1090 lines
44 KiB
Python
1090 lines
44 KiB
Python
import os
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import re
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import base64
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import warnings
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import imageio
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import whisper
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import numpy as np
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import pdfkit
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from PIL import Image
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from skimage.metrics import structural_similarity as ssim
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from collections import defaultdict
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import subprocess
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from jinja2 import Environment
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import cv2
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from scipy.signal import find_peaks
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from skimage.feature import hog
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from skimage.color import rgb2gray
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import concurrent.futures
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import threading
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import queue
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import time
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import gc
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from functools import lru_cache
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import multiprocessing
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import signal
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import sys
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# ======================== 全局配置 ========================
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warnings.filterwarnings("ignore", message="FP16 is not supported on CPU; using FP32 instead")
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VIDEO_PATH = "D:/python项目文件/1/input.mp4" # 输入视频路径
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MODEL_DIR = "D:/whisper_models" # Whisper模型目录
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FFMPEG_BIN = r"D:\Program Files\ffmpeg\bin" # FFmpeg安装路径
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WKHTMLTOPDF_PATH = r"D:\wkhtmltopdf\bin\wkhtmltopdf.exe" # wkhtmltopdf路径
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SSIM_THRESHOLD = 0.85 # 关键帧去重阈值
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FRAME_INTERVAL = 2 # 抽帧间隔（秒）
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OUTPUT_DIR = "D:\桌面文件\python\output1" # 输出目录
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TRANSITION_WORDS = ["接下来", "下一页", "如图"] # 过渡词过滤列
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HOG_THRESHOLD = 0.7 # HOG特征相似度阈值
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COLOR_THRESHOLD = 0.8 # 颜色直方图相似度阈值
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WHISPER_MODEL = "base" # Whisper模型大小
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PROFESSIONAL_TERMS = {
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"人工智能": "AI",
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"机器学习": "ML",
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"深度学习": "DL",
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"神经网络": "NN",
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"卷积神经网络": "CNN",
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"循环神经网络": "RNN",
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"自然语言处理": "NLP",
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"计算机视觉": "CV",
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"大数据": "Big Data",
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"云计算": "Cloud Computing"
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} # 专业术语词典
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# 性能优化配置
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MAX_WORKERS = max(1, multiprocessing.cpu_count() - 1) # 并行处理的工作线程数
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BATCH_SIZE = 10 # 批处理大小
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CACHE_SIZE = 100 # 缓存大小
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MEMORY_LIMIT = 0.8 # 内存使用限制（占总内存的比例）
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TIMEOUT_SECONDS = 300 # 操作超时时间（秒）
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PROGRESS_UPDATE_INTERVAL = 1 # 进度更新间隔（秒）
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# ========================================================
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# 进度跟踪类
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class ProgressTracker:
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def __init__(self, total_steps, description="处理中"):
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self.total_steps = total_steps
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self.current_step = 0
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self.description = description
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self.start_time = time.time()
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self.last_update_time = self.start_time
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self._lock = threading.Lock()
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def update(self, step=1, message=None):
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with self._lock:
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self.current_step += step
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current_time = time.time()
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# 控制更新频率
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if current_time - self.last_update_time >= PROGRESS_UPDATE_INTERVAL:
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elapsed = current_time - self.start_time
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progress = (self.current_step / self.total_steps) * 100
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if message:
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print(
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f"[进度] {self.description}: {progress:.1f}% ({self.current_step}/{self.total_steps}) - {message}")
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else:
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print(f"[进度] {self.description}: {progress:.1f}% ({self.current_step}/{self.total_steps})")
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self.last_update_time = current_time
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def complete(self, message="完成"):
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with self._lock:
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elapsed = time.time() - self.start_time
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print(f"[完成] {self.description}: 100% - {message} (耗时: {elapsed:.1f}秒)")
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# 超时处理类
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class TimeoutHandler:
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def __init__(self, timeout_seconds=TIMEOUT_SECONDS):
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self.timeout_seconds = timeout_seconds
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self.timer = None
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self._lock = threading.Lock()
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def start(self, operation_name):
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with self._lock:
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if self.timer:
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self.timer.cancel()
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self.timer = threading.Timer(self.timeout_seconds, self._timeout_callback, args=[operation_name])
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self.timer.start()
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print(f"[信息] 开始{operation_name}，超时时间: {self.timeout_seconds}秒")
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def stop(self):
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with self._lock:
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if self.timer:
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self.timer.cancel()
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self.timer = None
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def _timeout_callback(self, operation_name):
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print(f"[警告] {operation_name}操作超时，正在尝试恢复...")
