demo_utils.py

# IMPORTANT: Import spaces first, before any CUDA-related packages (torch, etc.)
try:
    import spaces
    ZEROGPU_AVAILABLE = True
except ImportError:
    ZEROGPU_AVAILABLE = False

import torch
import torch.nn.functional as F
import numpy as np
import av
import imageio
from transformers import VivitImageProcessor
from PIL import Image, ImageDraw, ImageFont
from omegaconf import OmegaConf
from einops import rearrange
from tqdm import trange
from autogaze.models.autogaze import AutoGaze
from autogaze.datasets.video_utils import read_video_pyav, transform_video_for_pytorch
from autogaze.tasks.video_mae_reconstruction import VideoMAEReconstruction
from autogaze.utils import UnNormalize


def image_to_video(image_path, output_path, fps):
    """
    Convert a single image to a single-frame video file.

    Args:
        image_path: Path to input image
        output_path: Path to output video file
        fps: Frame rate for the video

    Returns:
        Dictionary with video metadata (width, height, frames, fps)
    """
    img = Image.open(image_path)
    if img.mode != 'RGB':
        img = img.convert('RGB')

    img_array = np.array(img)

    with imageio.get_writer(output_path, fps=fps, format='FFMPEG', codec='libx264', pixelformat='yuv444p', macro_block_size=1) as writer:
        writer.append_data(img_array)

    return {
        'width': img_array.shape[1],
        'height': img_array.shape[0],
        'frames': 1,
        'fps': fps
    }


def load_model(device='cuda'):
    print("Loading AutoGaze model from HuggingFace...")
    model = AutoGaze.from_pretrained("nvidia/AutoGaze")
    model = model.to(device)
    model.eval()

    transform = VivitImageProcessor.from_pretrained(
        "facebook/vit-mae-large",
        size=model.scales[-1],
        crop_size=model.scales[-1]
    )

    unnorm = UnNormalize(
        mean=transform.image_mean,
        std=transform.image_std,
        rescale_factor=transform.rescale_factor
    )

    print("Loading VideoMAE model from HuggingFace...")
    scales_str = '+'.join(map(str, model.scales))
    recon_model_config = OmegaConf.create({
        'scale_embed': True,
        'max_num_frames': 256,
        'time_embed': True,
        'causal': True,
        'loss_type': 'l1+dinov2_reg+siglip2',
        'loss_weights': '1',
        'l1_loss_config': {},
        'dinov2_reg_loss_config': {
            'model': 'facebook/dinov2-with-registers-base'
        },
        'siglip2_loss_config': {
            'model': 'google/siglip2-base-patch16-224'
        }
    })
    task = VideoMAEReconstruction(
        recon_model='facebook/vit-mae-large',
        recon_model_config=recon_model_config,
        scales=scales_str,
        recon_sample_rate=1,
        attn_mode='sdpa'
    )

    # Load fine-tuned weights from HuggingFace
    from huggingface_hub import hf_hub_download
    checkpoint_path = hf_hub_download(repo_id="bfshi/VideoMAE_AutoGaze", filename="videomae.pt")
    print(f"Loading VideoMAE checkpoint from {checkpoint_path}...")
    task_sd = torch.load(checkpoint_path, map_location='cpu')
    task_sd = {k.replace('module.mae.', ''): v for k, v in task_sd.items()}
    task.mae.load_state_dict(task_sd, strict=True)
    print("Loaded VideoMAE checkpoint from HuggingFace")

    task = task.to(device)
    task.eval()

    return {
        'model': model,
        'task': task,
        'unnorm': unnorm,
        'scales': model.scales,
        'transform': transform,
    }


def process_video(video_path, setup, gazing_ratio=0.75, task_loss_requirement=0.6, progress_callback=None, spatial_batch_size=16):
    """
    Process a video file with AutoGaze using chunking for any resolution/duration.

