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, load_models 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") # Load models once task_name = 'depth' pipe_g, pipe_d = load_models(task_name, device) # 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(height=max_resolution[1]) # Adjust FPS if target_fps is specified if target_fps > 0: video = video.set_fps(target_fps) return video # Process a batch of frames through the depth model def process_frames_batch(frames_batch, seed=0): """Process a batch of frames and return depth maps.""" try: # Convert frames to PIL Images images_batch = [Image.fromarray(frame).convert('RGB') for frame in frames_batch] # Run batch inference depth_maps = lotus(images_batch, 'depth', seed, device, pipe_g, pipe_d) return depth_maps except Exception as e: logger.error(f"Error processing batch: {e}") return [None] * len(frames_batch) # Process video frames and generate depth maps def process_video(video_path, fps=0, seed=0, batch_size=16): """Process video frames in batches and generate depth maps.""" # Create a persistent temporary directory temp_dir = tempfile.mkdtemp() 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 directory for frame sequence and outputs 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_frames_batch(frames_batch, seed) for j, depth_map in enumerate(depth_maps): frame_index = i + j if depth_map is not None: # Save frame frame_path = os.path.join(frames_dir, f"frame_{frame_index:06d}.png") depth_map.save(frame_path) # Update live 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 depth_map, 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, quiet=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!") # Yield the file paths yield None, zip_path, output_video_path, 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 (unchanged) 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)