Update app.py

app.py

@@ -1,14 +1,14 @@
 import gradio as gr
-from PIL import Image, ImageFilter
+from PIL import Image, ImageFilter, ImageOps
 import numpy as np
 import io
 import tempfile
 import vtracer
-from skimage import color, filters
+from skimage import color, filters, exposure
 import torch
 from torchvision import transforms
 
-# Load a pretrained edge detection model (e.g., HED - Holistically-Nested Edge Detection)
+# AI-based edge detection using a pre-trained PyTorch model (e.g., ResNet)
 class EdgeDetectionModel:
     def __init__(self, model_name='resnet18', device='cpu'):
         self.device = device
@@ -25,18 +25,25 @@ class EdgeDetectionModel:
         input_tensor = preprocess(image).unsqueeze(0).to(self.device)
         with torch.no_grad():
             output = self.model(input_tensor)
+
         output = output.cpu().numpy()[0].transpose((1, 2, 0))
-        edges = filters.sobel(color.rgb2gray(output))
+        gray_output = color.rgb2gray(output)
+        edges = filters.sobel(gray_output)
         return edges
 
 # Load the edge detection model
-edge_model = EdgeDetectionModel(device='cpu')  # Use 'cuda' if you have a GPU
+edge_model = EdgeDetectionModel(device='cpu')
 
-def preprocess_image(image, blur_radius, edge_enhance):
+def preprocess_image(image, blur_radius, edge_enhance, contrast_stretch):
     """Applies advanced preprocessing steps to the image before tracing."""
     if blur_radius > 0:
         image = image.filter(ImageFilter.GaussianBlur(blur_radius))
     
+    if contrast_stretch:
+        # Apply contrast stretching
+        p2, p98 = np.percentile(np.array(image), (2, 98))
+        image = ImageOps.autocontrast(image, cutoff=(p2, p98))
+    
     if edge_enhance:
         edges = edge_model.detect_edges(image)
         edges_img = Image.fromarray((edges * 255).astype(np.uint8))
@@ -44,13 +51,13 @@ def preprocess_image(image, blur_radius, edge_enhance):
     
     return image
 
-def convert_image(image, blur_radius, edge_enhance, color_mode, hierarchical, mode, filter_speckle,
+def convert_image(image, blur_radius, edge_enhance, contrast_stretch, 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 and AI enhancements."""
 
     # Preprocess the image
-    image = preprocess_image(image, blur_radius, edge_enhance)
+    image = preprocess_image(image, blur_radius, edge_enhance, contrast_stretch)
 
     # Convert Gradio image to bytes for vtracer compatibility
     img_byte_array = io.BytesIO()
@@ -87,13 +94,14 @@ def convert_image(image, blur_radius, edge_enhance, color_mode, hierarchical, mo
 iface = gr.Blocks()
 
 with iface:
-    gr.Markdown("# 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("# Advanced 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, contrast stretching, and other preprocessing options ensure superior vectorization.")
 
     with gr.Row():
         image_input = gr.Image(type="pil", label="Upload Image")
         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")
+        contrast_stretch_input = gr.Checkbox(value=False, label="Contrast Stretch")
 
     with gr.Row():
         color_mode_input = gr.Radio(choices=["Color", "Binary"], value="Color", label="Color Mode")
@@ -121,7 +129,7 @@ with iface:
     convert_button.click(
         fn=convert_image,
         inputs=[
-            image_input, blur_radius_input, edge_enhance_input, color_mode_input, hierarchical_input, mode_input,
+            image_input, blur_radius_input, edge_enhance_input, contrast_stretch_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
         ],