通过棋盘格定义的ROI盒

本教程演示了如何使用 Zivid calibration board box 定义 ROI 盒的参数以及如何使用它进行过滤。我们假设标定板放置在料箱的右下角。料箱大小也假定为已知，该数据用于设置ROI盒的尺寸。这样你就可以自动找到相机坐标系中的ROI参数了。

小技巧

如果您使用的是Zivid One+ Small相机，那么棋盘格对于此应用程序来说可能太大了。对于这种情况，我们有一个通过ArUco标记定义的ROI盒的教程。

本教程使用了一个档案相机（file camera）对下图中的场景进行演示。

备注

档案相机可以从 Sample Data（示例数据）下载。

首先，我们需要捕获棋盘格的点云。

C++

const auto fileCamera = std::string(ZIVID_SAMPLE_DATA_DIR) + "/BinWithCalibrationBoard.zfc";

std::cout << "Creating virtual camera using file: " << fileCamera << std::endl;
auto camera = zivid.createFileCamera(fileCamera);

auto settings = Zivid::Settings{ Zivid::Settings::Acquisitions{ Zivid::Settings::Acquisition{} } };

const auto originalFrame = camera.capture(settings);
auto pointCloud = originalFrame.pointCloud();

Python

file_camera = get_sample_data_path() / "BinWithCalibrationBoard.zfc"

print(f"Creating virtual camera using file: {file_camera}")
camera = app.create_file_camera(file_camera)

settings = zivid.Settings([zivid.Settings.Acquisition()])

original_point_cloud = camera.capture(settings).point_cloud()

棋盘格坐标系的原点位于棋盘格左上角的四个格子之间的交叉点。

我们定义了ROI盒右下角相对于棋盘坐标系的位置，以及ROI盒尺寸。然后我们从长度和宽度中减去箱边缘的宽度以移除箱壁。

C++

// Coordinates are relative to the checkerboard origin which lies in the intersection between the four checkers
// in the top-left corner of the checkerboard: Positive x-axis is "East", y-axis is "South" and z-axis is "Down"
const Zivid::PointXYZ roiBoxLowerRightCornerInCheckerboardFrame{ 240.F, 260.F, 5.F };
const Zivid::PointXYZ roiBoxUpperRightCornerInCheckerboardFrame{ roiBoxLowerRightCornerInCheckerboardFrame.x,
                                                                 roiBoxLowerRightCornerInCheckerboardFrame.y
                                                                     - roiBoxWidth,
                                                                 roiBoxLowerRightCornerInCheckerboardFrame.z };
const Zivid::PointXYZ roiBoxLowerLeftCornerInCheckerboardFrame{ roiBoxLowerRightCornerInCheckerboardFrame.x
                                                                    - roiBoxLength,
                                                                roiBoxLowerRightCornerInCheckerboardFrame.y,
                                                                roiBoxLowerRightCornerInCheckerboardFrame.z };
const float roiBoxLength = 545.F;
const float roiBoxWidth = 345.F;
const float roiBoxHeight = 150.F;

Python

roi_box_lower_right_corner = np.array([240, 260, 0.5])
roi_box_upper_right_corner = np.array(
    [
        roi_box_lower_right_corner[0],
        roi_box_lower_right_corner[1] - roi_box_width,
        roi_box_lower_right_corner[2],
    ]
)
roi_box_lower_left_corner = np.array(
    [roi_box_lower_right_corner[0] - roi_box_length, roi_box_lower_right_corner[1], roi_box_lower_right_corner[2]]
)
roi_box_length = 545
roi_box_width = 345
roi_box_height = 150

然后可以设置定义ROI盒基坐标系的三个点。

C++

const Zivid::PointXYZ pointOInCheckerboardFrame = roiBoxLowerRightCornerInCheckerboardFrame;
const Zivid::PointXYZ pointAInCheckerboardFrame = roiBoxUpperRightCornerInCheckerboardFrame;
const Zivid::PointXYZ pointBInCheckerboardFrame = roiBoxLowerLeftCornerInCheckerboardFrame;

Python

point_o_in_checkerboard_frame = roi_box_lower_right_corner
point_a_in_checkerboard_frame = roi_box_upper_right_corner
point_b_in_checkerboard_frame = roi_box_lower_left_corner

然后我们需要估计棋盘格的位姿，将这三个点转换到相机参考系。

C++

std::cout << "Detecting and estimating pose of the Zivid checkerboard in the camera frame" << std::endl;
const auto detectionResult = Zivid::Calibration::detectFeaturePoints(pointCloud);
const auto transformCheckerboardToCamera = detectionResult.pose().toMatrix();

std::cout << "Transforming the ROI base frame points to the camera frame" << std::endl;
const auto roiPointsInCameraFrame = transformPoints(
    std::vector<Zivid::PointXYZ>{
        pointOInCheckerboardFrame, pointAInCheckerboardFrame, pointBInCheckerboardFrame },
    transformCheckerboardToCamera);

Python

print("Detecting and estimating pose of the Zivid checkerboard in the camera frame")
detection_result = zivid.calibration.detect_feature_points(original_point_cloud)
transform_checkerboard_to_camera = detection_result.pose().to_matrix()

print("Transforming the ROI base frame points to the camera frame")
roi_points_in_camera_frame = _transform_points(
    [point_o_in_checkerboard_frame, point_a_in_checkerboard_frame, point_b_in_checkerboard_frame],
    transform_checkerboard_to_camera,
)

提示

详细了解位置、方向和坐标变换来了解其工作原理。

现在我们可以根据ROI盒的大小和位置过滤点云。将第一个范围（extent）设置为一个较小的负数值来避免过滤掉底板，第二个范围（extent）设置为所需的盒子高度。

C++

settings.set(Zivid::Settings::RegionOfInterest{
    Zivid::Settings::RegionOfInterest::Box::Enabled::yes,
    Zivid::Settings::RegionOfInterest::Box::PointO{ roiPointsInCameraFrame[0] },
    Zivid::Settings::RegionOfInterest::Box::PointA{ roiPointsInCameraFrame[1] },
    Zivid::Settings::RegionOfInterest::Box::PointB{ roiPointsInCameraFrame[2] },
    Zivid::Settings::RegionOfInterest::Box::Extents{ -10, roiBoxHeight } });

Python

settings.region_of_interest.box.enabled = True
settings.region_of_interest.box.point_o = roi_points_in_camera_frame[0]
settings.region_of_interest.box.point_a = roi_points_in_camera_frame[1]
settings.region_of_interest.box.point_b = roi_points_in_camera_frame[2]
settings.region_of_interest.box.extents = (-10, roi_box_height)

最后，我们进行新的捕获并可视化过滤后的点云。

C++

const auto roiFrame = camera.capture(settings);

std::cout << "Displaying the ROI-filtered point cloud" << std::endl;
visualizeZividPointCloud(roiFrame);

Python

roi_point_cloud = camera.capture(settings).point_cloud()

print("Displaying the ROI-filtered point cloud")
display_pointcloud(roi_point_cloud.copy_data("xyz"), roi_point_cloud.copy_data("rgba")[:, :, :3])