Samples: Improve benchmark reporting and add connect options

README.md

@@ -36,6 +36,7 @@ tested compatibility with earlier SDK versions, please check out
     - [File Camera]
     - [Projector]
   - **Maintenance and Prevention**
+    - [Benchmarking Your System]
     - [Infield Correction]
     - [Warm-up]
     - [Firmware Update]
@@ -87,9 +88,8 @@ from the camera can be used.
       normals and print a subset.
     - [CaptureHalconViaGenICam] - Capture and save a point cloud, with
       colors, using GenICam interface and Halcon C++ SDK.
-    - [CaptureHalconViaZivid] - Capture a point cloud, with colors,
-      using Zivid SDK, transform it to a Halcon point cloud and save it
-      using Halcon C++ SDK.
+    - [CaptureHalconViaZivid] - Capture a colored point cloud with the
+      Zivid SDK, convert it to a HALCON point cloud and save it.
     - [CaptureViaGenICam] - Capture using the GenICam interface.
     - **MultiCamera**
       - [MultiCameraCaptureInParallel] - Capture point clouds with
@@ -106,17 +106,15 @@ from the camera can be used.
     - [CaptureWithDiagnostics] - Capture a 2D+3D frame and a 2D frame
       from the Zivid camera with diagnostics enabled.
     - [CheckHealth] - Poll the camera health check from a separate
-      thread while capturing in the main thread, printing the statuses
-      and values every second.
+      thread while capturing, printing statuses and values each second.
     - [ExploreSettingsMetaData] - Recursively iterates through all leaf
       parameters in a Zivid camera’s settings.
     - [FirmwareUpdater] - Update firmware on the Zivid camera.
     - [FrameInfo] - Read frame info from the Zivid camera.
     - [GetCameraIntrinsics] - Read intrinsic parameters from the Zivid
       camera (OpenCV model) or estimate them from the point cloud.
-    - [MeasureSceneConditions] - Measure ambient light conditions in the
-      scene and output the measured flickering frequency of the ambient
-      light if flickering is detected.
+    - [MeasureSceneConditions] - Measure ambient light in the scene and
+      report the flickering frequency when flickering is detected.
     - [SettingsInfo] - Read settings info from the Zivid camera.
     - [Warmup] - Short example of a basic way to warm up the camera with
       specified time and capture cycle.
@@ -153,7 +151,7 @@ from the camera can be used.
         it.
     - **FileFormats**
       - [ConvertZDF] - Convert point cloud data from a ZDF file to your
-        preferred format
+        preferred format.
       - [ReadIterateZDF] - Read point cloud data from a ZDF file,
         iterate through it, and extract individual points.
   - **Advanced**
@@ -176,17 +174,17 @@ from the camera can be used.
         with the Zivid SDK and perform RGBA to grayscale conversion on a
         CUDA device using OpenCV.
       - [CaptureAndConvertToDlpackTensorOnCuda] - Capture a 2D+3D frame
-        with the Zivid SDK and build DLPack tensors directly from the
-        Zivid DeviceArrays on the CUDA device.
+        and build DLPack tensors directly from the Zivid DeviceArrays on
+        the CUDA device.
     - **Transform**
       - [TransformPointCloudFromMillimetersToMeters] - Transform point
         cloud data from millimeters to meters.
       - [TransformPointCloudViaArucoMarker] - Transform a point cloud
-        from camera to ArUco marker coordinate frame by estimating the
-        marker's pose from the point cloud.
+        from camera to ArUco marker coordinate frame using the marker's
+        estimated pose.
       - [TransformPointCloudViaCheckerboard] - Transform a point cloud
-        from camera to checkerboard (Zivid Calibration Board) coordinate
-        frame by getting checkerboard pose from the API.
+        from camera to checkerboard coordinate frame using the pose from
+        the API.
     - **Roi**
       - [ROIBoxViaArucoMarker] - Filter the point cloud based on a ROI
         box given relative to the ArUco marker on a Zivid Calibration
@@ -195,8 +193,8 @@ from the camera can be used.
         box given relative to the Zivid Calibration Board.
     - **Stitching**
       - [StitchContinuouslyRotatingObject] - Stitch point clouds from a
-        continuously rotating object without pre-alignment using Local
-        Point Cloud Registration and apply Voxel Downsample.
+        continuously rotating object using Local Point Cloud
+        Registration, then downsample.
       - [StitchUsingRobotMountedCamera] - Stitch multiple point clouds
         captured with a robot mounted camera.
       - [StitchViaLocalPointCloudRegistration] - Stitch two point clouds
@@ -208,31 +206,31 @@ from the camera can be used.
     - **HandEyeCalibration**
       - [HandEyeCalibration] - Perform Hand-Eye calibration.
       - [PoseConversions] - Convert to/from Transformation Matrix
-        (Rotation Matrix + Translation Vector)
-      - [UtilizeHandEyeCalibration] - Transform single data point or
-        entire point cloud from camera to robot base reference frame
-        using Hand-Eye calibration
+        (Rotation Matrix + Translation Vector).
+      - [UtilizeHandEyeCalibration] - Transform a data point or entire
+        point cloud from camera to robot base frame using the Hand-Eye
+        calibration matrix.
     - **MultiCamera**
-      - [MultiCameraCalibration] - Use captures of a calibration object
-        to generate transformation matrices to a single coordinate
-        frame, from multiple connected cameras.
+      - [MultiCameraCalibration] - Generate transformation matrices to a
+        single coordinate frame from captures by multiple connected
+        cameras.
       - [MultiCameraCalibrationFromZDF] - Use captures of a calibration
         object to generate transformation matrices to a single
         coordinate frame, from a ZDF files.
-      - [StitchByTransformation] - Use transformation matrices from
-        Multi-Camera calibration to transform point clouds into single
-        coordinate frame, from connected cameras.
-      - [StitchByTransformationFromZDF] - Use transformation matrices
-        from Multi-Camera calibration to transform point clouds into
-        single coordinate frame, from a ZDF files.
+      - [StitchByTransformation] - Transform point clouds from connected
+        cameras into one coordinate frame using Multi-Camera
+        calibration.
+      - [StitchByTransformationFromZDF] - Transform point clouds from
+        ZDF files into one coordinate frame using Multi-Camera
+        calibration.
     - **Projector**
       - [ProjectAndFindMarker] - Show a marker using the projector,
         capture a set of 2D images to find the marker coordinates (2D
         and 3D).
       - [ReadProjectAndCaptureImage] - Read a 2D image from file and
         project it using the camera projector.
       - [ReprojectPoints] - Illuminate checkerboard (Zivid Calibration
-        Board) centers by getting the checkerboard feature points
+        Board) centers by getting the checkerboard feature points.
 
