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Mask Detection

Mask Detection Node Documentation
The Mask Detection node in AugeLab Studio is used to detect whether masks are being worn properly in an image. It applies a mask detection model to the input image and provides information about the presence and correctness of masks.
Node Overview
The Mask Detection node takes an image as input and applies a mask detection model to determine if masks are being worn properly. It provides the following outputs:
Processed Image: The input image with the mask detection results visualized.
Masked: The number of instances where masks are detected and worn properly.
Uncorrect Masked: The number of instances where masks are detected but not worn properly.
No Mask: The number of instances where no masks are detected.
Inputs
The Mask Detection node requires the following input:
Image: The input image to be processed. It should be in RGB format.
Outputs
The Mask Detection node provides the following outputs:
Processed Image: The input image with the mask detection results visualized.
Masked: The number of instances where masks are detected and worn properly.
Uncorrect Masked: The number of instances where masks are detected but not worn properly.
No Mask: The number of instances where no masks are detected.
Node Interaction
1.Drag and drop the Mask Detection node from the node library onto the canvas in AugeLab Studio.
2.Connect the input image to the Image input socket of the Mask Detection node.
3.Configure the node properties:
Confidence Threshold (%): Set the confidence threshold for mask detection. This determines the minimum confidence level required for a mask to be considered detected.
4.The output sockets provide the following information:
Processed Image: The input image with the mask detection results visualized.
Masked: The number of instances where masks are detected and worn properly.
Uncorrect Masked: The number of instances where masks are detected but not worn properly.
No Mask: The number of instances where no masks are detected.
5.Connect the output sockets to other nodes for further processing or analysis.
Implementation Details
The Mask Detection node is implemented as a subclass of the CalcNode base class. It overrides the evalImplementation method to perform the mask detection.
The node reads the input image from the input socket.
The mask detection model is initialized with the provided weights, configuration, and class names.
The confidence threshold is obtained from the node's configuration.
The mask detection model is applied to the input image, and the results are obtained.
The processed image with the mask detection results is returned, along with the counts for masked, uncorrectly masked, and no mask instances.
Usage
1.Drag and drop the Mask Detection node from the node library onto the canvas in AugeLab Studio.
2.Connect the input image to the Image input socket of the Mask Detection node.
3.Configure the node properties:
Confidence Threshold (%): Set the confidence threshold for mask detection. Adjust this value to control the sensitivity of the mask detection.
4.The output sockets provide the following information:
Processed Image: View the input image with the mask detection results visualized.
Masked: Get the count of instances where masks are detected and worn properly.
Uncorrect Masked: Get the count of instances where masks are detected but not worn properly.
No Mask: Get the count of instances where no masks are detected.
5.Connect the output sockets to other nodes for further processing or analysis.
6.Continue building your pipeline by connecting other nodes as needed.
Notes
The Mask Detection node applies a mask detection model to determine if masks are being worn properly in an image.
The confidence threshold can be adjusted to control the sensitivity of the mask detection. Higher values result in stricter detection.
The Mask Detection node provides both the processed image with the mask detection results and counts of different mask instances.
Use the outputs of the Mask Detection node to perform further analysis or visualization, or as input for other nodes in the pipeline.
The Mask Detection node is typically used in combination with other nodes for building AI applications or conducting research in areas such as public health and safety.
Experiment with different confidence thresholds and observe the impact on the mask detection results.
Ensure that the input image is in RGB format for accurate mask detection.
The Mask Detection node leverages the YOLOv4 model for mask detection, using the provided weights, configuration, and class names.
Consider combining the Mask Detection node with other nodes to create a more comprehensive AI application or analysis pipeline.
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Last modified 4mo ago