import os import gc 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 from utils.dc_utils import read_video_frames, save_video title = """**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.""" def stitch_rgbd_videos( processed_video: str, depth_vis_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(processed_video, max_len, target_fps, max_res) video_name = os.path.basename(processed_video) if not os.path.exists(output_dir): os.makedirs(output_dir) stitched_video_path = None if stitch: # For stitching: read the original video in full resolution (without downscaling). full_frames, _ = read_video_frames(processed_video, max_len, target_fps, max_res=-1) depths, _ = read_video_frames(depth_vis_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) if len(depth_color.shape) == 3 and depth_color.shape[2] in [3, 4]: depth_gray = cv2.cvtColor(depth_color, cv2.COLOR_RGB2GRAY) else: depth_gray = depth_color 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) # Ensure depth_vis is valid and contiguous if depth_vis is None or depth_vis.size == 0: raise ValueError("depth_vis is empty or not properly computed.") else: depth_vis = np.ascontiguousarray(depth_vis) # 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)) # Ensure both images have 3 channels. if len(rgb_full.shape) == 2: rgb_full = cv2.cvtColor(rgb_full, cv2.COLOR_GRAY2BGR) if len(depth_vis_resized.shape) == 2: depth_vis_resized = cv2.cvtColor(depth_vis_resized, cv2.COLOR_GRAY2BGR) # Ensure same data type. if rgb_full.dtype != depth_vis_resized.dtype: depth_vis_resized = depth_vis_resized.astype(rgb_full.dtype) # Ensure images are contiguous in memory. rgb_full = np.ascontiguousarray(rgb_full) depth_vis_resized = np.ascontiguousarray(depth_vis_resized) # Now safely concatenate. 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=target_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", processed_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) # Return stitched video. return [stitched_video_path, None] 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. processed_video = gr.Video(label="Input Video") depth_vis_video = gr.Video(label="Generated Depth Video") with gr.Column(scale=2): with gr.Row(equal_height=True): 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 generate_btn.click( fn=stitch_rgbd_videos, inputs=[processed_video, depth_vis_video, max_len, target_fps, max_res, stitch_option, grayscale_option, convert_from_color_option, blur_slider], outputs=[stitched_video], ) return demo if __name__ == "__main__": demo = construct_demo() demo.queue(max_size=2).launch()