 ## Installation
 
@@ -329,7 +327,7 @@ The following HALCON versions have been tested and confirmed to work
 with Zivid cameras:
 
 - 19.05 Progress, 20.05 Progress, 21.11 Progress, 24.05 Progress, 24.11
-  Progress-Steady, 25.05 Progress
+  Progress-Steady, 25.05 Progress, 25.11 Progress, 26.05 Progress
 
 We recommend using one of the HALCON versions we have tested.
 
@@ -377,6 +375,7 @@ If your build hangs, try to increase the memory available to Docker.
   [Multiple Zivid Cameras]: https://support.zivid.com/en/latest/camera/academy/camera/multiple-zivid-cameras.html
   [File Camera]: https://support.zivid.com/en/latest/camera/academy/camera/file-camera.html
   [Projector]: https://support.zivid.com/en/latest/camera/academy/camera/2d-image-projection.html
+  [Benchmarking Your System]: https://support.zivid.com/en/latest/camera/api-reference/benchmarks/benchmarking-your-system.html
   [Infield Correction]: https://support.zivid.com/en/latest/camera/academy/camera/infield-correction.html
   [Warm-up]: https://support.zivid.com/en/latest/camera/academy/camera/warmup.html
   [Firmware Update]: https://support.zivid.com/en/latest/camera/academy/camera/firmware-update.html

source/Applications/Advanced/Cuda/CaptureAndConvertToDlpackTensorOnCuda/CaptureAndConvertToDlpackTensorOnCuda.cpp

@@ -1,6 +1,5 @@
 /*
-Capture a 2D+3D frame with the Zivid SDK and build DLPack tensors directly from the Zivid DeviceArrays on the CUDA
-device.
+Capture a 2D+3D frame and build DLPack tensors directly from the Zivid DeviceArrays on the CUDA device.
 
