The Global Shutter Camera Module enables your OpenMV Cam to capture high-quality grayscale images without motion blur. The module features the MT9V034 Global Shutter Camera Module, capable of taking snapshot pictures on demand and running at 80 FPS in QVGA mode, 200 FPS in QQVGA mode, and 400 FPS in QQQVGA mode. The Global Shutter Camera Module is the ultimate sensor for machine vision enthusiasts who want to use their OpenMV Cam in professional products. Progressive Scan (Global Shutter) Versus Rolling Shutter - GraftekImaging Applications The global shutter camera is extremely useful for high FPS tracking applications and taking precision snapshots triggered by an external event. When running at a high FPS with low exposure the sensor allows you to capture very fast movement of illuminated objects. When in triggered mode you can easily take snapshots at the exact time an object is within the field of view. Product Parameters Processor: ARM® 32-bit Cortex®-M7 CPU with Double Precision FPU. Clock speed: 480 MHz (1027 DMIPS). Core Mark Score: 2400 (comparable to Raspberry Pi 2). RAM Layout (1MB Total): 256KB for .DATA/.BSS/Heap/Stack. 512KB for Frame Buffer/Stack. 256KB for DMA Buffers. Flash Layout (2MB Total): 128KB Bootloader / 128KB Embedded Flash Drive / 1792KB Firmware. Image Support: Formats: Grayscale, RGB565, JPEG, and BAYER. Max Resolution: Up to 640x480 for Grayscale and JPEG; up to 320x240 for raw RGB565. R2 Lens Specifications: Focal Length: 2.1mm | Aperture: F2.0 | Format: 1/6". Field of View: HFOV 60.7°, VFOV 47.5°. Mount: M12 * 0.5. Filter: Removable 650nm IR-cut filter. Electrical Information: I/O pins are 5V tolerant; output voltage is 3.3V. Maximum current per pin: 25mA (source or sink). Core Features Summary Line Tracking Automatically recognizes line continuity, enabling visual line-following for smart robots and vehicles. Color Tracking Capable of detecting up to 16 colors simultaneously, outputting data on the blob's position, size, center, and orientation. Circle Detection Easily detects circular shapes within the image using OpenMV. Rectangle Detection Utilizes square detection code from the AprilTag library to identify rectangular shapes. Digit Recognition Identifies numerical digits within the frame and displays the content in the IDE. QR Code Detection Reads and decodes QR codes, allowing intelligent robots to scan and recognize labels in their environment. Object Recognition Supports custom object recognition by capturing photos for learning, after which it can automatically identify specific items. Face & Eye Tracking Detects human faces and eyes, with the ability to determine gaze direction based on pupil movement.
