main files

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+
+def extract_outline(image, blur_level, block_size, c_value):
+    # Convert to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Adjust Gaussian blur kernel size based on blur level
+    blur_kernel_size = (5 + (blur_level * 2), 5 + (blur_level * 2))  # Adjust kernel size for blur
+    blurred = cv2.GaussianBlur(gray, blur_kernel_size, 0)
+
+    # Use adaptive thresholding with specified block size and constant
+    binary = cv2.adaptiveThreshold(blurred, 255, 
+                                   cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                                   cv2.THRESH_BINARY_INV, 
+                                   blockSize=max(block_size, 3) | 1,  # Ensure block size is odd
+                                   C=c_value)
+
+    # Dilate the binary image to enhance primary structure
+    kernel = np.ones((3, 3), np.uint8)
+    dilated = cv2.dilate(binary, kernel, iterations=1)
+
+    # Apply morphological thinning to get single-pixel-wide lines
+    thinned = cv2.ximgproc.thinning(dilated)
+
+    # Invert colors to get a white background and black outline
+    skeleton_on_white = cv2.bitwise_not(thinned)
+
+    return skeleton_on_white
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Basic Structure Outline Extractor")
+    gr.Markdown("Upload an image and adjust the sliders to control the amount of detail captured in the outline.")
+
+    with gr.Row():
+        image_input = gr.Image(type="numpy", label="Input Image")
+        
+        with gr.Column():
+            blur_slider = gr.Slider(
+                minimum=0, maximum=5, value=2, step=1,
+                label="Gaussian Blur Level",
+                info="Higher values apply more blur to the image."
+            )
+            block_size_slider = gr.Slider(
+                minimum=3, maximum=21, value=11, step=2,
+                label="Adaptive Threshold Block Size",
+                info="Odd values control the size of the blocks for thresholding."
+            )
+            c_value_slider = gr.Slider(
+                minimum=0, maximum=20, value=5, step=1,
+                label="Adaptive Threshold Constant (C)",
+                info="Adjust the constant subtracted from the mean in adaptive thresholding."
+            )
+    
+    output_image = gr.Image(type="numpy", label="Output Outline Image")
+    
+    process_button = gr.Button("Generate Outline")
+    process_button.click(fn=extract_outline, inputs=[image_input, blur_slider, block_size_slider, c_value_slider], outputs=output_image)
+
+# Launch the Gradio app
+demo.launch()