 This sample captures a combined 2D+3D frame and converts two common buffers to DLPack: the point cloud (PointXYZ) and
 the color image (ColorRGBA_SRGB). It constructs a DLManagedTensor from the raw building blocks each DeviceArray exposes -

source/Applications/Advanced/HandEyeCalibration/PoseConversions/PoseConversions.cpp

@@ -1,5 +1,5 @@
 /*
-Convert to/from Transformation Matrix (Rotation Matrix + Translation Vector)
+Convert to/from Transformation Matrix (Rotation Matrix + Translation Vector).
 
 Zivid primarily operate with a (4x4) transformation matrix. This example shows how to use Eigen to
 convert to and from: AxisAngle, Rotation Vector, Roll-Pitch-Yaw, Quaternion

source/Applications/Advanced/HandEyeCalibration/UtilizeHandEyeCalibration/UtilizeHandEyeCalibration.cpp

@@ -1,6 +1,5 @@
 /*
-Transform single data point or entire point cloud from camera to robot base reference frame using Hand-Eye calibration
-matrix.
+Transform a data point or entire point cloud from camera to robot base frame using the Hand-Eye calibration matrix.
 
 This example shows how to utilize the result of Hand-Eye calibration to transform either (picking) point coordinates
 or the entire point cloud from the camera to the robot base reference frame.

source/Applications/Advanced/MultiCamera/MultiCameraCalibration/MultiCameraCalibration.cpp

@@ -1,5 +1,5 @@
 /*
-Use captures of a calibration object to generate transformation matrices to a single coordinate frame, from multiple connected cameras.
+Generate transformation matrices to a single coordinate frame from captures by multiple connected cameras.
 
 For more information on multi-camera calibration, check out this tutorial:
 https://support.zivid.com/en/latest/camera/academy/applications/multi-camera-calibration.html

source/Applications/Advanced/MultiCamera/StitchByTransformation/StitchByTransformation.cpp

@@ -1,5 +1,5 @@
 /*
-Use transformation matrices from Multi-Camera calibration to transform point clouds into single coordinate frame, from connected cameras.
+Transform point clouds from connected cameras into one coordinate frame using Multi-Camera calibration.
 
 Note: This example uses experimental SDK features, which may be modified, moved, or deleted in the future without notice.
 

source/Applications/Advanced/MultiCamera/StitchByTransformationFromZDF/StitchByTransformationFromZDF.cpp

@@ -1,5 +1,5 @@
 /*
-Use transformation matrices from Multi-Camera calibration to transform point clouds into single coordinate frame, from a ZDF files.
+Transform point clouds from ZDF files into one coordinate frame using Multi-Camera calibration.
 
 Note: This example uses experimental SDK features, which may be modified, moved, or deleted in the future without notice.
 

source/Applications/Advanced/Projector/ReprojectPoints/ReprojectPoints.cpp

@@ -1,5 +1,5 @@
 /*
-Illuminate checkerboard (Zivid Calibration Board) centers by getting the checkerboard feature points
+Illuminate checkerboard (Zivid Calibration Board) centers by getting the checkerboard feature points.
 and illuminating them with the projector.
 
 The checkerboard feature points are first found through the API. These points are then used to get the

source/Applications/Advanced/Stitching/StitchContinuouslyRotatingObject/StitchContinuouslyRotatingObject.cpp

@@ -1,5 +1,5 @@
 /*
-Stitch point clouds from a continuously rotating object without pre-alignment using Local Point Cloud Registration and apply Voxel Downsample.
+Stitch point clouds from a continuously rotating object using Local Point Cloud Registration, then downsample.
 
 It is assumed that the object is rotating around its own axis and the camera is stationary.
 The camera settings should have defined a region of interest box that removes unnecessary points, keeping only the object to be stitched.

source/Applications/Advanced/Transform/TransformPointCloudViaArucoMarker/TransformPointCloudViaArucoMarker.cpp

@@ -1,5 +1,5 @@
 /*
-Transform a point cloud from camera to ArUco marker coordinate frame by estimating the marker's pose from the point cloud.
+Transform a point cloud from camera to ArUco marker coordinate frame using the marker's estimated pose.
 
