Added Skeletonizing and Pruning Operations - DIP

DIRECTORY.md

@@ -336,6 +336,8 @@
  * 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: int, y: int) -> list:
+ """
+ Return 8-neighbours of point (x, y), 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 + dx < img.shape[0] and 0 <= y + dy < img.shape[1]:
+ neighbour_points.append(img[x + dx][y + dy])
+ else:
+ neighbour_points.append(False)
+
+ return neighbour_points
+
+
+def is_endpoint(image: np.ndarray, x: int, y: 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, y))) == 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")