import os import gc import torch import cv2 import gradio as gr import numpy as np import matplotlib.cm as cm import matplotlib # New import for the updated colormap API import subprocess import sys import spaces 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 for the Gradio Demo. # Each example now contains 8 parameters: # [video_path, max_len, target_fps, max_res, stitch, grayscale, convert_from_color, blur] examples = [ ['assets/example_videos/octopus_01.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/chicken_01.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/gorilla_01.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/davis_rollercoaster.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/Tokyo-Walk_rgb.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/4158877-uhd_3840_2160_30fps_rgb.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/4511004-uhd_3840_2160_24fps_rgb.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/1753029-hd_1920_1080_30fps.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/davis_burnout.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/example_5473765-l.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/Istanbul-26920.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/obj_1.mp4', -1, -1, 1280, True, True, True, 0.3], ['assets/example_videos/sheep_cut1.mp4', -1, -1, 1280, True, True, True, 0.3], ] # Use GPU if available; otherwise, use CPU. DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' # Model configuration for different encoder variants. 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' model_name = encoder2name[encoder] # Initialize the model. 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 + RGBD sbs output" description = """**Video Depth Anything** + RGBD sbs output for viewing with Looking Glass Factory displays. 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(enable_queue=True) def infer_video_depth( input_video: str, max_len: int = -1, target_fps: int = -1, max_res: int = 1280, stitch: bool = True, grayscale: bool = True, convert_from_color: bool = True, blur: float = 0.3, output_dir: str = './outputs', input_size: int = 518, ): # 1. Read input video frames for inference (downscaled to max_res). frames, target_fps = read_video_frames(input_video, max_len, target_fps, max_res) # 2. Perform depth inference using the model. depths, fps = video_depth_anything.infer_video_depth(frames, target_fps, input_size=input_size, device=DEVICE) video_name = os.path.basename(input_video) if not os.path.exists(output_dir): os.makedirs(output_dir) # Save the preprocessed (RGB) video and the generated depth visualization. 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') save_video(frames, processed_video_path, fps=fps) save_video(depths, depth_vis_path, fps=fps, is_depths=True) stitched_video_path = None if stitch: # For stitching: read the original video in full resolution (without downscaling). full_frames, _ = read_video_frames(input_video, max_len, target_fps, max_res=-1) # For each frame, create a visual depth image from the inferenced depths. d_min, d_max = depths.min(), depths.max() stitched_frames = [] for i in range(min(len(full_frames), len(depths))): rgb_full = full_frames[i] # Full-resolution RGB frame. depth_frame = depths[i] # Normalize the depth frame to the range [0, 255]. depth_norm = ((depth_frame - d_min) / (d_max - d_min) * 255).astype(np.uint8) # Generate depth visualization: if grayscale: if convert_from_color: # First, generate a color depth image using the inferno colormap, # then convert that color image to grayscale. cmap = matplotlib.colormaps.get_cmap("inferno") depth_color = (cmap(depth_norm / 255.0)[..., :3] * 255).astype(np.uint8) depth_gray = cv2.cvtColor(depth_color, cv2.COLOR_RGB2GRAY) depth_vis = np.stack([depth_gray] * 3, axis=-1) else: # Directly generate a grayscale image from the normalized depth values. depth_vis = np.stack([depth_norm] * 3, axis=-1) else: # Generate a color depth image using the inferno colormap. cmap = matplotlib.colormaps.get_cmap("inferno") depth_vis = (cmap(depth_norm / 255.0)[..., :3] * 255).astype(np.uint8) # Apply Gaussian blur if requested. if blur > 0: kernel_size = int(blur * 20) * 2 + 1 # Ensures an odd kernel size. depth_vis = cv2.GaussianBlur(depth_vis, (kernel_size, kernel_size), 0) # Resize the depth visualization to match the full-resolution RGB frame. H_full, W_full = rgb_full.shape[:2] depth_vis_resized = cv2.resize(depth_vis, (W_full, H_full)) # Concatenate the full-resolution RGB frame (left) and the resized depth visualization (right). stitched = cv2.hconcat([rgb_full, depth_vis_resized]) stitched_frames.append(stitched) stitched_frames = np.array(stitched_frames) # Use only the first 20 characters of the base name for the output filename and append '_RGBD.mp4' base_name = os.path.splitext(video_name)[0] short_name = base_name[:20] stitched_video_path = os.path.join(output_dir, short_name + '_RGBD.mp4') save_video(stitched_frames, stitched_video_path, fps=fps) # Merge audio from the input video into the stitched video using ffmpeg. temp_audio_path = stitched_video_path.replace('_RGBD.mp4', '_RGBD_audio.mp4') cmd = [ "ffmpeg", "-y", "-i", stitched_video_path, "-i", input_video, "-c:v", "copy", "-c:a", "aac", "-map", "0:v:0", "-map", "1:a:0?", "-shortest", temp_audio_path ] subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) os.replace(temp_audio_path, stitched_video_path) gc.collect() torch.cuda.empty_cache() # Return the preprocessed RGB video, depth visualization, and (if created) the stitched video. return [processed_video_path, depth_vis_path, stitched_video_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): # Video input component for file upload. 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) stitched_video = gr.Video(label="Stitched RGBD 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.Accordion("Advanced Settings", open=False): max_len = gr.Slider(label="Max process length", minimum=-1, maximum=1000, value=-1, step=1) target_fps = gr.Slider(label="Target FPS", minimum=-1, maximum=30, value=-1, step=1) max_res = gr.Slider(label="Max side resolution", minimum=480, maximum=1920, value=1280, step=1) stitch_option = gr.Checkbox(label="Stitch RGB & Depth Videos", value=True) grayscale_option = gr.Checkbox(label="Output Depth as Grayscale", value=True) convert_from_color_option = gr.Checkbox(label="Convert Grayscale from Color", value=True) blur_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="Depth Blur (can reduce edge artifacts on display)", value=0.3) generate_btn = gr.Button("Generate") with gr.Column(scale=2): pass gr.Examples( examples=examples, inputs=[input_video, max_len, target_fps, max_res, stitch_option, grayscale_option, convert_from_color_option, blur_slider], outputs=[processed_video, depth_vis_video, stitched_video], fn=infer_video_depth, cache_examples=False, cache_mode="lazy", ) generate_btn.click( fn=infer_video_depth, inputs=[input_video, max_len, target_fps, max_res, stitch_option, grayscale_option, convert_from_color_option, blur_slider], outputs=[processed_video, depth_vis_video, stitched_video], ) return demo if __name__ == "__main__": demo = construct_demo() #demo.queue() # Enable asynchronous processing. #demo.launch(share=True) demo.queue(max_size=2).launch()