Settings Selector based on Capture Speed

This section provides detailed guidance on the best camera settings based on your time budget from when you trigger an acquisition until you get the point cloud. Here we also consider your PC specs because data transfer and point cloud processing depend on that as well. We address settings for 3D captures to get good point clouds and 2D captures to get good color images.

You have determined which Zivid camera to use for your Piece Picking application, and you have mounted it and positioned it correctly. The next step is to select the camera settings that meet your cycle time requirements, given your PC specs. If you haven’t got a PC yet, then this section is also about finding the PC specs that will meet your capture time requirements in combination with the settings used.

3D settings

We have split the Piece Picking application into two categories based on scene complexity. For each category, we specify and recommend settings that meet the given imaging requirements and capture speed requirements.

Piece Picking (Quality)

With Quality settings for Piece Picking, the camera covers a reasonably wide dynamic range, providing good point clouds for a large variety of objects. The scene can, in this case, include anything from bright and reflective plastic-wrapped objects and items in poly bags to dark absorptive material. The image below depicts a typical complex piece picking scene that requires Quality settings.

Tip

If you want to be sure that the camera will not miss seeing anything, choose Quality settings.

Piece Picking (Fast)

If your scene does not consist of very bright, shiny, and dark objects, you can save significant cycle time by choosing Fast settings for Piece Picking. The Fast settings cover a narrower dynamic range but provide faster captures. Here is an example of a scene where Fast settings work well. As you can see, there are no items that cause strong direct reflections, highlights and saturation, and there are no very dark items.

Tip

If there are no bright, shiny, and dark items in your scene, choose Fast settings.

For a parcel picking application, you can also choose Fast settings to achieve the shortest cycle times. These settings have a limited dynamic range but are good enough for objects that are relatively straightforward for imaging, for example:

• cardboard boxes

• white envelopes

• carton envelopes

Tip

If you are only imaging boxes, parcels, satchels, etc., choose Fast settings.

Settings Selector Table

The following table represents a settings selector tool based on the required capture time budget. The same table indicates the performance and thus the cost of a PC that can meet your capture speed requirements. Check out Recommended Industrial PCs to see examples of High-End, Mid-End, and Low-End configurations.

Application	Capture Time (2D + 3D)			Camera Settings
	High-End PC	Mid-End PC	Low-End PC
Piece Picking (Quality)	~600 ms	~700 ms	~800 ms	Z2 M70 Quality
				Z2 L100 Quality
				Z1+ M Quality
				Z1+ L Quality
Piece Picking (Fast)	~250 ms	~350 ms	~450 ms	Z2 M70 Fast
				Z2 L100 Fast
				Z1+ M Fast
				Z1+ L Fast

To demonstrate reaching the above requirements for piece picking we continuously run benchmark capture programs with our cameras on various PCs. The following table shows the results.