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# 这里可以添加恢复逻辑
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# ---------------------- 核心功能模块 ----------------------
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class VideoProcessor:
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def __init__(self):
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os.environ["PATH"] = FFMPEG_BIN + os.pathsep + os.environ["PATH"]
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self.frame_cache = {}
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self.feature_cache = {}
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self._lock = threading.Lock()
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self.timeout_handler = TimeoutHandler()
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@staticmethod
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def check_ffmpeg():
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"""验证FFmpeg可用性"""
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try:
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subprocess.run(["ffmpeg", "-version"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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print("[系统] FFmpeg验证成功")
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return True
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except Exception as e:
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print(f"[错误] FFmpeg验证失败: {str(e)}")
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return False
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@lru_cache(maxsize=CACHE_SIZE)
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def calculate_color_histogram(self, frame_key):
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"""计算颜色直方图特征（带缓存）"""
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frame = self.frame_cache.get(frame_key)
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if frame is None:
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return None
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hist = cv2.calcHist([frame], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
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cv2.normalize(hist, hist)
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return hist.flatten()
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@lru_cache(maxsize=CACHE_SIZE)
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def calculate_hog_features(self, frame_key):
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"""计算HOG特征（带缓存）"""
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frame = self.frame_cache.get(frame_key)
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if frame is None:
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return None
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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features = hog(gray, orientations=8, pixels_per_cell=(16, 16),
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cells_per_block=(1, 1), visualize=False)
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return features
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@staticmethod
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def is_ppt_transition(frame1, frame2):
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"""检测PPT页面切换"""
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# 转换为灰度图
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gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
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gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
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# 计算边缘
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edges1 = cv2.Canny(gray1, 100, 200)
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edges2 = cv2.Canny(gray2, 100, 200)
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# 计算边缘差异
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diff = cv2.absdiff(edges1, edges2)
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return np.mean(diff) > 50 # 阈值可调整
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@staticmethod
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def is_blank_frame(frame, threshold=30):
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"""检测是否为无信息帧（纯黑屏或纯白屏）"""
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try:
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# 转换为灰度图
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# 计算图像统计特征
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mean = np.mean(gray)
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std_dev = np.std(gray)
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# 检查是否为纯黑或纯白
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is_black = mean < 10 and std_dev < 5
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is_white = mean > 245 and std_dev < 5
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# 检查是否有足够的细节
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has_detail = std_dev > threshold
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return is_black or is_white or not has_detail
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except Exception as e:
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print(f"[警告] 检查无信息帧时出错: {str(e)}")
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return True
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def process_frame_batch(self, frames_batch, start_idx):
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"""处理一批帧"""
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results = []
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for i, frame in enumerate(frames_batch):
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idx = start_idx + i
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frame_key = f"frame_{idx}"
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self.frame_cache[frame_key] = frame
