Update morphing.py

morphing.py

@@ -1,83 +1,84 @@
-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])
+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
+from model import model
+
+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])