Delete live_wire.py

live_wire.py

-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-from skimage import exposure
-from skimage.filters import gaussian
-from skimage.feature import canny
-from skimage.graph import route_through_array
-from scipy.signal import convolve2d
-
-### Disk live wire cost image
-
-def compute_disk_size(user_radius, upscale_factor=1.2):
-    return int(np.ceil(upscale_factor * 2 * user_radius + 1) // 2 * 2 + 1)
-
-
-def load_image(path):
-    return cv2.imread(path, cv2.IMREAD_GRAYSCALE)
-
-def preprocess_image(image, sigma=3, clip_limit=0.01):
-    # Apply histogram equalization
-    image_contrasted = exposure.equalize_adapthist(image, clip_limit=clip_limit)
-
-    # Apply smoothing
-    smoothed_img = gaussian(image_contrasted, sigma=sigma)
-
-    return smoothed_img
-
-
-def compute_cost_image(path, user_radius, sigma=3, clip_limit=0.01):
-
-    disk_size = compute_disk_size(user_radius)
-
-    ### Load image
-    image = load_image(path)
-
-    # Apply smoothing
-    smoothed_img = preprocess_image(image, sigma=sigma, clip_limit=clip_limit)
-
-    # Apply Canny edge detection
-    canny_img = canny(smoothed_img)
-
-    # Do disk thing
-    binary_img = canny_img
-    kernel = circle_edge_kernel(k_size=disk_size)
-    convolved = convolve2d(binary_img, kernel, mode='same', boundary='fill')
-
-    # Create cost image
-    cost_img = (convolved.max() - convolved)**4  # Invert edges: higher cost where edges are stronger
-
-    return cost_img
-
-
-def find_path(cost_image, points):
-
-    if len(points) != 2:
-        raise ValueError("Points should be a list of 2 points: seed and target.")
-    
-    seed_rc, target_rc = points
-
-    path_rc, cost = route_through_array(
-        cost_image, 
-        start=seed_rc, 
-        end=target_rc, 
-        fully_connected=True
-    )
-
-    return path_rc
-
-
-def circle_edge_kernel(k_size=5, radius=None):
-    """
-    Create a k_size x k_size array whose values increase linearly
-    from 0 at the center to 1 at the circle boundary (radius).
-
-    Parameters
-    ----------
-    k_size : int
-        The size (width and height) of the kernel array.
-    radius : float, optional
-        The circle's radius. By default, set to (k_size-1)/2.
-
-    Returns
-    -------
-    kernel : 2D numpy array of shape (k_size, k_size)
-        The circle-edge-weighted kernel.
-    """
-    if radius is None:
-        # By default, let the radius be half the kernel size
-        radius = (k_size - 1) / 2
-
-    # Create an empty kernel
-    kernel = np.zeros((k_size, k_size), dtype=float)
-
-    # Coordinates of the center
-    center = radius  # same as (k_size-1)/2 if radius is default
-
-    # Fill the kernel
-    for y in range(k_size):
-        for x in range(k_size):
-            dist = np.sqrt((x - center)**2 + (y - center)**2)
-            if dist <= radius:
-                # Weight = distance / radius => 0 at center, 1 at boundary
-                kernel[y, x] = dist / radius
-
-    return kernel
-
-
-# Other functions (to be implemented?)
-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)
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