import gradio as gr import torch import os import tempfile import shutil import time import ffmpeg import numpy as np from PIL import Image import moviepy.editor as mp from infer import lotus # Import the depth model inference function import logging # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Set device to use the L40s GPU explicitly device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Preprocess the video to adjust resolution and frame rate def preprocess_video(video_path, target_fps=24, max_resolution=(1920, 1080)): """Preprocess the video to resize and adjust its frame rate.""" video = mp.VideoFileClip(video_path) # Resize video if it's larger than the target resolution if video.size[0] > max_resolution[0] or video.size[1] > max_resolution[1]: video = video.resize(newsize=max_resolution) # Adjust FPS if target_fps is specified if target_fps > 0: video = video.set_fps(target_fps) return video # Process a single frame through the depth model def process_frame(image, seed=0): """Process a single frame through the depth model and return depth map.""" try: # Set seeds for reproducibility torch.manual_seed(seed) np.random.seed(seed) # Process through the depth model (assuming lotus accepts image data) _, output_d = lotus(image, 'depth', seed, device) # Convert depth output to numpy array depth_array = np.array(output_d) return depth_array except Exception as e: logger.error(f"Error processing frame: {e}") return None # Process video frames and generate depth maps def process_video(video_path, fps=0, seed=0, batch_size=16): """Process video, batch frames, and use L40s GPU to generate depth maps.""" try: start_time = time.time() # Preprocess the video video = preprocess_video(video_path, target_fps=fps) # Use original video FPS if not specified if fps == 0: fps = video.fps frames = list(video.iter_frames(fps=video.fps)) total_frames = len(frames) logger.info(f"Processing {total_frames} frames at {fps} FPS...") # Create temporary directory for frame sequence and outputs with tempfile.TemporaryDirectory() as temp_dir: frames_dir = os.path.join(temp_dir, "frames") os.makedirs(frames_dir, exist_ok=True) processed_frames = [] # Process frames in batches for i in range(0, total_frames, batch_size): frames_batch = frames[i:i+batch_size] depth_maps = [] # Process each frame in the batch for frame in frames_batch: depth_map = process_frame(Image.fromarray(frame), seed) depth_maps.append(depth_map) for j, depth_map in enumerate(depth_maps): if depth_map is not None: # Save frame frame_index = i + j frame_path = os.path.join(frames_dir, f"frame_{frame_index:06d}.png") Image.fromarray(depth_map).save(frame_path) # Collect processed frame for preview processed_frames.append(depth_map) # Update preview every 10% progress if frame_index % max(1, total_frames // 10) == 0: elapsed_time = time.time() - start_time progress = (frame_index / total_frames) * 100 yield processed_frames[-1], None, None, f"Processed {frame_index}/{total_frames} frames... ({progress:.2f}%) Elapsed: {elapsed_time:.2f}s" else: logger.error(f"Error processing frame {frame_index}") logger.info("Creating output files...") # Create ZIP of frame sequence zip_filename = f"depth_frames_{int(time.time())}.zip" zip_path = os.path.join(temp_dir, zip_filename) shutil.make_archive(zip_path[:-4], 'zip', frames_dir) # Create MP4 video video_filename = f"depth_video_{int(time.time())}.mp4" output_video_path = os.path.join(temp_dir, video_filename) try: # FFmpeg settings for high-quality MP4 ( ffmpeg .input(os.path.join(frames_dir, 'frame_%06d.png'), pattern_type='sequence', framerate=fps) .output(output_video_path, vcodec='libx264', pix_fmt='yuv420p', crf=17) .run(overwrite_output=True) ) logger.info("MP4 video created successfully!") except ffmpeg.Error as e: logger.error(f"Error creating video: {e.stderr.decode() if e.stderr else str(e)}") output_video_path = None total_time = time.time() - start_time logger.info("Processing complete!") # Read output files to return as bytes with open(zip_path, 'rb') as f: zip_data = f.read() with open(output_video_path, 'rb') as f: video_data = f.read() yield None, (zip_filename, zip_data), (video_filename, video_data), f"Processing complete! Total time: {total_time:.2f} seconds" except Exception as e: logger.error(f"Error: {e}") yield None, None, None, f"Error processing video: {e}" # Wrapper function with error handling def process_wrapper(video, fps=0, seed=0, batch_size=16): if video is None: raise gr.Error("Please upload a video.") try: outputs = [] # Use video directly, since it's the file path for output in process_video(video, fps, seed, batch_size): outputs.append(output) yield output return outputs[-1] except Exception as e: raise gr.Error(f"Error processing video: {str(e)}") # Custom CSS for styling custom_css = """ .title-container { text-align: center; padding: 10px 0; } #title { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; font-size: 36px; font-weight: bold; color: #000000; padding: 10px; border-radius: 10px; display: inline-block; background: linear-gradient( 135deg, #e0f7fa, #e8f5e9, #fff9c4, #ffebee, #f3e5f5, #e1f5fe, #fff3e0, #e8eaf6 ); background-size: 400% 400%; animation: gradient-animation 15s ease infinite; } @keyframes gradient-animation { 0% { background-position: 0% 50%; } 50% { background-position: 100% 50%; } 100% { background-position: 0% 50%; } } """ # Gradio Interface with gr.Blocks(css=custom_css) as demo: gr.HTML('''
Video Depth Estimation
''') with gr.Row(): with gr.Column(): video_input = gr.Video(label="Upload Video", interactive=True) fps_slider = gr.Slider(minimum=0, maximum=60, step=1, value=0, label="Output FPS (0 for original)") seed_slider = gr.Number(value=0, label="Seed") batch_size_slider = gr.Slider(minimum=1, maximum=64, step=1, value=16, label="Batch Size") btn = gr.Button("Process Video") with gr.Column(): preview_image = gr.Image(label="Live Preview") output_frames_zip = gr.File(label="Download Frame Sequence (ZIP)") output_video = gr.File(label="Download Video (MP4)") time_textbox = gr.Textbox(label="Status", interactive=False) btn.click( fn=process_wrapper, inputs=[video_input, fps_slider, seed_slider, batch_size_slider], outputs=[preview_image, output_frames_zip, output_video, time_textbox] ) demo.queue() if __name__ == "__main__": demo.launch(debug=True)