Added method using sato and kernel for centerline extraction

sato_test.py

+import time
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage.morphology import skeletonize
+from skimage.filters import gaussian, sato
+from skimage.feature import canny
+from skimage.graph import route_through_array
+
+#### Helper functions ####
+
+def load_image(path, type):
+    """
+    Load an image in either gray or color mode (then convert color to gray).
+    """
+    if type == 'gray':
+        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
+        if img is None:
+            raise FileNotFoundError(f"Could not read {path}")
+    elif type == 'color':
+        img = cv2.imread(path, cv2.IMREAD_COLOR)
+        if img is None:
+            raise FileNotFoundError(f"Could not read {path}")
+        else:
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    else:
+        raise ValueError("type must be 'gray' or 'color'")
+    return img
+
+def downscale(img, points, scale_percent):
+    """
+    Downsample `img` to `scale_percent` size and scale the given points accordingly.
+    Returns (downsampled_img, (scaled_seed, scaled_target)).
+    """
+    if scale_percent == 100:
+        return img, (tuple(points[0]), tuple(points[1]))
+    else:
+        # Compute new dimensions
+        width = int(img.shape[1] * scale_percent / 100)
+        height = int(img.shape[0] * scale_percent / 100)
+        new_dimensions = (width, height)
+
+        # Downsample
+        downsampled_img = cv2.resize(img, new_dimensions, interpolation=cv2.INTER_AREA)
+
+        # Scaling factors
+        scale_x = width / img.shape[1]
+        scale_y = height / img.shape[0]
+
+        # Scale the points (x, y)
+        seed_xy = tuple(points[0])
+        target_xy = tuple(points[1])
+        scaled_seed_xy = (int(seed_xy[0] * scale_x), int(seed_xy[1] * scale_y))
+        scaled_target_xy = (int(target_xy[0] * scale_x), int(target_xy[1] * scale_y))
+
+        return downsampled_img, (scaled_seed_xy, scaled_target_xy)
+
+## NO LONGER INVERSE (NOT 1/...)
+def compute_cost(image, sigma=1.0, epsilon=1e-1):
+    """
+    Smooth the image, run Canny edge detection, then invert the edge map into a cost image.
+    """
+
+    smoothed_img = gaussian(image, sigma=sigma)
+    canny_img = sato(smoothed_img)
+
+    canny_thresh = canny_img > 0.08
+
+    skeleton = skeletonize(canny_thresh)
+
+    cost_img = 1 /(skeleton + epsilon)  # Invert edges: higher cost where edges are stronger
+    return cost_img, skeleton
+
+def backtrack_pixels_on_image(img_color, path_coords, bgr_color=(0, 0, 255)):
+    """
+    Color the path on the (already converted BGR) image in the specified color.
+    `path_coords` should be a list of (row, col) or (y, x).
+    """
+    for (row, col) in path_coords:
+        img_color[row, col] = bgr_color
+    return img_color
+
+#### Main Script ####
+
+def main():
+    # Define input parameters
+    image_path = './tests/slice_60_volQ.png'
+    image_type = 'gray'        # 'gray' or 'color'
+    downscale_factor = 100     # % of original size
+    points_path = './tests/LiveWireEndPoints.npy'
+
+    # Load image
+    image = load_image(image_path, image_type)
+
+    # Load seed and target points
+    points = np.int0(np.round(np.load(points_path)))  # shape: (2, 2), i.e. [[x_seed, y_seed], [x_target, y_target]]
+
+    # Downscale image and points
+    scaled_image, scaled_points = downscale(image, points, downscale_factor)
+    seed, target = scaled_points  # Each is (x, y)
+
+    # Convert to row,col for scikit-image (which uses (row, col) = (y, x))
+    seed_rc = (seed[1], seed[0])
+    target_rc = (target[1], target[0])
+
+    # Compute cost image
+    cost_image, canny_img = compute_cost(scaled_image)
+
+    # Find path using route_through_array
+    # route_through_array expects: route_through_array(image, start, end, fully_connected=True/False)
+    start_time = time.time()
+    path_rc, cost = route_through_array(
+        cost_image, 
+        start=seed_rc, 
+        end=target_rc, 
+        fully_connected=True
+    )
+    end_time = time.time()
+
+    print(f"Elapsed time for pathfinding: {end_time - start_time:.3f} seconds")
+
+    # Convert single-channel image to BGR for coloring
+    color_img = cv2.cvtColor(scaled_image, cv2.COLOR_GRAY2BGR)
+
+    # Draw path. `path_rc` is a list of (row, col).
+    # If you want to mark it in red, do (0,0,255) because OpenCV uses BGR format.
+    color_img = backtrack_pixels_on_image(color_img, path_rc, bgr_color=(0, 0, 255))
+
+    # Display results
+    plt.figure(figsize=(20, 8))
+    plt.subplot(1, 2, 1)
+    plt.title("Canny Edges")
+    plt.imshow(canny_img, cmap='gray')
+
+    plt.subplot(1, 2, 2)
+    plt.title("Path from Seed to Target")
+    # Convert BGR->RGB for pyplot
+    plt.imshow(color_img[..., ::-1])
+    plt.show()
+
+if __name__ == "__main__":
+    main()