    Args:
        video_path: Path to video file
        setup: Dictionary with model, task, unnorm, scales, transform
        gazing_ratio: Maximum percentage of patches to gaze per frame
        task_loss_requirement: Reconstruction loss threshold
        progress_callback: Optional callback function for progress updates

    Yields:
        Dictionary with original frames, gazing frames, reconstruction frames, and statistics
    """
    model = setup['model']
    task = setup['task']
    transform = setup['transform']
    device = next(model.parameters()).device
    if device == 'cuda':
        torch.cuda.empty_cache()

    container = av.open(video_path)
    video_stream = container.streams.video[0]
    total_frames_available = video_stream.frames
    fps = float(video_stream.average_rate)
    container.close()

    container = av.open(video_path)
    sample_indices = list(range(total_frames_available))
    video = read_video_pyav(container=container, indices=sample_indices)  # (T, H, W, 3) numpy array
    container.close()

    # Keep video on CPU for preprocessing to save GPU memory
    video_tensor = torch.from_numpy(video).float()  # (T, H, W, 3)
    video_tensor = video_tensor / 255.0  # Normalize to [0, 1]
    video_tensor = video_tensor.permute(0, 3, 1, 2)  # (T, C, H, W)
    T, C, H, W = video_tensor.shape
    if T > 200:
        print(f'Video has {T} frames, which may require significant GPU memory. Decreasing spatial_batch_size to 2.')
        spatial_batch_size //= 2

    # Clone for later visualization (keep on CPU)
    video_tensor_original = video_tensor.clone()

    # Pad video to be divisible by 224x224 and 16 frames
    pad_t = (16 - T % 16) % 16
    pad_h = (224 - H % 224) % 224
    pad_w = (224 - W % 224) % 224

    if pad_t > 0 or pad_h > 0 or pad_w > 0:
        video_tensor = F.pad(video_tensor, (0, pad_w, 0, pad_h, 0, 0, 0, pad_t))

    # Chunk video into 16-frame, 224x224 chunks (following QUICK_START.md)
    video_tensor = video_tensor.unsqueeze(0)  # 1 * T * C * H * W

    # Calculate chunking dimensions
    nt = (T + pad_t) // 16
    nh = (H + pad_h) // 224
    nw = (W + pad_w) // 224
    num_spatial_chunks = nh * nw
    num_chunks = nt * num_spatial_chunks

    # Chunk into (num_chunks, 16, C, 224, 224)
    video_chunks = rearrange(video_tensor, 'B (nt t) C (nh h) (nw w) -> (B nt nh nw) t C h w', t=16, h=224, w=224)

    print(f"Video chunked into {num_chunks} chunks ({nt} temporal x {num_spatial_chunks} spatial) of shape (16, {C}, 224, 224). Original shape: ({T}, {C}, {H}, {W})")

    # Apply VivitImageProcessor normalization to chunks
    # Rearrange chunks to process all frames: (num_chunks, 16, C, H, W) -> (num_chunks * 16, C, H, W)
    chunks_flat = rearrange(video_chunks, 'b t c h w -> (b t) c h w')

    # Apply normalization using VivitImageProcessor's mean and std (on CPU)
    mean = torch.tensor(transform.image_mean).view(1, 3, 1, 1)
    std = torch.tensor(transform.image_std).view(1, 3, 1, 1)
    chunks_flat = (chunks_flat - mean) / std

    video_chunks = rearrange(chunks_flat, '(b t) c h w -> b t c h w', b=num_chunks, t=16)
    video_chunks = rearrange(video_chunks, '(ns nt) t c h w -> ns nt t c h w', ns=num_spatial_chunks, nt=nt)