Expected median (±stddev) in ms

Piece Picking Settings	Zivid One+			Zivid Two
	Intel UHD 750	Intel UHD G1	NVIDIA 3070	Intel UHD 750	Intel UHD G1	NVIDIA 3070
	High-end [1]	Low-end [2]	High-end [3]	High-end [1]	Low-end [2]	High-end [3]
Z2 M70 Fast	NA	NA	NA	233 (±3) ms	421 (±274) ms	112 (±0.7) ms
Z2 L100 Fast	NA	NA	NA	230 (±3) ms	428 (±291) ms	112 (±0.7) ms
Z1+ M Fast	231 (±3) ms	329 (±2) ms	112 (±2) ms	NA	NA	NA
Z1+ L Fast	230 (±3) ms	328 (±1) ms	112 (±0.7) ms	NA	NA	NA
Z2 M70 Fast Stripe	NA	NA	NA	531 (±2) ms	989 (±431) ms	274 (±2) ms
Z2 L100 Fast Stripe	NA	NA	NA	532 (±2) ms	1009 (±441) ms	274 (±1) ms
Z1+ M Fast Stripe	516 (±2) ms	704 (±3) ms	279 (±0.6) ms	NA	NA	NA
Z1+ L Fast Stripe	517 (±2) ms	703 (±4) ms	278 (±2) ms	NA	NA	NA
Z2 M70 Quality	NA	NA	NA	477 (±5) ms	744 (±384) ms	276 (±2) ms
Z2 L100 Quality	NA	NA	NA	498 (±3) ms	760 (±389) ms	301 (±1) ms
Z1+ M Quality	468 (±2) ms	670 (±6) ms	292 (±1) ms	NA	NA	NA
Z1+ L Quality	511 (±2) ms	715 (±8) ms	339 (±2) ms	NA	NA	NA
Z2 M70 Quality Stripe	NA	NA	NA	1018 (±33) ms	1655 (±441) ms	623 (±3) ms
Z2 L100 Quality Stripe	NA	NA	NA	1059 (±12) ms	1665 (±484) ms	688 (±2) ms
Z1+ M Quality Stripe	1098 (±6) ms	1439 (±31) ms	612 (±3) ms	NA	NA	NA
Z1+ L Quality Stripe	1140 (±5) ms	1481 (±39) ms	656 (±2) ms	NA	NA	NA

[1] (1,2)

High-end machine with GPU: Intel UHD Graphics 750 (ID:0x4C8A) and CPU: 11th Gen Intel(R) Core(TM) i9-11900K @ 3.50GHz, 10GbE

[2] (1,2)

Low-end machine with GPU: Intel UHD Graphics G1 (ID:0x8A56) and CPU: Intel(R) Core(TM) i3-1005G1 CPU @ 1.20GHz, 1GbE

[3] (1,2)

High-end machine with GPU: NVIDIA GeForce RTX 3070 and CPU: 11th Gen Intel(R) Core(TM) i9-11900K @ 3.50GHz, 10GbE

Testing 3D Settings

We can use Zivid Studio to test 3D settings.

To import the settings in Zivid Studio:

• Click on File → Import Capture Settings

• Navigate to the location of your settings file

• Click Open

Tip

Drag and drop a YML file to import the settings to Zivid Studio - added in Zivid SDK 2.5.

Settings can be loaded using the Zivid SDK as well.

C++

source

std::cout << "Loading settings from file" << std::endl;
const auto cameraModel = camera.info().model().toString().substr(0, 8);
const auto settingsFile = std::string(ZIVID_SAMPLE_DATA_DIR) + "/Settings/" + cameraModel + "/Settings01.yml";
const auto settings = Zivid::Settings(settingsFile);

C#

source

Console.WriteLine("Loading settings from file");
var cameraModel = camera.Info.Model.ToString().Substring(0, 8);
var settingsFile = Environment.GetFolderPath(Environment.SpecialFolder.CommonApplicationData)
                   + "/Zivid/Settings/" + cameraModel + "/Settings01.yml";
var settings = new Zivid.NET.Settings(settingsFile);

Python

source

print("Loading settings from file")
settings_file = Path(user_options.settings_path)
settings = zivid.Settings.load(settings_file)

2D settings

Although you get color images through 3D captures, they may not be adequate for your 2D application due to the light from the projector of the camera. You can read more about this in 2D + 3D Capture Strategy. We provide two options for acquiring 2D settings for your Piece Picking application:

• Get 2D settings automatically using the Zivid calibration board with a code sample

• Use a look-up table and find your appropriate 2D settings

Automatic 2D Settings

Run the auto_2d_settings.py sample to automatically find the acquisition settings and color balance for a 2D capture using a Zivid calibration board. Place the calibration board at either the top or bottom of the bin and run the sample by specifying the calibration board location and the desired focus range (bin height) as command line arguments. If you, for example, place the calibration board at the bin top and the bin height is 400 mm, run the sample as:

python auto_2d_settings.py --desired-focus-range 400 --checkerboard-at-start-of-range

If you place the calibration board at the bin bottom, run:

python auto_2d_settings.py --desired-focus-range 400 --checkerboard-at-end-of-range

This sample does not use the internal projector when finding 2D settings by default, but this can be set by the optional argument --use-projector. Additionally, you can choose to not balance colors by the optional argument --no-color-balance. Since the sample calibrates with the ambient light in the scene you should use similar lighting conditions as in your actual application.