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results.append((idx, frame))
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return results
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def extract_keyframes(self, video_path: str) -> tuple:
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"""提取去重关键帧及其时间戳（多特征融合，并行处理）"""
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try:
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self.timeout_handler.start("关键帧提取")
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reader = imageio.get_reader(video_path)
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fps = reader.get_meta_data()["fps"]
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total_frames = reader.count_frames()
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print(f"[信息] 视频总帧数: {total_frames}")
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keyframes = []
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timestamps = []
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prev_frame = None
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frame_count = 0
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# 创建进度跟踪器
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progress = ProgressTracker(total_frames, "关键帧提取")
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# 设置最后处理帧的阈值和超时
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last_frames_threshold = 30 # 增加到30帧
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last_frame_time = time.time()
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last_frame_timeout = 10 # 降低到10秒超时
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# 批处理大小动态调整
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current_batch_size = BATCH_SIZE
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# 使用队列存储结果
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result_queue = queue.Queue()
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# 最后阶段的简化处理标志
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simplified_processing = False
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# 使用线程池进行并行处理
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with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
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futures = []
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frames_batch = []
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batch_start_idx = 0
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try:
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for idx, frame in enumerate(reader):
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# 更新进度
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progress.update(1)
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# 检查是否接近结束
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if idx >= total_frames - last_frames_threshold:
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if not simplified_processing:
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print("[信息] 进入最后阶段，启用简化处理模式")
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simplified_processing = True
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# 清理现有资源
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self.frame_cache.clear()
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self.feature_cache.clear()
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gc.collect()
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current_time = time.time()
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if current_time - last_frame_time > last_frame_timeout:
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print(f"[警告] 处理最后{last_frames_threshold}帧时卡住，跳过剩余帧")
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# 强制处理当前批次
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if frames_batch:
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future = executor.submit(self.process_frame_batch, frames_batch, batch_start_idx)
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futures.append(future)
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break
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# 在最后阶段使用最小批处理大小
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current_batch_size = 1
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last_frame_time = current_time
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curr_time = idx / fps
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if curr_time - (timestamps[-1] if timestamps else 0) < FRAME_INTERVAL:
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continue
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# 检查是否为无信息帧（使用简化版本的检查）
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if self.is_blank_frame(frame, simplified=True):
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continue
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frames_batch.append(frame)
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# 当批次达到指定大小时提交处理
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if len(frames_batch) >= current_batch_size:
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future = executor.submit(self.process_frame_batch, frames_batch, batch_start_idx)
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futures.append(future)
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batch_start_idx += len(frames_batch)
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frames_batch = []
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# 及时清理完成的future
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self._clean_completed_futures(futures, result_queue)
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# 强制垃圾回收
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if frame_count % 20 == 0: # 更频繁的垃圾回收
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gc.collect()
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# 处理剩余的帧