    # Keep video_chunks on CPU - only move mini-batches to GPU as needed
    print(f'video_chunks shape (spatial, temporal, frames, C, H, W): {video_chunks.shape}')

    del video_tensor, chunks_flat, mean, std

    with torch.inference_mode():
        # Process spatial locations in mini-batches (keep all temporal chunks together per spatial location)
        num_spatial_batches = (num_spatial_chunks + spatial_batch_size - 1) // spatial_batch_size

        all_gaze_outputs = []
        total_gazing_tokens = 0

        for batch_idx in range(num_spatial_batches):
            start_idx = batch_idx * spatial_batch_size
            end_idx = min(start_idx + spatial_batch_size, num_spatial_chunks)
            batch_size = end_idx - start_idx

            gazing_pct = int(((batch_idx + 1) / num_spatial_batches) * 100)
            if progress_callback:
                progress_callback(0.1 + 0.4 * (batch_idx / num_spatial_batches), f"Gazing progress: {gazing_pct}%")
            yield None

            spatial_batch = video_chunks[start_idx:end_idx].to(device)
            spatial_batch = rearrange(spatial_batch, 'bs nt t c h w -> (bs nt) t c h w')
            print(f'Processing spatial batch {batch_idx+1}/{num_spatial_batches} with {batch_size} spatial locations x {nt} temporal = {spatial_batch.shape[0]} chunks')

            # Run AutoGaze on this mini-batch
            batch_gaze_output = model({"video": spatial_batch}, gazing_ratio=gazing_ratio, task_loss_requirement=task_loss_requirement)

            num_gazing_each_frame = batch_gaze_output['num_gazing_each_frame'][:T]
            num_gazing_total = num_gazing_each_frame.sum().item()

            # Free GPU memory after forward pass
            del spatial_batch

            # Count gazing tokens for this batch
            if_padded = batch_gaze_output.get('if_padded_gazing')
            if if_padded is not None:
                print(f'shape of if_padded: {if_padded.shape}')
                if_padded = if_padded[:, :min(num_gazing_total, if_padded.shape[1])]
                new_gazing_tokens = (~if_padded).sum().item()
            else:
                new_gazing_tokens = (batch_gaze_output['gazing_pos'] < (196 * T)).sum().item()
            total_gazing_tokens += new_gazing_tokens
            print(f'Batch {batch_idx+1}: Gazing tokens = {new_gazing_tokens}, Total gazing tokens so far = {total_gazing_tokens}')

            # Store the output
            all_gaze_outputs.append(batch_gaze_output)
            if torch.cuda.is_available():
                torch.cuda.empty_cache()

        print("Merging mini-batch results...")

        # Find max sequence length across all mini-batches
        max_seq_len = max(out['gazing_pos'].shape[1] for out in all_gaze_outputs)

        # Pad gazing_pos and if_padded_gazing to same length (they have variable seq length)
        # gazing_mask doesn't need padding since all chunks have same shape
        padded_gazing_pos = []
        padded_if_padded_gazing = []

        for out in all_gaze_outputs:
            seq_len = out['gazing_pos'].shape[1]
            pad_len = max_seq_len - seq_len

            # Pad gazing_pos with zeros
            padded_pos = F.pad(out['gazing_pos'], (0, pad_len), value=0)
            padded_gazing_pos.append(padded_pos)

            # Pad if_padded_gazing and mark new positions as True (padded)
            if 'if_padded_gazing' in out:
                padded_if_pad = F.pad(out['if_padded_gazing'], (0, pad_len), value=True)
                padded_if_padded_gazing.append(padded_if_pad)

        # Store num_gazing_each_frame per mini-batch for later per-chunk extraction
        num_gazing_each_frame_list = [out['num_gazing_each_frame'] for out in all_gaze_outputs]
        batch_sizes = [out['gazing_pos'].shape[0] for out in all_gaze_outputs]

        gaze_output = {
            'gazing_pos': torch.cat(padded_gazing_pos, dim=0),
            'gazing_mask': [torch.cat([out['gazing_mask'][i] for out in all_gaze_outputs], dim=0) for i in range(4)],
            'num_gazing_each_frame_list': num_gazing_each_frame_list,  # List of values per mini-batch
            'batch_sizes': batch_sizes,  # Track which chunks came from which mini-batch
            'frame_sampling_rate': all_gaze_outputs[0]['frame_sampling_rate'],
            'num_vision_tokens_each_frame': all_gaze_outputs[0]['num_vision_tokens_each_frame'],
        }
        if len(padded_if_padded_gazing) > 0:
            gaze_output['if_padded_gazing'] = torch.cat(padded_if_padded_gazing, dim=0)

