Topaz Video AI视频智能放大

import numpy as npfrom typing import Tuple, List, Optionalfrom dataclasses import dataclass, field @dataclassclass VideoFrame:    """视频帧数据结构"""    width: int    height: int    channels: int = 3    data: np.ndarray = field(default=None)     def __post_init__(self):        if self.data is None:            self.data = np.zeros((self.height, self.width, self.channels),                                 dtype=np.float32)  @dataclassclass UpscaleConfig:    """放大配置参数"""    scale_factor: int = 2    model_name: str = "artemis"    use_temporal: bool = True    temporal_radius: int = 3    denoise_strength: float = 0.3  class OpticalFlow:    """简化版光流估计器（模拟帧间运动分析）"""     def __init__(self, window_size: int = 15, max_level: int = 3):        self.window_size = window_size        self.max_level = max_level     def estimate(self, frame1: VideoFrame, frame2: VideoFrame) -> np.ndarray:        """        估计两帧之间的光流（运动向量场）         Args:            frame1: 第一帧            frame2: 第二帧         Returns:            光流场，shape为(height, width, 2)，通道0为x方向，通道1为y方向        """        h, w = frame1.height, frame1.width        # 模拟光流：使用帧差作为运动估计的近似        diff = frame2.data - frame1.data        # 计算梯度的近似        flow = np.zeros((h, w, 2), dtype=np.float32)        if h > 1 and w > 1:            grad_x = np.mean(diff[:, 1:, :], axis=2) * 10            grad_y = np.mean(diff[1:, :, :], axis=2) * 10            flow[:, 1:, 0] = grad_x            flow[1:, :, 1] = grad_y        return flow     def warp_frame(self, frame: VideoFrame, flow: np.ndarray) -> VideoFrame:        """根据光流对帧进行变形（warping）"""        h, w = frame.height, frame.width        x, y = np.meshgrid(np.arange(w), np.arange(h))        map_x = (x + flow[:, :, 0]).astype(np.float32)        map_y = (y + flow[:, :, 1]).astype(np.float32)        # 简化的双线性插值变形        map_x = np.clip(map_x, 0, w - 1)        map_y = np.clip(map_y, 0, h - 1)        x0 = map_x.astype(int)        y0 = map_y.astype(int)        x1 = np.minimum(x0 + 1, w - 1)        y1 = np.minimum(y0 + 1, h - 1)        wx = map_x - x0        wy = map_y - y0        result = (frame.data[y0, x0] * (1 - wx)[:, :, None] * (1 - wy)[:, :, None] +                  frame.data[y0, x1] * wx[:, :, None] * (1 - wy)[:, :, None] +                  frame.data[y1, x0] * (1 - wx)[:, :, None] * wy[:, :, None] +                  frame.data[y1, x1] * wx[:, :, None] * wy[:, :, None])        return VideoFrame(w, h, frame.channels, result.astype(np.float32))  class AIUpscaleModel:    """模拟AI放大模型的推断过程"""     def __init__(self, model_name: str = "artemis"):        self.model_name = model_name        # 模拟模型参数        self.weights = np.random.randn(3, 3, 3, 64) * 0.01     def infer(self, frame: VideoFrame,              ref_frames: List[VideoFrame] = None) -> VideoFrame:        """        对单帧进行AI放大推断         Args:            frame: 输入低分辨率帧            ref_frames: 时域参考帧（已对齐）         Returns:            放大后的高分辨率帧        """        h, w = frame.height, frame.width        # 模拟卷积放大：先用双线性插值放大，再模拟残差学习        up_h, up_w = h * 2, w * 2        result = self._bilinear_upscale(frame.data, 2)         # 模拟AI残差细节恢复        if self.model_name == "artemis":            # Artemis: 强细节恢复            detail = self._simulate_detail(result, strength=0.15)        elif self.model_name == "proteus":            # Proteus: 适中细节 + 去噪            detail = self._simulate_detail(result, strength=0.08)            result = result * 0.9 + np.mean(result) * 0.1        elif self.model_name == "gaia":            # Gaia: 锐利边缘（适合动画）            detail = self._simulate_edge_enhance(result, strength=0.12)        else:            detail = self._simulate_detail(result, strength=0.05)         result = np.clip(result + detail, 0, 255)        return VideoFrame(up_w, up_h, frame.channels, result)     def _bilinear_upscale(self, data: np.ndarray, factor: int) -> np.ndarray:        """双线性插值放大"""        h, w, c = data.shape        new_h, new_w = h * factor, w * factor        # 使用repeat+平均模拟双线性        upscaled = np.repeat(np.repeat(data, factor, axis=0), factor, axis=1)        # 平滑处理        kernel = np.ones((3, 3, 1)) / 9        from scipy.ndimage import convolve        for ch in range(c):            upscaled[:, :, ch] = convolve(upscaled[:, :, ch], kernel[:, :, 0])        return upscaled[:new_h, :new_w, :]     def _simulate_detail(self, data: np.ndarray,                         strength: float = 0.1) -> np.ndarray:        """模拟AI恢复的高频细节"""        noise = np.random.randn(*data.shape) * strength * 10        return noise     def _simulate_edge_enhance(self, data: np.ndarray,                               strength: float = 0.1) -> np.ndarray:        """模拟边缘增强（适合动画/CG）"""        from scipy.ndimage import sobel        result = np.zeros_like(data)        for ch in range(data.shape[2]):            edge = sobel(data[:, :, ch])            result[:, :, ch] = edge * strength        return result  class TopazVideoAIProcessor:    """Topaz Video AI处理管线模拟器"""     def __init__(self, config: UpscaleConfig):        self.config = config        self.flow_estimator = OpticalFlow()        self.model = AIUpscaleModel(config.model_name)     def process_video(self, frames: List[VideoFrame]) -> List[VideoFrame]:        """        处理视频帧序列         Args:            frames: 输入帧列表         Returns:            放大后的帧列表        """        output_frames = []        total = len(frames)         for i in range(total):            if self.config.use_temporal and total > 1:                # 收集时域参考帧                radius = self.config.temporal_radius                start = max(0, i - radius)                end = min(total, i + radius + 1)                ref_indices = [j for j in range(start, end) if j != i]                ref_frames = []                for j in ref_indices:                    flow = self.flow_estimator.estimate(frames[i], frames[j])                    warped = self.flow_estimator.warp_frame(frames[j], flow)                    ref_frames.append(warped)                # 时域融合放大                upscaled = self.model.infer(frames[i], ref_frames)            else:                upscaled = self.model.infer(frames[i])             output_frames.append(upscaled)             if (i + 1) % 10 == 0 or i == total - 1:                print(f"  处理进度: {i+1}/{total} 帧 "                      f"({(i+1)/total*100:.1f}%)")         return output_frames  # ============================================================# 演示：Topaz Video AI放大流程# ============================================================if __name__ == "__main__":    print("=" * 60)    print("Topaz Video AI 放大流程模拟")    print("=" * 60)     # 创建测试视频帧（模拟10帧 480x270 视频）    print("\n[1] 创建测试视频帧 (480x270, 10帧)...")    frames = []    for i in range(10):        frame = VideoFrame(480, 270, 3)        # 模拟渐变+运动        for y in range(270):            for x in range(480):                frame.data[y, x] = [                    (x + i * 5) % 256,                    (y + i * 3) % 256,                    ((x + y) // 2 + i * 2) % 256                ]        frames.append(frame)    print(f"  创建了 {len(frames)} 帧, 分辨率: {frames[0].width}x{frames[0].height}")     # 配置放大参数    print("\n[2] 配置放大参数...")    config = UpscaleConfig(        scale_factor=2,        model_name="artemis",        use_temporal=True,        temporal_radius=2,        denoise_strength=0.3    )    print(f"  模型: {config.model_name}")    print(f"  放大倍数: {config.scale_factor}x")    print(f"  时域融合: {'开启' if config.use_temporal else '关闭'}")     # 处理视频    print("\n[3] 开始AI放大处理...")    processor = TopazVideoAIProcessor(config)    output_frames = processor.process_video(frames)     # 输出结果    print(f"\n[4] 处理完成!")    print(f"  输入: {frames[0].width}x{frames[0].height} -> "          f"输出: {output_frames[0].width}x{output_frames[0].height}")    print(f"  总帧数: {len(output_frames)}")    print(f"  像素总数: {frames[0].width * frames[0].height} -> "          f"{output_frames[0].width * output_frames[0].height}")     print("\n" + "=" * 60)    print("Topaz Video AI 放大流程模拟完成!")    print("=" * 60)