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if frames_batch:
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future = executor.submit(self.process_frame_batch, frames_batch, batch_start_idx)
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futures.append(future)
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# 等待所有future完成，但设置更短的超时
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try:
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for future in concurrent.futures.as_completed(futures, timeout=15):
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try:
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batch_results = future.result(timeout=3) # 更短的超时
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for idx, frame in batch_results:
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result_queue.put((idx, frame))
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except Exception as e:
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print(f"[警告] 处理批次时出错: {str(e)}")
|
||
except concurrent.futures.TimeoutError:
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print("[警告] 部分批次处理超时，继续处理已完成的结果")
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except Exception as e:
|
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print(f"[警告] 帧处理过程中出错: {str(e)}")
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finally:
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# 处理队列中的所有结果
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while not result_queue.empty():
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try:
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idx, frame = result_queue.get_nowait()
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curr_time = idx / fps
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# 使用简化版本的特征比较
|
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if prev_frame is not None:
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try:
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if not self._is_frame_different(prev_frame, frame, simplified=True):
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continue
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except Exception as e:
|
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print(f"[警告] 特征比较失败: {str(e)}")
|
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continue
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keyframes.append(Image.fromarray(frame))
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timestamps.append(curr_time)
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prev_frame = frame
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frame_count += 1
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||
|
||
# 在最后阶段更频繁地清理资源
|
||
if simplified_processing and frame_count % 5 == 0:
|
||
gc.collect()
|
||
except queue.Empty:
|
||
break
|
||
|
||
reader.close()
|
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print(f"[图像] 关键帧提取完成，共{len(keyframes)}帧")
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||
|
||
# 清理资源
|
||
self.frame_cache.clear()
|
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self.feature_cache.clear()
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gc.collect()
|
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|
||
# 停止超时处理
|
||
self.timeout_handler.stop()
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||
progress.complete(f"提取了{len(keyframes)}个关键帧")
|
||
|
||
return keyframes, timestamps
|
||
except Exception as e:
|
||
print(f"[错误] 关键帧提取失败: {str(e)}")
|
||
self.timeout_handler.stop()
|
||
return [], []
|
||
|
||
def _clean_completed_futures(self, futures, result_queue):
|
||
"""清理已完成的future并存储结果"""
|
||
done = []
|
||
for future in futures:
|
||
if future.done():
|
||
try:
|
||
batch_results = future.result(timeout=1)
|
||
for result in batch_results:
|
||
result_queue.put(result)
|
||
done.append(future)
|
||
except Exception as e:
|
||
print(f"[警告] 获取future结果时出错: {str(e)}")
|
||
|
||
# 从futures列表中移除已完成的
|
||
for future in done:
|
||
futures.remove(future)
|
||
|
||
# 强制垃圾回收
|
||
if len(done) > 0:
|
||
gc.collect()
|
||
|
||
def _is_frame_different(self, frame1, frame2, simplified=False):
|
||
"""简化版本的帧差异检测"""
|
||
if simplified:
|
||
try:
|
||
# 使用更简单的比较方法
|
||
gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
|
||
gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
|
||
|
||
# 计算平均差异
|
||
diff = cv2.absdiff(gray1, gray2)
|
||
mean_diff = np.mean(diff)
|
||
|
||
# 如果差异小于阈值，认为帧相同
|
||
return mean_diff > 10 # 可调整的阈值
|
||
except Exception:
|
||
return True
|
||
else:
|
||
# 完整的特征比较逻辑
|
||
return True # 默认认为不同，具体实现可以根据需要添加
|
||
|
||
def is_blank_frame(self, frame, simplified=False):
|
||
"""检测是否为无信息帧（支持简化版本）"""
|
||
try:
|
||
if simplified:
|
||
# 简化版本：只检查亮度和方差
|
||
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||
mean = np.mean(gray)
|
||
std = np.std(gray)
|
||
return mean < 10 or mean > 245 or std < 20
|
||
else:
|
||
# 完整版本的检查逻辑
|
||
return super().is_blank_frame(frame)
|
||
except Exception as e:
|
||
print(f"[警告] 检查无信息帧时出错: {str(e)}")
|
||
return True
|
||
|
||
@staticmethod
|
||
def transcribe_audio(video_path: str, model_name: str = WHISPER_MODEL) -> list:
|
||
"""语音识别与时间戳获取（支持中英文混合）"""
|
||
try:
|
||
# 创建进度跟踪器
|
||
progress = ProgressTracker(100, "语音识别")
|
||
progress.update(10, "加载模型")
|
||
|
||
# 使用更大的模型提高准确率
|
||
model = whisper.load_model(model_name, device="cpu", download_root=MODEL_DIR)
|
||
|
||
progress.update(20, "开始转写")
|
||
|
||
# 配置转写参数
|
||
result = model.transcribe(
|
||
video_path,
|
||
fp16=False,
|
||
language="zh",
|
||
task="transcribe",
|
||
verbose=True,
|
||
initial_prompt="这是一段包含中英文的PPT讲解视频，可能包含专业术语。"
|
||
)
|
||
|
||
progress.update(60, "处理转写结果")
|
||
segments = result.get("segments", [])
|
||
|
||
# 后处理：专业术语替换
|
||
for i, seg in enumerate(segments):
|
||
text = seg["text"]
|
||
for cn, en in PROFESSIONAL_TERMS.items():
|
||
text = text.replace(cn, f"{cn}({en})")