        # Clean up mini-batch outputs
        del all_gaze_outputs

        total_possible_tokens = 196 * min(T, 16) * num_chunks

        # Extract gazing masks for later visualization (already in batched form)
        gazing_masks_batched = gaze_output['gazing_mask']  # List of 4 scales, each (num_chunks, 16, num_patches)

        # Flatten video_chunks back to (num_chunks, 16, C, H, W) for reconstruction
        video_chunks_flat = rearrange(video_chunks, 'ns nt t c h w -> (ns nt) t c h w').cpu()

        # Pre-allocate reconstruction tensor on CPU to avoid memory accumulation
        total_frames = num_chunks * 16
        C = video_chunks_flat.shape[2]
        reconstruction_chunks = torch.zeros((total_frames, C, 224, 224), dtype=torch.float32)
        frame_idx_counter = 0

        # Process reconstruction in mini-batches matching AutoGaze batch structure
        num_autogaze_batches = len(gaze_output['num_gazing_each_frame_list'])
        print(f'Reconstructing {num_chunks} chunks in {num_autogaze_batches} batches (aligned with AutoGaze batches)...')

        chunk_idx = 0
        for autogaze_batch_idx in range(num_autogaze_batches):
            batch_size = gaze_output['batch_sizes'][autogaze_batch_idx]
            start_chunk_idx = chunk_idx
            end_chunk_idx = chunk_idx + batch_size

            print(f'Reconstructing chunks {start_chunk_idx+1}-{end_chunk_idx}/{num_chunks}...')

            # Extract videos for all chunks in this AutoGaze batch
            batch_videos = video_chunks_flat[start_chunk_idx:end_chunk_idx].to(device)  # (batch_size, 16, C, H, W)

            # Extract gazing data for all chunks in this AutoGaze batch
            batch_gazing_pos = gaze_output['gazing_pos'][start_chunk_idx:end_chunk_idx]
            batch_gazing_mask = [scale_mask[start_chunk_idx:end_chunk_idx] for scale_mask in gaze_output['gazing_mask']]
            batch_num_gazing_each_frame = gaze_output['num_gazing_each_frame_list'][autogaze_batch_idx]

            # Trim to expected sequence length for this AutoGaze batch
            expected_seq_len = batch_num_gazing_each_frame.sum().item()
            batch_gazing_pos = batch_gazing_pos[:, :expected_seq_len]

            chunk_idx = end_chunk_idx

            batch_gaze_output = {
                'gazing_pos': batch_gazing_pos,
                'gazing_mask': batch_gazing_mask,
                'num_gazing_each_frame': batch_num_gazing_each_frame,
                'frame_sampling_rate': gaze_output['frame_sampling_rate'],
                'num_vision_tokens_each_frame': gaze_output['num_vision_tokens_each_frame'],
            }

            if 'if_padded_gazing' in gaze_output:
                batch_if_padded = gaze_output['if_padded_gazing'][start_chunk_idx:end_chunk_idx]
                batch_if_padded = batch_if_padded[:, :expected_seq_len]
                batch_gaze_output['if_padded_gazing'] = batch_if_padded

            # Reconstruct frame by frame for this batch
            batch_video_dict = {"video": batch_videos}
            # Pre-allocate batch_reconstructions tensor to avoid list + stack memory spike
            batch_reconstructions = torch.zeros((16, batch_size, C, 224, 224), device=device)
            for frame_idx in range(16):
                # Update progress for each frame
                frame_pct = int(((autogaze_batch_idx * 16 + frame_idx + 1) / (num_autogaze_batches * 16)) * 100)
                if progress_callback:
                    progress_callback(0.5 + 0.4 * ((autogaze_batch_idx * 16 + frame_idx + 1) / (num_autogaze_batches * 16)), f"Reconstruction progress: {frame_pct}%")
                yield None

                task_output = task.forward_output(batch_video_dict, batch_gaze_output, frame_idx_to_reconstruct=[frame_idx])
                batch_reconstructions[frame_idx] = task_output['reconstruction'][:, 0]  # (recon_batch_size, C, H, W)
                del task_output

