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Mask Detection
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.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.
The
Mask Detection
node requires the following input:- Image: The input image to be processed. It should be in RGB format.
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.
- 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 theMask 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.
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.
- 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 theMask 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.
- 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.