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| import gradio as gr | |
| from PIL import Image, ImageFilter | |
| import numpy as np | |
| import io | |
| import tempfile | |
| import vtracer | |
| from skimage import feature, filters, morphology | |
| import cv2 | |
| from rembg import remove | |
| from sklearn.cluster import KMeans | |
| def quantize_colors(image, num_colors): | |
| """Reduce the number of colors in an image.""" | |
| try: | |
| image_np = np.array(image) | |
| h, w, c = image_np.shape | |
| image_reshaped = image_np.reshape((-1, 3)) | |
| kmeans = KMeans(n_clusters=num_colors, random_state=42).fit(image_reshaped) | |
| new_colors = kmeans.cluster_centers_[kmeans.labels_].reshape(h, w, 3).astype(np.uint8) | |
| return Image.fromarray(new_colors) | |
| except Exception as e: | |
| print(f"Error during color quantization: {e}") | |
| raise | |
| def preprocess_image(image, blur_radius, sharpen_radius, noise_reduction, detail_level, edge_method, color_quantization, enhance_with_ai, remove_bg): | |
| """Advanced preprocessing of the image before vectorization.""" | |
| try: | |
| if blur_radius > 0: | |
| image = image.filter(ImageFilter.GaussianBlur(blur_radius)) | |
| if sharpen_radius > 0: | |
| image = image.filter(ImageFilter.UnsharpMask(radius=sharpen_radius, percent=150, threshold=3)) | |
| if noise_reduction > 0: | |
| image_np = np.array(image) | |
| image_np = cv2.fastNlMeansDenoisingColored(image_np, None, h=noise_reduction, templateWindowSize=7, searchWindowSize=21) | |
| image = Image.fromarray(image_np) | |
| if detail_level > 0: | |
| sigma = max(0.5, 3.0 - (detail_level * 0.5)) | |
| image_np = np.array(image.convert('L')) | |
| if edge_method == 'Canny': | |
| edges = feature.canny(image_np, sigma=sigma) | |
| elif edge_method == 'Sobel': | |
| edges = filters.sobel(image_np) | |
| elif edge_method == 'Scharr': | |
| edges = filters.scharr(image_np) | |
| else: # Prewitt | |
| edges = filters.prewitt(image_np) | |
| edges = morphology.dilation(edges, morphology.square(max(1, 6 - detail_level))) | |
| edges_img = Image.fromarray((edges * 255).astype(np.uint8)) | |
| image = Image.blend(image.convert('RGB'), edges_img.convert('RGB'), alpha=0.5) | |
| if color_quantization > 0: | |
| image = quantize_colors(image, color_quantization) | |
| if enhance_with_ai: | |
| image_np = np.array(image) | |
| # AI-based enhancement for smoothing edges without background removal | |
| image_np = cv2.detailEnhance(image_np, sigma_s=10, sigma_r=0.15) | |
| if remove_bg: | |
| image_np = remove(image_np) | |
| image = Image.fromarray(image_np) | |
| except Exception as e: | |
| print(f"Error during preprocessing: {e}") | |
| raise | |
| return image | |
| def convert_image(image, blur_radius, sharpen_radius, noise_reduction, detail_level, edge_method, color_quantization, | |
| color_mode, hierarchical, mode, filter_speckle, color_precision, layer_difference, | |
| corner_threshold, length_threshold, max_iterations, splice_threshold, path_precision, | |
| enhance_with_ai, remove_bg, upscale_factor): | |
| """Convert an image to SVG using vtracer with customizable and advanced parameters.""" | |
| try: | |
| # Preprocess the image with additional detail level settings | |
| image = preprocess_image(image, blur_radius, sharpen_radius, noise_reduction, detail_level, edge_method, color_quantization, enhance_with_ai, remove_bg) | |
| # Upscale the image if needed | |
| if upscale_factor > 1: | |
| new_size = (int(image.width * upscale_factor), int(image.height * upscale_factor)) | |
| image = image.resize(new_size, Image.LANCZOS) | |
| # Convert Gradio image to bytes for vtracer compatibility | |
| img_byte_array = io.BytesIO() | |
| image.save(img_byte_array, format='PNG') | |
| img_bytes = img_byte_array.getvalue() | |
| # Perform the conversion | |
| svg_str = vtracer.convert_raw_image_to_svg( | |
| img_bytes, | |
| img_format='png', | |
| colormode=color_mode.lower(), | |
| hierarchical=hierarchical.lower(), | |
| mode=mode.lower(), | |
| filter_speckle=int(filter_speckle), | |
| color_precision=int(color_precision), | |
| layer_difference=int(layer_difference), | |
| corner_threshold=int(corner_threshold), | |
| length_threshold=float(length_threshold), | |
| max_iterations=int(max_iterations), | |
| splice_threshold=int(splice_threshold), | |
| path_precision=int(path_precision) | |
| ) | |
| # Save the SVG string to a temporary file | |
| temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.svg') | |
| temp_file.write(svg_str.encode('utf-8')) | |
| temp_file.close() | |
| # Display the SVG in the Gradio interface and provide the download link | |
| svg_html = f'<svg viewBox="0 0 {image.width} {image.height}">{svg_str}</svg>' | |
| return gr.HTML(svg_html), temp_file.name | |
| except Exception as e: | |
| print(f"Error during vectorization: {e}") | |
| return f"Error: {e}", None | |
| # Gradio interface | |
| iface = gr.Blocks() | |
| with iface: | |
| gr.Markdown("# Super-Advanced Image to SVG Converter with Enhanced Models") | |
| with gr.Row(): | |
| image_input = gr.Image(type="pil", label="Upload Image") | |
| blur_radius_input = gr.Slider(minimum=0, maximum=10, value=0, step=0.1, label="Blur Radius (for smoothing)") | |
| sharpen_radius_input = gr.Slider(minimum=0, maximum=5, value=0, step=0.1, label="Sharpen Radius") | |
| noise_reduction_input = gr.Slider(minimum=0, maximum=30, value=0, step=1, label="Noise Reduction") | |
| enhance_with_ai_input = gr.Checkbox(label="AI Edge Enhance", value=False) | |
| remove_bg_input = gr.Checkbox(label="Remove Background", value=False) | |
| upscale_factor_input = gr.Slider(minimum=1, maximum=4, value=1, step=0.1, label="Upscale Factor (1 = No Upscaling)") | |
| with gr.Row(): | |
| detail_level_input = gr.Slider(minimum=0, maximum=10, value=5, step=1, label="Detail Level") | |
| edge_method_input = gr.Radio(choices=["Canny", "Sobel", "Scharr", "Prewitt"], value="Canny", label="Edge Detection Method") | |
| color_quantization_input = gr.Slider(minimum=2, maximum=64, value=0, step=2, label="Color Quantization (0 to disable)") | |
| 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") | |
| convert_button = gr.Button("Convert Image to SVG") | |
| svg_output = gr.HTML(label="SVG Output") | |
| download_output = gr.File(label="Download SVG") | |
| convert_button.click( | |
| fn=convert_image, | |
| inputs=[ | |
| image_input, blur_radius_input, sharpen_radius_input, noise_reduction_input, detail_level_input, edge_method_input, color_quantization_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, enhance_with_ai_input, remove_bg_input, upscale_factor_input | |
| ], | |
| outputs=[svg_output, download_output] | |
| ) | |
| iface.launch() |