Pick Anything V2

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Function

This Step performs surface segmentation on the input depth map and color image based on the Pick Anything V2 Model Package. It identifies each individual pickable surface and overlapped surface and outputs a list of masks sorted by picking priority. The results can be used to generate pick points in subsequent Steps.

To use the Pick Anything V2 Step, please contact Mech-Mind sales to obtain a software license that supports this feature. After updating the software license, you can access this feature.

Usage Scenario

This Step is suitable for generalized object picking and high-speed sorting applications. It typically follows image processing and frame transformation Steps, and precedes point cloud extraction and pose adjustment Steps.

Go to Download Center to get the Pick Anything V2 deep learning model package.

Input and Output

Input

Input port Data type Description

Camera Depth Map

Image/Depth

Original depth map of the object.

Camera Color Image

Image/Color

Original color image of the object.

Output

Output port Data type Description

Visualization Output

Image/Color

Visualized results.

Sorted Mask Images

Image/Color/Mask []

List of segmented surface mask images, sorted first by pickable priority (pickable surfaces before overlapped surfaces), and then by area from largest to smallest within each category.

Pickable Flags

Bool []

List of pickable flags for segmented surface masks, in one-to-one correspondence with the sorted mask list. true means the surface is pickable, and false means the surface is overlapped.

System Requirements

The following system requirements need to be met when using this Step.

  • CPU: needs to support the AVX2 instruction set and meets any of the following conditions:

    • IPC or PC without any discrete graphics card: Intel i5-12400 or higher.

    • IPC or PC with a discrete graphics card: Intel i7-6700 or higher, with the graphics card not lower than GeForce GTX 1660.

    This Step has been thoroughly tested on Intel CPUs but has not been tested on AMD CPUs yet. Therefore, Intel CPUs are recommended.

  • GPU: GeForce GTX 1660 or above (if with a discrete graphics card).

Parameter Description

Model Package Settings

Parameter Description

Model Manager Tool

Parameter description: This parameter is used to open the deep learning model package management tool and import the deep learning model package. The model package file is a “.dlkpack” file exported by Mech-DLK.
Tuning instruction: Please refer to Deep Learning Model Package Management Tool for the usage.

Model Name

Parameter description: This parameter is used to select the model package that has been imported for this Step.
Tuning instruction: After importing a deep learning model package with the Deep Learning Model Package Management Tool, select the corresponding model package name from the drop-down list.

Release Original Model Package After Switching

Parameter description: This parameter determines whether to release the resources occupied by the original model package immediately when the model package is switched.
Default setting: Selected.
Instruction: Once this option is selected, when the Step switches to another model package, the system will immediately release the original model package resources, even if the model package is still in use by other Steps. When this option is not selected, the system will automatically release the original model package only when it is no longer used by any Steps.

Model Package Type

Parameter description: Once a Model Name is selected, the DI Algo Type Translated String will be filled automatically.

Input Batch Size

Parmeter description: The number of images processed during each inference.

GPU ID

Parameter description: This parameter is used to select the device ID of the GPU that will be used for the inference.
Tuning instruction: Once you have selected the model name, you can select the GPU ID in the drop-down list of this parameter.

Pre-Process

Parameter Description

ROI File

Parameter description: This parameter is used to set or modify the ROI of the input image.

Tuning instruction: Once the deep learning model is imported, a default ROI will be applied. If you need to edit the ROI, click Open the editor. Edit the ROI in the pop-up Set ROI window, and fill in the ROI name.

Before the inference, please check whether the ROI set here is consistent with the one set in Mech-DLK. If not, the recognition result may be affected.

During the inference, the ROI set during model training, i.e. the default ROI, is usually used. If the position of the object changes in the camera’s field of view, please adjust the ROI.

If you would like to use the default ROI again, please delete the ROI file name below the Open the editor button.

Post-Process

Parameter Description

Inference Configuration

Parameter description: This parameter is used to configure parameters related to Pick Anything model package inference. You can click Open the editor to open the inference configuration window.
Tuning instructions: For details, see Inference Configuration Tool.

Visualization Settings

Parameter Description

Draw Segmentation Mask on Image

Parameter description: This parameter is used to display the segmentation mask on the image.

Tuning instructions: Select this option to enable visualization. The segmentation masks are displayed directly on the image, as shown below:

visualization output

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