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+import os
+import re
+import base64
+import warnings
+import imageio
+import whisper
+import numpy as np
+import pdfkit
+from PIL import Image
+from skimage.metrics import structural_similarity as ssim
+from collections import defaultdict
+import subprocess
+from jinja2 import Environment
+import cv2
+from scipy.signal import find_peaks
+from skimage.feature import hog
+from skimage.color import rgb2gray
+
+# ======================== 全局配置 ========================
+warnings.filterwarnings("ignore", message="FP16 is not supported on CPU; using FP32 instead")
+VIDEO_PATH = "D:/python项目文件/1/input3.mp4" # 输入视频路径
+MODEL_DIR = "D:/whisper_models" # Whisper模型目录
+FFMPEG_BIN = r"D:\Program Files\ffmpeg\bin" # FFmpeg安装路径
+WKHTMLTOPDF_PATH = r"D:\wkhtmltopdf\bin\wkhtmltopdf.exe" # wkhtmltopdf路径
+SSIM_THRESHOLD = 0.85 # 关键帧去重阈值
+FRAME_INTERVAL = 2 # 抽帧间隔（秒）
+OUTPUT_DIR = "D:\桌面文件\python\output" # 输出目录
+TRANSITION_WORDS = ["接下来", "下一页", "如图"] # 过渡词过滤列
+HOG_THRESHOLD = 0.7 # HOG特征相似度阈值
+COLOR_THRESHOLD = 0.8 # 颜色直方图相似度阈值
+WHISPER_MODEL = "base" # Whisper模型大小
+PROFESSIONAL_TERMS = {
+ "人工智能": "AI",
+ "机器学习": "ML",
+ "深度学习": "DL",
+ "神经网络": "NN",
+ "卷积神经网络": "CNN",
+ "循环神经网络": "RNN",
+ "自然语言处理": "NLP",
+ "计算机视觉": "CV",
+ "大数据": "Big Data",
+ "云计算": "Cloud Computing"
+} # 专业术语词典
+
+
+# ========================================================
+
+# ---------------------- 核心功能模块 ----------------------
+class VideoProcessor:
+ def __init__(self):
+ os.environ["PATH"] = FFMPEG_BIN + os.pathsep + os.environ["PATH"]
+
+ @staticmethod
+ def check_ffmpeg():
+ """验证FFmpeg可用性"""
+ try:
+ subprocess.run(["ffmpeg", "-version"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+ print("[系统] FFmpeg验证成功")
+ return True
+ except Exception as e:
+ print(f"[错误] FFmpeg验证失败: {str(e)}")
+ return False
+
+ @staticmethod
+ def calculate_color_histogram(frame):
+ """计算颜色直方图特征"""
+ hist = cv2.calcHist([frame], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
+ cv2.normalize(hist, hist)
+ return hist.flatten()
+
+ @staticmethod
+ def calculate_hog_features(frame):
+ """计算HOG特征"""
+ gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+ features = hog(gray, orientations=8, pixels_per_cell=(16, 16),
+ cells_per_block=(1, 1), visualize=False)
+ return features
+
+ @staticmethod
+ def is_ppt_transition(frame1, frame2):
+ """检测PPT页面切换"""
+ # 转换为灰度图
+ gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
+ gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
+
+ # 计算边缘
+ edges1 = cv2.Canny(gray1, 100, 200)
+ edges2 = cv2.Canny(gray2, 100, 200)
+
+ # 计算边缘差异
+ diff = cv2.absdiff(edges1, edges2)
+ return np.mean(diff) > 50 # 阈值可调整
+
+ @staticmethod
+ def extract_keyframes(video_path: str) -> tuple:
+ """提取去重关键帧及其时间戳（多特征融合）"""
+ try:
+ reader = imageio.get_reader(video_path)
+ fps = reader.get_meta_data()["fps"]
+ total_frames = reader.count_frames()
+ print(f"[信息] 视频总帧数: {total_frames}")
+
+ keyframes = []
+ timestamps = []
+ prev_frame = None
+ frame_count = 0
+ last_progress = 0
+
+ for idx, frame in enumerate(reader):
+ # 显示进度
+ progress = int((idx / total_frames) * 100)
+ if progress != last_progress and progress % 5 == 0: # 每5%显示一次进度
+ print(f"[进度] 处理中: {progress}% ({idx}/{total_frames}帧)")
+ last_progress = progress
+
+ curr_time = idx / fps
+ if curr_time - (timestamps[-1] if timestamps else 0) < FRAME_INTERVAL:
+ continue
+
+ # 多特征相似度计算
+ if prev_frame is not None:
+ try:
+ # 1. SSIM相似度（使用简化版本）
+ gray_prev = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
+ gray_curr = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+ ssim_score = ssim(gray_prev, gray_curr, win_size=3)
+
+ # 2. 颜色直方图相似度
+ hist_prev = VideoProcessor.calculate_color_histogram(prev_frame)
+ hist_curr = VideoProcessor.calculate_color_histogram(frame)
