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Image segmentation is a crucial task in computer vision that involves dividing an image into meaningful and distinct regions. In this tutorial, we'll explore image segmentation using Python and the OpenCV library. We'll focus on a simple yet powerful technique known as thresholding.
Before we begin, make sure you have Python and OpenCV installed on your system. You can install OpenCV using the following command:
Image segmentation is the process of partitioning an image into different segments to simplify its representation or make it more meaningful for analysis. In thresholding, we'll separate an image into foreground and background based on pixel intensity.
Let's create a simple Python script to perform image segmentation using OpenCV.
Replace "path/to/your/image.jpg" with the path to your input image. Adjust the threshold_value based on the intensity level that separates the foreground and background in your image.
Save the script with a .py extension, for example, image_segmentation.py. Run the script using the following command:
The script will display the original image and its segmented version.
In this tutorial, we covered a basic image segmentation technique using thresholding in Python with OpenCV. Image segmentation is a vast field with more advanced techniques, but this simple example serves as a starting point for understanding the basics. Feel free to explore and experiment with different images and threshold values to see how the segmentation changes.
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