|
||
seg["text"] = text
|
||
progress.update(30 / len(segments), f"处理第{i + 1}/{len(segments)}个片段")
|
||
|
||
progress.complete(f"识别了{len(segments)}个语音片段")
|
||
return segments
|
||
except Exception as e:
|
||
print(f"[错误] 语音识别失败: {str(e)}")
|
||
return []
|
||
|
||
|
||
# ---------------------- 业务逻辑模块 ----------------------
|
||
class ContentAligner:
|
||
@staticmethod
|
||
def generate_page_intervals(timestamps: list, duration: float) -> list:
|
||
"""生成页面时间段"""
|
||
intervals = []
|
||
for i in range(len(timestamps)):
|
||
start = timestamps[i]
|
||
end = timestamps[i + 1] if i < len(timestamps) - 1 else duration
|
||
intervals.append((start, end))
|
||
return intervals
|
||
|
||
@staticmethod
|
||
@lru_cache(maxsize=CACHE_SIZE)
|
||
def calculate_text_similarity(text1: str, text2: str) -> float:
|
||
"""计算文本相似度（带缓存）"""
|
||
# 使用简单的词重叠度计算
|
||
words1 = set(re.findall(r'\w+', text1.lower()))
|
||
words2 = set(re.findall(r'\w+', text2.lower()))
|
||
if not words1 or not words2:
|
||
return 0.0
|
||
intersection = words1.intersection(words2)
|
||
union = words1.union(words2)
|
||
return len(intersection) / len(union)
|
||
|
||
@staticmethod
|
||
def _process_segment(seg, seg_start, intervals, all_segments):
|
||
"""处理单个语音片段（用于并行处理）"""
|
||
# 首先尝试时间戳匹配
|
||
for page_idx, (start, end) in enumerate(intervals):
|
||
if start <= seg_start < end:
|
||
return page_idx, seg
|
||
|
||
# 如果时间戳匹配失败，尝试文本相似度匹配
|
||
best_page = None
|
||
best_score = 0.0
|
||
|
||
for page_idx, (start, end) in enumerate(intervals):
|
||
# 获取该页面的所有文本
|
||
page_text = " ".join([s["text"] for s in all_segments if start <= s["start"] < end])
|
||
similarity = ContentAligner.calculate_text_similarity(seg["text"], page_text)
|
||
if similarity > best_score:
|
||
best_score = similarity
|
||
best_page = page_idx
|
||
|
||
if best_page is not None:
|
||
return best_page, seg
|
||
return None
|
||
|
||
@staticmethod
|
||
def find_best_match(segments: list, intervals: list) -> dict:
|
||
"""为每个语音片段找到最佳匹配的页面（并行处理）"""
|
||
page_texts = defaultdict(list)
|
||
unmatched_segments = []
|
||
|
||
# 创建进度跟踪器
|
||
progress = ProgressTracker(len(segments), "内容对齐")
|
||
|
||
# 使用线程池进行并行处理
|
||
with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
|
||
futures = []
|
||
|
||
for seg in segments:
|
||
seg_start = seg["start"]
|
||
future = executor.submit(ContentAligner._process_segment, seg, seg_start, intervals, segments)
|
||
futures.append(future)
|
||
|
||
# 收集结果
|
||
for i, future in enumerate(concurrent.futures.as_completed(futures)):
|
||
try:
|
||
result = future.result()
|
||
if result:
|
||
page_idx, seg = result
|
||
page_texts[page_idx].append(seg)
|
||
else:
|
||
unmatched_segments.append(seg)
|
||
progress.update(1, f"处理第{i + 1}/{len(segments)}个片段")
|
||
except Exception as e:
|
||
print(f"[警告] 处理语音片段时出错: {str(e)}")
|
||
|
||
# 处理未匹配的片段
|
||
if unmatched_segments:
|
||
print(f"[警告] 发现{len(unmatched_segments)}个未匹配的语音片段")
|
||
# 将未匹配片段添加到最近的页面
|
||
for seg in unmatched_segments:
|
||
closest_page = min(range(len(intervals)),
|
||
key=lambda i: abs(seg["start"] - (intervals[i][0] + intervals[i][1]) / 2))
|
||
page_texts[closest_page].append(seg)
|
||
|
||
progress.complete(f"对齐了{len(segments)}个语音片段")
|
||
return page_texts
|
||
|
||
@staticmethod
|
||
def align_content(video_path: str, timestamps: list) -> list:
|
||
"""语音-画面对齐主逻辑（改进版，并行处理）"""
|
||
try:
|
||
# 创建超时处理器
|
||
timeout_handler = TimeoutHandler()
|
||
timeout_handler.start("内容对齐")
|
||
|
||
reader = imageio.get_reader(video_path)
|
||
duration = reader.get_meta_data()["duration"]
|
||
reader.close()
|
||
except:
|
||
duration = timestamps[-1] + FRAME_INTERVAL
|
||
|
||
segments = VideoProcessor.transcribe_audio(video_path)
|
||
intervals = ContentAligner.generate_page_intervals(timestamps, duration)
|
||
|
||
# 使用改进的匹配算法（并行处理）
|
||
page_texts = ContentAligner.find_best_match(segments, intervals)
|
||
|
||
# 生成最终的对齐数据
|
||
aligned_data = []
|
||
for idx in range(len(intervals)):
|
||
text = " ".join([seg["text"] for seg in page_texts.get(idx, [])])
|
||
aligned_data.append({
|
||
"page": idx,
|
||
"start_time": intervals[idx][0],
|
||
"end_time": intervals[idx][1],
|
||
"text": text
|
||
})
|
||
|
||
# 停止超时处理
|
||
timeout_handler.stop()
|
||
|
||
return aligned_data
|
||
|
||
|
||
# ---------------------- 摘要生成模块 ----------------------
|
||
class SummaryGenerator:
|
||
@staticmethod
|
||
def optimize_text(text: str) -> str:
|
||
"""文本浓缩优化，过滤重复句子"""
|
||
# 分割句子
|
||
sentences = re.split(r'[。！？]', text)
|
||
filtered = []
|
||
seen = defaultdict(int) # 用于记录句子出现次数
|
||
|
||
# 预处理句子：去除空白字符，转换为小写
|
||
processed_sentences = [sent.strip().lower() for sent in sentences]
|
||
|
||
# 过滤重复句子
|
||
for sent, processed_sent in zip(sentences, processed_sentences):
|
||
sent = sent.strip()
|
||
if (len(sent) >= 10 # 句子长度至少10个字符
|
||
and not any(word in sent for word in TRANSITION_WORDS) # 不包含过渡词
|
||
and seen[processed_sent] < 5): # 出现次数少于5次
|
||
filtered.append(sent)
|
||
seen[processed_sent] += 1
|
||
|
||
# 如果过滤后没有句子，返回空字符串
|
||
if not filtered:
|
||
return ""
|
||
|
||
# 重新组合句子
|
||
return '。'.join(filtered) + '。'
|
||
|
||
@staticmethod
|
||
def generate_html(aligned_data: list, keyframes: list, output_dir: str):
|
||
"""生成HTML报告（并行处理）"""
|
||
# 创建超时处理器
|
||
timeout_handler = TimeoutHandler()
|
||
timeout_handler.start("HTML报告生成")
|
||
|
||
# 创建进度跟踪器
|
||
progress = ProgressTracker(len(keyframes), "HTML报告生成")
|
||
|
||
pages_data = []
|
||
temp_img_dir = os.path.join(output_dir, "_temp_images")
|
||
os.makedirs(temp_img_dir, exist_ok=True)
|
||
|
||
try:
|
||
# 使用线程池进行并行处理
|
||
with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
|
||
futures = []
|
||
|
||
for idx, frame in enumerate(keyframes):
|
||
future = executor.submit(SummaryGenerator._process_frame, idx, frame, aligned_data, temp_img_dir)
|
||
futures.append(future)
|
||
|
||
# 收集结果
|
||
for i, future in enumerate(concurrent.futures.as_completed(futures)):
|
||
try:
|
||
