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import os
import gc
import numpy as np
import torch
import spaces
import gradio as gr
from moviepy.editor import VideoFileClip, concatenate_videoclips
from video_depth_anything.video_depth import VideoDepthAnything
from utils.dc_utils import read_video_frames, save_video
from huggingface_hub import hf_hub_download
examples = [
['assets/example_videos/davis_rollercoaster.mp4', -1, -1, 1280],
['assets/example_videos/Tokyo-Walk_rgb.mp4', -1, -1, 1280],
['assets/example_videos/4158877-uhd_3840_2160_30fps_rgb.mp4', -1, -1, 1280],
['assets/example_videos/4511004-uhd_3840_2160_24fps_rgb.mp4', -1, -1, 1280],
['assets/example_videos/1753029-hd_1920_1080_30fps.mp4', -1, -1, 1280],
['assets/example_videos/davis_burnout.mp4', -1, -1, 1280],
['assets/example_videos/example_5473765-l.mp4', -1, -1, 1280],
['assets/example_videos/Istanbul-26920.mp4', -1, -1, 1280],
['assets/example_videos/obj_1.mp4', -1, -1, 1280],
['assets/example_videos/sheep_cut1.mp4', -1, -1, 1280],
]
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
model_configs = {
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
}
encoder2name = {
'vits': 'Small',
'vitl': 'Large',
}
#encoder = 'vitl'
encoder = 'vits'
model_name = encoder2name[encoder]
video_depth_anything = VideoDepthAnything(**model_configs[encoder])
filepath = hf_hub_download(repo_id=f"depth-anything/Video-Depth-Anything-{model_name}", filename=f"video_depth_anything_{encoder}.pth", repo_type="model")
video_depth_anything.load_state_dict(torch.load(filepath, map_location='cpu'))
video_depth_anything = video_depth_anything.to(DEVICE).eval()
title = "# Video Depth Anything"
description = """Official demo for **Video Depth Anything**.
Please refer to our [paper](https://arxiv.org/abs/2501.12375), [project page](https://videodepthanything.github.io/), and [github](https://github.com/DepthAnything/Video-Depth-Anything) for more details."""
@spaces.GPU(duration=240)
def infer_video_depth(
input_video: str,
max_len: int = -1,
target_fps: int = -1,
max_res: int = 1280,
grayscale: bool = False,
output_dir: str = './outputs',
input_size: int = 518,
):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
video_name = os.path.basename(input_video)
processed_video_path = os.path.join(output_dir, os.path.splitext(video_name)[0]+'_src.mp4')
depth_vis_path = os.path.join(output_dir, os.path.splitext(video_name)[0]+'_vis.mp4')
# Load the video
clip = VideoFileClip(input_video)
fps = clip.fps
total_frames = int(clip.duration * fps)
# Define the number of frames per segment
frames_per_segment = 45 # Adjust this value based on your GPU memory
segments = []
for start_frame in range(0, total_frames, frames_per_segment):
end_frame = min(start_frame + frames_per_segment, total_frames)
start_time = start_frame / fps
end_time = end_frame / fps
segment = clip.subclip(start_time, end_time)
segment_path = os.path.join(output_dir, f'segment_{start_frame}.mp4')
segment.write_videofile(segment_path, codec='libx264')
segments.append(segment_path)
# Save the processed video (concatenated segments)
processed_segments = [VideoFileClip(segment) for segment in segments]
final_processed_clip = concatenate_videoclips(processed_segments)
final_processed_clip.write_videofile(processed_video_path, codec='libx264')
# Process each segment
depth_segments = []
for segment in segments:
frames, target_fps = read_video_frames(segment, max_len, target_fps, max_res)
print("frame length", len(frames))
depths, fps = video_depth_anything.infer_video_depth(frames, target_fps, input_size=input_size, device=DEVICE)
depth_segment_path = os.path.join(output_dir, f'depth_{os.path.basename(segment)}')
save_video(depths, depth_segment_path, fps=fps, is_depths=True, grayscale=grayscale)
depth_segments.append(depth_segment_path)
# Merge depth segments
depth_clips = [VideoFileClip(depth_segment) for depth_segment in depth_segments]
final_depth_clip = concatenate_videoclips(depth_clips)
final_depth_clip.write_videofile(depth_vis_path, codec='libx264')
# Clean up
for segment in segments:
os.remove(segment)
for depth_segment in depth_segments:
os.remove(depth_segment)
gc.collect()
torch.cuda.empty_cache()
return [processed_video_path, depth_vis_path]
def construct_demo():
with gr.Blocks(analytics_enabled=False) as demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown("### If you find this work useful, please help ⭐ the [$$Github Repo$$](https://github.com/DepthAnything/Video-Depth-Anything). Thanks for your attention!")
with gr.Row(equal_height=True):
with gr.Column(scale=1):
input_video = gr.Video(label="Input Video")
with gr.Column(scale=2):
with gr.Row(equal_height=True):
processed_video = gr.Video(
label="Preprocessed video",
interactive=False,
autoplay=True,
loop=True,
show_share_button=True,
scale=5,
)
depth_vis_video = gr.Video(
label="Generated Depth Video",
interactive=False,
autoplay=True,
loop=True,
show_share_button=True,
scale=5,
)
with gr.Row(equal_height=True):
with gr.Column(scale=1):
with gr.Row(equal_height=False):
with gr.Accordion("Advanced Settings", open=False):
max_len = gr.Slider(
label="max process length",
minimum=-1,
maximum=1000,
value=500,
step=1,
)
target_fps = gr.Slider(
label="target FPS",
minimum=-1,
maximum=30,
value=15,
step=1,
)
max_res = gr.Slider(
label="max side resolution",
minimum=480,
maximum=1920,
value=1280,
step=1,
)
grayscale = gr.Checkbox(
label="grayscale",
value=False,
)
generate_btn = gr.Button("Generate")
with gr.Column(scale=2):
pass
gr.Examples(
examples=examples,
inputs=[
input_video,
max_len,
target_fps,
max_res
],
outputs=[processed_video, depth_vis_video],
fn=infer_video_depth,
cache_examples="lazy",
)
generate_btn.click(
fn=infer_video_depth,
inputs=[
input_video,
max_len,
target_fps,
max_res,
grayscale
],
outputs=[processed_video, depth_vis_video],
)
return demo
if __name__ == "__main__":
demo = construct_demo()
demo.queue()
demo.launch(share=True) |