Initial clean project import

2026-06-19 18:41:41 +08:00
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+"""
+工具函数模块
+提供字幕封面生成过程中需要的通用工具函数
+"""
+
+import os
+import sys
+from pathlib import Path
+from typing import Tuple, Optional, List, Dict, Any
+
+# 尝试导入loguru，如果不可用则使用print作为替代
+try:
+    from loguru import logger
+except ImportError:
+    # Fallback logger that uses print
+    class logger:
+        @staticmethod
+        def info(msg, *args, **kwargs):
+            print(f"[INFO] {msg}", file=sys.stderr)
+        @staticmethod
+        def warning(msg, *args, **kwargs):
+            print(f"[WARN] {msg}", file=sys.stderr)
+        @staticmethod
+        def error(msg, *args, **kwargs):
+            print(f"[ERROR] {msg}", file=sys.stderr)
+        @staticmethod
+        def debug(msg, *args, **kwargs):
+            print(f"[DEBUG] {msg}", file=sys.stderr)
+
+# 尝试导入图像处理库
+try:
+    import cv2
+    import numpy as np
+    CV2_AVAILABLE = True
+except ImportError:
+    CV2_AVAILABLE = False
+    logger.warning("opencv-python不可用，图像处理功能受限")
+
+try:
+    from PIL import Image
+    PIL_AVAILABLE = True
+except ImportError:
+    PIL_AVAILABLE = False
+    logger.warning("PIL不可用，图像处理功能受限")
+
+
+def ensure_dir(path: str) -> Path:
+    """确保目录存在"""
+    path_obj = Path(path)
+    path_obj.mkdir(parents=True, exist_ok=True)
+    return path_obj
+
+
+def get_video_info(video_path: str) -> Dict[str, Any]:
+    """获取视频信息"""
+    if not CV2_AVAILABLE:
+        # 如果opencv不可用，返回基本信息
+        try:
+            import os
+            file_size = os.path.getsize(video_path)
+            return {
+                'fps': 30,  # 默认值
+                'frame_count': 0,
+                'width': 1920,  # 默认值
+                'height': 1080,  # 默认值
+                'duration': 0,  # 未知
+                'path': video_path,
+                'file_size': file_size,
+                'note': '视频信息不完整，opencv不可用'
+            }
+        except Exception as e:
+            logger.error(f"获取基本视频信息失败: {e}")
+            raise
+
+    try:
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            raise ValueError(f"无法打开视频文件: {video_path}")
+
+        # 获取视频属性
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        duration = frame_count / fps if fps > 0 else 0
+
+        cap.release()
+
+        return {
+            'fps': fps,
+            'frame_count': frame_count,
+            'width': width,
+            'height': height,
+            'duration': duration,
+            'path': video_path
+        }
+    except Exception as e:
+        logger.error(f"获取视频信息失败: {e}")
+        raise
+
+
+def extract_frame_at_time(video_path: str, time_seconds: float):
+    """在指定时间抽取视频帧"""
+    if not CV2_AVAILABLE:
+        logger.warning("opencv不可用，无法抽取视频帧")
+        # 返回一个占位符
+        return None
+
+    try:
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            raise ValueError(f"无法打开视频文件: {video_path}")
+
+        # 设置帧位置
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        frame_number = int(time_seconds * fps)
+        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
+
+        ret, frame = cap.read()
+        cap.release()
+
+        if not ret:
+            raise ValueError(f"无法读取帧: {frame_number}")
+
+        return frame
+    except Exception as e:
+        logger.error(f"抽帧失败: {e}")
+        raise
+
+
+def save_image(image, output_path: str, quality: int = 95) -> None:
+    """保存图像
+    
+    如果输出路径是PNG格式，会保留alpha通道（透明背景）
+    如果是JPEG格式，会转换为RGB格式
+    """
+    try:
+        ensure_dir(os.path.dirname(output_path))
+        
+        is_png = output_path.lower().endswith('.png')
+
+        if CV2_AVAILABLE and isinstance(image, np.ndarray):
+            # 使用OpenCV保存
+            if is_png and image.shape[2] == 4:
+                # PNG格式且有alpha通道，保存为BGRA（OpenCV使用BGR格式）
+                # 确保图像是BGRA格式（B, G, R, A）
+                # 如果输入是RGBA（R, G, B, A），需要转换为BGRA
+                if image.dtype != np.uint8:
+                    image = image.astype(np.uint8)
+                # OpenCV的imwrite会自动处理BGRA格式
+                success = cv2.imwrite(output_path, image, [cv2.IMWRITE_PNG_COMPRESSION, 3])
+            elif is_png:
+                # PNG格式但没有alpha通道，转换为RGB
+                if image.shape[2] == 3:
+                    success = cv2.imwrite(output_path, image, [cv2.IMWRITE_PNG_COMPRESSION, 3])
+                else:
+                    # 如果是单通道，转换为3通道
+                    if len(image.shape) == 2:
+                        image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
+                    success = cv2.imwrite(output_path, image, [cv2.IMWRITE_PNG_COMPRESSION, 3])
+            else:
+                # JPEG格式，确保是3通道BGR
+                if image.shape[2] == 4:
