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from skimage.feature import canny
from scipy.signal import convolve2d
from compute_disk_size import compute_disk_size
from load_image import load_image
from preprocess_image import preprocess_image
from circle_edge_kernel import circle_edge_kernel
import numpy as np
def compute_cost_image(path: str, user_radius: int, sigma: int = 3, clip_limit: float = 0.01) -> np.ndarray:
"""
Compute the cost image for a given image path, user radius, and optional parameters.
Args:
path: The path to the image file.
user_radius: The radius of the disk.
sigma: The standard deviation for Gaussian smoothing.
clip_limit: The limit for contrasting the image.
Returns:
The cost image as a NumPy array.
"""
disk_size = compute_disk_size(user_radius)
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)
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