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import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image, ImageFilter
import gradio as gr
import numpy as np
import os
import uuid
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
transform = transforms.Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
resize_transform = transforms.Resize((512, 512))
def load_image(image):
image = Image.fromarray(image).convert('RGB')
image = transform(image)
return image.unsqueeze(0).to(device)
def interpolate_vectors(v1, v2, num_steps):
return [v1 * (1 - alpha) + v2 * alpha for alpha in np.linspace(0, 1, num_steps)]
def infer_and_interpolate(image1, image2, num_interpolations=24):
image1 = load_image(image1)
image2 = load_image(image2)
with torch.no_grad():
mu1, logvar1 = model.encode(image1)
mu2, logvar2 = model.encode(image2)
interpolated_vectors = interpolate_vectors(mu1, mu2, num_interpolations)
decoded_images = [model.decode(vec).squeeze(0) for vec in interpolated_vectors]
return decoded_images
def create_gif(decoded_images, duration=200, apply_blur=False):
reversed_images = decoded_images[::-1]
all_images = decoded_images + reversed_images
pil_images = []
for img in all_images:
img = (img - img.min()) / (img.max() - img.min())
img = (img * 255).byte()
pil_img = transforms.ToPILImage()(img.cpu()).convert("RGB")
pil_img = resize_transform(pil_img)
if apply_blur:
pil_img = pil_img.filter(ImageFilter.GaussianBlur(radius=1))
pil_images.append(pil_img)
gif_filename = f"/tmp/morphing_{uuid.uuid4().hex}.gif"
pil_images[0].save(gif_filename, save_all=True, append_images=pil_images[1:], duration=duration, loop=0)
return gif_filename
def create_morphing_gif(image1, image2, num_interpolations=24, duration=200):
decoded_images = infer_and_interpolate(image1, image2, num_interpolations)
gif_path = create_gif(decoded_images, duration)
return gif_path
examples = [
["example_images/image1.jpg", "example_images/image2.png", 24, 200],
["example_images/image3.jpg", "example_images/image4.jpg", 30, 150],
]
with gr.Blocks() as morphing:
with gr.Column():
with gr.Column():
num_interpolations = gr.Slider(minimum=2, maximum=50, value=24, step=1, label="Number of interpolations")
duration = gr.Slider(minimum=100, maximum=1000, value=200, step=50, label="Duration per frame (ms)")
generate_button = gr.Button("Generate Morphing GIF")
output_gif = gr.Image(label="Morphing GIF")
with gr.Row():
image1 = gr.Image(label="Upload first image", type="numpy")
image2 = gr.Image(label="Upload second image", type="numpy")
generate_button.click(fn=create_morphing_gif, inputs=[image1, image2, num_interpolations, duration], outputs=output_gif)
gr.Examples(examples=examples, inputs=[image1, image2, num_interpolations, duration])
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