Camera Intrinsics

Zivid camera model is more complex, using more intrinsic parameters, than some well-known pinhole camera models (e.g., OpenCV camera model). In addition, our cameras utilize a rolling calibration, which means that the point clouds are generated as a function of aperture, temperature, and color channels.

Zivid camera model is proprietary, which is why our internal camera intrinsics are not available in the SDK. However, we provide approximations of OpenCV and Halcon models since many machine vision algorithms rely on them.

小心

In general, we tend to discourage using camera intrinsics. One reason is that valuable information is lost in the approximation. Another reason is that there are often better methods to achieve the same result. Because of the complexity of the topic, we advise reaching out to us to discuss your use case. Intrinsics are complex; use point cloud whenever possible!

OpenCV camera intrinsics

Zivid SDK offers two options to get OpenCV camera intrinsics.

Hard-coded camera intrinsics

The hard-coded camera intrinsics are given for a single aperture and a single temperature. This temperature and apertures correspond to what we consider typical conditions.

Camera Model	Lens Temperature (°C)	Aperture
One+	35	6.2
Two	35	2.38

To get the hard-coded camera intrinsics, you first have to connect to the camera:

C++

std::cout << "Connecting to camera" << std::endl;
auto camera = zivid.connectCamera();

C#

Console.WriteLine("Connecting to camera");
var camera = zivid.ConnectCamera();

Python

print("Connecting to camera")
camera = app.connect_camera()

Then, you can get the OpenCV camera intrinsics (this function returns right away):

C++

std::cout << "Getting camera intrinsics" << std::endl;
const auto intrinsics = Zivid::Experimental::Calibration::intrinsics(camera);

C#

Console.WriteLine("Getting camera intrinsics");
var intrinsics = Zivid.NET.Experimental.Calibration.Calibrator.Intrinsics(camera);

Python

print("Getting camera intrinsics")
intrinsics = calibration.intrinsics(camera)

小心

The hard-coded OpenCV intrinsics are not a function of temperature and aperture, unlike our point clouds. Therefore, they will not correspond perfectly to a capture taken with a different aperture at a different temperature. The situation becomes even more complicated if a point cloud is captured using the HDR mode where each acquisition uses a different aperture. This is because it is not trivial to determine the intrinsics for such a case. Because of these complexities, we provide an alternative method to get OpenCV camera intrinsics: the camera intrinsics estimated from the point cloud.

Camera intrinsics estimated from the point cloud

These camera intrinsics are estimated from the point cloud. Using our calibration, a point cloud is generated as a function of temperature and aperture. Therefore, the estimated intrinsics indirectly utilize the temperature and the aperture.

To get the estimated OpenCV intrinsics, you first have to connect to the camera:

C++

std::cout << "Connecting to camera" << std::endl;
auto camera = zivid.connectCamera();

C#

Console.WriteLine("Connecting to camera");
var camera = zivid.ConnectCamera();

Python

print("Connecting to camera")
camera = app.connect_camera()

Then you have to capture a frame:

C++

const auto frame = camera.capture(settings);

C#

var frame = camera.Capture(settings);

Python

with camera.capture(settings=settings) as frame:

Then, you can estimate the intrinsics from the frame (because there is computation involved, this function does not return right away):

C++

const auto estimated_intrinsics = Zivid::Experimental::Calibration::estimateIntrinsics(frame);

C#

var estimatedIntrinsics = Zivid.NET.Experimental.Calibration.Calibrator.EstimateIntrinsics(frame);

Python

estimated_intrinsics = calibration.estimate_intrinsics(frame)

Saving camera intrinsics

It is possible to save OpenCV camera intrinsics to a YML file:

C++

const std::string outputFile = "Intrinsics.yml";
std::cout << "Saving camera intrinsics to file: " << outputFile << std::endl;
intrinsics.save(outputFile);

C#

var intrinsicsFile = "Intrinsics.yml";
Console.WriteLine("Saving camera intrinsics to file: " + intrinsicsFile);
intrinsics.Save(intrinsicsFile);

Python

output_file = "Intrinsics.yml"
print(f"Saving camera intrinsics to file: {output_file}")
intrinsics.save(output_file)

Halcon camera intrinsics

Halcon uses a camera model different than Zivid and OpenCV camera models. There are two options to get Halcon intrinsics and both are based on approximation from the OpenCV model.

