Update app.py

app.py

@@ -6,27 +6,55 @@ import tempfile
 import vtracer
 from skimage import color, filters
 from skimage.feature import canny
-from skimage.transform import resize
-
-def preprocess_image(image, blur_radius, edge_enhance, edge_threshold):
-    """Applies advanced preprocessing steps to the image before tracing."""
-    if blur_radius > 0:
-        image = image.filter(ImageFilter.GaussianBlur(blur_radius))
-
-    if edge_enhance:
-        # Convert image to grayscale and apply edge detection
-        gray_image = np.array(image.convert('L'))
-        edges = canny(gray_image, sigma=edge_threshold)
-        edges_img = Image.fromarray((edges * 255).astype(np.uint8))
-        image = Image.blend(image.convert('RGB'), edges_img.convert('RGB'), alpha=0.5)
-
-    return image
-
-def convert_image(image, blur_radius, edge_enhance, edge_threshold, color_mode, hierarchical, mode, 
+from skimage.measure import shannon_entropy
+
+def analyze_image(image):
+    """Automatically adjust settings based on image analysis."""
+    # Convert image to grayscale for analysis
+    gray_image = np.array(image.convert('L'))
+
+    # Calculate basic metrics
+    entropy = shannon_entropy(gray_image)
+    edges = canny(gray_image, sigma=1.0).mean()  # Mean edge intensity
+
+    # Adjust parameters based on metrics
+    if entropy > 5.0:
+        blur_radius = 0
+        edge_enhance = True
+        edge_threshold = 2.0
+    else:
+        blur_radius = 2
+        edge_enhance = False
+        edge_threshold = 1.0
+
+    color_mode = "Color" if entropy > 4.0 else "Binary"
+    hierarchical = "Stacked" if edges > 0.1 else "Cutout"
+    mode = "Spline" if edges > 0.15 else "Polygon"
+
+    filter_speckle = int(5 if edges > 0.12 else 15)
+    color_precision = int(8 if entropy > 4.5 else 10)
+    layer_difference = int(12 if edges > 0.2 else 20)
+    corner_threshold = int(60 if edges > 0.12 else 75)
+    length_threshold = float(5.0 if edges > 0.15 else 10.0)
+    max_iterations = int(15 if entropy > 4.5 else 10)
+    splice_threshold = int(50 if entropy > 4.0 else 70)
+    path_precision = int(10 if edges > 0.15 else 5)
+
+    return (blur_radius, edge_enhance, edge_threshold, color_mode, hierarchical, mode, 
+            filter_speckle, color_precision, layer_difference, corner_threshold, length_threshold,
+            max_iterations, splice_threshold, path_precision)
+
+def convert_image(image, auto_settings, blur_radius, edge_enhance, edge_threshold, color_mode, hierarchical, mode, 
                   filter_speckle, color_precision, layer_difference, corner_threshold, length_threshold,
                   max_iterations, splice_threshold, path_precision):
     """Converts an image to SVG using vtracer with customizable parameters."""
 
+    # Auto-adjust settings based on image if auto_settings is enabled
+    if auto_settings:
+        (blur_radius, edge_enhance, edge_threshold, color_mode, hierarchical, mode, 
+        filter_speckle, color_precision, layer_difference, corner_threshold, length_threshold,
+        max_iterations, splice_threshold, path_precision) = analyze_image(image)
+
     # Preprocess the image
     image = preprocess_image(image, blur_radius, edge_enhance, edge_threshold)
 
@@ -61,38 +89,52 @@ def convert_image(image, blur_radius, edge_enhance, edge_threshold, color_mode,
     svg_html = f'<svg viewBox="0 0 {image.width} {image.height}">{svg_str}</svg>'
     return gr.HTML(svg_html), temp_file.name
 
