draw on frames before saving them.
You can use the OpenMV Cam to record up to Grayscale/RGB565 MJPEG video or GIF images (or RAW video). You directly control how each frame of video is recorded in your Python script and have total control on how video recording starts and finishes. And, like capturing images, you can preform machine vision functions and/or draw on video frames before saving them.
TensorFlow Lite support lets you run custom image classification and segmentation models on board your OpenMV Cam. With TensorFlow Lite support you can easily classify complex regions of interest in view and control I/O pins based on what you see. See the TensorFlow module for more information.
Click here to view a larger version of the above pinout diagram.
OpenMV Cam Base Schematic (.pdf)
OpenMV Cam OV5640 Schematic (.pdf)
Processor Datasheet: STM32H743II (.pdf)
Reference Manual (.pdf)
Camera Datasheet: OV5640 (.pdf)
Software Application Note (.pdf)
Regulator Datasheet: PAM2305AAB330 (.pdf)
Camera Regulator Datasheet: TPS731 (.pdf)
SDRAM Datasheet: IS42S32800G (.pdf)
FLASH Datasheet: W25Q256JV (.pdf)