Upload 2 files

godraveapp.py

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+# -*- coding: utf-8 -*-
+"""godraveapp.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1MHm84PnI1EaofvmNUzaIVeLj7kBB3FL1
+"""
+
+!pip install gradio pillow
+
+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()

nginx.rtf

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+{\rtf1\ansi\ansicpg1252\cocoartf2822
+\cocoatextscaling0\cocoaplatform0{\fonttbl\f0\fswiss\fcharset0 Helvetica;}
+{\colortbl;\red255\green255\blue255;}
+{\*\expandedcolortbl;;}
+\margl1440\margr1440\vieww11520\viewh8400\viewkind0
+\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
+
+\f0\fs24 \cf0 gradio\
+pillow\
+numpy\
+opencv-python\
+scipy\
+}