# Import spaces first import spaces # Import spaces for @spaces.GPU decorator import gradio as gr import os import tempfile import shutil import time import ffmpeg import numpy as np from PIL import Image import moviepy.editor as mp import logging import torch # Import torch after spaces from infer import lotus, load_models # Import infer after spaces and torch # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Set device to use GPU if available device = torch.device("cuda" 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 frame rate def preprocess_video(video_path, target_fps=24): """Preprocess the video to adjust its frame rate.""" video = mp.VideoFileClip(video_path) # 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(frame, seed=0): """Process a single frame and return depth map.""" try: # Convert frame to PIL Image image = Image.fromarray(frame).convert('RGB') # Run inference without resizing depth_map = lotus(image, 'depth', seed, device, pipe_g, pipe_d) return depth_map except Exception as e: logger.error(f"Error processing frame: {e}") return None @spaces.GPU # Decorate the function to use Hugging Face Spaces GPUs def process_video(video_path, fps=0, seed=0): """Process video frames individually 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()) 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) # Process frames individually for i, frame in enumerate(frames): depth_map = process_frame(frame, seed) if depth_map is not None: # Save frame in lossless PNG format frame_path = os.path.join(frames_dir, f"frame_{i:06d}.png") depth_map.save(frame_path, format='PNG', compress_level=0) # Update live preview every 10% progress if i % max(1, total_frames // 10) == 0: elapsed_time = time.time() - start_time progress = (i / total_frames) * 100 yield depth_map, None, None, f"Processed {i}/{total_frames} frames... ({progress:.2f}%) Elapsed: {elapsed_time:.2f}s" else: logger.error(f"Error processing frame {i}") 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 with lossless encoding video_filename = f"depth_video_{int(time.time())}.mp4" output_video_path = os.path.join(temp_dir, video_filename) try: # FFmpeg settings for lossless MP4 input_pattern = os.path.join(frames_dir, 'frame_%06d.png') ( ffmpeg .input(input_pattern, pattern_type='sequence', framerate=fps) .output(output_video_path, vcodec='libx264', pix_fmt='yuv420p', preset='veryslow', crf=0) .run(overwrite_output=True, quiet=True) ) logger.info("Lossless 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}" finally: # Clean up temporary directory if necessary pass # Wrapper function with error handling def process_wrapper(video, fps=0, seed=0): 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): 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") 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], outputs=[preview_image, output_frames_zip, output_video, time_textbox] ) demo.queue() if __name__ == "__main__": demo.launch(debug=True)