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