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computer_vision/intensity_based_segmentation.py
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Intensity_based_Segmentation (#12491)
30 дек 2024, 18:12
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30 дек 2024, 18:12
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# Source: "https://www.ijcse.com/docs/IJCSE11-02-03-117.pdf" # Importing necessary libraries import matplotlib.pyplot as plt import numpy as np from PIL import Image def segment_image(image: np.ndarray, thresholds: list[int]) -> np.ndarray: """ Performs image segmentation based on intensity thresholds. Args: image: Input grayscale image as a 2D array. thresholds: Intensity thresholds to define segments. Returns: A labeled 2D array where each region corresponds to a threshold range. Example: >>> img = np.array([[80, 120, 180], [40, 90, 150], [20, 60, 100]]) >>> segment_image(img, [50, 100, 150]) array([[1, 2, 3], [0, 1, 2], [0, 1, 1]], dtype=int32) """ # Initialize segmented array with zeros segmented = np.zeros_like(image, dtype=np.int32) # Assign labels based on thresholds for i, threshold in enumerate(thresholds): segmented[image > threshold] = i + 1 return segmented if __name__ == "__main__": # Load the image image_path = "path_to_image" # Replace with your image path original_image = Image.open(image_path).convert("L") image_array = np.array(original_image) # Define thresholds thresholds = [50, 100, 150, 200] # Perform segmentation segmented_image = segment_image(image_array, thresholds) # Display the results plt.figure(figsize=(10, 5)) plt.subplot(1, 2, 1) plt.title("Original Image") plt.imshow(image_array, cmap="gray") plt.axis("off") plt.subplot(1, 2, 2) plt.title("Segmented Image") plt.imshow(segmented_image, cmap="tab20") plt.axis("off") plt.show()