app.py

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('''
        <div class="title-container">
            <div id="title">Video Depth Estimation</div>
        </div>
    ''')
    
    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)