OpenMV Cam H7 Plus

The OpenMV Cam is a small, low power, microcontroller board which allows you to easily implement applications using machine vision in the real-world. 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 video on external events or execute machine vision algorithms to figure out how to control your I/O pins.

 

Interface Library：

The OpenMV Cam comes built-in with an RPC (Remote Python/Procedure Call) library which makes it easy to connect the OpenMV Cam to your computer, a SBC (single board computer) like the RaspberryPi or Beaglebone, or a microcontroller like the Arduino or ESP8266/32. The RPC Interface Library works over:

• Async Serial (UART) - at up 7.5 Mb/s.
• I2C Bus - at up to 1 Mb/s.
• SPI Bus - at up to 80 Mb/s.
• CAN Bus - at up to 1 Mb/s.
• USB Virtual COM Port (VCP) - at up to 12 Mb/s.
• WiFi using the WiFi Shield - at up to 12 Mb/s.

OpenMV provides the following libraries for interfacing your OpenMV Cam to other systems below:

• Generic Python Interface Library for USB and WiFi Comms
• Provides Python code for connecting your OpenMV Cam to a Windows, Mac, or Linux computer (or Raspberry Pi/Beaglebone, etc.) with python programmatically over USB VCP or Ethernet/ WiFi (i.e. with sockets).
• Arduino Interface Library for I2C, SPI, CAN, and UART Comms

• The STM32H743II ARM Cortex M7 processor running at 480 MHz with 32MBs SDRAM + 1MB of SRAM and 32 MB of external flash + 2 MB of internal 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.
• A μSD Card socket capable of 100Mbs reads/writes
• A SPI bus that can run up to 80Mbs allowing you to easily stream image data off the system to either the LCD Shield, the WiFi Shield, or another microcontroller.
• A 12-bit ADC and a 12-bit DAC
• Two 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.

• TensorFlow Lite for Microcontrollers Support

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.

• 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.

• 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.

• 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.

• Person Detection

You can detect if there's a person in the field of view using our built-in person detector TensorFlow Lite model.

• 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.

• Data Matrix Detection/Decoding

The OpenMV Cam H7 can also detect and decode data matrix 2D barcodes too.

• Linear Barcode Decoding

The OpenMV Cam H7 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.

• AprilTag Tracking

Even better than QR Codes above, the OpenMV Cam H7 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.

• 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.

• Circle Detection

You can use the OpenMV Cam H7 to easily detect circles in the image.

• Rectangle Detection

The OpenMV Cam H7 can also detect rectangles using our AprilTag library's quad detector code.

• 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 640x480 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.

• Video Recording

You can use the OpenMV Cam to record up to 640x480 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.

• ARM® 32-bit Cortex®-M7 CPU
• w/ Double Precision FPU
• 480 MHz (1027 DMIPS)
• Core Mark Score: 2400 (compare w/ Raspberry Pi 2: 2340)
• 64KB Stack
• 256KB .DATA/.BSS/Heap
• 32MB Frame Buffer/Stack
• 512KB SDRAM Cache
• 256KB DMA Buffers
• 128KB Bootloader
• 16MB Embedded Flash Drive
• 1792KB Firmware
• Output/Input:3.3v/5v
• Power Consumption
•  Idle - No μSD Card 140mA @ 3.3V
•  Idle - μSD Card 140mA @ 3.3V
•  Active - No μSD Card 230mA @ 3.3V
•  Active - μSD Card 240mA @ 3.3V
• Temperature Range
•  Storage -40°C to 125°C
•  Operating -20°C to 70°C

• OpenMV Cam H7 Plus x1