Duplicate from CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model

.gitattributes

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README.md

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+---
+title: Image Animation Using Thin Plate Spline Motion Model
+emoji: 👁
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.0.19
+app_file: app.py
+pinned: false
+duplicated_from: CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+import shutil
+import torch
+from PIL import Image
+import argparse
+import pathlib
+
+os.system("git clone https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model")
+os.chdir("Thin-Plate-Spline-Motion-Model")
+os.system("mkdir checkpoints")
+os.system("wget -c https://cloud.tsinghua.edu.cn/f/da8d61d012014b12a9e4/?dl=1 -O checkpoints/vox.pth.tar")
+
+
+
+title = "# Thin-Plate Spline Motion Model for Image Animation"
+DESCRIPTION = '''### Gradio demo for <b>Thin-Plate Spline Motion Model for Image Animation</b>, CVPR 2022. <a href='https://arxiv.org/abs/2203.14367'>[Paper]</a><a href='https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model'>[Github Code]</a>
+
+<img id="overview" alt="overview" src="https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model/raw/main/assets/vox.gif" />
+'''
+FOOTER = '<img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.Image-Animation-using-Thin-Plate-Spline-Motion-Model" />'
+
+
+def get_style_image_path(style_name: str) -> str:
+    base_path = 'assets'
+    filenames = {
+        'source': 'source.png',
+        'driving': 'driving.mp4',
+    }
+    return f'{base_path}/{filenames[style_name]}'
+
+
+def get_style_image_markdown_text(style_name: str) -> str:
+    url = get_style_image_path(style_name)
+    return f'<img id="style-image" src="{url}" alt="style image">'
+
+
+def update_style_image(style_name: str) -> dict:
+    text = get_style_image_markdown_text(style_name)
+    return gr.Markdown.update(value=text)
+
+
+def set_example_image(example: list) -> dict:
+    return gr.Image.update(value=example[0])
+
+def set_example_video(example: list) -> dict:
+    return gr.Video.update(value=example[0])
+
+def inference(img,vid):
+  if not os.path.exists('temp'):
+    os.system('mkdir temp')
+  
+  img.save("temp/image.jpg", "JPEG")
+  os.system(f"python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image 'temp/image.jpg' --driving_video {vid} --result_video './temp/result.mp4' --cpu")
+  return './temp/result.mp4'
+  
+
+
+def main():
+    with gr.Blocks(theme="huggingface", css='style.css') as demo:
+        gr.Markdown(title)
+        gr.Markdown(DESCRIPTION)
+
+        with gr.Box():
+            gr.Markdown('''## Step 1 (Provide Input Face Image)
+- Drop an image containing a face to the **Input Image**.
+    - If there are multiple faces in the image, use Edit button in the upper right corner and crop the input image beforehand.
+''')
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        input_image = gr.Image(label='Input Image',
+                                               type="pil")
+                        
+            with gr.Row():
+                paths = sorted(pathlib.Path('assets').glob('*.png'))
+                example_images = gr.Dataset(components=[input_image],
+                                            samples=[[path.as_posix()]
+                                                     for path in paths])
+
+        with gr.Box():
+            gr.Markdown('''## Step 2 (Select Driving Video)
+- Select **Style Driving Video for the face image animation**.
+''')
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        driving_video = gr.Video(label='Driving Video',
+                                               format="mp4")
+
+            with gr.Row():
+                paths = sorted(pathlib.Path('assets').glob('*.mp4'))
+                example_video = gr.Dataset(components=[driving_video],
+                                            samples=[[path.as_posix()]
+                                                     for path in paths])
+
+        with gr.Box():
+            gr.Markdown('''## Step 3 (Generate Animated Image based on the Video)
+- Hit the **Generate** button. (Note: As it runs on the CPU, it takes ~ 3 minutes to generate final results.)
+''')
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        generate_button = gr.Button('Generate')
+
+                with gr.Column():
+                    result = gr.Video(type="file", label="Output")
+        gr.Markdown(FOOTER)
+        generate_button.click(fn=inference,
+                              inputs=[
+                                  input_image,
+                                  driving_video
+                              ],
+                              outputs=result)
+        example_images.click(fn=set_example_image,
+                             inputs=example_images,
+                             outputs=example_images.components)
+        example_video.click(fn=set_example_video,
+                             inputs=example_video,
+                             outputs=example_video.components)
+
+    demo.launch(
+        enable_queue=True,
+        debug=True
+    )
+
+if __name__ == '__main__':
+    main()

packages.txt

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+bzip2
+cmake
+ninja-build

requirements.txt

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+cffi==1.14.6
+cycler==0.10.0
+decorator==5.1.0
+face-alignment==1.3.5
+imageio==2.9.0
+imageio-ffmpeg==0.4.5
+kiwisolver==1.3.2
+matplotlib==3.4.3
+networkx==2.6.3
+numpy==1.20.3
+pandas==1.3.3
+Pillow
+pycparser==2.20
+pyparsing==2.4.7
+python-dateutil==2.8.2
+pytz==2021.1
+PyWavelets==1.1.1
+PyYAML==5.4.1
+scikit-image==0.18.3
+scikit-learn==1.0
+scipy==1.7.1
+six==1.16.0
+torch==1.11.0
+torchvision==0.12.0
+tqdm==4.62.3
+gradio

requirements.txt

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+cffi==1.14.6
+cycler==0.10.0
+decorator==5.1.0
+face-alignment==1.3.5
+imageio==2.9.0
+imageio-ffmpeg==0.4.5
+kiwisolver==1.3.2
+matplotlib==3.4.3
+networkx==2.6.3
+numpy==1.20.3
+pandas==1.3.3
+Pillow==8.3.2
+pycparser==2.20
+pyparsing==2.4.7
+python-dateutil==2.8.2
+pytz==2021.1
+PyWavelets==1.1.1
+PyYAML==5.4.1
+scikit-image==0.18.3
+scikit-learn==1.0
+scipy==1.7.1
+six==1.16.0
+torch==1.10.0
+torchvision==0.11.0
+tqdm==4.62.3

style.css

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+h1 {
+  text-align: center;
+}
+img#overview {
+  max-width: 1000px;
+  max-height: 600px;
+  display: block;
+  margin: auto;
+}
+img#style-image {
+  max-width: 1000px;
+  max-height: 600px;
+  display: block;
+  margin: auto;
+}
+img#visitor-badge {
+  display: block;
+  margin: auto;
+}