# -*- coding: utf-8 -*- """godraveapp.ipynb Automatically generated by Colab. Original file is located at https://colab.research.google.com/drive/1MHm84PnI1EaofvmNUzaIVeLj7kBB3FL1 """ import gradio as gr from PIL import Image, ImageOps import numpy as np import os import uuid # Ensure there's a directory for outputs os.makedirs("outputs", exist_ok=True) def make_square(img, size=3000, fill_color=(0, 0, 0)): x, y = img.size scale = size / max(x, y) new_size = (int(x * scale), int(y * scale)) # Replace deprecated ANTIALIAS with modern equivalent img = img.resize(new_size, Image.Resampling.LANCZOS) new_img = Image.new("RGB", (size, size), fill_color) new_img.paste(img, ((size - new_size[0]) // 2, (size - new_size[1]) // 2)) return new_img def blend_images(images): if len(images) < 2: return "Upload at least two images.", None try: # Add error handling for image processing processed = [] for img in images: try: processed.append(make_square(Image.open(img))) except Exception as e: return f"Error processing image: {str(e)}", None base = np.array(processed[0]).astype(np.float32) for img in processed[1:]: base = (base + np.array(img).astype(np.float32)) / 2 final = Image.fromarray(np.uint8(base)) # Save to file output_path = f"outputs/amalgam_{uuid.uuid4().hex[:8]}.png" final.save(output_path) return final, output_path except Exception as e: return f"Error during blending: {str(e)}", None demo = gr.Interface( fn=blend_images, inputs=gr.File(file_types=["image"], file_count="multiple", label="Upload 2–5 stills"), outputs=[ gr.Image(label="Blended Image"), gr.File(label="Download Image") ], title="Amalgamator", description="Upload up to 5 stills. Outputs a 3000x3000 blended image preserving the aesthetic. Save it as PNG below." ) demo.launch() import gradio as gr from PIL import Image, ImageOps import numpy as np import os import uuid import random from scipy import ndimage # Ensure there's a directory for outputs os.makedirs("outputs", exist_ok=True) def make_square(img, size=3000, fill_color=(0, 0, 0)): x, y = img.size scale = size / max(x, y) new_size = (int(x * scale), int(y * scale)) img = img.resize(new_size, Image.Resampling.LANCZOS) new_img = Image.new("RGB", (size, size), fill_color) new_img.paste(img, ((size - new_size[0]) // 2, (size - new_size[1]) // 2)) return new_img def pixel_shuffle(img_array, block_size=10, shuffle_strength=0.5): """Shuffle pixels in blocks to create generative effect""" height, width, channels = img_array.shape result = np.copy(img_array) # Create blocks for shuffling h_blocks = height // block_size w_blocks = width // block_size # Create list of block coordinates blocks = [] for i in range(h_blocks): for j in range(w_blocks): blocks.append((i, j)) # Shuffle a percentage of blocks based on strength num_blocks_to_shuffle = int(len(blocks) * shuffle_strength) blocks_to_shuffle = random.sample(blocks, num_blocks_to_shuffle) # Create a shuffled version of these blocks target_positions = blocks_to_shuffle.copy() random.shuffle(target_positions) # Perform the shuffling for (src_i, src_j), (tgt_i, tgt_j) in zip(blocks_to_shuffle, target_positions): src_y, src_x = src_i * block_size, src_j * block_size tgt_y, tgt_x = tgt_i * block_size, tgt_j * block_size # Swap blocks temp = np.copy(result[src_y:src_y+block_size, src_x:src_x+block_size]) result[src_y:src_y+block_size, src_x:src_x+block_size] = result[tgt_y:tgt_y+block_size, tgt_x:tgt_x+block_size] result[tgt_y:tgt_y+block_size, tgt_x:tgt_x+block_size] = temp return result def flow_distortion(img_array, strength=10): """Apply flow-based distortion to simulate generative models""" height, width, channels = img_array.shape result = np.zeros_like(img_array, dtype=np.float32) # Create random flow fields for x and y directions flow_x = np.random.normal(0, strength, (height, width)) flow_y = np.random.normal(0, strength, (height, width)) # Smooth the flow fields flow_x = ndimage.gaussian_filter(flow_x, sigma=30) flow_y = ndimage.gaussian_filter(flow_y, sigma=30) # Create meshgrid for coordinate mapping y_coords, x_coords = np.meshgrid(np.arange(height), np.arange(width), indexing='ij') # Add flow to coordinates x_mapped = x_coords + flow_x y_mapped = y_coords + flow_y # Clip to ensure we stay within bounds x_mapped = np.clip(x_mapped, 0, width-1) y_mapped = np.clip(y_mapped, 0, height-1) # Sample from the original image using the warped coordinates for c in range(channels): result[:, :, c] = ndimage.map_coordinates(img_array[:, :, c], [y_mapped, x_mapped], order=1) return result def blend_images_with_rearrangement(images, block_size=20, shuffle_strength=0.3, flow_strength=5): if len(images) < 2: return "Upload at least two images.", None try: # Process images processed = [] for img in images: try: processed.append(make_square(Image.open(img))) except Exception as e: return f"Error processing image: {str(e)}", None # Convert images to numpy arrays img_arrays = [np.array(img).astype(np.float32) for img in processed] # Create a base canvas base = np.zeros_like(img_arrays[0]) # Divide the images into a grid and randomly select pixels from different images height, width, _ = base.shape for i in range(0, height, block_size): for j in range(0, width, block_size): # Get end coordinates for the block end_i = min(i + block_size, height) end_j = min(j + block_size, width) # Randomly select which image to pull this block from source_img = random.choice(img_arrays) base[i:end_i, j:end_j] = source_img[i:end_i, j:end_j] # Apply pixel shuffling to the composite image base = pixel_shuffle(base, block_size, shuffle_strength) # Apply flow distortion to further randomize base = flow_distortion(base, flow_strength) # Blend with original images to preserve some coherence for img_array in img_arrays: base = base * 0.7 + img_array * 0.3 / len(img_arrays) final = Image.fromarray(np.uint8(np.clip(base, 0, 255))) # Save to file output_path = f"outputs/amalgam_{uuid.uuid4().hex[:8]}.png" final.save(output_path) return final, output_path except Exception as e: return f"Error during blending: {str(e)}", None demo = gr.Interface( fn=blend_images_with_rearrangement, inputs=[ gr.File(file_types=["image"], file_count="multiple", label="Upload 2–5 stills"), gr.Slider(minimum=5, maximum=100, value=20, step=5, label="Block Size (pixels)"), gr.Slider(minimum=0.0, maximum=1.0, value=0.3, step=0.05, label="Shuffle Strength"), gr.Slider(minimum=0, maximum=20, value=5, step=1, label="Flow Distortion") ], outputs=[ gr.Image(label="Generated Image"), gr.File(label="Download Image") ], title="Amalgamator", description="Upload up to 5 stills. Outputs a 3000x3000 image with pixel rearrangement to create a truly generative look." ) demo.launch()