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import gradio as gr |
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import spaces |
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import gc |
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import numpy as np |
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import os |
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import torch |
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from video_depth_anything.video_depth import VideoDepthAnything |
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from utils.dc_utils import read_video_frames, save_video |
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from huggingface_hub import hf_hub_download |
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DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' |
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model_configs = { |
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'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, |
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'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, |
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} |
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encoder2name = { |
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'vits': 'Small', |
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'vitl': 'Large', |
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} |
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encoder='vitl' |
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model_name = encoder2name[encoder] |
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video_depth_anything = VideoDepthAnything(**model_configs[encoder]) |
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filepath = hf_hub_download(repo_id=f"depth-anything/Video-Depth-Anything-{model_name}", filename=f"video_depth_anything_{encoder}.pth", repo_type="model") |
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video_depth_anything.load_state_dict(torch.load(filepath, map_location='cpu')) |
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video_depth_anything = video_depth_anything.to(DEVICE).eval() |
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@spaces.GPU(duration=240) |
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def infer_video_depth( |
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input_video: str, |
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max_len: int = -1, |
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target_fps: int = -1, |
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max_res: int = 1280, |
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grayscale: bool = False, |
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output_dir: str = './outputs', |
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input_size: int = 518, |
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): |
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""" |
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Generate depth maps from input video. |
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This function processes the input video to generate corresponding depth maps |
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using the Video Depth Anything model. |
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Args: |
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input_video (str): Path to the input video file |
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max_len (int): Maximum number of frames to process |
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target_fps (int): Target frames per second for processing |
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max_res (int): Maximum resolution for processing |
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grayscale (bool): Whether to output in grayscale |
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output_dir (str): Directory to save output videos |
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input_size (int): Input size for the model |
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Returns: |
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List[str]: Paths to the processed video and depth visualization |
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""" |
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frames, target_fps = read_video_frames(input_video, max_len, target_fps, max_res) |
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depths, fps = video_depth_anything.infer_video_depth(frames, target_fps, input_size=input_size, device=DEVICE) |
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video_name = os.path.basename(input_video) |
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if not os.path.exists(output_dir): |
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os.makedirs(output_dir) |
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processed_video_path = os.path.join(output_dir, os.path.splitext(video_name)[0]+'_src.mp4') |
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depth_vis_path = os.path.join(output_dir, os.path.splitext(video_name)[0]+'_vis.mp4') |
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save_video(frames, processed_video_path, fps=fps) |
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save_video(depths, depth_vis_path, fps=fps, is_depths=True, grayscale=grayscale) |
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gc.collect() |
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torch.cuda.empty_cache() |
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return [processed_video_path, depth_vis_path] |
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theme = gr.themes.Base().set( |
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body_background_fill="#1A1A1A", |
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body_background_fill_dark="#1A1A1A", |
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body_text_color="#CCCCCC", |
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body_text_color_dark="#CCCCCC", |
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block_background_fill="#2C2C2C", |
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block_background_fill_dark="#2C2C2C", |
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block_border_color="#3C3C3C", |
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block_border_color_dark="#3C3C3C", |
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button_primary_background_fill="#FF8C00", |
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button_primary_background_fill_dark="#FF8C00", |
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button_primary_background_fill_hover="#FF9F33", |
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button_primary_border_color="*primary_500", |
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button_primary_text_color="white", |
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button_primary_text_color_dark="white", |
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block_border_width="1px", |
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block_radius="8px" |
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) |
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with gr.Blocks( |
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theme=theme, |
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css=""" |
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.gradio-container { |
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background: #1A1A1A !important; |
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color: #CCCCCC !important; |
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font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif !important; |
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} |
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.gradio-container .footer, |
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.gradio-container footer, |
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.gradio-container [data-testid="footer"], |
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.gradio-container .gradio-footer { |
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display: none !important; |
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} |
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.gradio-container .gradio-container { |
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padding-bottom: 0 !important; |
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} |
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.gradio-container h1, .gradio-container h2, .gradio-container h3 { |
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color: #FFFFFF !important; |
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font-weight: bold !important; |
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} |
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.gradio-container .markdown { |
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color: #CCCCCC !important; |
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} |
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.gradio-container .tab-nav { |
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background: #2C2C2C !important; |
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border: none !important; |
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} |
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.gradio-container .tab-nav button { |
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background: #2C2C2C !important; |
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color: #CCCCCC !important; |
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border: none !important; |
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border-radius: 8px 8px 0 0 !important; |
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} |
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.gradio-container .tab-nav button.selected { |
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background: #FF8C00 !important; |
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color: #FFFFFF !important; |
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} |
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.gradio-container .tab-nav button:hover { |
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background: #3C3C3C !important; |
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} |
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.gradio-container .tab-nav button.selected:hover { |
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background: #FF8C00 !important; |
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} |
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.gradio-container .tab-content { |
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background: #2C2C2C !important; |
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border: none !important; |
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border-radius: 0 0 8px 8px !important; |
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padding: 20px !important; |
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} |
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.gradio-container .accordion { |
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background: #2C2C2C !important; |
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border: 1px solid #3C3C3C !important; |
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border-radius: 8px !important; |
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margin: 10px 0 !important; |
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} |
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.gradio-container .accordion .accordion-header { |
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background: #2C2C2C !important; |