result = future.result()
|
||
if result:
|
||
pages_data.append(result)
|
||
progress.update(1, f"处理第{i + 1}/{len(keyframes)}个页面")
|
||
except Exception as e:
|
||
print(f"[警告] 处理帧时出错: {str(e)}")
|
||
|
||
# 按页面顺序排序
|
||
pages_data.sort(key=lambda x: x["num"])
|
||
|
||
progress.update(10, "生成HTML模板")
|
||
env = Environment()
|
||
template = env.from_string("""
|
||
<!DOCTYPE html>
|
||
<html>
|
||
<head>
|
||
<meta charset="UTF-8">
|
||
<title>PPT视频摘要报告</title>
|
||
<style>
|
||
.page { margin: 20px; padding: 15px; border: 1px solid #eee; }
|
||
img { max-width: 800px; height: auto; }
|
||
.timestamp { color: #666; font-size: 0.9em; }
|
||
.content { margin-top: 10px; }
|
||
</style>
|
||
</head>
|
||
<body>
|
||
<h1>PPT视频结构化摘要</h1>
|
||
{% for page in pages %}
|
||
<div class="page">
|
||
<h2>页面 {{ page.num }}</h2>
|
||
<div class="timestamp">{{ page.time }}</div>
|
||
<img src="{{ page.image }}" alt="页面截图">
|
||
<div class="content">{{ page.text }}</div>
|
||
</div>
|
||
{% endfor %}
|
||
</body>
|
||
</html>
|
||
""")
|
||
|
||
progress.update(10, "保存HTML文件")
|
||
output_path = os.path.join(output_dir, "summary.html")
|
||
with open(output_path, "w", encoding="utf-8") as f:
|
||
f.write(template.render(pages=pages_data))
|
||
print(f"[输出] HTML报告已生成: {output_path}")
|
||
|
||
# 停止超时处理
|
||
timeout_handler.stop()
|
||
progress.complete("HTML报告生成完成")
|
||
finally:
|
||
for f in os.listdir(temp_img_dir):
|
||
os.remove(os.path.join(temp_img_dir, f))
|
||
os.rmdir(temp_img_dir)
|
||
|
||
@staticmethod
|
||
def _process_frame(idx, frame, aligned_data, temp_img_dir):
|
||
"""处理单个帧（用于并行处理）"""
|
||
try:
|
||
img_path = os.path.join(temp_img_dir, f"page_{idx}.jpg")
|
||
frame.save(img_path)
|
||
with open(img_path, "rb") as f:
|
||
img_data = base64.b64encode(f.read()).decode("utf-8")
|
||
|
||
return {
|
||
"num": idx + 1,
|
||
"time": f"{aligned_data[idx]['start_time']:.1f}s - {aligned_data[idx]['end_time']:.1f}s",
|
||
"image": f"data:image/jpeg;base64,{img_data}",
|
||
"text": SummaryGenerator.optimize_text(aligned_data[idx]["text"])
|
||
}
|
||
except Exception as e:
|
||
print(f"[警告] 处理帧 {idx} 时出错: {str(e)}")
|
||
return None
|
||
|
||
@staticmethod
|
||
def generate_pdf(aligned_data: list, keyframes: list, output_dir: str):
|
||
"""生成PDF报告（优化版，并行处理）"""
|
||
# 创建超时处理器
|
||
timeout_handler = TimeoutHandler()
|
||
timeout_handler.start("PDF报告生成")
|
||
|
||
# 创建进度跟踪器
|
||
progress = ProgressTracker(len(keyframes) + 20, "PDF报告生成")
|
||
|
||
temp_html = os.path.join(output_dir, "_temp_pdf.html")
|
||
temp_img_dir = os.path.join(output_dir, "_temp_pdf_images")
|
||
os.makedirs(temp_img_dir, exist_ok=True)
|
||
|
||
try:
|
||
# 使用绝对路径
|
||
abs_temp_img_dir = os.path.abspath(temp_img_dir)
|
||
|
||
progress.update(5, "准备HTML模板")
|
||
html_content = """
|
||
<!DOCTYPE html>
|
||
<html>
|
||
<head>
|
||
<meta charset="UTF-8">
|
||
<style>
|
||
@page {
|
||
margin: 20mm;
|
||
size: A4;
|
||
}
|
||
body {
|
||
font-family: "Microsoft YaHei", "SimSun", sans-serif;
|
||
line-height: 1.6;
|
||
color: #333;
|
||
}
|
||
.page {
|
||
page-break-inside: avoid;
|
||
margin-bottom: 30px;
|
||
padding: 20px;
|
||
border: 1px solid #eee;
|
||
border-radius: 5px;
|
||
}
|
||
.page-number {
|
||
text-align: center;
|
||
font-size: 24pt;
|
||
font-weight: bold;
|
||
margin-bottom: 20px;
|
||
color: #2c3e50;
|
||
}
|
||
.timestamp {
|
||
color: #666;
|
||
font-size: 12pt;
|
||
margin-bottom: 15px;
|
||
}
|
||
.image-container {
|
||
text-align: center;
|
||
margin: 20px 0;
|
||
}
|
||
img {
|
||
max-width: 90% !important;
|
||
height: auto;
|
||
display: block;
|
||
margin: 0 auto;
|
||
box-shadow: 0 2px 5px rgba(0,0,0,0.1);
|
||
}
|
||
.content {
|
||
font-size: 14pt;
|
||
line-height: 1.8;
|
||
margin-top: 20px;
|
||
padding: 15px;
|
||
background: #f9f9f9;
|
||
border-radius: 5px;
|
||
}
|
||
.professional-term {
|
||
color: #2980b9;
|
||
font-weight: bold;
|
||
}
|
||
</style>
|
||
</head>
|
||
<body>
|
||
<h1 style="text-align: center; color: #2c3e50; margin-bottom: 40px;">PPT视频结构化摘要</h1>
|
||
{% for page in pages %}
|
||
<div class="page">
|
||
<div class="page-number">第 {{ page.num }} 页</div>
|
||
<div class="timestamp">时间区间：{{ page.time }}</div>
|
||
<div class="image-container">
|
||
<img src="{{ page.image_path }}" alt="页面截图">
|
||
</div>
|
||
<div class="content">{{ page.text }}</div>
|
||
</div>
|
||
{% endfor %}
|
||
</body>
|
||
</html>
|
||
"""
|
||
|
||
pages_data = []
|
||
|
||
# 使用线程池进行并行处理
|
||
with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
|
||
futures = []
|
||
|
||
for idx, frame in enumerate(keyframes):
|
||
future = executor.submit(SummaryGenerator._process_frame_for_pdf, idx, frame, aligned_data,
|
||
abs_temp_img_dir)
|
||
futures.append(future)
|
||
|
||
# 收集结果
|
||
for i, future in enumerate(concurrent.futures.as_completed(futures)):
|
||
try:
|
||
result = future.result()
|
||
if result:
|
||
pages_data.append(result)
|
||
progress.update(1, f"处理第{i + 1}/{len(keyframes)}个页面")
|
||
except Exception as e:
|
||
print(f"[警告] 处理帧时出错: {str(e)}")
|
||
|
||
# 按页面顺序排序
|
||
pages_data.sort(key=lambda x: x["num"])
|
||
|
||
progress.update(5, "生成HTML文件")
|
||
env = Environment()
|
||
template = env.from_string(html_content)
|
||
with open(temp_html, "w", encoding="utf-8") as f:
|
||
f.write(template.render(pages=pages_data))
|
||
|
||
# PDF生成选项
|
||
progress.update(5, "配置PDF生成选项")
|
||
options = {
|
||
"enable-local-file-access": "",
|
||
"encoding": "UTF-8",
|
||
"margin-top": "20mm",
|
||
"margin-bottom": "20mm",
|
||
"margin-left": "20mm",
|
||
"margin-right": "20mm",
|
||
"no-stop-slow-scripts": "",
|
||
"quiet": "",
|
||
"dpi": "300",
|
||
"image-quality": "100",
|
||
"enable-smart-shrinking": "",
|
||
"print-media-type": ""
|
||
}
|
||
config = pdfkit.configuration(wkhtmltopdf=WKHTMLTOPDF_PATH)
|
||
|
||
progress.update(5, "生成PDF文件")
|
||
pdf_path = os.path.join(output_dir, "summary.pdf")
|
||
|
||
# 使用子进程生成PDF，设置超时
|
||
try:
|
||
print("[信息] 尝试使用子进程生成PDF...")
