Update app.py

app.py

@@ -1,39 +1,14 @@
-import cv2
-import numpy as np
+pip install gradio
+import gradio as gr
 
-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)
+def ui_interface(input_image):
+    segmented = watershed_segmentation(input_image)
+    return segmented
 
-    # 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)
+iface = gr.Interface(
+    fn=ui_interface,
+    inputs=gr.inputs.Image(),
+    outputs=gr.outputs.Image()
+)
 
-    # 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
+iface.launch()