python segmentation metrics

Описание: Download this code from https://codegive.com
In image segmentation tasks, evaluating the performance of segmentation algorithms is crucial. Segmentation metrics help quantify how well the algorithm has performed in accurately delineating objects or regions of interest in an image. In this tutorial, we'll explore some common segmentation metrics in Python, along with code examples using the popular image processing library, scikit-image.
We will cover three commonly used segmentation metrics:
Intersection over Union (IoU): Also known as the Jaccard Index, it measures the ratio of the intersection area between the predicted and ground truth masks to the union area.
Dice Coefficient: Similar to IoU, the Dice Coefficient is another measure of overlap between the predicted and ground truth masks.
Pixel Accuracy: This metric calculates the percentage of correctly classified pixels in the segmentation result.
First, make sure you have the necessary libraries installed:
Let's create a Python script with example code for each segmentation metric.
This script assumes binary masks, where pixel values are either 0 or 1. You may need to adapt the code based on the specific requirements of your segmentation task.
Remember to replace "ground_truth_image.jpg" and "predicted_image.jpg" with the paths to your ground truth and predicted segmentation masks.
Feel free to customize the script according to your needs, especially if you are working with multi-class segmentation or other types of segmentation tasks.
ChatGPT