|
||
process = subprocess.Popen(
|
||
[WKHTMLTOPDF_PATH, "--enable-local-file-access", temp_html, pdf_path],
|
||
stdout=subprocess.PIPE,
|
||
stderr=subprocess.PIPE
|
||
)
|
||
|
||
# 等待进程完成，设置超时
|
||
try:
|
||
print("[信息] 等待PDF生成进程完成...")
|
||
stdout, stderr = process.communicate(timeout=60)
|
||
if process.returncode != 0:
|
||
print(f"[警告] PDF生成返回非零状态码: {process.returncode}")
|
||
print(f"[警告] 错误输出: {stderr.decode('utf-8', errors='ignore')}")
|
||
raise Exception(f"PDF生成失败，返回码: {process.returncode}")
|
||
print("[信息] PDF生成进程完成")
|
||
except subprocess.TimeoutExpired:
|
||
print("[警告] PDF生成超时，终止进程")
|
||
process.kill()
|
||
print("[信息] 尝试使用备用方法")
|
||
|
||
# 备用方法：使用pdfkit
|
||
print("[信息] 使用pdfkit库生成PDF...")
|
||
pdfkit.from_file(
|
||
temp_html,
|
||
pdf_path,
|
||
configuration=config,
|
||
options=options
|
||
)
|
||
print("[信息] pdfkit生成PDF完成")
|
||
except Exception as e:
|
||
print(f"[警告] 使用子进程生成PDF失败: {str(e)}")
|
||
# 备用方法：使用pdfkit
|
||
print("[信息] 使用pdfkit库生成PDF...")
|
||
try:
|
||
pdfkit.from_file(
|
||
temp_html,
|
||
pdf_path,
|
||
configuration=config,
|
||
options=options
|
||
)
|
||
print("[信息] pdfkit生成PDF完成")
|
||
except Exception as e2:
|
||
print(f"[错误] pdfkit生成PDF也失败: {str(e2)}")
|
||
# 最后的备用方法：使用简化的HTML
|
||
print("[信息] 尝试使用简化的HTML生成PDF...")
|
||
try:
|
||
# 创建一个简化的HTML文件
|
||
simple_html = os.path.join(output_dir, "_simple_pdf.html")
|
||
with open(simple_html, "w", encoding="utf-8") as f:
|
||
f.write("""
|
||
<!DOCTYPE html>
|
||
<html>
|
||
<head>
|
||
<meta charset="UTF-8">
|
||
<title>PPT视频摘要报告</title>
|
||
<style>
|
||
body { font-family: Arial, sans-serif; }
|
||
.page { margin: 20px; padding: 15px; border: 1px solid #eee; }
|
||
</style>
|
||
</head>
|
||
<body>
|
||
<h1>PPT视频结构化摘要</h1>
|
||
""")
|
||
|
||
for page in pages_data:
|
||
f.write(f"""
|
||
<div class="page">
|
||
<h2>页面 {page['num']}</h2>
|
||
<div>时间区间：{page['time']}</div>
|
||
<div>{page['text']}</div>
|
||
</div>
|
||
""")
|
||
|
||
f.write("</body></html>")
|
||
|
||
# 使用简化的HTML生成PDF
|
||
pdfkit.from_file(
|
||
simple_html,
|
||
pdf_path,
|
||
configuration=config,
|
||
options=options
|
||
)
|
||
print("[信息] 使用简化HTML生成PDF完成")
|
||
|
||
# 清理简化HTML
|
||
if os.path.exists(simple_html):
|
||
os.remove(simple_html)
|
||
except Exception as e3:
|
||
print(f"[错误] 所有PDF生成方法都失败: {str(e3)}")
|
||
print("[警告] 无法生成PDF报告，请检查HTML报告")
|
||
|
||
print(f"[输出] PDF报告已生成: {pdf_path}")
|
||
|
||
# 停止超时处理
|
||
timeout_handler.stop()
|
||
progress.complete("PDF报告生成完成")
|
||
|
||
finally:
|
||
# 清理临时文件
|
||
print("[信息] 清理临时文件...")
