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import gradio as gr
import cv2
import numpy as np
from flask import Flask, render_template
# A placeholder function; Actual LiveWire code would be much more involved
#def live_wire_segmentation(image, points):
# This is a placeholder. Implement actual live wire segmentation here.
# return image
import cv2
import numpy as np
def compute_cost(image):
gradient_x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3)
gradient_y = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=3)
gradient_magnitude = cv2.magnitude(gradient_x, gradient_y)
return gradient_magnitude
def dijkstra(cost, start, end):
# Dimensions of the image
h, w = cost.shape
visited = np.zeros((h, w), dtype=np.bool_)
distance = np.full((h, w), np.inf)
parent = np.zeros((h, w, 2), dtype=np.int16) # to store the path
distance[start[1], start[0]] = 0
for _ in range(h * w):
min_distance = np.inf
for y in range(h):
for x in range(w):
if not visited[y, x] and distance[y, x] < min_distance:
u = (x, y)
min_distance = distance[y, x]
visited[u[1], u[0]] = True
# Check neighbors
for i in [-1, 0, 1]:
for j in [-1, 0, 1]:
if 0 <= u[1] + i < h and 0 <= u[0] + j < w:
v = (u[0] + j, u[1] + i)
alt = distance[u[1], u[0]] + cost[v[1], v[0]]
if alt < distance[v[1], v[0]]:
distance[v[1], v[0]] = alt
parent[v[1], v[0]] = u
# Reconstruct path from end to start by following parents
path = []
while end != start:
path.append(end)
end = tuple(parent[end[1], end[0]])
path.append(start)
return path
def live_wire_segmentation(image, start, end):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cost = compute_cost(gray)
path = dijkstra(cost, start, end)
for point in path:
cv2.circle(image, point, 1, (0, 255, 0), -1) # Draw path on the image
return image
def main_app():
interface = gr.Interface(
fn=live_wire_segmentation,
inputs=["image", gr.inputs.Sketchpad()], # You may have to adjust this for latest version of Gradio
outputs="image",
live=True
)
interface.launch()
app = Flask(__name__)
@app.route('/')
def index():
main_app()
return "Gradio App Running!"
if __name__ == "__main__":
app.run(debug=True)
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