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Detect image edges using Canny edge detection for object recognition and image enhancement.
The Canny node is designed to detect edges within an image using the Canny edge detection algorithm, a popular technique in computer vision. This node is particularly useful for identifying the boundaries and contours of objects within an image, which can be beneficial for various image processing tasks such as object recognition, image segmentation, and artistic effects. By applying this node, you can enhance the structural details of your images, making it easier to highlight and manipulate specific features. The Canny edge detection method works by identifying areas of rapid intensity change, which typically correspond to edges, and it uses two threshold values to determine the strength of the edges to be detected.
The image
parameter is the input image on which the Canny edge detection will be performed. This parameter expects an image in the form of a tensor, which will be processed to detect edges. The image should be in a format compatible with the node's processing capabilities.
The low_threshold
parameter sets the lower bound for edge detection. Edges with intensity gradients below this threshold will be discarded. This parameter helps in controlling the sensitivity of the edge detection process. The value should be a float between 0.01 and 0.99, with a default value of 0.4. Adjusting this value can help in fine-tuning the detection of weaker edges.
The high_threshold
parameter sets the upper bound for edge detection. Edges with intensity gradients above this threshold are considered strong edges and are more likely to be detected. This parameter works in conjunction with the low_threshold
to filter out noise and irrelevant details. The value should be a float between 0.01 and 0.99, with a default value of 0.8. Adjusting this value can help in capturing more prominent edges.
The output image
parameter is the result of the Canny edge detection process. This output is an image tensor where the detected edges are highlighted, typically in a binary format where edges are marked with high intensity values. This processed image can be used for further image analysis or as a base for additional artistic effects.
low_threshold
to control the sensitivity to weaker edges and the high_threshold
to ensure that only significant edges are detected, reducing noise and irrelevant details.low_threshold
or high_threshold
values are set outside the allowed range of 0.01 to 0.99. - Solution: Adjust the threshold values to be within the specified range to avoid this error.© Copyright 2024 RunComfy. All Rights Reserved.