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diff --git a/3.0/毕设.py b/3.0/毕设.py
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+++ b/3.0/毕设.py
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+import cv2
+import numpy as np
+from skimage.metrics import structural_similarity as ssim
+from moviepy.editor import VideoFileClip
+from PIL import Image
+import os
+from scipy.signal import find_peaks
+import torch
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import time
+import whisper
+from collections import defaultdict
+import re
+from reportlab.lib.pagesizes import A4
+from reportlab.pdfgen import canvas
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.platypus import Paragraph, Image as RLImage
+from reportlab.lib.units import inch
+import threading
+import pdfkit
+from jinja2 import Environment
+import io
+import base64
+
+# 全局配置
+SSIM_THRESHOLD = 0.85 # 关键帧去重阈值
+FRAME_INTERVAL = 2 # 抽帧间隔（秒）
+TRANSITION_WORDS = ["接下来", "下一页", "如图"] # 过渡词过滤列表
+WKHTMLTOPDF_PATH = r"D:\wkhtmltopdf\bin\wkhtmltopdf.exe"
+
+
+class PPTSummarizer:
+ def __init__(self, video_path, output_dir=None):
+ self.video_path = video_path
+ # 如果没有指定输出目录，则使用默认目录
+ if output_dir is None:
+ # 使用视频文件名作为输出目录名
+ video_name = os.path.splitext(os.path.basename(video_path))[0]
+ self.output_dir = os.path.join("output", video_name)
+ else:
+ self.output_dir = output_dir
+
+ self.frames = []
+ self.key_frames = []
+ self.text_content = []
+ self.frame_timestamps = []
+ self.aligned_data = []
+ self.processing_complete = threading.Event()
+
+ # 创建输出目录
+ if not os.path.exists(self.output_dir):
+ os.makedirs(self.output_dir)
+
+ # 初始化whisper模型
+ self.whisper_model = whisper.load_model("tiny", device="cpu")
+
+ def extract_frames(self):
+ """提取视频帧"""
+ try:
+ # 使用VideoFileClip替代cv2
+ video = VideoFileClip(self.video_path)
+ duration = video.duration
+ fps = video.fps
+
+ # 计算采样间隔
+ sample_interval = max(1 / fps, FRAME_INTERVAL)
+ timestamps = np.arange(0, duration, sample_interval)
+
+ print(f"开始提取帧，视频时长：{duration:.2f}秒，FPS：{fps}")
+
+ # 提取帧
+ for t in timestamps:
+ try:
+ frame = video.get_frame(t)
+ # 转换为BGR格式（OpenCV格式）
+ frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+ self.frames.append(frame_bgr)
+ self.frame_timestamps.append(t)
+ except Exception as e:
+ print(f"提取帧 {t}s 时出错: {str(e)}")
+ continue
+
+ video.close()
+ print(f"成功提取 {len(self.frames)} 帧")
+ return fps
+
+ except Exception as e:
+ print(f"视频处理出错: {str(e)}")
+ return 0
+
+ def process_audio(self):
+ """处理音频"""
+ try:
+ print("开始语音识别...")