Settings Selector Table

The table below is a settings selector tool for 2D settings based on the illuminance at the bottom of the bin and the distance to the bin top. These 2D settings assume not using the internal projector as the light source, only the external light source.

To use the table, first determine the illuminance at the bottom of the bin given the expected lighting conditions. Then, choose the settings based on the distance from the camera to the bin top.

Note

Illuminance, measured in lux (lx), is the total luminous power incident on a surface per unit area. Luminous power, measured in lumen (lm), is a measure of the total quantity of visible light emitted by a source per unit of time.

Tip

You can roughly measure the illuminance in the scene with a light meter app for Android or Apple; for an accurate measurement, you need a Light / Lux Meter.

At bin bottom	To bin top
	600 mm	800 mm	1100 mm	1400 mm	1700 mm	1900 mm
~300 lx	Zivid Two M70	Zivid Two M70	Zivid Two L100	Zivid One+ M	Zivid One+ L	Zivid One+ L
		Zivid Two L100	Zivid One+ M	Zivid One+ L
		Zivid One+ M
~1000 lx	Zivid Two M70	Zivid Two M70	Zivid Two L100	Zivid One+ M	Zivid One+ L	Zivid One+ L
		Zivid Two L100	Zivid One+ M	Zivid One+ L
		Zivid One+ M

Tip

To benchmark 3D and/or 2D settings on your PC, you can run ZividBenchmark.cpp sample with settings loaded from YML files. Go to Samples, and select C++ for instructions.

Testing 2D Settings

To test 2D settings, you can run our code sample to capture 2D images with settings from YML file.

First, we provide the path to the 2D acquisition settings.

Python

source

def _options() -> argparse.Namespace:
    """Configure and take command line arguments from user.

    Returns:
        Arguments from user

    """
    parser = argparse.ArgumentParser(
        description=(
            "Balance the color of a 2D image\n"
            "Example:\n"
            "\t $ python color_balance.py path/to/settings.yml\n\n"
            "where path/to/settings.yml is the path to the 2D acquisition settings you want to find color balance for."
        ),
        formatter_class=argparse.RawTextHelpFormatter,
    )

    parser.add_argument(
        dest="path",
        type=Path,
        help="Path to YML containing 2D capture settings",
    )

    return parser.parse_args()

Then, we connect to the camera, load the 2D settings, capture the image, and display it.

Python

source

user_options = _options()
camera = app.connect_camera()
settings_2d = zivid.Settings2D.load(user_options.path)
with camera.capture(settings_2d) as frame_2d:
    image = frame_2d.image_rgba()
    rgba = image.copy_data()
    display_rgb(rgba[:, :, 0:3], title="RGB image", block=True)

Lastly, the image is saved.

Python

source

image_file = "Image.png"
print(f"Saving 2D color image to file: {image_file}")
image.save(image_file)

Note

We have tested the 3D and 2D settings we recommend for the above typical scenes in low (~300 lx) and high (~1000 lx) ambient light conditions. This we have done for the range of distances that we specify in the Camera Selector section, and all with various items in the scene. Therefore, we are confident that these settings will work the majority of scenes that fit into these categories. However, if you know exactly what items you expect in your scene and you are an expert camera user, you can manually configure settings to optimize them for your case. Check out Capturing High Quality Point Clouds to learn how to do that.

In the next section, we cover machine vision processes we recommend carrying out to help you prepare your Zivid camera for production.