# 示意代码，不可直接运行；官方开源实现见上文仓库链接
# 注意：论文实际方法为「外观-生理解耦 + 皮肤分割 + 循环一致性」，并非本示意中的简单条件注入，结构以论文/官方仓库为准
import torch
import torch.nn as nn

class rePPGGenerator(nn.Module):
    """
    rePPG生成器：将PPG信号嵌入到面部视频帧中（示意）

    论文核心思想：
    1. 解耦面部外观与生理特征
    2. 皮肤分割限制修改区域
    3. 循环一致性保证注入信号可被可靠恢复
    """
    
    def __init__(self):
        super().__init__()
        # 编码器：提取面部特征
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, 3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, 128, 3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(128, 256, 3, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
        )
        
        # PPG条件注入
        self.ppg_proj = nn.Linear(1, 256)
        
        # 解码器：生成调制后的面部帧
        self.decoder = nn.Sequential(
            nn.Conv2d(256, 128, 3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(128, 64, 3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, 3, 3, padding=1),
            nn.Tanh(),
        )
        
    def forward(self, frame: torch.Tensor, 
               ppg_value: torch.Tensor,
               skin_mask: torch.Tensor) -> torch.Tensor:
        """
        Args:
            frame: [B, 3, H, W] 原始面部帧
            ppg_value: [B, 1] PPG信号值（归一化）
            skin_mask: [B, 1, H, W] 皮肤区域掩码
            
        Returns:
            relit_frame: [B, 3, H, W] 嵌入PPG的视频帧
        """
        # 编码面部特征
        feat = self.encoder(frame)  # [B, 256, H, W]
        
        # PPG信号条件注入
        ppg_feat = self.ppg_proj(ppg_value)  # [B, 256]
        ppg_feat = ppg_feat.unsqueeze(-1).unsqueeze(-1)  # [B, 256, 1, 1]
        
        # 融合
        fused = feat + ppg_feat  # 广播加
        
        # 解码
        modulated = self.decoder(fused)  # [B, 3, H, W]
        
        # 仅调制皮肤区域
        relit = frame * (1 - skin_mask) + modulated * skin_mask
        
        return relit


# PPG信号到视频序列生成（示意：逐帧注入目标PPG值）
def embed_ppg_to_video(
    frames: np.ndarray,  # [T, H, W, 3]
    ppg_signal: np.ndarray,  # [T]
    skin_mask: np.ndarray,  # [H, W]
    generator: rePPGGenerator
) -> np.ndarray:
    """
    将PPG信号嵌入到视频序列中（示意）

    Args:
        frames: 原始视频帧
        ppg_signal: 目标PPG信号
        skin_mask: 皮肤区域掩码
        generator: rePPG生成器

    Returns:
        relit_frames: 注入目标PPG的视频
    """
    T = len(frames)
    relit_frames = np.zeros_like(frames)

    for t in range(T):
        frame = torch.from_numpy(frames[t]).permute(2, 0, 1).float() / 255
        frame = frame.unsqueeze(0)  # [1, 3, H, W]

        ppg = torch.tensor([[ppg_signal[t]]], dtype=torch.float32)
        mask = torch.from_numpy(skin_mask).float().unsqueeze(0).unsqueeze(0)

        with torch.no_grad():
            relit = generator(frame, ppg, mask)

        relit_frames[t] = (relit.squeeze(0).permute(1, 2, 0).numpy() * 255).astype(np.uint8)

    return relit_frames


if __name__ == "__main__":
    import numpy as np

    gen = rePPGGenerator()

    # 测试
    frame = torch.randn(1, 3, 64, 64)
    ppg = torch.tensor([[0.5]])
    mask = torch.ones(1, 1, 64, 64)

    output = gen(frame, ppg, mask)
    print(f"输入帧: {frame.shape}")
    print(f"PPG值: {ppg.shape}")
    print(f"输出帧: {output.shape}")
    print(f"参数量: {sum(p.numel() for p in gen.parameters()):,}")