            # Reorder from (16, recon_batch_size, C, H, W) to (recon_batch_size, 16, C, H, W) to match expected chunk ordering
            # batch_reconstructions already in shape (16, recon_batch_size, C, H, W)
            batch_reconstructions = rearrange(batch_reconstructions, 't b c h w -> (b t) c h w')  # (recon_batch_size * 16, C, H, W)

            # Write directly into pre-allocated tensor
            batch_size_frames = batch_reconstructions.shape[0]
            reconstruction_chunks[frame_idx_counter:frame_idx_counter+batch_size_frames] = batch_reconstructions.cpu()
            frame_idx_counter += batch_size_frames

            # Clean up batch-specific variables
            del batch_videos, batch_gaze_output, batch_video_dict, batch_reconstructions
        print('Reconstruction complete.')
        # Manually reverse the mean/std normalization to get back to [0, 1] range
        mean = torch.tensor(transform.image_mean).view(1, 3, 1, 1).to(reconstruction_chunks.device)
        std = torch.tensor(transform.image_std).view(1, 3, 1, 1).to(reconstruction_chunks.device)
        reconstruction_chunks = reconstruction_chunks * std + mean

        # Clean up video chunks and gaze output to free GPU memory (keep gazing_masks_batched for later)
        del video_chunks, video_chunks_flat, gaze_output

    # Reshape chunks back to original structure (nt, nh, nw already calculated earlier)
    print(f'Reshaping reconstructed chunks back to video tensor...')
    reconstruction_tensor = rearrange(reconstruction_chunks, '(nt nh nw t) C h w -> (nt t) C (nh h) (nw w)', nt=nt, nh=nh, nw=nw, t=16)
    reconstruction_tensor = reconstruction_tensor[:T, :, :H, :W]  # Remove padding

    # Move reconstruction to GPU for visualization
    reconstruction_tensor = reconstruction_tensor.to(device)

    gazing_mask_assembled = []
    for scale_idx in range(4):
        scale_masks_stacked = gazing_masks_batched[scale_idx]

        # Reshape: (num_chunks, 16, num_patches) -> (num_chunks * 16, num_patches)
        scale_masks_flat = scale_masks_stacked.reshape(-1, scale_masks_stacked.shape[-1])

        # Rearrange back to original video structure
        scale_masks_reshaped = rearrange(scale_masks_flat, '(nt nh nw t) n -> (nt t) (nh nw) n', nt=nt, nh=nh, nw=nw, t=16)
        scale_masks_reshaped = scale_masks_reshaped[:T]  # Remove temporal padding

        gazing_mask_assembled.append(scale_masks_reshaped)

        del scale_masks_stacked, scale_masks_flat, scale_masks_reshaped

    del gazing_masks_batched

    pct = total_gazing_tokens / total_possible_tokens

    # Move original video to GPU for visualization
    video_viz = video_tensor_original.to(device)

    # Generate frame-by-frame visualizations
    original_frames = []
    composite_frames = []
    reconstruction_frames = []
    scales_stitch_frames = []

    print('Visualizing...')
    if progress_callback:
        progress_callback(0.9, "Visualizing...")
    yield None
    for t in trange(T):
        # Original frame
        frame = video_viz[t].permute(1, 2, 0)
        frame = torch.clip(frame, 0, 1)
        frame_uint8 = (frame * 255).byte().cpu().numpy()
        original_frames.append(frame_uint8)