+ # 使用更大的模型以提高识别准确度
+ result = self.whisper_model.transcribe(
+ self.video_path,
+ fp16=False,
+ language="zh",
+ task="transcribe",
+ verbose=True
+ )
+ segments = result.get("segments", [])
+ print(f"语音识别完成，共识别出 {len(segments)} 个片段")
+
+ # 打印识别结果
+ for i, seg in enumerate(segments):
+ try:
+ print(f"片段 {i + 1}: {seg['start']:.1f}s - {seg['end']:.1f}s: {seg['text']}")
+ except UnicodeEncodeError:
+ print(f"片段 {i + 1}: {seg['start']:.1f}s - {seg['end']:.1f}s: [文本包含特殊字符]")
+
+ # 生成页面时间段
+ intervals = []
+ for i in range(len(self.frame_timestamps)):
+ start = self.frame_timestamps[i]
+ end = self.frame_timestamps[i + 1] if i < len(self.frame_timestamps) - 1 else self.frame_timestamps[
+ -1] + 1
+ intervals.append((start, end))
+ print(f"页面 {i + 1} 时间段: {start:.1f}s - {end:.1f}s")
+
+ # 改进的对齐逻辑
+ page_texts = defaultdict(list)
+ page_segments = defaultdict(list) # 存储每个页面对应的片段
+ all_texts = [] # 存储所有文本片段
+ used_texts = set() # 跟踪已使用的文本
+
+ # 第一步：收集所有文本片段
+ for seg in segments:
+ try:
+ seg_start = seg["start"]
+ seg_end = seg["end"]
+ seg_text = seg["text"].strip()
+ all_texts.append((seg_start, seg_end, seg_text))
+ except Exception as e:
+ print(f"处理片段时出错: {str(e)}")
+ continue
+
+ # 第二步：将文本分配到各个页面
+ for start, end, text in all_texts:
+ try:
+ # 找到与当前片段时间重叠的所有页面
+ overlapping_pages = []
+ for page_idx, (page_start, page_end) in enumerate(intervals):
+ if (start <= page_end and end >= page_start):
+ overlapping_pages.append((page_idx, page_start, page_end))
+
+ # 如果找到重叠页面，将文本添加到最合适的页面
+ if overlapping_pages:
+ # 计算每个页面的重叠时间
+ page_overlaps = []
+ for page_idx, page_start, page_end in overlapping_pages:
+ overlap_start = max(start, page_start)
+ overlap_end = min(end, page_end)
+ overlap_duration = overlap_end - overlap_start
+ page_overlaps.append((page_idx, overlap_duration))
+
+ # 按重叠时间排序
+ page_overlaps.sort(key=lambda x: x[1], reverse=True)
+
+ # 将文本添加到重叠时间最长的页面
+ best_page = page_overlaps[0][0]
+ if text not in used_texts: # 确保文本未被使用
+ page_texts[best_page].append(text)
+ page_segments[best_page].append((start, end, text))
+ used_texts.add(text)
+ print(f"将文本 '{text}' 添加到页面 {best_page + 1}")
+ except Exception as e:
+ print(f"分配文本时出错: {str(e)}")
+ continue
+
+ # 第三步：优化每个页面的文本
+ self.aligned_data = []
+ for idx in range(len(intervals)):
+ try:
+ # 获取当前页面的所有片段
+ segments = page_segments[idx]
+
+ # 按时间排序
+ segments.sort(key=lambda x: x[0])
+
+ # 合并相邻的相似文本
+ merged_texts = []
+ current_text = ""
+ last_end_time = 0
+
+ for start, end, text in segments:
+ # 如果当前文本为空，直接添加
+ if not current_text:
+ current_text = text
+ last_end_time = end
+ continue
+
+ # 计算时间间隔
+ time_gap = start - last_end_time
+
+ # 如果时间间隔小于3秒，合并文本
+ if time_gap < 3.0: # 增加时间间隔阈值
+ current_text += " " + text
+ else:
+ merged_texts.append(current_text)
+ current_text = text
+
+ last_end_time = end
+
+ # 添加最后一个文本
+ if current_text:
+ merged_texts.append(current_text)
+
+ # 合并所有文本
+ final_text = " ".join(merged_texts)
+
+ # 如果当前页面文本为空，尝试从前一页面获取
+ if not final_text and idx > 0:
+ final_text = self.aligned_data[idx - 1]["text"]
+
+ # 优化文本
+ optimized_text = self.optimize_text(final_text)
+
+ if optimized_text:
+ print(f"页面 {idx + 1} 的优化后文本内容: {optimized_text}")
+
+ self.aligned_data.append({
+ "page": idx,
+ "start_time": intervals[idx][0],
+ "end_time": intervals[idx][1],
+ "text": optimized_text
+ })
+ except Exception as e:
+ print(f"处理页面 {idx + 1} 时出错: {str(e)}")
+ # 添加空数据
+ self.aligned_data.append({
+ "page": idx,
+ "start_time": intervals[idx][0],
+ "end_time": intervals[idx][1],
+ "text": ""
+ })
+
+ # 第四步：确保所有文本都被包含
+ # 检查是否有遗漏的文本片段
+ for start, end, text in all_texts:
+ try:
+ if text not in used_texts:
+ # 找到最后一个非空页面
+ last_non_empty_page = -1
+ for i in range(len(self.aligned_data) - 1, -1, -1):
+ if self.aligned_data[i]["text"]:
+ last_non_empty_page = i
+ break
+
+ if last_non_empty_page >= 0:
+ self.aligned_data[last_non_empty_page]["text"] += " " + text
+ print(f"将遗漏的文本 '{text}' 添加到页面 {last_non_empty_page + 1}")
+ except Exception as e:
+ print(f"处理遗漏文本时出错: {str(e)}")
+ continue
+
+ # 保存对齐数据到文件
+ try:
+ with open(os.path.join(self.output_dir, 'aligned_data.txt'), 'w', encoding='utf-8') as f:
+ for data in self.aligned_data:
+ f.write(f"页面 {data['page'] + 1}:\n")
+ f.write(f"时间: {data['start_time']:.1f}s - {data['end_time']:.1f}s\n")
+ f.write(f"文本: {data['text']}\n\n")
+ print("对齐数据已保存到文件")
+ except Exception as e:
+ print(f"保存对齐数据时出错: {str(e)}")
+
+ except Exception as e:
+ print(f"音频处理出错: {str(e)}")
+ self.aligned_data = []
+
+ def process_frames(self):
+ """处理视频帧"""
+ try:
+ print("开始处理视频帧...")