|
||
try:
|
||
if os.path.exists(temp_html):
|
||
os.remove(temp_html)
|
||
print("[信息] 已删除临时HTML文件")
|
||
|
||
if os.path.exists(temp_img_dir):
|
||
for f in os.listdir(temp_img_dir):
|
||
try:
|
||
os.remove(os.path.join(temp_img_dir, f))
|
||
except Exception as e:
|
||
print(f"[警告] 删除临时图片文件失败: {str(e)}")
|
||
try:
|
||
os.rmdir(temp_img_dir)
|
||
print("[信息] 已删除临时图片目录")
|
||
except Exception as e:
|
||
print(f"[警告] 删除临时图片目录失败: {str(e)}")
|
||
except Exception as e:
|
||
print(f"[警告] 清理临时文件时出错: {str(e)}")
|
||
|
||
@classmethod
|
||
def generate_all(cls, aligned_data: list, keyframes: list, output_dir: str):
|
||
"""生成所有格式报告（并行处理）"""
|
||
# 创建进度跟踪器
|
||
progress = ProgressTracker(2, "报告生成")
|
||
|
||
# 使用线程池并行生成HTML和PDF
|
||
with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
|
||
html_future = executor.submit(cls.generate_html, aligned_data, keyframes, output_dir)
|
||
pdf_future = executor.submit(cls.generate_pdf, aligned_data, keyframes, output_dir)
|
||
|
||
# 等待HTML生成完成
|
||
try:
|
||
html_future.result(timeout=300) # 设置5分钟超时
|
||
progress.update(1, "HTML报告生成完成")
|
||
except concurrent.futures.TimeoutError:
|
||
print("[警告] HTML报告生成超时")
|
||
except Exception as e:
|
||
print(f"[警告] HTML报告生成出错: {str(e)}")
|
||
|
||
# 等待PDF生成完成
|
||
try:
|
||
pdf_future.result(timeout=300) # 设置5分钟超时
|
||
progress.update(1, "PDF报告生成完成")
|
||
except concurrent.futures.TimeoutError:
|
||
print("[警告] PDF报告生成超时")
|
||
except Exception as e:
|
||
print(f"[警告] PDF报告生成出错: {str(e)}")
|
||
|
||
progress.complete("所有报告生成完成")
|
||
|
||
@staticmethod
|
||
def _process_frame_for_pdf(idx, frame, aligned_data, abs_temp_img_dir):
|
||
"""处理单个帧用于PDF生成（用于并行处理）"""
|
||
try:
|
||
img_filename = f"page_{idx}.jpg"
|
||
img_path = os.path.join(abs_temp_img_dir, img_filename)
|
||
frame.save(img_path)
|
||
|
||
return {
|
||
"num": idx + 1,
|
||
"time": f"{aligned_data[idx]['start_time']:.1f}s - {aligned_data[idx]['end_time']:.1f}s",
|
||
"image_path": img_path,
|
||
"text": SummaryGenerator.optimize_text(aligned_data[idx]["text"])
|
||
}
|
||
except Exception as e:
|
||
print(f"[警告] 处理帧 {idx} 时出错: {str(e)}")
|
||
return None
|
||
|
||
|
||
# ---------------------- 主流程控制 ----------------------
|
||
def main_process():
|
||
# 环境检查
|
||
processor = VideoProcessor()
|
||
if not processor.check_ffmpeg():
|
||
return
|
||
if not os.path.exists(VIDEO_PATH):
|
||
print(f"[错误] 视频文件不存在: {VIDEO_PATH}")
|
||
return
|
||
|
||
# 创建总进度跟踪器
|
||
total_progress = ProgressTracker(4, "总体进度")
|
||
|
||
# 关键帧提取
|
||
total_progress.update(1, "开始关键帧提取")
|
||
keyframes, timestamps = processor.extract_keyframes(VIDEO_PATH)
|
||
if not keyframes:
|
||
print("[错误] 未提取到关键帧")
|
||
return
|
||
total_progress.update(1, "关键帧提取完成")
|
||
|
||
# 内容对齐
|
||
total_progress.update(1, "开始内容对齐")
|
||
aligned_data = ContentAligner.align_content(VIDEO_PATH, timestamps)
|
||
if not aligned_data:
|
||
print("[警告] 未识别到有效语音内容")
|
||
total_progress.update(1, "内容对齐完成")
|
||
|
||
# 生成摘要
|
||
print("[信息] 开始生成报告...")
|
||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||
|
||
try:
|
||
SummaryGenerator.generate_all(aligned_data, keyframes, OUTPUT_DIR)
|
||
print("[信息] 报告生成完成")
|
||
except Exception as e:
|
||
print(f"[错误] 报告生成过程中出错: {str(e)}")
|
||
|
||
total_progress.complete("处理完成")
|
||
print("[完成] 所有处理已完成，请查看输出目录中的报告文件")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
try:
|
||
main_process()
|
||
except KeyboardInterrupt:
|
||
print("\n[中断] 用户中断了处理")
|
||
except Exception as e:
|
||
print(f"[错误] 程序执行过程中出现未处理的异常: {str(e)}")
|
||
import traceback
|
||
|
||
traceback.print_exc()
|