Added Skeletonizing and Pruning Operations - DIP

DIRECTORY.md

@@ -195,6 +195,7 @@
     * [Permutations](data_structures/arrays/permutations.py)
     * [Prefix Sum](data_structures/arrays/prefix_sum.py)
     * [Product Sum](data_structures/arrays/product_sum.py)
+    * [Rotate Array](data_structures/arrays/rotate_array.py)
     * [Sparse Table](data_structures/arrays/sparse_table.py)
     * [Sudoku Solver](data_structures/arrays/sudoku_solver.py)
   * Binary Tree
@@ -336,6 +337,8 @@
   * Morphological Operations
     * [Dilation Operation](digital_image_processing/morphological_operations/dilation_operation.py)
     * [Erosion Operation](digital_image_processing/morphological_operations/erosion_operation.py)
+    * [Pruning Operation](digital_image_processing/morphological_operations/pruning_operation.py)
+    * [Skeletonization Operation](digital_image_processing/morphological_operations/skeletonization_operation.py)
   * Resize
     * [Resize](digital_image_processing/resize/resize.py)
   * Rotation

digital_image_processing/morphological_operations/pruning_operation.py

@@ -0,0 +1,189 @@
+# @Author: @joydipb01
+# @File: pruning_operation.py
+# @Time: 2025-10-03 19:45
+
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+
+def rgb_to_gray(rgb: np.ndarray) -> np.ndarray:
+    """
+    Return gray image from rgb image
+
+    >>> rgb_to_gray(np.array([[[127, 255, 0]]]))
+    array([[187.6453]])
+    >>> rgb_to_gray(np.array([[[0, 0, 0]]]))
+    array([[0.]])
+    >>> rgb_to_gray(np.array([[[2, 4, 1]]]))
+    array([[3.0598]])
+    >>> rgb_to_gray(np.array([[[26, 255, 14], [5, 147, 20], [1, 200, 0]]]))
+    array([[159.0524,  90.0635, 117.6989]])
+    """
+    r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
+    return 0.2989 * r + 0.5870 * g + 0.1140 * b
+
+
+def gray_to_binary(gray: np.ndarray) -> np.ndarray:
+    """
+    Return binary image from gray image
+
+    >>> gray_to_binary(np.array([[127, 255, 0]]))
+    array([[False,  True, False]])
+    >>> gray_to_binary(np.array([[0]]))
+    array([[False]])
+    >>> gray_to_binary(np.array([[26.2409, 4.9315, 1.4729]]))
+    array([[False, False, False]])
+    >>> gray_to_binary(np.array([[26, 255, 14], [5, 147, 20], [1, 200, 0]]))
+    array([[False,  True, False],
+           [False,  True, False],
+           [False,  True, False]])
+    """
+    return (gray > 127) & (gray <= 255)
+
+
+def neighbours(image: np.ndarray, x_coord: int, y_coord: int) -> list:
+    """
+    Return 8-neighbours of point (x_coord, y_coord), in clockwise order
+
+    >>> neighbours(
+    ...     np.array(
+    ...         [
+    ...             [True, True, False],
+    ...             [True, False, False],
+    ...             [False, True, False]
+    ...         ]
+    ...     ), 1, 1
+    ... )
+    [np.True_, np.False_, np.False_, np.False_, np.True_, np.False_, np.True_, np.True_]
+    >>> neighbours(
+    ...     np.array(
+    ...         [
+    ...             [True, True, False, True],
+    ...             [True, False, False, True],
+    ...             [False, True, False, True]
+    ...         ]
+    ...     ), 1, 2
+    ... )
+    [np.False_, np.True_, np.True_, np.True_, np.False_, np.True_, np.False_, np.True_]
+    """
+    img = image
+
+    neighborhood = [
+        (-1, 0),
+        (-1, 1),
+        (0, 1),
+        (1, 1),
+        (1, 0),
+        (1, -1),
+        (0, -1),
+        (-1, -1),
+    ]
+
+    neighbour_points = []
+
+    for dx, dy in neighborhood:
+        if 0 <= x_coord + dx < img.shape[0] and 0 <= y_coord + dy < img.shape[1]:
+            neighbour_points.append(img[x_coord + dx][y_coord + dy])
+        else:
+            neighbour_points.append(False)
+
+    return neighbour_points
+
+
+def is_endpoint(image: np.ndarray, x_coord: int, y_coord: int) -> bool:
+    """
+    Check if a pixel is an endpoint based on its 8-neighbors.
+
+    An endpoint is defined as a pixel that has exactly one neighboring pixel
+    that is part of the foreground (True).
+
+    >>> is_endpoint(
+    ...     np.array(
+    ...         [
+    ...             [True, True, False],
+    ...             [True, False, False],
+    ...             [False, True, False]
+    ...         ]
+    ...     ), 1, 1
+    ... )
+    False
+    >>> is_endpoint(
+    ...     np.array(
+    ...         [
+    ...             [True, True, False, True],
+    ...             [True, False, False, True],
+    ...             [False, True, False, True]
+    ...         ]
+    ...     ), 2, 3
+    ... )
+    True
+    """
+    img = image
+    return int(sum(neighbours(img, x_coord, y_coord))) == 1
+
+
+def prune_skeletonized_image(
+    image: np.ndarray, spur_branch_length: int = 50
+) -> np.ndarray:
+    """
+    Return pruned image by removing spurious branches of specified length
+    Source: https://www.scribd.com/doc/15792184/042805-04
+
+    >>> arr = np.array([
+    ...     [False, True, False],
+    ...     [False, True, False],
+    ...     [False, True, True]
+    ... ])
+    >>> prune_skeletonized_image(arr, spur_branch_length=1)
+    array([[False,  True, False],
+           [False,  True, False],
+           [False,  True,  True]])
+    >>> arr2 = np.array([
+    ...     [False, False, False, False],
+    ...     [False, True, True, False],
+    ...     [False, False, False, False]
+    ... ])
+    >>> prune_skeletonized_image(arr2, spur_branch_length=1)
+    array([[False, False, False, False],
+           [False, False, False, False],
+           [False, False, False, False]])
+    >>> arr3 = np.array([
+    ...     [False, True, False],
+    ...     [False, True, False],
+    ...     [False, True, False]
+    ... ])
+    >>> prune_skeletonized_image(arr3, spur_branch_length=2)
+    array([[False,  True, False],
+           [False,  True, False],
+           [False,  True, False]])
+    """
+    img = image.copy()
+    rows, cols = img.shape
+
+    for _ in range(spur_branch_length):
+        endpoints = []
+
+        for i in range(1, rows - 1):
+            for j in range(1, cols - 1):
+                if img[i][j] and is_endpoint(img, i, j):
+                    endpoints.append((i, j))
+        for x, y in endpoints:
+            img[x][y] = False
+    return img
+
+
+if __name__ == "__main__":
+    # Read original (skeletonized) image
+    skeleton_lena_path = (
+        Path(__file__).resolve().parent.parent / "image_data" / "skeleton_lena.png"
+    )
+    skeleton_lena = np.array(Image.open(skeleton_lena_path))
+
+    # Apply pruning operation to a skeletonized image
+    output = prune_skeletonized_image(gray_to_binary(rgb_to_gray(skeleton_lena)))
+
+    # Save the output image
+    pil_img = Image.fromarray(output).convert("RGB")
+    pil_img.save("result_pruned.png")