+ # 计算帧间相似度
+ similarities = []
+ for i in range(len(self.frames) - 1):
+ try:
+ frame1 = cv2.cvtColor(self.frames[i], cv2.COLOR_BGR2GRAY)
+ frame2 = cv2.cvtColor(self.frames[i + 1], cv2.COLOR_BGR2GRAY)
+ similarity = ssim(frame1, frame2)
+ similarities.append(similarity)
+ except Exception as e:
+ print(f"计算帧 {i} 相似度时出错: {str(e)}")
+ similarities.append(1.0) # 出错时假设帧相似
+
+ # 使用自适应阈值
+ mean_similarity = np.mean(similarities)
+ std_similarity = np.std(similarities)
+ adaptive_threshold = mean_similarity - 2 * std_similarity
+
+ # 使用峰值检测找到关键帧
+ peaks, _ = find_peaks([1 - s for s in similarities],
+ height=1 - adaptive_threshold,
+ distance=int(len(similarities) / 20)) # 最小距离
+
+ # 保存关键帧
+ for peak in peaks:
+ if not self.is_blank_frame(self.frames[peak]):
+ self.key_frames.append(self.frames[peak])
+
+ print(f"找到 {len(self.key_frames)} 个关键帧")
+
+ except Exception as e:
+ print(f"处理视频帧时出错: {str(e)}")
+
+ def is_blank_frame(self, frame, threshold=30):
+ """检测是否为空白帧"""
+ try:
+ # 转换为灰度图
+ gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+ # 计算图像统计特征
+ mean = np.mean(gray)
+ std_dev = np.std(gray)
+
+ # 检查是否为纯黑或纯白
+ is_black = mean < 10 and std_dev < 5
+ is_white = mean > 245 and std_dev < 5
+
+ # 检查是否有足够的细节
+ has_detail = std_dev > threshold
+
+ return is_black or is_white or not has_detail
+ except Exception as e:
+ print(f"检查空白帧时出错: {str(e)}")
+ return True
+
+ def optimize_text(self, text):
+ """文本优化"""
+ try:
+ if not text:
+ return ""
+
+ # 过滤过渡词
+ sentences = re.split(r'[。！？]', text)
+ filtered = []
+ seen = set()
+ for sent in sentences:
+ sent = sent.strip()
+ if (len(sent) >= 10
+ and not any(word in sent for word in TRANSITION_WORDS)
+ and sent not in seen):
+ filtered.append(sent)
+ seen.add(sent)
+
+ result = '。'.join(filtered) + '。' if filtered else ""
+ if result:
+ print(f"优化后的文本: {result}")
+ return result
+ except Exception as e:
+ print(f"文本优化时出错: {str(e)}")
+ return text
+
+ def save_results(self):
+ """保存结果"""
+ try:
+ # 检查输出目录权限
+ if not os.access(self.output_dir, os.W_OK):
+ print(f"错误：没有写入权限: {self.output_dir}")
+ return
+
+ # 生成PDF文档
+ pdf_path = os.path.join(self.output_dir, 'summary.pdf')
+
+ # 创建临时HTML文件
+ temp_html = os.path.join(self.output_dir, "_temp_pdf.html")
+ temp_img_dir = os.path.join(self.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)
+
+ html_content = """
+
+
+
+
+
+
+
+ PPT视频结构化摘要
+ {% for page in pages %}
+
+
页面 {{ page.num }}
+
{{ page.time }}
+
+
{{ page.text }}
+