polarisation function

Author: taryaksama

find_polarisation.py

@@ -0,0 +1,71 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage import feature, measure
+from typing import Tuple
+
+def find_polarisation(
+        image: np.ndarray, 
+        mask: np.ndarray,
+        display:bool = False) -> Tuple[[Tuple[int, int]], Tuple[float, float]]:
+    
+    """
+    Finds the global maximum and centroid of a mask in an image and optionally displays the results.
+
+    Parameters:
+    - image (np.ndarray): The input image as a 2D NumPy array.
+    - mask (np.ndarray): The binary mask as a 2D NumPy array.
+    - display (bool): Flag to display the image, mask, and annotations. Default is False.
+
+    Returns:
+    - Tuple[Optional[Tuple[int, int]], Tuple[float, float]]: A tuple containing:
+        - The coordinates of the global maximum as a tuple of integers (row, column), or None if no local maxima are found.
+        - The coordinates of the centroid of the mask as a tuple of floats (row, column).
+
+    Description:
+    This function performs the following steps:
+    1. Identifies local maxima in the input image.
+    2. Determines the global maximum among these local maxima.
+    3. Calculates the centroid of the provided binary mask.
+    4. Optionally displays the image, mask, global maximum, centroid, and an arrow between them.
+
+    The function is useful for analyzing features in images, such as identifying the brightest spot and the center of a region of interest.
+    """
+
+    # Find local maxima in the image
+    local_maxima_coords = feature.peak_local_max(image, min_distance=10)
+    # Find the global maximum among the local maxima
+    if len(local_maxima_coords) > 0:
+        maxima_values = [image[coord[0], coord[1]] for coord in local_maxima_coords]
+        global_max_index = np.argmax(maxima_values)
+        global_max_coord = local_maxima_coords[global_max_index]
+    else:
+        global_max_coord = None
+
+    # Find the centroid of the mask using region properties
+    properties = measure.regionprops(mask)
+    centroid = properties[0].centroid
+
+    if display:
+        fig, ax = plt.subplots(1,3)
+        ax[0].imshow(image, cmap='gray')
+        ax[0].set_title('Image')
+
+        ax[1].imshow(mask, cmap='gray')
+        ax[1].set_title('Mask')
+        
+        ax[2].imshow(image, cmap='gray')
+        ax[2].plot(centroid[1], centroid[0], 'bo')
+        ax[2].plot(global_max_coord[1], global_max_coord[0], 'ro')
+        # Define the start and end points of the arrow
+        start_point = (centroid[1], centroid[0])
+        end_point = (global_max_coord[1], global_max_coord[0])
+        plt.annotate(
+            '', xy=end_point, xytext=start_point,
+            arrowprops=dict(arrowstyle='->', color='red', lw=2)
+        )
+        ax[2].set_title('Annotated Image')
+    
+    return global_max_coord, centroid
+
+if __name__ == "__main__":
+    pass