+                    # 有alpha通道，先合成到白色背景
+                    bgr = image[:, :, :3]
+                    alpha = image[:, :, 3:4] / 255.0
+                    white_bg = np.ones_like(bgr) * 255
+                    image = (bgr * alpha + white_bg * (1 - alpha)).astype(np.uint8)
+                elif len(image.shape) == 2:
+                    image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
+                success = cv2.imwrite(output_path, image, [cv2.IMWRITE_JPEG_QUALITY, quality])
+            
+            if not success:
+                raise ValueError(f"保存图像失败: {output_path}")
+        elif PIL_AVAILABLE and hasattr(image, 'save'):
+            # 使用PIL保存
+            if is_png:
+                image.save(output_path, 'PNG', compress_level=3)
+            else:
+                # JPEG格式，确保是RGB
+                if image.mode == 'RGBA':
+                    # 合成到白色背景
+                    rgb = Image.new('RGB', image.size, (255, 255, 255))
+                    rgb.paste(image, mask=image.split()[3])
+                    image = rgb
+                image.save(output_path, 'JPEG', quality=quality)
+        else:
+            raise ValueError("没有可用的图像保存方法")
+
+        logger.info(f"图像已保存: {output_path}")
+    except Exception as e:
+        logger.error(f"保存图像失败: {e}")
+        raise
+
+
+def format_time(seconds: float) -> str:
+    """格式化时间为 HH:MM:SS.mmm"""
+    hours = int(seconds // 3600)
+    minutes = int((seconds % 3600) // 60)
+    secs = int(seconds % 60)
+    milliseconds = int((seconds % 1) * 1000)
+
+    return "02d"
+
+
+def create_temp_dir(prefix: str = "subtitle_cover_") -> str:
+    """创建临时目录"""
+    import tempfile
+    temp_dir = tempfile.mkdtemp(prefix=prefix)
+    logger.info(f"创建临时目录: {temp_dir}")
+    return temp_dir
+
+
+def cleanup_temp_dir(temp_dir: str) -> None:
+    """清理临时目录"""
+    try:
+        import shutil
+        if os.path.exists(temp_dir):
+            shutil.rmtree(temp_dir)
+            logger.info(f"清理临时目录: {temp_dir}")
+    except Exception as e:
+        logger.warning(f"清理临时目录失败: {e}")
+
+
+def validate_file_exists(file_path: str, file_type: str = "文件") -> None:
+    """验证文件是否存在"""
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"{file_type}不存在: {file_path}")
+
+    if not os.path.isfile(file_path):
+        raise ValueError(f"{file_type}不是文件: {file_path}")
+
+
+def get_file_size_mb(file_path: str) -> float:
+    """获取文件大小(MB)"""
+    size_bytes = os.path.getsize(file_path)
+    return size_bytes / (1024 * 1024)
+
+
+def calculate_aspect_ratio(width: int, height: int) -> float:
+    """计算宽高比"""
+    return width / height if height > 0 else 0
+
+
+def resize_image(image: np.ndarray, target_width: int, target_height: int,
+                keep_aspect_ratio: bool = True) -> np.ndarray:
+    """调整图像大小"""
+    if keep_aspect_ratio:
+        # 保持宽高比
+        h, w = image.shape[:2]
+        aspect_ratio = w / h
+
+        if target_width / target_height > aspect_ratio:
+            # 目标更宽，以高度为准
+            new_width = int(target_height * aspect_ratio)
+            new_height = target_height
+        else:
+            # 目标更高，以宽度为准
+            new_width = target_width
+            new_height = int(target_width / aspect_ratio)
+
+        resized = cv2.resize(image, (new_width, new_height))
+    else:
+        # 不保持宽高比，直接缩放
+        resized = cv2.resize(image, (target_width, target_height))
+
+    return resized
+
+
+def blend_images(background: np.ndarray, foreground: np.ndarray,
+                position: Tuple[int, int] = (0, 0)) -> np.ndarray:
+    """将前景图像合成到背景图像上"""
+    x, y = position
+    h, w = foreground.shape[:2]
+
+    # 确保位置不超出边界
+    bg_h, bg_w = background.shape[:2]
+    x = max(0, min(x, bg_w - w))
+    y = max(0, min(y, bg_h - h))
+
+    # 创建ROI
+    roi = background[y:y+h, x:x+w]
+
+    # 如果前景有alpha通道，进行透明合成
+    if foreground.shape[2] == 4:
+        # 分离颜色和alpha通道
+        foreground_rgb = foreground[:, :, :3]
+        alpha = foreground[:, :, 3] / 255.0
+
+        # 扩展alpha到3通道
+        alpha = np.stack([alpha] * 3, axis=2)
+
+        # 透明合成
+        blended = foreground_rgb * alpha + roi * (1 - alpha)
+        background[y:y+h, x:x+w] = blended.astype(np.uint8)
+    else:
+        # 直接覆盖
+        background[y:y+h, x:x+w] = foreground
+
+    return background
+
+
+def apply_blur(image: np.ndarray, kernel_size: int = 15) -> np.ndarray:
+    """应用模糊效果"""
+    return cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)
+
+
+def apply_gradient_overlay(image: np.ndarray, color: Tuple[int, int, int],
+                          opacity: float = 0.5) -> np.ndarray:
+    """应用渐变覆盖"""
+    overlay = np.full_like(image, color, dtype=np.uint8)
+    return cv2.addWeighted(image, 1 - opacity, overlay, opacity, 0)