the old school DB9 port on the back of a PC, USB to serial RS232/RS422/RS485/TTL adapters, and TTL serial on I/O pins on SBCs like the RaspberryPi/Beaglebone. Supports RS232/RS422/RS485/TTL UARTs on all systems.Provides Python code for connecting your OpenMV Cam to a Windows, Mac, or Linux computer (or RaspberryPi/Beaglebone, etc.).Generic Python Interface Library for USB VCP, Ethernet/WiFi, UART, Kvarser CAN, and I2C/SPI Comms.Finally, OpenMV provides the following libraries for interfacing your OpenMV Cam to other systems below: You can find examples that run on the OpenMV Cam under File->Examples->Remote Control in OpenMV IDE and online here. With the RPC Library you can easily get image processing results, stream RAW or JPG image data, or have the OpenMV Cam control another Microcontroller for lower-level hardware control like driving motors. WiFi using the WiFi Shield - at up to 12 Mb/s on the OpenMV Cam M4/M7/H7.USB Virtual COM Port (VCP) - at up to 12 Mb/s on the OpenMV Cam M4/M7/H7.CAN Bus - at up to 1 Mb/s on the OpenMV Cam H7.Up to 80 Mb/s or 40 Mb/s is achievable with short enough wires.SPI Bus - at up to 20 Mb/s on the OpenMV Cam H7.I2C Bus - at up to 1 Mb/s on the OpenMV Cam H7.Async Serial (UART) - at up 7.5 Mb/s on the OpenMV Cam H7.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. For information on embedding Tensorflow models into the firmware, and loading them, please see Tensorflow Support. To load an external Tensorflow model from the filesystem from Python use tf Python module. The firmware supports loading external models that reside on the filesystem to memory (on boards with SDRAM), and internal models (embedded into the firmware) in place. The OpenMV firmware supports loading quantized Tensorflow Lite models. For more information, please visit Tensorflow support The OpenMV project was successfully funded via Kickstarter back in 2015 and has come a long way since then. Additionally, the OpenMV Cam supports extension modules (shields) using the I/O headers for adding a WiFi adapter, a LCD Display, a Thermal Vision Sensor, a Motor Driver, and more. The boards have built-in RGB and IR LEDs, USB FS support for programming and video streaming, a uSD socket, and I/O headers breaking out PWM, UARTs, SPI, I2C, CAN, and more. The first generation of OpenMV cameras is based on STM32 ARM Cortex-M Digital Signal Processors (DSPs) and OmniVision sensors. The IDE allows viewing the camera's frame buffer, accessing sensor controls, uploading scripts to the camera via serial over USB (or WiFi/BLE if available) and includes a set of image processing tools to generate tags, thresholds, keypoints, and etc. The OpenMV Cam comes with a cross-platform IDE (based on Qt Creator) designed specifically to support programmable cameras. OpenMV cameras are programmable in Python3 and come with an extensive set of machine learning and image processing functions such as face detection, keypoints descriptors, color tracking, QR and Bar code decoding, AprilTags, GIF and MJPEG recording, and more. The OpenMV project aims at making machine vision more accessible to beginners by developing a user-friendly, open-source, low-cost machine vision platform.
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