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| import gradio as gr | |
| from PIL import Image, ImageStat | |
| import numpy as np | |
| import os | |
| import shutil | |
| import random | |
| def get_dominant_color(image): | |
| """Get the dominant color of an image or return None.""" | |
| try: | |
| stat = ImageStat.Stat(image) | |
| r, g, b = stat.mean[:3] | |
| return int(r), int(g), int(b) | |
| except: | |
| return None | |
| def resize_and_pad(image, target_size): | |
| """Resize the image and pad it to match target size.""" | |
| img = image.convert("RGB") | |
| aspect_ratio = img.width / img.height | |
| target_aspect_ratio = target_size[0] / target_size[1] | |
| if aspect_ratio > target_aspect_ratio: | |
| # Wider than target aspect ratio | |
| new_width = target_size[0] | |
| new_height = int(new_width / aspect_ratio) | |
| else: | |
| # Taller than target aspect ratio | |
| new_height = target_size[1] | |
| new_width = int(new_height * aspect_ratio) | |
| # Use Image.Resampling.LANCZOS instead of Image.ANTIALIAS | |
| img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) | |
| # Determine padding color | |
| dominant_color = get_dominant_color(img) | |
| if dominant_color is None: | |
| dominant_color = (255, 255, 255) if random.choice([True, False]) else (0, 0, 0) | |
| # Create a new image with the determined background color | |
| new_img = Image.new("RGB", target_size, dominant_color) | |
| offset = ((target_size[0] - new_width) // 2, (target_size[1] - new_height) // 2) | |
| new_img.paste(img, offset) | |
| return new_img | |
| def combine_images(images, target_size=(2048, 2048)): | |
| """Combine four images into a single one.""" | |
| num_images = len(images) | |
| output_images = [] | |
| # Group images into chunks of 4 | |
| for i in range(0, num_images, 4): | |
| group = images[i:i+4] | |
| if len(group) < 4: | |
| continue | |
| # Resize and pad each image | |
| resized_images = [resize_and_pad(img, (512, 512)) for img in group] | |
| # Create a blank canvas | |
| combined_img = Image.new("RGB", (1024, 1024), (0, 0, 0)) | |
| combined_img.paste(resized_images[0], (0, 0)) | |
| combined_img.paste(resized_images[1], (512, 0)) | |
| combined_img.paste(resized_images[2], (0, 512)) | |
| combined_img.paste(resized_images[3], (512, 512)) | |
| # Resize if the final size exceeds target | |
| if combined_img.width > target_size[0] or combined_img.height > target_size[1]: | |
| combined_img = combined_img.resize(target_size, Image.Resampling.LANCZOS) | |
| output_images.append(combined_img) | |
| return output_images | |
| def process_images(uploaded_images): | |
| """Main processing function.""" | |
| images = [Image.open(img) for img in uploaded_images] | |
| combined_images = combine_images(images) | |
| result_paths = [] | |
| # Save combined images | |
| output_dir = "output_images" | |
| os.makedirs(output_dir, exist_ok=True) | |
| for idx, img in enumerate(combined_images): | |
| output_path = os.path.join(output_dir, f"combined_{idx + 1}.png") | |
| img.save(output_path) | |
| result_paths.append(output_path) | |
| # Create a ZIP file | |
| zip_path = "combined_images.zip" | |
| shutil.make_archive("combined_images", 'zip', output_dir) | |
| return zip_path | |
| # Gradio UI | |
| interface = gr.Interface( | |
| fn=process_images, | |
| inputs=gr.Files(file_types=["image"], label="Upload Images"), | |
| outputs=gr.File(label="Download ZIP"), | |
| title="Image Resizer and Combiner", | |
| description="Upload multiple images. This tool will resize and pad them to match a consistent size, then combine every four images into one. The output will be resized if it exceeds 2048x2048, and all results are available as a ZIP file." | |
| ) | |
| interface.launch() |