k means clustering for image segmentation python

Image segmentation is a crucial task in computer vision, allowing us to divide an image into distinct regions based on certain characteristics. K-Means clustering is a popular technique for image segmentation, where pixels with similar features are grouped together. In this tutorial, we will walk through the process of using K-Means clustering for image segmentation in Python with a code example.
Before we start, make sure you have the following Python libraries installed:
Load the image you want to segment. For this example, let's use a sample image:
Reshape the image to a 2D array of pixels (rows * columns, channels) for K-Means clustering.
Apply K-Means clustering to the reshaped image. Choose the number of clusters based on the desired segmentation.
Assign cluster labels to each pixel in the original image.
Visualize the original image and the segmented image side by side.
Adjust the parameters and the number of clusters to fine-tune the segmentation results. Experiment with different images to see how well K-Means clustering can segment different types of content.
This tutorial provides a basic example of using K-Means clustering for image segmentation in Python. Depending on your specific use case, you may want to explore more advanced techniques and pre-processing steps for better segmentation results.
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