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color: #FFFFFF !important; |
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border: none !important; |
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border-radius: 8px !important; |
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} |
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.gradio-container .accordion .accordion-content { |
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background: #2C2C2C !important; |
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color: #CCCCCC !important; |
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border: none !important; |
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border-radius: 0 0 8px 8px !important; |
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} |
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.gradio-container .button { |
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background: #FF8C00 !important; |
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color: #FFFFFF !important; |
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border: none !important; |
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border-radius: 8px !important; |
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font-weight: bold !important; |
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padding: 12px 24px !important; |
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} |
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.gradio-container .button:hover { |
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background: #FF9F33 !important; |
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} |
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.gradio-container .button.secondary { |
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background: #3C3C3C !important; |
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color: #CCCCCC !important; |
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} |
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.gradio-container .button.secondary:hover { |
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background: #4C4C4C !important; |
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} |
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.gradio-container .slider { |
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background: #3C3C3C !important; |
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} |
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.gradio-container .slider .slider-handle { |
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background: #FF8C00 !important; |
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border: 2px solid #FFFFFF !important; |
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} |
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.gradio-container .slider .slider-track { |
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background: #3C3C3C !important; |
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} |
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.gradio-container .slider .slider-track-fill { |
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background: #FF8C00 !important; |
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} |
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.gradio-container .checkbox { |
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color: #CCCCCC !important; |
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} |
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.gradio-container .radio { |
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color: #CCCCCC !important; |
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} |
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.gradio-container .gallery { |
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background: #2C2C2C !important; |
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border: 1px solid #3C3C3C !important; |
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border-radius: 8px !important; |
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} |
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.gradio-container .image { |
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background: #2C2C2C !important; |
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border: 1px solid #3C3C3C !important; |
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border-radius: 8px !important; |
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} |
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.gradio-container .video { |
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background: #2C2C2C !important; |
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border: 1px solid #3C3C3C !important; |
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border-radius: 8px !important; |
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} |
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.gradio-container .model3d { |
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background: #2C2C2C !important; |
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border: 1px solid #3C3C3C !important; |
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border-radius: 8px !important; |
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} |
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.gradio-container .row { |
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gap: 20px !important; |
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} |
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.gradio-container .column { |
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background: #2C2C2C !important; |
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border: 1px solid #3C3C3C !important; |
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border-radius: 8px !important; |
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padding: 20px !important; |
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} |
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.gradio-container .row { |
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align-items: flex-start !important; |
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justify-content: center !important; |
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} |
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.gradio-container .container { |
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max-width: 1200px !important; |
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margin: 0 auto !important; |
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padding: 20px !important; |
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} |
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""" |
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) as demo: |
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gr.Markdown(""" |
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<div style="text-align: center; margin-bottom: 30px; padding: 20px; background: #2C2C2C; border: 1px solid #3C3C3C; border-radius: 8px;"> |
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<h3 style="color: #FFFFFF; margin-bottom: 15px;">Instructions</h3> |
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<p style="color: #CCCCCC; margin-bottom: 10px;"> |
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• Upload a video and click "Generate Depth" to create depth maps |
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</p> |
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<p style="color: #CCCCCC; margin-bottom: 10px;"> |
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• Adjust settings in Generation Settings for optimal results |
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</p> |
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<p style="color: #CCCCCC;"> |
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• Download the processed video and depth visualization |
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</p> |
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</div> |
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""") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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gr.Markdown(""" |
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<div style="background: #2C2C2C; border: 1px solid #3C3C3C; border-radius: 8px; padding: 20px; margin-bottom: 20px;"> |
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<h3 style="color: #FFFFFF; margin-bottom: 15px;">Video Depth Generation</h3> |
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<p style="color: #CCCCCC; margin-bottom: 20px;">Generate depth maps from video content for compositing and 3D effects.</p> |
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</div> |
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""") |
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input_video = gr.Video(label="Input Video", height=300) |
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with gr.Accordion(label="Generation Settings", open=False): |
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max_len = gr.Slider(0, 1000, label="Max Process Length", value=500, step=1) |
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target_fps = gr.Slider(-1, 30, label="Target FPS", value=15, step=1) |
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max_res = gr.Slider(480, 1920, label="Max Side Resolution", value=1280, step=1) |
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grayscale = gr.Checkbox(label="Grayscale Output", value=False) |
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generate_btn = gr.Button("Generate Depth", variant="primary", size="lg") |
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with gr.Column(scale=1): |
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gr.Markdown(""" |
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<div style="background: #2C2C2C; border: 1px solid #3C3C3C; border-radius: 8px; padding: 20px; margin-bottom: 20px;"> |
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<h3 style="color: #FFFFFF; margin-bottom: 15px;">Generated Depth Maps</h3> |
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<p style="color: #CCCCCC; margin-bottom: 20px;">Preview and download your generated depth maps.</p> |
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</div> |
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""") |
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video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300) |
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model_output = gr.Video(label="Generated Depth Video", autoplay=True, loop=True, height=300) |
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generate_btn.click( |
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fn=infer_video_depth, |
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inputs=[input_video, max_len, target_fps, max_res, grayscale], |
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outputs=[video_output, model_output], |
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) |
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gr.Markdown(""" |
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<div style="text-align: center; margin-top: 40px; padding: 20px; background: #2C2C2C; border: 1px solid #3C3C3C; border-radius: 8px;"> |
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<p style="color: #CCCCCC; font-size: 0.9rem; margin: 0;"> |
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Powered by <span style="color: #FF8C00;">Mean Cat Entertainment</span> • Built for the future of VFX |
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</p> |
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</div> |
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""") |
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if __name__ == "__main__": |
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demo.launch(share=True) |
