运行现场标定

在相机上运行现场标定有两种选择：使用CLI工具或通过SDK实现。

CLI功能概述

DESCRIPTION
Tool for verifying, computing and updating in-field correction parameters on a Zivid camera

SYNOPSIS
        ZividExperimentalInfieldCorrection.exe (verify|correct|read|reset) [-h]

OPTIONS
        verify      verify camera correction quality based on a single capture
        correct     calculate in-field correction based on a series of captures at different distances and positions
        read        get information about the correction currently on the connected camera
        reset       reset correction on connected camera to factory settings

• verify ：此功能使用单次捕获来确定放置的Zivid标定板点云的局部尺寸真实度。

• correct ：此功能通过拍摄Zivid标定板来确定必要的参数，以提高点云的精度。该功能还将返回校正后的误差，即在拍摄图像的工作距离上的1σ（标准差）统计确定性。

• reset ： 使用重置功能将删除之前正确应用的现场标定的结果。 在进行新的现场标定之前不需要进行重置。

• read ：读取功能将返回上一次将现场标定结果写入相机的时间。

Windows

如果在安装期间将Zivid安装到自定义路径，则CLI工具也位于该路径下，否则，CLI工具位于 C:\Program Files\Zivid\bin 下。

Ubuntu

CLI工具位于主路径中。

Verify（验证）

将标定板放置需要验证的位置。

CLI

使用命令行运行验证功能： ZividExperimentalInfieldCorrection verify。

SDK

如果想要通过SDK验证相机的局部尺寸真实度，请先连接到相机。

C++

源码

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

Python

源码

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

然后，通过捕获Zivid标定板来收集数据。

C++

source

std::cout << "Capturing calibration board" << std::endl;
const auto detectionResult = Zivid::Experimental::Calibration::detectFeaturePoints(camera);

Python

source

print("Capturing calibration board")
detection_result = calibration.detect_feature_points(camera)

之后，准备数据并检查它是否适合现场验证。

C++

source

const auto input = Zivid::Experimental::Calibration::InfieldCorrectionInput{ detectionResult };
if(!input.valid())
{
    throw std::runtime_error(
        "Capture not valid for in-field verification! Feedback: " + input.statusDescription());
}

Python

source

infield_input = calibration.InfieldCorrectionInput(detection_result)
if not infield_input.valid():
    raise RuntimeError(
        f"Capture not valid for in-field verification! Feedback: {infield_input.status_description()}"
    )

最后显示验证结果。

C++

source

std::cout << "Successful measurement at " << detectionResult.centroid() << std::endl;
const auto verification = Zivid::Experimental::Calibration::verifyCamera(input);
std::cout << "Estimated dimension trueness at measured position: " << std::setprecision(2) << std::fixed
          << verification.localDimensionTrueness() * 100.0F << "%" << std::endl;

Python

source

print(f"Successful measurement at {detection_result.centroid()}")
camera_verification = calibration.verify_camera(infield_input)
print(
    f"Estimated dimension trueness at measured position: {camera_verification.local_dimension_trueness()*100:.3f}%"
)

Correct（校正）

首次将标定板对齐以进行校正时，请使用Zivid studio中的实时模式，将棋盘格对齐到要进行现场标定拍照的最近点。 使标定板与相机的坐标系尽可能平行，并确保整个标定板在相机视野中可见。

CLI

通过在计算机上的终端输入指令 ZividExperimentalInfieldCorrection correct 调用现场标定工具（不要忘记从Zivid Studio中断开与相机的连接）。

SDK

如果想要通过SDK在相机上执行现场校正，请先连接到相机。

C++

source

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

Python

source

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

然后，将相机对准Zivid标定板并收集数据以进行现场校正。

C++

源码

std::vector<Zivid::Experimental::Calibration::InfieldCorrectionInput> dataset;
std::cout << "Please point the camera at a Zivid in-field calibration board. " << std::endl;

const std::string printLine = "------------------------------------------------------------------------";
while(true)
{
    std::cout << printLine << std::endl;
    if(yesNoPrompt("Capture (y) or finish (n)?"))
    {
        std::cout << "Capturing calibration board" << std::endl;
        const auto detectionResult = Zivid::Experimental::Calibration::detectFeaturePoints(camera);
        const auto input = Zivid::Experimental::Calibration::InfieldCorrectionInput{ detectionResult };

        if(input.valid())
        {
            dataset.emplace_back(input);
            std::cout << "Valid measurement at: " << input.detectionResult().centroid() << std::endl;
        }
        else
        {
            std::cout << "****INVALID****" << std::endl;
            std::cout << "Feedback: " << input.statusDescription() << std::endl;
        }
        std::cout << printLine << std::endl;
    }
    else
    {
        std::cout << "End of capturing stage." << std::endl;
        std::cout << printLine << std::endl;
        break;
    }
    std::cout << "You have collected " << dataset.size() << " valid measurements so far." << std::endl;
}

