scripts/visualize_3d.py

import os
import numpy as np
import plotly.graph_objects as go
from action_state.utils import (
    CO3DDataLoader, 
    get_camera_center, 
    get_view_direction, 
    get_camera_up,
    get_sequence_geometry
)

# --- 配置 ---
ROOT_PATH = "/run/determined/NAS1/public/lixinyuan/interleaved-co3d"
CATEGORY = "tv"
SEQUENCE_NAME = "398_50483_99041" 
OUTPUT_FILE = "interactive_3d_aligned.html"

HIGHLIGHT_FRAMES = [74, 85, 157, 98, 116]


def main():
    print(f"Loading sequence: {SEQUENCE_NAME}...")
    loader = CO3DDataLoader(ROOT_PATH, CATEGORY)
    
    if SEQUENCE_NAME not in loader.get_sequences():
        print(f"Error: Sequence {SEQUENCE_NAME} not found.")
        return

    frame_ids = sorted(loader.get_frames(SEQUENCE_NAME))
    seq_data = loader.seq_data[SEQUENCE_NAME]
    
    # 使用CO3D官方对齐方法
    mean_center, basis, aligned_seq_data = get_sequence_geometry(
        seq_data, align_to_standard=True
    )
    
    print(f"\n{'='*60}")
    print(f"Aligned to Standard Y-Up Coordinate System")
    print(f"Original CO3D ground normal → Standard [0,1,0]")
    print(f"{'='*60}\n")
    
    # 收集对齐后的相机数据
    camera_centers = []
    view_dirs = []
    up_vectors = []
    
    for fid in frame_ids:
        info = aligned_seq_data[fid]
        C = get_camera_center(info['R'], info['T'])
        V = get_view_direction(info['R'])
        U = get_camera_up(info['R'])
        
        camera_centers.append(C)
        view_dirs.append(V)
        up_vectors.append(U)

    camera_centers = np.array(camera_centers)
    view_dirs = np.array(view_dirs)
    up_vectors = np.array(up_vectors)
    
    # 计算统计
    dists = np.linalg.norm(camera_centers - mean_center, axis=1)
    avg_dist = np.mean(dists)
    arrow_len = avg_dist * 0.3
    
    # 分析Up向量对齐度
    mean_camera_up = np.mean(up_vectors, axis=0)
    mean_camera_up_normalized = mean_camera_up / np.linalg.norm(mean_camera_up)
    alignment = np.dot(mean_camera_up_normalized, basis[2])
    
    # === 创建Plotly图形 ===
    fig = go.Figure()
    
    # 1. 地面平面（XZ平面）
    plane_size = avg_dist * 2
    x_range = np.linspace(mean_center[0] - plane_size, mean_center[0] + plane_size, 20)
    z_range = np.linspace(mean_center[2] - plane_size, mean_center[2] + plane_size, 20)
    X_grid, Z_grid = np.meshgrid(x_range, z_range)
    Y_grid = np.ones_like(X_grid) * mean_center[1]
    
    fig.add_trace(go.Surface(
        x=X_grid, y=Y_grid, z=Z_grid,
        colorscale=[[0, 'lightblue'], [1, 'lightblue']],
        showscale=False,
        opacity=0.15,
        name='Ground Plane (XZ)',
        hoverinfo='skip'
    ))
    
    # 2. 世界坐标系轴
    axis_len = avg_dist * 0.5
    
    # X轴 - 红色
    fig.add_trace(go.Scatter3d(
        x=[mean_center[0], mean_center[0] + axis_len],
        y=[mean_center[1], mean_center[1]],
        z=[mean_center[2], mean_center[2]],
        mode='lines+markers',
        line=dict(color='red', width=8),
        marker=dict(size=[0, 10], symbol='arrow', angleref='previous'),
        name='World X',
        hovertemplate='<b>X Axis</b><extra></extra>'
    ))
    
    # Y轴 - 绿色（Up，对齐后的地面法向量）
    fig.add_trace(go.Scatter3d(
        x=[mean_center[0], mean_center[0]],
        y=[mean_center[1], mean_center[1] + axis_len],
        z=[mean_center[2], mean_center[2]],
        mode='lines+markers',
        line=dict(color='lime', width=8),
        marker=dict(size=[0, 10], symbol='arrow', angleref='previous'),
        name='World Y (Up/Ground Normal)',
        hovertemplate='<b>Y Axis (Up)</b><br>Aligned from CO3D ground normal<extra></extra>'
    ))
    
