Create app.py

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+
+# Global variables to track the state of manual segmentation
+drawing = False
+roi_points = []
+
+# Function to handle mouse events
+def mouse_event(event, x, y, flags, param):
+    global roi_points, drawing
+    if event == cv2.EVENT_LBUTTONDOWN:
+        drawing = True
+        roi_points = [(x, y)]
+    elif event == cv2.EVENT_MOUSEMOVE:
+        if drawing:
+            roi_points.append((x, y))
+    elif event == cv2.EVENT_LBUTTONUP:
+        drawing = False
+        roi_points.append((x, y))
+
+# Function to perform image segmentation using the watershed algorithm
+def segment_image(input_image):
+    # Convert the input image (NumPy array) to a format that OpenCV can use
+    image = input_image.astype(np.uint8)
+    
+    # Create a copy of the image for manual segmentation
+    manual_segmentation_image = image.copy()
+    
+    # Create a mask for manual segmentation
+    mask = np.zeros(image.shape[:2], dtype=np.uint8)
+    
+    # Create a window for manual segmentation
+    cv2.namedWindow("Manual Segmentation")
+    cv2.setMouseCallback("Manual Segmentation", mouse_event)
+    
+    while True:
+        for point in roi_points:
+            cv2.circle(manual_segmentation_image, point, 5, (0, 0, 255), -1)
+        
+        cv2.imshow("Manual Segmentation", manual_segmentation_image)
+        key = cv2.waitKey(1) & 0xFF
+        
+        # Press 's' to perform segmentation
+        if key == ord("s"):
+            break
+        # Press 'r' to reset manual segmentation
+        elif key == ord("r"):
+            manual_segmentation_image = image.copy()
+            roi_points = []
+    
+    # Close the manual segmentation window
+    cv2.destroyWindow("Manual Segmentation")
+    
+    # Convert the image to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    
+    # Create a mask based on the manually segmented ROIs
+    if roi_points:
+        roi_points = np.array(roi_points, np.int32)
+        cv2.fillPoly(mask, [roi_points], 255)
+    
+    # Apply the watershed algorithm to segment the image
+    cv2.watershed(image, mask)
+    
+    # Create a segmented output image
+    output_image = image.copy()
+    output_image[mask == -1] = [0, 0, 255]  # Mark watershed boundaries in red
+    
+    return output_image
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=segment_image,
+    inputs="image",
+    outputs="image",
+    title="Manual Image Segmentation using Watershed Algorithm",
+    description="Upload an image and perform manual image segmentation by drawing regions of interest (ROIs) before segmenting. Press 's' to segment and 'r' to reset ROIs.",
+)
+
+# Launch the Gradio app
+iface.launch()