Python

source

dataset = []
print("Please point the camera at a Zivid in-field calibration board. ")

print_line = "------------------------------------------------------------------------"
while True:
    print(print_line)
    if _yes_no_prompt("Capture (y) or finish (n)? "):
        print("Capturing calibration board")
        detection_result = calibration.detect_feature_points(camera)
        infield_input = calibration.InfieldCorrectionInput(detection_result)

        if infield_input.valid():
            dataset.append(infield_input)
        else:
            print("****INVALID****")
            print(f"Feedback: {infield_input.status_description()}")

        print(print_line)
    else:
        print("End of capturing stage.")
        print(print_line)
        break

    print(f"You have collected {len(dataset)} valid measurements so far.")

如果标定板偏离方向太远或捕获质量太差导致无法提供足够的数据，您将被告知存在潜在的设置问题：

1. 标定板在X，Y或Z平面中倾斜角度过大。

2. 并非所有特征点都是可检测的。

3. 基准标记不可见。

4. 标定板的图像质量太差，无法提供足够的数据。

通过在不同距离和FOV中的不同位置捕获标定板图像来继续收集数据，具体可参考 进行现场标定的准则。在完成现场校正所需要的捕获后，即可使用这些数据生成校正参数。

C++

source

std::cout << "Computing new camera correction..." << std::endl;
const auto correction = Zivid::Experimental::Calibration::computeCameraCorrection(dataset);

Python

source

print("Computing new camera correction...")
correction = calibration.compute_camera_correction(dataset)

在应用新的标定数据之前，请先估计新校正的预期尺寸精度。

C++

source

const auto accuracyEstimate = correction.accuracyEstimate();
std::cout
    << "If written to the camera, this correction can be expected to yield a dimension accuracy of "
    << std::fixed << std::setprecision(2) << 100.0F * accuracyEstimate.dimensionAccuracy()
    << "% or better in the range of z=[" << static_cast<int>(std::round(accuracyEstimate.zMin())) << ","
    << static_cast<int>(std::round(accuracyEstimate.zMax()))
    << "] across the full FOV. Accuracy close to where the correction data was collected is likely better."
    << std::endl;

Python

源码

accuracy_estimate = correction.accuracy_estimate()

print(
    "If written to the camera, this correction can be expected to yield a dimension accuracy of ",
    f"{accuracy_estimate.dimension_accuracy()*100:.3f} or better in the range of z=[{accuracy_estimate.z_min():.3f}, {accuracy_estimate.z_max():.3f}] across the full FOV.",
    "Accuracy close to where the correction data was collected is likely better.",
)

完成此过程返回的信息包括：

1. 最新计算出的FOV大部分区域的最大尺寸真实度误差。

2. 该误差的范围取决于拍摄校正图像的距离。

如果对校正的结果满意，请选择将校正参数应用于相机。

最后，在相机上应用现场校正的结果。

C++

source

std::cout << "Writing camera correction..." << std::endl;
Zivid::Experimental::Calibration::writeCameraCorrection(camera, correction);

Python

source

print("Writing correction to camera")
calibration.write_camera_correction(camera, correction)

Read（读取）

可使用读取功能查看上次校正是什么时间写入相机的。 这将返回上一次将标定参数写入相机的日期和时间。

CLI

ZividExperimentalInfieldCorrection read

SDK

C++

source

if(Zivid::Experimental::Calibration::hasCameraCorrection(camera))
{
    const auto timestamp = Zivid::Experimental::Calibration::cameraCorrectionTimestamp(camera);
    const auto time = std::chrono::system_clock::to_time_t(timestamp);
    std::cout << "Timestamp of current camera correction: " << std::put_time(std::gmtime(&time), "%FT%TZ")
              << std::endl;
}
else
{
    std::cout << "This camera has no in-field correction written to it." << std::endl;
}

Python

source

if calibration.has_camera_correction(camera):
    timestamp = calibration.camera_correction_timestamp(camera)
    print(f"Timestamp of current camera correction: {timestamp.strftime(r'%Y-%m-%d %H:%M:%S')}")
else:
    print("This camera has no in-field correction written to it.")

Reset（重置）

使用重置功能可删除已应用到相机的标定结果。 这会将相机的标定参数重置为Zivid的出厂标定参数。 在测试和应用新标定参数之前，不需要删除已有的标定参数。

CLI

ZividExperimentalInfieldCorrection reset

SDK

C++

源码

std::cout << "Reset in-field correction on the camera" << std::endl;
Zivid::Experimental::Calibration::resetCameraCorrection(camera);

Python

源码

print("Reset in-field correction on the camera")
calibration.reset_camera_correction(camera)