    # Z轴 - 蓝色
    fig.add_trace(go.Scatter3d(
        x=[mean_center[0], mean_center[0]],
        y=[mean_center[1], mean_center[1]],
        z=[mean_center[2], mean_center[2] + axis_len],
        mode='lines+markers',
        line=dict(color='cyan', width=8),
        marker=dict(size=[0, 10], symbol='arrow', angleref='previous'),
        name='World Z',
        hovertemplate='<b>Z Axis</b><extra></extra>'
    ))
    
    # 3. 物体中心
    fig.add_trace(go.Scatter3d(
        x=[mean_center[0]],
        y=[mean_center[1]],
        z=[mean_center[2]],
        mode='markers',
        marker=dict(size=15, color='black', symbol='x', 
                   line=dict(width=3, color='yellow')),
        name='Object Center',
        hovertemplate='<b>Object Center</b><br>X: %{x:.2f}<br>Y: %{y:.2f}<br>Z: %{z:.2f}<extra></extra>'
    ))
    
    # 4. 相机轨迹线
    fig.add_trace(go.Scatter3d(
        x=camera_centers[:, 0],
        y=camera_centers[:, 1],
        z=camera_centers[:, 2],
        mode='lines',
        line=dict(color='gray', width=2, dash='dash'),
        name='Camera Trajectory',
        opacity=0.5,
        hoverinfo='skip'
    ))
    
    # 5. 所有相机位置
    fig.add_trace(go.Scatter3d(
        x=camera_centers[:, 0],
        y=camera_centers[:, 1],
        z=camera_centers[:, 2],
        mode='markers',
        marker=dict(size=3, color=frame_ids, colorscale='Viridis', 
                   colorbar=dict(title="Frame ID"), opacity=0.6),
        text=[f'Frame {fid}' for fid in frame_ids],
        name='All Cameras',
        hovertemplate='<b>%{text}</b><br>X: %{x:.2f}<br>Y: %{y:.2f}<br>Z: %{z:.2f}<extra></extra>'
    ))
    
    # 6. 高亮帧
    highlight_indices = [i for i, fid in enumerate(frame_ids) if fid in HIGHLIGHT_FRAMES]
    
    if highlight_indices:
        highlight_centers = camera_centers[highlight_indices]
        highlight_fids = [frame_ids[i] for i in highlight_indices]
        
        fig.add_trace(go.Scatter3d(
            x=highlight_centers[:, 0],
            y=highlight_centers[:, 1],
            z=highlight_centers[:, 2],
            mode='markers+text',
            marker=dict(size=10, color='red', symbol='circle', 
                       line=dict(width=2, color='black')),
            text=[str(fid) for fid in highlight_fids],
            textposition='top center',
            textfont=dict(size=12, color='red', family='Arial Black'),
            name='Highlighted Frames',
            hovertemplate='<b>Frame %{text}</b><br>X: %{x:.2f}<br>Y: %{y:.2f}<br>Z: %{z:.2f}<extra></extra>'
        ))
        
        # 7. 高亮帧的相机朝向和Up向量
        for idx in highlight_indices:
            C = camera_centers[idx]
            V = view_dirs[idx]
            U = up_vectors[idx]
            
            # View direction (深蓝色)
            view_end = C + V * arrow_len
            fig.add_trace(go.Scatter3d(
                x=[C[0], view_end[0]],
                y=[C[1], view_end[1]],
                z=[C[2], view_end[2]],
                mode='lines',
                line=dict(color='darkblue', width=4),
                name=f'View Dir (Frame {frame_ids[idx]})',
                showlegend=False,
                hovertemplate=f'<b>View Direction</b><br>Frame {frame_ids[idx]}<extra></extra>'
            ))
            
            # Up vector (洋红色)
            up_end = C + U * arrow_len
            fig.add_trace(go.Scatter3d(
                x=[C[0], up_end[0]],
                y=[C[1], up_end[1]],
                z=[C[2], up_end[2]],
                mode='lines',
                line=dict(color='magenta', width=4),
                name=f'Up Vec (Frame {frame_ids[idx]})',
                showlegend=False,
                hovertemplate=f'<b>Camera Up Vector</b><br>Frame {frame_ids[idx]}<extra></extra>'
            ))
            