 The ZDF file for this sample can be found under the main instructions for Zivid samples.
 

source/Applications/Advanced/Transform/TransformPointCloudViaCheckerboard/TransformPointCloudViaCheckerboard.cpp

@@ -1,5 +1,5 @@
 /*
-Transform a point cloud from camera to checkerboard (Zivid Calibration Board) coordinate frame by getting checkerboard pose from the API.
+Transform a point cloud from camera to checkerboard coordinate frame using the pose from the API.
 
 The ZDF file for this sample can be found under the main instructions for Zivid samples.
 

source/Applications/Advanced/Visualization/CaptureVis3DInLoop/CaptureVis3DInLoop.cpp

@@ -5,7 +5,8 @@ Capture point clouds, with color, from the Zivid camera, and visualize them in a
 #include <Zivid/Visualization/Visualizer.h>
 #include <Zivid/Zivid.h>
 
-#include <future>
+#include <atomic>
+#include <exception>
 #include <iostream>
 #include <thread>
 
@@ -26,49 +27,48 @@ int main()
                                  Zivid::Settings2D::Acquisitions{ Zivid::Settings2D::Acquisition{} } } } };
 
         std::cout << "Capturing frame" << std::endl;
-        auto frame = camera.capture2D3D(settings);
+        const auto frame = camera.capture2D3D(settings);
         std::cout << "Settings:" << frame.settings() << std::endl;
 
         std::cout << "Setting up visualization" << std::endl;
-        std::atomic_bool visualizerRunning{ false };
-        auto visualizerPromise = std::promise<Zivid::Visualization::Visualizer *>();
-        auto visualizerFuture = visualizerPromise.get_future();
+        auto visualizer = Zivid::Visualization::Visualizer();
 
-        std::thread visualizationThread([&frame, &visualizerPromise, &visualizerRunning]() {
-            auto visualizer = Zivid::Visualization::Visualizer();
+        std::cout << "Visualizing point cloud" << std::endl;
+        visualizer.showMaximized();
+        visualizer.show(frame);
+        visualizer.resetToFit();
 
-            // Pass the visualizer to the main thread
-            visualizerPromise.set_value(&visualizer);
+        std::atomic_bool visualizerRunning{ true };
+        std::exception_ptr captureException;
 
-            std::cout << "Visualizing point cloud" << std::endl;
-            visualizer.showMaximized();
-            visualizer.show(frame);
-            visualizer.resetToFit();
-
-            std::cout << "Running visualizer. Blocking until window closes." << std::endl;
-            visualizerRunning = true;
-            visualizer.run();
-            visualizerRunning = false;
+        std::thread captureThread([&camera, &settings, &visualizer, &visualizerRunning, &captureException]() {
+            try
+            {
+                while(visualizerRunning)
+                {
+                    const auto newFrame = camera.capture2D3D(settings);
+                    if(visualizerRunning)
+                    {
+                        visualizer.show(newFrame);
+                    }
+                    std::this_thread::sleep_for(std::chrono::milliseconds(10));
+                }
+            }
+            catch(...)
+            {
+                captureException = std::current_exception();
+                visualizer.close();
+            }
         });
 
-        // Get the visualizer handle in the main thread
-        auto visualizerHandle = visualizerFuture.get();
-
-        while(!visualizerRunning)
-        {
-            std::this_thread::sleep_for(std::chrono::milliseconds(1));
-        }
-        while(visualizerRunning)
+        std::cout << "Running visualizer. Blocking until window closes." << std::endl;
+        visualizer.run();
+        visualizerRunning = false;
+        captureThread.join();
+        if(captureException)
         {
-            frame = camera.capture2D3D(settings);
-            if(!visualizerRunning)
-            {
-                break;
-            }
-            visualizerHandle->show(frame);
-            std::this_thread::sleep_for(std::chrono::milliseconds(10));
+            std::rethrow_exception(captureException);
         }
-        visualizationThread.join();
         std::cout << "Visualizer closed" << std::endl;
     }
     catch(const std::exception &e)