Update app.py

app.py

@@ -1,85 +1,37 @@
 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)
+# Function to perform image segmentation using OpenCV Watershed Algorithm
+def watershed_segmentation(input_image, scribble_image):
+    # Load the input image and scribble image
+    image = cv2.cvtColor(input_image.astype('uint8'), cv2.COLOR_RGBA2BGR)
+    scribble = cv2.cvtColor(scribble_image.astype('uint8'), cv2.COLOR_RGBA2GRAY)
+
+    # Convert scribble to markers (0 for background, 1 for unknown, 2 for foreground)
+    markers = np.zeros_like(scribble)
+    markers[scribble == 0] = 0
+    markers[scribble == 255] = 1
+    markers[scribble == 128] = 2
+
+    # Apply watershed algorithm
+    cv2.watershed(image, markers)
+
+    # Create a segmented mask
+    segmented_mask = np.zeros_like(image, dtype=np.uint8)
+    segmented_mask[markers == 2] = [0, 0, 255]  # Red color for segmented regions
+
+    return segmented_mask
+
+# Gradio interface
+input_image = gr.inputs.Image(type='pil', label="Upload an image")
+scribble_image = gr.inputs.Image(type='pil', label="Scribble on the image")
+output_image = gr.outputs.Image(type='pil', label="Segmented Image")
+
+gr.Interface(
+    fn=watershed_segmentation,
+    inputs=[input_image, scribble_image],
+    outputs=output_image,
+    title="Image Segmentation using Watershed Algorithm",
+    description="Upload an image and scribble on it to perform segmentation using the Watershed Algorithm.",
+).launch()