Intensity_based_Segmentation

computer_vision/intensity_based_segmentation.py

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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()