+def preprocess_image(image, blur_radius, edge_enhance, edge_threshold):
+    """Applies advanced preprocessing steps to the image before tracing."""
+    if blur_radius > 0:
+        image = image.filter(ImageFilter.GaussianBlur(blur_radius))
+
+    if edge_enhance:
+        # Convert image to grayscale and apply edge detection
+        gray_image = np.array(image.convert('L'))
+        edges = canny(gray_image, sigma=edge_threshold)
+        edges_img = Image.fromarray((edges * 255).astype(np.uint8))
+        image = Image.blend(image.convert('RGB'), edges_img.convert('RGB'), alpha=0.5)
+
+    return image
+
 # Gradio interface
 iface = gr.Blocks()
 
 with iface:
-    gr.Markdown("# CPU-Optimized AI-Enhanced Image to SVG Vectors")
-    gr.Markdown("Upload an image and customize the conversion parameters for high-quality vector results. AI-enhanced edge detection and preprocessing ensure superior vectorization.")
+    gr.Markdown("# CPU-Optimized AI-Enhanced Image to SVG Vectors with Auto Settings")
+    gr.Markdown("Upload an image and either use custom parameters or let the auto settings analyze and adjust everything for you.")
 
     with gr.Row():
         image_input = gr.Image(type="pil", label="Upload Image")
+        auto_settings_input = gr.Checkbox(value=False, label="Auto Settings")
+
+    with gr.Row(visible=False) as manual_settings:
         blur_radius_input = gr.Slider(minimum=0, maximum=10, value=0, step=0.5, label="Blur Radius (for smoothing)")
         edge_enhance_input = gr.Checkbox(value=False, label="AI Edge Enhance")
         edge_threshold_input = gr.Slider(minimum=0.1, maximum=3.0, value=1.0, step=0.1, label="Edge Detection Threshold")
-
-    with gr.Row():
         color_mode_input = gr.Radio(choices=["Color", "Binary"], value="Color", label="Color Mode")
         hierarchical_input = gr.Radio(choices=["Stacked", "Cutout"], value="Stacked", label="Hierarchical")
         mode_input = gr.Radio(choices=["Spline", "Polygon", "None"], value="Spline", label="Mode")
-
-    with gr.Row():
         filter_speckle_input = gr.Slider(minimum=1, maximum=100, value=4, step=1, label="Filter Speckle")
         color_precision_input = gr.Slider(minimum=1, maximum=100, value=6, step=1, label="Color Precision")
         layer_difference_input = gr.Slider(minimum=1, maximum=100, value=16, step=1, label="Layer Difference")
-
-    with gr.Row():
         corner_threshold_input = gr.Slider(minimum=1, maximum=100, value=60, step=1, label="Corner Threshold")
         length_threshold_input = gr.Slider(minimum=1, maximum=100, value=4.0, step=0.5, label="Length Threshold")
         max_iterations_input = gr.Slider(minimum=1, maximum=100, value=10, step=1, label="Max Iterations")
-
-    with gr.Row():
         splice_threshold_input = gr.Slider(minimum=1, maximum=100, value=45, step=1, label="Splice Threshold")
         path_precision_input = gr.Slider(minimum=1, maximum=100, value=8, step=1, label="Path Precision")
 
+    def toggle_settings(auto_settings):
+        return gr.update(visible=not auto_settings)
+
+    auto_settings_input.change(toggle_settings, inputs=[auto_settings_input], outputs=manual_settings)
+
     convert_button = gr.Button("Convert Image to SVG")
     svg_output = gr.HTML(label="SVG Output")
     download_output = gr.File(label="Download SVG")
@@ -100,10 +142,10 @@ with iface:
     convert_button.click(
         fn=convert_image,
         inputs=[
-            image_input, blur_radius_input, edge_enhance_input, edge_threshold_input, color_mode_input,
-            hierarchical_input, mode_input, filter_speckle_input, color_precision_input, layer_difference_input,
-            corner_threshold_input, length_threshold_input, max_iterations_input, splice_threshold_input,
-            path_precision_input
+            image_input, auto_settings_input, blur_radius_input, edge_enhance_input, edge_threshold_input, 
+            color_mode_input, hierarchical_input, mode_input, filter_speckle_input, color_precision_input,
+            layer_difference_input, corner_threshold_input, length_threshold_input, max_iterations_input, 
+            splice_threshold_input, path_precision_input
         ],
         outputs=[svg_output, download_output]
     )