Update app.py

app.py

@@ -5,9 +5,9 @@ import matplotlib.pyplot as plt
 from prediction import predict_mask
 
 # Placeholder for your 3D model
-def process_3d_image(image):
+def process_3d_image(image, resx, resy, resz):
     # Dummy model implementation: Replace with your actual model logic
-    binary_mask = predict_mask(image)
+    binary_mask = predict_mask(image, resx, resy, resz)
     return binary_mask
 
 def auximread(filepath):
@@ -28,7 +28,7 @@ def auximread(filepath):
     return image
 
 # Function to handle file input and processing
-def process_file(file):
+def process_file(file, resx, resy, resz):
     """
     Process the uploaded file and return the binary mask.
     """
@@ -43,7 +43,7 @@ def process_file(file):
         raise ValueError("Input image is not 3D.")
 
     # Process image through the model
-    binary_mask = process_3d_image(image)
+    binary_mask = process_3d_image(image, resx, resy, resz)
 
     # Save binary mask to a .tif file to return
     output_path = "output_mask.tif"
@@ -81,9 +81,9 @@ def visualize_slice(image, mask, slice_index):
 processed_image = None
 processed_mask = None
 
-def segment_button_click(file):
+def segment_button_click(file, resx, resy, resz):
     global processed_image, processed_mask
-    processed_image, processed_mask, output_path = process_file(file)
+    processed_image, processed_mask, output_path = process_file(file, resx, resy, resz)
     num_slices = processed_image.shape[2]
     return "Segmentation completed! Use the slider to explore slices.", output_path, gr.update(visible=True, maximum=num_slices - 1)
 
@@ -99,6 +99,12 @@ with gr.Blocks() as iface:
     Use the slider to navigate through the 2D slices. This is the official implementation of 3DVascNet, described in this paper: https://www.ahajournals.org/doi/10.1161/ATVBAHA.124.320672.
     The raw code is available at https://github.com/HemaxiN/3DVascNet.
     """)
+    
+    # Input fields for resolution in micrometers
+    with gr.Row():
+        resx_input = gr.Number(value=0.333, label="Resolution in X (µm)", precision=3)
+        resy_input = gr.Number(value=0.333, label="Resolution in Y (µm)", precision=3)
+        resz_input = gr.Number(value=0.5, label="Resolution in Z (µm)", precision=3)
 
     with gr.Row():
         file_input = gr.File(label="Upload 3D Image (.tif)")
@@ -112,7 +118,9 @@ with gr.Blocks() as iface:
         visualization_output = gr.Plot(label="2D Slice Visualization")
 
     # Button click triggers segmentation
-    segment_button.click(segment_button_click, inputs=file_input, outputs=[status_output, download_output, slice_slider])
+    segment_button.click(segment_button_click, 
+                         inputs=[file_input, resx_input, resy_input, resz_input], 
+                         outputs=[status_output, download_output, slice_slider])
 
     # Slider changes trigger visualization updates
     slice_slider.change(update_visualization, inputs=slice_slider, outputs=visualization_output)