The OpenMV Cam is an all in one embedded machine vision developer platform. Build your own machine vision system faster using high level python scrips (based on MicroPythonn). Using python instead of C/C++ make is considerably easier to deal with complex data structures used with machine vision algorithms. Hoever this high level programming approach does not limit you as you can still have total control over the I/O pins via Python. Included with your OpenMV cam is a Python openMV IDE that lets you easily build and deploy your own algorithms and has many examples from face detection to optic flow. The IO pins of the openMV cam also let you add you own shield to extend the functionality. You can think of the openMV cam as an Arduino for machine vision.

Key Features

• Build and deploy your machine vision algorithms quickly using the openMV python IDE
• Loads of example scripts to get your started, inlcuding face tracking, template matching, optic flow, MAVlink (to integrate with ardupilot systems)
• Lots of horsepower, powered by a 216 MHz ARM Cortex M7 processor with 512KB of RAM
• Expandable through the use of shields.  Add WiFi, Bluetooth, thermopile etc..
• Sensor uses M12 Lens mount, giving you the flexibility of using whatever lens you want.

The OpenMV Cam hardware features:

• The STM32F765VI ARM Cortex M7 processor running at 216 MHz with 512KB of RAM and 2 MB of flash. All I/O pins output 3.3V and are 5V tolerant. The processor has the following I/O interfaces:
• A full speed USB (12Mbs) interface to your computer. Your OpenMV Cam will appear as a Virtual COM Port and a USB Flash Drive when plugged in.
• A μSD Card socket capable of 100Mbs reads/writes which allows your OpenMV Cam to record video and easy pull machine vision assets off of the μSD card.
• A SPI bus that can run up to 54Mbs allowing you to easily stream image data off the system to either the LCD Shield, the WiFi Shield, or another microcontroller.
• An I2C Bus, CAN Bus, and an Asynchronous Serial Bus (TX/RX) for interfacing with other microcontrollers and sensors.
• A 12-bit ADC and a 12-bit DAC.
• Three I/O pins for servo control.
• Interrupts and PWM on all I/O pins (there are 10 I/O pins on the board).
• And, an RGB LED and two high power 850nm IR LEDs.
• The OV7725 image sensor is capable of taking 640x480 8-bit Grayscale images or 320x240 16-bit RGB565 images at 30 FPS. Your OpenMV Cam comes with a 2.8mm lens on a standard M12 lens mount. If you want to use more specialized lenses with your OpenMV Cam you can easily buy and attach them yourself.

For more information about the OpenMV Cam please see our documentation.

OpenMV Cam M7 Pinout

Applications

The OpenMV Cam can be used for the following things currently (more in the future):

• Frame Differencing
• 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.
• Color Tracking
• 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.
• Marker Tracking
• 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.
• Face Detection
• 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.
• Eye Tracking
• 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.
• Optical Flow
• 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.
• QR Code Detection/Decoding
• 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.
• Data Matrix Detection/Decoding
• The OpenMV Cam M7 can also detect and decode data matrix 2D barcodes too. You can see our video on this feature here.
• Linear Barcode Decoding
• The OpenMV Cam M7 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.
• AprilTag Tracking
• Even better than QR Codes above, the OpenMV Cam M7 can also track AprilTags at 160x120 at up to about 12 FPS. AprilTags are rotation, scale, shear, and lighting invariant state-of-the-art fidicual markers. We have a video on this feature here.
• Line Detection
• 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.
• Template Matching
• 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.
• Image Capture
• You can use the OpenMV Cam to capture up to 320x240 RGB565 (or 640x480 Grayscale) 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.
• Video Recording
• You can use the OpenMV Cam to record up to 320x240 RGB565 (or 640x480 Grayscale) MJPEG video or GIF images. 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.

Finally, all the above features can be mixed and matched in your own custom application along with I/O pin control to talk to the real world.

Specifications

Power Consumption

Temperature Range