小心

If you need to use Halcon intrinsics (Camera internal parameters), use one of the approaches described below to get them. Do not calibrate our 2D camera in Halcon to get Halcon intrinsics; it does not work well with our camera.

From Camera

Requirements:

Python installed and skills to run Python scripts
Zivid-Python installed

The simplest method to get Halcon intrinsics for your camera is to run convert_intrinsics_opencv_to_halcon.py code sample. Read the sample description with a focus on example when reading from camera.

备注

This method is limited to getting the hard-coded camera intrinsics.

From YML file

Requirements:

Python installed and skills to run Python scripts
Skills to build C++ or C# code samples

The other method to get Halcon intrinsics for your camera is to load the OpenCV camera intrinsics from the YML file and convert them to Halcon format.

To get OpenCV camera intrinsics and save them to a file, run one of the following samples:

C++

To get OpenCV camera intrinsics and save them to a file, run GetCameraIntrinsics.cpp. See 使用 CMake 配置 C++ 示例并在 Windows 的 Visual Studio 中构建它们.

C#

To get OpenCV camera intrinsics and save them to a file, run GetCameraIntrinsics.cs. See 使用Visual Studio构建C#示例 for building C# samples.

Python

To get OpenCV camera intrinsics and save them to a file, run get_camera_intrinsics.py.

Then, run convert_intrinsics_opencv_to_halcon.py code sample. Read the sample description with a focus on Example when reading from file.

备注

This method allows getting both hard-coded camera intrinsics and camera intrinsics estimated from the point cloud. However, for the latter, this assumes saving and loading from the file for each capture you need intrinsics for.

Which camera intrinsics should I use?

Hard-coded camera intrinsics

We recommend using hard-coded camera intrinsics only assuming the following:

You get the color image from either of:
- 2D capture
- Single acquisition 3D capture
- Multi-acquisition HDR 3D capture with Color Mode set to UseFirstAcquisition.
For your relevant acquisition (one of the three options above) you use the aperture value for which we provide the hard-coded intrinsics (see the table).
For temperatures close to the room temperature.
For applications where you use only 2D data, e.g., undistorting the image to detect straight lines.

Camera intrinsics estimated from point cloud

We recommend using estimated intrinsics assuming:

You get the color image from multi-acquisition HDR 3D capture with Color Mode set to Automatic or ToneMapping. This is because the point cloud is likely a result of acquisitions with different apertures and at a temperature different than the one corresponding to the hard-coded intrinsics.
Any other use case when using OpenCV intrinsics is necessary, for example:
- projecting 3D data to a 2D image using projectPoints().
- estimating 3D pose from a 2D image using solvePnP().

备注

The estimated camera intrinsics also work well for all cases where we recommend the hard-coded camera intrinsics. Therefore, for simplicity, you could always use the estimated intrinsics. However, estimating intrinsics from the point cloud takes time, whereas getting the hard-coded intrinsics from the camera is instantaneous.

Hand-Eye Calibration

In general, the hand-eye calibration we recommend is our 手眼标定, as it is best suited for Zivid cameras. Our hand-eye calibration method does not require camera intrinsics; however, many other methods do. If you use one of the other methods, we recommend using camera intrinsics estimated from the point cloud. Read more about choosing the correct Hand-Eye calibration method.

Projecting 3D points to a 2D image plane

When projecting Zivid point clouds onto a 2D image plane, using estimated intrinsics will result in smaller re-projection errors than using hard-coded intrinsics. However, keep in mind that using our correction filters, e.g. Gaussian Smoothing（高斯平滑） and Contrast Distortion Filter（对比度失真过滤器） will result in higher re-projection errors. When using estimated intrinsics on a point cloud captured without correction filters, less than 1 pixel of re-projection error is expected. When using correction filters, the re-projection errors will be larger for the significantly corrected points. However, for most points, there should be less than 1 pixel re-projection error.

Estimating 3D pose from a 2D image

Let’s assume you are estimating a 3D pose of an object from a 2D image, e.g., using solvePnP() in OpenCV. Compared to hard-coded camera intrinsics, the intrinsics estimated from the point cloud will provide better results. However, we do not recommend estimating a pose from a 2D image, as it is possible to estimate the pose more accurately directly from the point cloud.

2D image processing algorithms

If you only use 2D data for your application, the hard-coded intrinsics and those estimated from the point cloud will work fine. An example is undistorting the 2D image to correct the lens distortion to guarantee the detection of straight lines.