        # Reconstruction frame
        recon_frame = reconstruction_tensor[t].permute(1, 2, 0)
        recon_frame = torch.clip(recon_frame, 0, 1)
        recon_uint8 = (recon_frame * 255).byte().cpu().numpy()
        reconstruction_frames.append(recon_uint8)

        composite = torch.zeros((H, W, 3)).to(device)
        scales = setup['scales']
        alpha_values = [0.4, 0.5, 0.6, 0.7]  # Per-scale opacity (coarse to fine)
        colors = [
            [1.0, 0.0, 0.0],  # Scale 0 (coarsest): Red
            [0.0, 1.0, 0.0],  # Scale 1: Green
            [0.0, 0.0, 1.0],  # Scale 2: Blue
            [1.0, 1.0, 0.0]   # Scale 3 (finest): Yellow
        ]

        for scale_idx in range(4):
            scale = scales[scale_idx]
            scale_h = int(scale * H / 224)
            scale_w = int(scale * W / 224)

            # Get mask for this scale and frame
            mask = gazing_mask_assembled[scale_idx][t]  # (nh * nw, num_patches)

            # print(f'Frame {t}, Scale {scale}: mask shape {mask.shape}')
            # print(mask)
            # print()

            # Reshape mask: (nh * nw, num_patches) where num_patches = s^2
            num_patches_per_chunk = mask.shape[-1]
            s = int(num_patches_per_chunk ** 0.5)

            # Rearrange to 2D spatial grid
            mask_2d = rearrange(mask, '(nh nw) (h w) -> (nh h) (nw w)', nh=nh, nw=nw, h=s, w=s)

            # Convert to tensor if needed
            if isinstance(mask_2d, np.ndarray):
                mask_tensor = torch.from_numpy(mask_2d)
            else:
                mask_tensor = mask_2d

            # Map mask through padded space then crop to original image dimensions
            H_pad, W_pad = nh * 224, nw * 224
            mask_full = F.interpolate(mask_tensor.unsqueeze(0).unsqueeze(0).float(), size=(H_pad, W_pad), mode='nearest')[0, 0]
            mask_resized = F.interpolate(mask_full[:H, :W].unsqueeze(0).unsqueeze(0), size=(scale_h, scale_w), mode='nearest')[0, 0]

            frame_tensor = video_viz[t]
            frame_scaled = F.interpolate(frame_tensor.unsqueeze(0), size=(scale_h, scale_w), mode='bicubic', align_corners=False).squeeze().clamp(0, 1)

            frame_scaled_masked = frame_scaled * mask_resized.unsqueeze(0)

            # Upsample both masked frame and mask to full size
            frame_upsampled = F.interpolate(frame_scaled_masked.unsqueeze(0), size=(H, W), mode='nearest').squeeze() #.cpu().numpy()
            mask_upsampled = F.interpolate(mask_resized.unsqueeze(0).unsqueeze(0), size=(H, W), mode='nearest').squeeze() #.cpu().numpy()

            frame_upsampled = frame_upsampled.permute(1, 2, 0)

            composite = composite * (1 - mask_upsampled[:, :, None] * alpha_values[scale_idx]) + frame_upsampled * alpha_values[scale_idx]

        composite_np = composite.detach().cpu().numpy()
        composite_np = (composite_np - composite_np.min()) / (composite_np.max() - composite_np.min() + 1e-8)
        composite_uint8 = (composite_np * 255).astype(np.uint8)
        composite_frames.append(composite_uint8)

        # Create individual scale visualizations for horizontal stitch
        scale_composites = []
        label_bar_height = 30

        for scale_idx in range(4):
            scale = scales[scale_idx]
            scale_h = int(scale * H / 224)
            scale_w = int(scale * W / 224)

            # Get mask for this scale and frame
            mask = gazing_mask_assembled[scale_idx][t]

            # Reshape mask to 2D spatial grid
            num_patches_per_chunk = mask.shape[-1]
            s = int(num_patches_per_chunk ** 0.5)
            mask_2d = rearrange(mask, '(nh nw) (h w) -> (nh h) (nw w)', nh=nh, nw=nw, h=s, w=s)

            if isinstance(mask_2d, np.ndarray):
                mask_tensor_scale = torch.from_numpy(mask_2d)
            else:
                mask_tensor_scale = mask_2d

