iRAYPLE A9507CX703 5MP Color CoaXPress Area Scan Camera

Applications this camera is commonly selected for. Open one to see the full recommended hardware and approach.

• datasheetiRAYPLE A9507CX703 — manufacturer page →
• datasheetiRAYPLE A9507CX703 — manufacturer page →
• softwareiRAYPLE Machine Vision SDK →
• downloadsiRAYPLE software & SDK downloads →

Follow these steps to bring up your iRAYPLE camera, capture a first image, and move from the viewer to your own application. The flow is the same across the A, AE, AX and L series; only the physical interface differs.

Software: iRAYPLE Machine Vision SDK

1. 1. Install the iRAYPLE SDK and viewer

   Download and install the iRAYPLE machine-vision SDK together with its viewer client for your operating system. The installer provides the camera drivers, the GenTL producer, and the viewer you will use for first light. For GigE Vision models, also install or enable the bundled filter/performance driver on the network adapter to maximize throughput.

2. 2. Connect the camera to the correct interface

   Wire the camera to its matching host interface: a dedicated Gigabit (or 10 GigE) NIC for GigE Vision models, a USB3 Vision port with a quality cable for USB3 models, or a frame grabber for CoaXPress models. Power the camera (PoE, USB bus power, or PoCXP / external supply as applicable). For GigE Vision, put the NIC and camera on the same subnet and, where supported, enable jumbo frames.

3. 3. Detect and open the camera in the viewer

   Launch the iRAYPLE viewer, run device enumeration, and select your camera from the discovered list. Open it to load the device feature tree (GenICam node map). If a GigE camera is found but cannot be opened, correct the IP configuration so the camera and NIC are in the same address range.

4. 4. Set exposure, gain and trigger, then grab a first image

   Start with continuous (free-run) acquisition under stable lighting. Adjust exposure time and gain for a well-exposed, low-noise image, set the pixel format (e.g. Mono8 or a Bayer/RGB format), and tune white balance on color models. Once the live image looks correct, switch trigger mode to hardware or software trigger to synchronize capture with your process, and verify a single triggered frame.

5. 5. Move from the viewer to the SDK or a GenICam library

   With imaging confirmed, reproduce the same settings in code using the SDK's C/C++ or Python API: enumerate, open, configure the relevant GenICam features, register a frame callback or grab loop, and convert frames to your pipeline's buffer format. Since the cameras are GenICam/GenTL-compliant, you can alternatively drive them from a third-party GenICam vision library. From here a stable image stream can feed a deep-learning inspection step such as Rosepetal for automated quality control.