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Commit 5d292d48 authored by Christian's avatar Christian
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optimized live_wire by a factor of 100

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1 merge request!3Live wire to be implemented into GUI
import time
import cv2
import numpy as np
import heapq
import matplotlib.pyplot as plt
from scipy.ndimage import convolve
from skimage.filters import gaussian
from skimage.feature import canny
from skimage.graph import route_through_array
#### Helper functions ####
def neighbors_8(x, y, width, height):
"""Return the 8-connected neighbors of (x, y)."""
for nx in (x-1, x, x+1):
for ny in (y-1, y, y+1):
if 0 <= nx < width and 0 <= ny < height:
if not (nx == x and ny == y):
yield nx, ny
def dijkstra(cost_img, seed):
"""
Dijkstra's algorithm on a 2D grid, using cost_img as the per-pixel cost.
Args:
cost_img (np.array): 2D array of costs (float).
seed (tuple): (x, y) starting coordinate.
Returns:
dist (np.float32): array of minimal cumulative cost from seed to each pixel.
parent (np.int32): array storing predecessor of each pixel for path reconstruction.
def load_image(path, type):
"""
height, width = cost_img.shape
# Initialize dist and parent
dist = np.full((height, width), np.inf, dtype=np.float32)
dist[seed[1], seed[0]] = 0.0
parent = -1 * np.ones((height, width, 2), dtype=np.int32)
visited = np.zeros((height, width), dtype=bool)
pq = [(0.0, seed[0], seed[1])] # (distance, x, y)
while pq:
curr_dist, cx, cy = heapq.heappop(pq)
if visited[cy, cx]:
continue
visited[cy, cx] = True
for nx, ny in neighbors_8(cx, cy, width, height):
if visited[ny, nx]:
continue
# We can take an average or sum—here, let's just sum the cost
move_cost = 0.5 * (cost_img[cy, cx] + cost_img[ny, nx])
ndist = curr_dist + move_cost
if ndist < dist[ny, nx]:
dist[ny, nx] = ndist
parent[ny, nx] = (cx, cy)
heapq.heappush(pq, (ndist, nx, ny))
return dist, parent
def backtrack_path(parent, start, end):
Load an image in either gray or color mode (then convert color to gray).
"""
Reconstruct path from 'end' back to 'start' using 'parent' array.
Args:
parent (np.array): shape (H, W, 2), storing (px, py) for each pixel.
start (tuple): (x, y) start coordinate.
end (tuple): (x, y) end coordinate.
Returns:
path (list of (x, y)): from start to end inclusive.
"""
path = []
current = end
while True:
path.append(current)
if current == start:
break
px, py = parent[current[1], current[0]]
current = (px, py)
path.reverse()
return path
def compute_cost(image, sigma=3.0, epsilon=1e-5):
smoothed_img = gaussian(image, sigma=sigma)
canny_img = canny(smoothed_img)
cost_img = 1 / (canny_img + epsilon)
return cost_img, canny_img
def load_image(path, type):
# Load image
if type == 'gray':
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
if img is None:
......@@ -104,82 +25,111 @@ def load_image(path, type):
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 the image
# Downsample
downsampled_img = cv2.resize(img, new_dimensions, interpolation=cv2.INTER_AREA)
### SCALE POINTS
# Original image dimensions
original_width = img.shape[1]
original_height = img.shape[0]
# Downsampled image dimensions
downsampled_width = width
downsampled_height = height
# Scaling factors
scale_x = downsampled_width / original_width
scale_y = downsampled_height / original_height
scale_x = width / img.shape[1]
scale_y = height / img.shape[0]
# Original points
# Scale the points (x, y)
seed_xy = tuple(points[0])
target_xy = tuple(points[1])
# Scale the points
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)
def compute_cost(image, sigma=3.0, epsilon=1e-5):
"""
Smooth the image, run Canny edge detection, then invert the edge map into a cost image.
"""
smoothed_img = gaussian(image, sigma=sigma)
canny_img = canny(smoothed_img)
cost_img = 1.0 / (canny_img + epsilon) # Invert edges: higher cost where edges are stronger
return cost_img, canny_img
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 ####
# Define the following
def main():
# Define input parameters
image_path = './tests/slice_60_volQ.png'
image_type = 'gray' # 'gray' or 'color'
downscale_factor = 100 # % of original size wanted
downscale_factor = 100 # % of original size
points_path = './tests/LiveWireEndPoints.npy'
# Load image
image = load_image(image_path, image_type)
# Load points
points = np.int0(np.round(np.load(points_path)))
# 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
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 and time it
# Find path using route_through_array
# route_through_array expects: route_through_array(image, start, end, fully_connected=True/False)
start_time = time.time()
dist, parent = dijkstra(cost_image, seed)
path = backtrack_path(parent, seed, target)
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)
for (x, y) in path:
color_img[y, x] = (0, 0, 255) # red (color of path)
# 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("Cost Image")
plt.title("Canny Edges")
plt.imshow(canny_img, cmap='gray')
plt.subplot(1, 2, 2)
plt.title("Path from Seed to Target")
plt.imshow(color_img[..., ::-1]) # BGR->RGB for plotting
# Convert BGR->RGB for pyplot
plt.imshow(color_img[..., ::-1])
plt.show()
if __name__ == "__main__":
main()
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