            # Map mask through padded space then crop to original image dimensions
            H_pad, W_pad = nh * 224, nw * 224
            mask_full_scale = F.interpolate(mask_tensor_scale.unsqueeze(0).unsqueeze(0).float(), size=(H_pad, W_pad), mode='nearest')[0, 0]
            mask_resized_scale = F.interpolate(mask_full_scale[:H, :W].unsqueeze(0).unsqueeze(0), size=(scale_h, scale_w), mode='nearest')[0, 0]

            frame_tensor_scale = video_viz[t]
            frame_scaled_scale = F.interpolate(frame_tensor_scale.unsqueeze(0), size=(scale_h, scale_w), mode='bicubic', align_corners=False).squeeze().clamp(0, 1)

            # Apply gazing pattern: gazed tiles = 1.0 brightness, ungazed tiles = 0.2 brightness
            frame_scaled_permuted = frame_scaled_scale.permute(1, 2, 0)
            scale_composite = frame_scaled_permuted * (mask_resized_scale[:, :, None] * 1.0 + (1 - mask_resized_scale[:, :, None]) * 0.2)

            scale_composite_np = scale_composite.detach().cpu().numpy()
            scale_composite_np = np.clip(scale_composite_np, 0, 1)
            scale_composite_uint8 = (scale_composite_np * 255).astype(np.uint8)

            # Resize visualization to common display height first (preserving aspect ratio)
            display_width = int(scale_w * H / scale_h)
            scale_composite_pil = Image.fromarray(scale_composite_uint8)
            scale_composite_resized = scale_composite_pil.resize((display_width, H), Image.NEAREST)
            scale_composite_resized_np = np.array(scale_composite_resized)

            # Create label bar matching the resized visualization width
            label_bar = np.ones((label_bar_height, display_width, 3), dtype=np.uint8) * 255
            label_bar_pil = Image.fromarray(label_bar)
            draw = ImageDraw.Draw(label_bar_pil)
            try:
                font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 20)
            except:
                font = ImageFont.load_default()

            label = f"Scale {scale_idx + 1}"
            draw.text((5, 5), label, fill=(0, 0, 0), font=font)
            label_bar_np = np.array(label_bar_pil)

            # Stack label bar above the visualization
            scale_with_label = np.vstack([label_bar_np, scale_composite_resized_np])

            scale_composites.append(scale_with_label)

        # Add 10px white padding between scales
        padding = np.ones((H + label_bar_height, 10, 3), dtype=np.uint8) * 255

        # Concatenate all scales horizontally with padding
        stitched = scale_composites[0]
        for i in range(1, 4):
            stitched = np.concatenate([stitched, padding, scale_composites[i]], axis=1)

        # Add white padding at the top to prevent Gradio's label from blocking content
        top_padding = np.ones((50, stitched.shape[1], 3), dtype=np.uint8) * 255
        stitched = np.vstack([top_padding, stitched])

        scales_stitch_frames.append(stitched)

        del frame_tensor, mask_tensor, mask_resized, frame_scaled, frame_scaled_masked, frame_upsampled, mask_upsampled

    del gazing_mask_assembled

    del video_tensor_original, reconstruction_tensor, video_viz, reconstruction_chunks

    if device == 'cuda':
        torch.cuda.empty_cache()

    yield {
        'original_frames': original_frames,
        'gazing_frames': composite_frames,
        'reconstruction_frames': reconstruction_frames,
        'scales_stitch_frames': scales_stitch_frames,
        'fps': fps,
        'gazing_pct': pct,
        'total_gazing_tokens': total_gazing_tokens,
        'total_possible_tokens': total_possible_tokens
    }


def save_video(frames, output_path, fps):
    with imageio.get_writer(output_path, fps=fps, format='FFMPEG', codec='libx264', pixelformat='yuv444p', macro_block_size=1) as writer:
        for frame in frames:
            writer.append_data(frame)