            # 连线到物体中心
            fig.add_trace(go.Scatter3d(
                x=[C[0], mean_center[0]],
                y=[C[1], mean_center[1]],
                z=[C[2], mean_center[2]],
                mode='lines',
                line=dict(color='black', width=1, dash='dot'),
                opacity=0.3,
                showlegend=False,
                hoverinfo='skip'
            ))
    
    # 8. 平均相机Up向量（用金色虚线表示）
    mean_up_end = mean_center + mean_camera_up_normalized * axis_len * 0.9
    fig.add_trace(go.Scatter3d(
        x=[mean_center[0], mean_up_end[0]],
        y=[mean_center[1], mean_up_end[1]],
        z=[mean_center[2], mean_up_end[2]],
        mode='lines',
        line=dict(color='gold', width=6, dash='dash'),
        name=f'Mean Camera Up (align={alignment:.2f})',
        hovertemplate=f'<b>Mean Camera Up</b><br>Alignment: {alignment:.3f}<extra></extra>'
    ))
    
    # 设置布局
    fig.update_layout(
        title=dict(
            text=f'<b>Interactive 3D Camera Trajectory (CO3D Aligned)</b><br>' +
                 f'Sequence: {CATEGORY}/{SEQUENCE_NAME}<br>' +
                 f'<span style="font-size:12px">Original CO3D Ground Normal: [-0.0396,-0.8306,-0.5554] → Aligned [0,1,0]</span><br>' +
                 f'<span style="font-size:12px; color:{"green" if abs(alignment) > 0.7 else "red"}">Camera Up Alignment: {alignment:.3f} {"✓" if abs(alignment) > 0.7 else "✗"}</span>',
            x=0.5,
            xanchor='center'
        ),
        scene=dict(
            xaxis=dict(
                title='X (PyTorch3D: Left)', 
                backgroundcolor="rgb(240, 240, 240)",
                gridcolor="white",
                showbackground=True
            ),
            yaxis=dict(
                title='Y (PyTorch3D: Up)', 
                backgroundcolor="rgb(230, 255, 230)",
                gridcolor="white",
                showbackground=True
            ),
            zaxis=dict(
                title='Z (PyTorch3D: Forward)', 
                backgroundcolor="rgb(240, 240, 240)",
                gridcolor="white",
                showbackground=True
            ),
            aspectmode='data',
            camera=dict(
                eye=dict(x=1.5, y=1.5, z=1.5),
                center=dict(x=0, y=0, z=0),
                up=dict(x=0, y=1, z=0)
            )
        ),
        hovermode='closest',
        width=1400,
        height=900,
        showlegend=True,
        legend=dict(
            x=0.02, 
            y=0.98, 
            bgcolor='rgba(255,255,255,0.9)',
            bordercolor='black',
            borderwidth=1
        )
    )
    
    # 添加注释说明
    fig.add_annotation(
        text="<b>🖱️ Controls:</b><br>" +
             "• Drag to rotate<br>" +
             "• Scroll to zoom<br>" +
             "• Hover for details<br>" +
             "• Double-click to reset",
        xref="paper", yref="paper",
        x=0.98, y=0.02,
        xanchor='right', yanchor='bottom',
        showarrow=False,
        bgcolor='rgba(255,255,200,0.8)',
        bordercolor='black',
        borderwidth=1,
        font=dict(size=10)
    )
    
    # 保存为HTML
    fig.write_html(OUTPUT_FILE)
    print(f"\n✅ Interactive 3D plot saved to {OUTPUT_FILE}")
    print(f"📂 Open it in your browser to interact!")
    print(f"\n📊 Analysis:")
    print(f"   Camera Up Alignment: {alignment:.3f}")
    print(f"   Status: {'✓ Well aligned' if abs(alignment) > 0.7 else '✗ Misaligned'}")
    print(f"   Total Frames: {len(frame_ids)}")
    print(f"   Highlighted Frames: {len(highlight_indices)}")


if __name__ == "__main__":
    main()