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89df499 79de4b9 89df499 d5aa24d 89df499 13bdc14 89df499 13bdc14 89df499 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import cv2
import numpy as np
def extract_edges(image_path, min_threshold=None, max_threshold=None, n_samples=None, ord=2):
"""
Function 1: Load image and extract edges using OpenCV Canny
Returns:
gray: Grayscale image
edges: Binary edge map
"""
gray = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
if gray is None:
raise ValueError(f"Image at path {image_path} could not be loaded.")
edges = cv2.Canny(gray, min_threshold, max_threshold)
y_coords, x_coords = np.where(edges > 0)
all_points = np.column_stack((x_coords, y_coords)) #stacking them along column
# Sample if too many points
if len(all_points) > n_samples:
indices = np.random.choice(len(all_points), n_samples, replace=False)
points = all_points[indices]
else:
points = all_points
print(f"Ordering {len(points)} edge points...")
# Order points using nearest neighbor
current = 0
visited = {current}
ordered_points = [points[current]]
while len(visited) < len(points):
current_point = points[current]
distances = np.linalg.norm(points - current_point, ord=ord,axis=1)
distances[list(visited)] = np.inf
next_idx = np.argmin(distances)
if distances[next_idx] == np.inf:
break
ordered_points.append(points[next_idx])
visited.add(next_idx)
current = next_idx
ordered_points = np.array(ordered_points)
return ordered_points
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