+ color_sim = cv2.compareHist(hist_prev, hist_curr, cv2.HISTCMP_CORREL)
+
+ # 3. HOG特征相似度（仅在SSIM和颜色相似度较高时计算）
+ if ssim_score > 0.8 and color_sim > 0.8:
+ hog_prev = VideoProcessor.calculate_hog_features(prev_frame)
+ hog_curr = VideoProcessor.calculate_hog_features(frame)
+ hog_sim = np.dot(hog_prev, hog_curr) / (np.linalg.norm(hog_prev) * np.linalg.norm(hog_curr))
+ else:
+ hog_sim = 0 # 如果SSIM和颜色相似度低，直接跳过HOG计算
+
+ # 4. PPT页面切换检测
+ is_transition = VideoProcessor.is_ppt_transition(prev_frame, frame)
+
+ # 综合判断
+ if (ssim_score > SSIM_THRESHOLD and
+ color_sim > COLOR_THRESHOLD and
+ hog_sim > HOG_THRESHOLD and
+ not is_transition):
+ continue
+ except Exception as e:
+ print(f"[警告] 特征计算失败: {str(e)}")
+ continue
+
+ keyframes.append(Image.fromarray(frame))
+ timestamps.append(curr_time)
+ prev_frame = frame
+ frame_count += 1
+
+ # 每处理100帧强制垃圾回收
+ if frame_count % 100 == 0:
+ import gc
+ gc.collect()
+
+ reader.close()
+ print(f"[图像] 关键帧提取完成，共{len(keyframes)}帧")
+ return keyframes, timestamps
+ except Exception as e:
+ print(f"[错误] 关键帧提取失败: {str(e)}")
+ return [], []
+
+ @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:
+ # 使用更大的模型提高准确率
+ model = whisper.load_model(model_name, device="cpu", download_root=MODEL_DIR)
+
+ # 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。"
+ )
+
+ segments = result.get("segments", [])
+
+ # 后处理：专业术语替换
+ for seg in segments:
+ text = seg["text"]
+ for cn, en in PROFESSIONAL_TERMS.items():
+ text = text.replace(cn, f"{cn}({en})")
+ seg["text"] = text
+
+ 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
+ def calculate_text_similarity(text1: str, text2: str) -> float:
+ """计算文本相似度"""
+ # 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 find_best_match(segments: list, intervals: list) -> dict:
+ """为每个语音片段找到最佳匹配的页面"""
+ page_texts = defaultdict(list)
+ unmatched_segments = []
+
+ for seg in segments:
+ seg_start = seg["start"]
+ best_match = None
+ best_score = 0.0
+
+ # 1. 首先尝试时间戳匹配
+ for page_idx, (start, end) in enumerate(intervals):
+ if start <= seg_start < end:
+ best_match = page_idx
+ break
+
+ # 2. 如果时间戳匹配失败，尝试文本相似度匹配
+ if best_match is None:
+ 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 segments if start <= s["start"] < end])
+ similarity = ContentAligner.calculate_text_similarity(seg["text"], page_text)
+ if similarity > best_score:
+ best_score = similarity
+ best_match = page_idx
+
+ # 3. 如果找到匹配，添加到对应页面
+ if best_match is not None:
+ page_texts[best_match].append(seg)
+ else:
+ unmatched_segments.append(seg)
+
+ # 4. 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)
+
+ 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）"""
+ try:
+ 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
+ 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
+ })
+
+ return aligned_data
+
+
+# ---------------------- 摘要生成模块 ----------------------
+class SummaryGenerator:
+ @staticmethod
+ def optimize_text(text: str) -> str:
+ """文本浓缩优化"""
+ 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)
+ return '。'.join(filtered) + '。' if filtered else ""
+
+ @staticmethod
+ def generate_html(aligned_data: list, keyframes: list, output_dir: str):
+ """生成HTML报告"""
+ pages_data = []
+ temp_img_dir = os.path.join(output_dir, "_temp_images")
+ os.makedirs(temp_img_dir, exist_ok=True)
+
+ try:
+ for idx, frame in enumerate(keyframes):
+ 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")
+
+ pages_data.append({
+ "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"])
+ })
+
+ env = Environment()
+ template = env.from_string("""
+
+
+
+
+ PPT视频摘要报告
+
+
+
+ PPT视频结构化摘要
+ {% for page in pages %}
+
+
页面 {{ page.num }}
+
{{ page.time }}
+
+
{{ page.text }}
+
PPT视频结构化摘要
+ {% for page in pages %}
+
+
第 {{ page.num }} 页
+
时间区间：{{ page.time }}
+
+
+
+
{{ page.text }}
+