- Overview
- TensorFlow support
- Interface library
- Building the firmware from source
- Contributing to the project
The OpenMV project aims at making machine vision more accessible to beginners by developing a user-friendly, open-source, low-cost machine vision platform. 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 Cam comes with a cross-platform IDE (based on Qt Creator) designed specifically to support programmable cameras. 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 first generation of OpenMV cameras is based on STM32 ARM Cortex-M Digital Signal Processors (DSPs) and OmniVision sensors. 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. 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 OpenMV project was successfully funded via Kickstarter back in 2015 and has come a long way since then. For more information, please visit https://openmv.io
The OpenMV firmware supports loading quantized TensorFlow Lite models. 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. To load an external TensorFlow model from the filesystem from Python use tf Python module. For information on embedding TensorFlow models into the firmware, and loading them, please see TensorFlow Support.
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 on the OpenMV Cam H7.
- I2C Bus - at up to 1 Mb/s on the OpenMV Cam H7.
- Using 1K pull up resistors.
- SPI Bus - at up to 20 Mb/s on the OpenMV Cam H7.
- Up to 80 Mb/s or 40 Mb/s is achievable with short enough wires.
- CAN Bus - at up to 1 Mb/s on the OpenMV Cam H7.
- USB Virtual COM Port (VCP) - at up to 12 Mb/s on the OpenMV Cam M4/M7/H7.
- WiFi using the WiFi Shield - at up to 12 Mb/s on the OpenMV Cam M4/M7/H7.
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.
You can find examples that run on the OpenMV Cam under File->Examples->Remote Control in OpenMV IDE and online here. Finally, OpenMV provides the following libraries for interfacing your OpenMV Cam to other systems below:
- Generic Python Interface Library for USB VCP, Ethernet/WiFi, UART, Kvarser CAN, and I2C/SPI Comms
- Provides Python code for connecting your OpenMV Cam to a Windows, Mac, or Linux computer (or RaspberryPi/Beaglebone, etc.).
- Supports USB VCP on all systems. E.g. direct USB connection to the OpenMV Cam.
- Supports Ethernet/WiFi on all systems.
- Supports RS232/RS422/RS485/TTL UARTs on all systems. E.g. 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 Kvarser CAN on Windows and Linux (Kvarser does not support Mac).
- Supports I2C/SPI on Linux SBCs like the RaspberryPi/Beaglebone, etc. (coming soon)
- Provides Python code for connecting your OpenMV Cam to a Windows, Mac, or Linux computer (or RaspberryPi/Beaglebone, etc.).
- Arduino Interface Library for CAN, I2C, SPI, UART Comms
- Works on all Arduino variants.
- CAN support via the MCP2515 over SPI or via the CAN peripheral on the ESP32.
If you only need to read print() output from a script running on an OpenMV camera over USB, then you only need to open the OpenMV camera Virtual COM Port and read lines of text from the serial port. For example (using pyserial):
import serial
ser = serial.Serial("COM3", timeout=1, dsrdtr=False)
while True:
line = ser.readline().strip()
if line: print(line)The above code works for Windows, Mac, or Linux. You just need to change the above port name to the same name of the USB VCP port the OpenMV Cam shows up as (it will be under /dev/ on Mac or Linux). Note that if you are opening the USB VCP port using another serial library and/or language make sure to set the DTR line to false - otherwise the OpenMV Cam will suppress printed output.
The easiest way to patch the firmware and rebuild it, is to fork this repository, enable Actions (from the Actions tab) in the forked repository, and pushing the changes. Our GitHub workflow rebuilds the firmware on pushes to the master branch and/or merging pull requests and generates a development release with attached separate firmware packages per supported board. For more complex changes, and building the OpenMV firmware from source locally, see Building the Firmware From Source.
Contributions are most welcome. If you are interested in contributing to the project, start by creating a fork of each of the following repositories:
Clone the forked openmv repository, and add a remote to the main openmv repository:
git clone --recursive https://github.com/<username>/openmv.git
git -C openmv remote add upstream https://github.com/openmv/openmv.gitSet the origin remote of the micropython submodule to the forked micropython repo:
git -C openmv/src/micropython remote set-url origin https://github.com/<username>/micropython.gitFinally add a remote to openmv's micropython fork:
git -C openmv/src/micropython remote add upstream https://github.com/openmv/micropython.gitNow the repositories are ready for pull requests. To send a pull request, create a new feature branch and push it to origin, and use Github to create the pull request from the forked repository to the upstream openmv/micropython repository. For example:
git checkout -b <some_branch_name>
<commit changes>
git push origin -u <some_branch_name>Please follow the best practices when sending pull requests upstream. In general, the pull request should:
- Fix one problem. Don't try to tackle multiple issues at once.
- Split the changes into logical groups using git commits.
- Pull request title should be less than 78 characters, and match this pattern:
<scope>:<1 space><description><.>
- Commit subject line should be less than 78 characters, and match this pattern:
<scope>:<1 space><description><.>
Example PR titles or commit subject lines:
github: Update workflows.
Libtf: Add support for built-in models.
RPC library: Remove CAN bit timing function.
OPENMV4: Add readme template file.
ports/stm32/main.c: Fix storage label.
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