K-means clustering

Description

K-means clustering:
Performs k-means clustering, grouping object with a distance formula

Real time: False

Usage

• Pre-processing: Transform the image to help segmentation, the image may not retain it's properties. Changes here will be ignored when extracting features

Parameters

• Color space (color_space):
• Cluster count (cluster_count): Number of clusters to split the set by.
• Max iterations allowed (max_iter_count): An integer specifying maximum number of iterations.
• Minium precision (Epsilon) (precision): Required accuracy
• Termination criteria - Stop when: (stop_crit):
• Centers initialization method (flags):
• Attempts (attempts): Flag to specify the number of times the algorithm is executed using different initial labelling. The algorithm returns the labels that yield the best compactness. This compactness is returned as output.
• Name of ROI to be used (roi_names): Operation will only be applied inside of ROI
• ROI selection mode (roi_selection_mode):
• Normalize histograms (normalize):

Example

Source

Parameters/Code

Default values are not needed when calling function

from ipapi.ipt import call_ipt

ret, label, center = call_ipt(ipt_id="IptKMeansClustering",
                              source="tomato_sample_plant.jpg",
                              cluster_count=6)

Result