Mạch OpenMV Cam H7 Machine Vision Camera Module được sử dụng trong các ứng dụng xử lý, nhận dạng hình ảnh, mạch có thể hoạt động độc lập, kết nối với Vi điều khiển qua giao tiếp UART hoặc kết nối với máy tính qua cổng USB trên mạch để lập trình với trình biên dịch IDE chuyên biệt bằng ngôn ngữ MicroPython giúp việc lập trình xử lý, nhận dạng hình ảnh trở nên cực kỳ dễ dàng, hãy xem video dưới đây để cảm nhận:
Lưu ý mạch là bản đi kèm camera OV7725, để giảm chi phí Hshop.vn nhập bản không đi kèm OpenMV Cam Board Key tuy nhiên vẫn có thể sử dụng đầy đủ chức năng với IDE (khi phần mềm hỏi key chỉ cần cancel là sử dụng bình thường), bạn cũng có thể mua key để đăng ký tại trang chủ của OpenMV nếu cần với giá 15USD.
The STM32H743VI ARM Cortex M7 processor running at 480 MHz with 1MB 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.
An SPI bus that can run up to 100Mbs allowing you to easily stream image data of 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.
A removable camera module system allowing the OpenMV Cam H7 to interface with different sensors:
The OpenMV Cam H7 comes with an OV7725 image sensor is capable of taking 640x480 8-bit grayscale images or 640x480 16-bit RGB565 images at 60 FPS when the resolution is above 320x240 and 120 FPS when it is below. Most simple algorithms will run at above 60 FPS. Your image sensor comes with a 2.8mm lens on a standard M12 lens mount. If you want to use more specialized lenses with your image sensor you can easily buy and attach them yourself.
A LiPo battery connector is compatible with 3.7V LiPo batteries commonly sold online for hobbyist robotics applications.
The OpenMV Cam can be used for the following things currently (more in the future):
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.
QR Code Detection/Decoding
You can use the OpenMV Cam to read QR Codes in its field of view. With QR Code Detection/Decoding you can make smart robots that can read labels in the environment. You can see our video on this feature here.
Data Matrix Detection/Decoding
The OpenMV Cam H7 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 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. You can see our video on this feature here.
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 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 to easily detect circles in the image. See for yourself in this video.
The OpenMV Cam H7 can also detect rectangles using our AprilTag library's quad detector code. Check out 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 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.
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.
TensorFlow Lite for Microcontrollers Support
TensorFlow Lite support lets you run custom image classification and segmentation models onboard 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.