ComfyUI Node: Gaze Detection

Gaze Detection:

Gaze Detection is a powerful node designed to analyze images and detect the direction of gaze from detected faces. This node leverages advanced machine learning models to identify faces within an image and determine where each face is looking. The primary benefit of this node is its ability to provide insights into visual attention and focus, which can be particularly useful in applications such as user experience research, behavioral studies, and interactive art installations. By visualizing gaze directions, this node helps you understand the dynamics of visual engagement in a given scene, offering a deeper understanding of how subjects interact with their environment.

Gaze Detection Input Parameters:

model

The model parameter specifies the machine learning model used for gaze detection. It is crucial as it determines the accuracy and efficiency of the gaze detection process. The model should be compatible with the Moondream framework, ensuring it can process images and detect gaze effectively. There are no specific minimum or maximum values, but it must be a valid MOONDREAM_MODEL.

image

The image parameter is the input image on which gaze detection will be performed. This parameter is essential as it provides the visual data for the node to analyze. The image should be in a format that the node can process, typically a standard image file. There are no specific constraints on the image size, but larger images may require more processing time.

use_ensemble

The use_ensemble parameter is a boolean option that determines whether to use an ensemble method for gaze detection. When set to True, the node prioritizes accuracy by using multiple models or techniques to improve detection results. The default value is False, which means the node will use a single model for detection. This parameter can significantly impact the accuracy and computational load of the node.

Gaze Detection Output Parameters:

image

The image output parameter provides the processed image with visualizations of detected faces and their gaze directions. This output is crucial for interpreting the results of the gaze detection process, as it visually represents where each detected face is looking. The output image can be used for further analysis or as a visual aid in presentations and reports.

Gaze Detection Usage Tips:

• To improve accuracy, consider enabling the use_ensemble parameter, especially when working with complex images or when high precision is required.
• Ensure that the input image is clear and well-lit to facilitate better face and gaze detection. Avoid images with excessive noise or low resolution.

Gaze Detection Common Errors and Solutions:

Model not loaded

• Explanation: This error occurs when the node attempts to use a model that has not been properly loaded.
• Solution: Ensure that the model is correctly initialized and loaded before running the gaze detection process. Check the model path and compatibility with the Moondream framework.

No faces detected

• Explanation: This error indicates that the node could not find any faces in the input image.
• Solution: Verify that the input image contains visible faces. If the image is too dark or faces are obscured, try using a clearer image. Adjust the image quality or lighting conditions if necessary.