import os import gc import torch import cv2 import gradio as gr import numpy as np import matplotlib.cm as cm import matplotlib import subprocess import sys import spaces from video_depth_anything.video_depth import VideoDepthAnything from utils.dc_utils import save_video from huggingface_hub import hf_hub_download # Examples for the Gradio Demo. 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.""" def get_video_info(video_path, max_len=-1, target_fps=-1): """Extract video information without loading all frames into memory.""" cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise ValueError(f"Could not open video file: {video_path}") # Get video properties original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) original_fps = cap.get(cv2.CAP_PROP_FPS) total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # Adjust based on max_len parameter if max_len > 0 and max_len < total_frames: frame_count = max_len else: frame_count = total_frames # Use target_fps if specified if target_fps > 0: fps = target_fps else: fps = original_fps cap.release() return { 'width': original_width, 'height': original_height, 'fps': fps, 'original_fps': original_fps, 'frame_count': frame_count, 'total_frames': total_frames } def process_frame(frame, max_res): """Process a single frame to the desired resolution.""" if max_res > 0: h, w = frame.shape[:2] scale = min(max_res / w, max_res / h) if scale < 1: new_w, new_h = int(w * scale), int(h * scale) frame = cv2.resize(frame, (new_w, new_h)) return frame def read_video_frames_chunked(video_path, max_len=-1, target_fps=-1, max_res=-1, chunk_size=32): """Read video frames in chunks to avoid loading the entire video into memory.""" cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise ValueError(f"Could not open video file: {video_path}") original_fps = cap.get(cv2.CAP_PROP_FPS) total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # Determine actual number of frames to process if max_len > 0 and max_len < total_frames: frame_count = max_len else: frame_count = total_frames # Use target_fps if specified if target_fps > 0: fps = target_fps # Calculate frame skip if downsampling fps if target_fps < original_fps: skip = int(round(original_fps / target_fps)) - 1 else: skip = 0 else: fps = original_fps skip = 0 frame_idx = 0 processed_count = 0 while processed_count < frame_count: frames_chunk = [] # Read frames up to chunk size or remaining frames chunk_limit = min(chunk_size, frame_count - processed_count) while len(frames_chunk) < chunk_limit: ret, frame = cap.read() if not ret: break # Process frame if we're not skipping it if frame_idx % (skip + 1) == 0: # Convert from BGR to RGB frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # Resize if necessary frame = process_frame(frame, max_res) frames_chunk.append(frame) processed_count += 1 if processed_count >= frame_count: break frame_idx += 1 if frames_chunk: yield frames_chunk, fps if processed_count >= frame_count or len(frames_chunk) < chunk_limit: break cap.release() @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, ): 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') # Get video info first video_info = get_video_info(input_video, max_len, target_fps) fps = video_info['fps'] frame_count = video_info['frame_count'] print(f"Processing video: {input_video}, {frame_count} frames at {fps} fps") # Process the video in chunks to manage memory chunk_size = 32 # Adjust based on available memory # We'll collect depths as we go to calculate global min/max all_depths = [] all_processed_frames = [] # First pass to collect frames and depths frame_idx = 0 for frames_chunk, fps in read_video_frames_chunked(input_video, max_len, target_fps, max_res, chunk_size): print(f"Processing chunk: frames {frame_idx+1}-{frame_idx+len(frames_chunk)}/{frame_count}") # Process this chunk of frames depths, _ = video_depth_anything.infer_video_depth(frames_chunk, fps, input_size=input_size, device=DEVICE) # Store results (we'll need both for the output videos) all_processed_frames.extend(frames_chunk) all_depths.extend(depths) frame_idx += len(frames_chunk) # Free memory gc.collect() torch.cuda.empty_cache() # Calculate global min/max for depth normalization depths_array = np.array(all_depths) d_min, d_max = depths_array.min(), depths_array.max() # Save the preprocessed video and depth visualization save_video(all_processed_frames, processed_video_path, fps=fps) save_video(all_depths, depth_vis_path, fps=fps, is_depths=True) # Free some memory before stitching del all_processed_frames gc.collect() # Process stitched video if requested stitched_video_path = None if stitch: # Use only the first 20 characters of the base name for the output filename 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') # For stitching: read the original video in full resolution and stitch frames one by one stitched_frames = [] # Process in chunks for memory efficiency frame_idx = 0 for frames_chunk, _ in read_video_frames_chunked(input_video, max_len, target_fps, -1, chunk_size): # No max_res for original resolution print(f"Stitching chunk: frames {frame_idx+1}-{frame_idx+len(frames_chunk)}/{frame_count}") # Process each frame in the chunk for i, rgb_full in enumerate(frames_chunk): depth_idx = frame_idx + i if depth_idx >= len(all_depths): break depth_frame = all_depths[depth_idx] # Normalize the depth frame depth_norm = ((depth_frame - d_min) / (d_max - d_min) * 255).astype(np.uint8) # Generate depth visualization if grayscale: if convert_from_color: # Convert from color 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 use grayscale depth_vis = np.stack([depth_norm] * 3, axis=-1) else: # Use color visualization cmap = matplotlib.colormaps.get_cmap("inferno") depth_vis = (cmap(depth_norm / 255.0)[..., :3] * 255).astype(np.uint8) # Apply blur if requested if blur > 0: kernel_size = int(blur * 20) * 2 + 1 # Ensures odd kernel size depth_vis = cv2.GaussianBlur(depth_vis, (kernel_size, kernel_size), 0) # Resize depth visualization to match original resolution H_full, W_full = rgb_full.shape[:2] depth_vis_resized = cv2.resize(depth_vis, (W_full, H_full)) # Concatenate RGB and depth stitched = cv2.hconcat([rgb_full, depth_vis_resized]) stitched_frames.append(stitched) frame_idx += len(frames_chunk) # Free memory after processing each chunk gc.collect() # Save the stitched video save_video(stitched_frames, stitched_video_path, fps=fps) # Merge audio from the input video 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) # Free memory del stitched_frames # Clean up del all_depths gc.collect() torch.cuda.empty_cache() 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(max_size=2).launch()