Camera Calibration

Basic Knowledge Requirements

Before diving into this code, here's a quick heads-up on what you'll need to be familiar with:

1. Python Programming: It's important to have a good grasp of Python, especially with concepts like functions, loops, and classes, since the example utilizes these fundamentals.
2. Asynchronous Programming with asyncio: Familiarity with Python's asyncio for writing concurrent code using the async/await syntax.
3. Camera Calibration Concepts: Basic knowledge about camera calibration, including what it is and why it's important, will help you understand the context and the results returned by the example.
4. farm-ng Oak Service Overview: This overview provides a base understanding of the gRPC service the client you create will connect to.

The Camera Calibration example operates as a standalone Python script, in which an EventClient to an Oak camera service running on an Amiga brain is created. The calibration of the Oak camera is printed in the terminal.

You can either run this example directly on a brain by ssh'ing in, or use your local PC. If using your local PC, it should be either connected to the same local network as the brain or linked to it through tailscale.

Ensure that a farm-ng brain running Oak cameras is active.

1. Install the farm-ng Brain ADK package

2. Setup

tip

It is recommended to also install these dependencies and run the example in the brain ADK virtual environment.

Create first a virtual environment

python3 -m venv venv
source venv/bin/activate

# assuming you're already in the amiga-dev-kit/ directory
cd farm-ng-amiga/py/examples/camera_calibration

3. Install the example's dependencies

pip3 install -r requirements.txt

4. Execute the Python script

info

To run this script from your PC, you need to update the service_config.json by modifying the host field with your Amiga brain name.

Please check out Amiga Development 101 for more details.

python3 main.py --service-config service_config.json

5. Customize run

# usage: amiga-camera-calibration [-h] --service-config SERVICE_CONFIG
#
# optional arguments:
#   -h, --help            show this help message and exit
#   --service-config SERVICE_CONFIG
#                         The camera config.

6. Code overview

In this example we use the EventClient with the request_reply method to receive the camera camera calibration. The request_reply method is a coroutine that returns a Future object. The Future object is used to retrieve the result of the request.

The path to the calibration service is /calibration and the request message is Empty. The response message is OakCalibration, which is automatically decoded by the request_reply method using the decode=True argument.

async def main(service_config_path: Path) -> None:
    """Request the camera calibration from the camera service.

    Args:
        service_config_path (Path): The path to the camera service config.
    """
    # create a client to the camera service
    config: EventServiceConfig = proto_from_json_file(service_config_path, EventServiceConfig())

    # get the calibration message
    calibration: oak_pb2.OakCalibration =
        await EventClient(config).request_reply("/calibration", Empty(), decode=True)
    print(calibration)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(prog="amiga-camera-calibration")
    parser.add_argument("--service-config", type=Path, required=True, help="The camera config.")
    args = parser.parse_args()

    asyncio.run(main(args.service_config))

tip

We highly recommend to have some basic knowledge about asyncio.