Update app.py

app.py

@@ -1,7 +1,39 @@
-import gradio as gr
+import cv2
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
+def watershed_segmentation(input_image):
+    # Convert the image to grayscale
+    gray = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
+    
+    # Apply adaptive thresholding
+    thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 2)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+    # Morphological operations to remove small noise - use morphologyEx
+    kernel = np.ones((3, 3), np.uint8)
+    opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
+    
+    # Identify sure background area
+    sure_bg = cv2.dilate(opening, kernel, iterations=3)
+
+    # Find sure foreground area using distance transform
+    dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
+    ret, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)
+
+    # Find unknown region
+    sure_fg = np.uint8(sure_fg)
+    unknown = cv2.subtract(sure_bg, sure_fg)
+    
+    # Marker labeling
+    ret, markers = cv2.connectedComponents(sure_fg)
+    
+    # Add one to all labels so that sure background is not 0, but 1
+    markers = markers + 1
+    
+    # Mark the unknown region with zero
+    markers[unknown == 255] = 0
+    
+    # Apply watershed
+    cv2.watershed(input_image, markers)
+    input_image[markers == -1] = [0, 0, 255]  # boundary region
+    
+    return input_image