quickshift(image, ratio=1.0, kernel_size=5, max_dist=10, return_tree=False, sigma=0, convert2lab=True, random_seed=42)
Produces an oversegmentation of the image using the quickshift mode-seeking algorithm.
The authors advocate to convert the image to Lab color space prior to segmentation, though this is not strictly necessary. For this to work, the image must be given in RGB format.
Input image.
Balances color-space proximity and image-space proximity. Higher values give more weight to color-space.
Width of Gaussian kernel used in smoothing the sample density. Higher means fewer clusters.
Cut-off point for data distances. Higher means fewer clusters.
Whether to return the full segmentation hierarchy tree and distances.
Width for Gaussian smoothing as preprocessing. Zero means no smoothing.
Whether the input should be converted to Lab colorspace prior to segmentation. For this purpose, the input is assumed to be RGB.
Random seed used for breaking ties.
Integer mask indicating segment labels.
Segments image using quickshift clustering in Color-(x,y) space.
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them