526 lines
20 KiB
Python
526 lines
20 KiB
Python
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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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# ======================== 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\output" # 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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# ========================================================
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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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@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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@staticmethod
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def calculate_color_histogram(frame):
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"""计算颜色直方图特征"""
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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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@staticmethod
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def calculate_hog_features(frame):
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"""计算HOG特征"""
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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 extract_keyframes(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）"""
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try:
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reader = imageio.get_reader(video_path)
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fps = reader.get_meta_data()["fps"]
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keyframes = []
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timestamps = []
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prev_frame = None
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prev_features = None
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for idx, frame in enumerate(reader):
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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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# 多特征相似度计算
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if prev_frame is not None:
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# 1. SSIM相似度
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gray_prev = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
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gray_curr = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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ssim_score = ssim(gray_prev, gray_curr)
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# 2. 颜色直方图相似度
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hist_prev = VideoProcessor.calculate_color_histogram(prev_frame)
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hist_curr = VideoProcessor.calculate_color_histogram(frame)
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color_sim = cv2.compareHist(hist_prev, hist_curr, cv2.HISTCMP_CORREL)
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# 3. HOG特征相似度
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hog_prev = VideoProcessor.calculate_hog_features(prev_frame)
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hog_curr = VideoProcessor.calculate_hog_features(frame)
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hog_sim = np.dot(hog_prev, hog_curr) / (np.linalg.norm(hog_prev) * np.linalg.norm(hog_curr))
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# 4. PPT页面切换检测
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is_transition = VideoProcessor.is_ppt_transition(prev_frame, frame)
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# 综合判断
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if (ssim_score > SSIM_THRESHOLD and
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color_sim > COLOR_THRESHOLD and
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hog_sim > HOG_THRESHOLD and
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not is_transition):
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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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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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return keyframes, timestamps
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except Exception as e:
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print(f"[错误] 关键帧提取失败: {str(e)}")
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return [], []
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@staticmethod
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def transcribe_audio(video_path: str, model_name: str = WHISPER_MODEL) -> list:
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"""语音识别与时间戳获取（支持中英文混合）"""
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try:
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# 使用更大的模型提高准确率
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model = whisper.load_model(model_name, device="cpu", download_root=MODEL_DIR)
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# 配置转写参数
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result = model.transcribe(
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video_path,
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fp16=False,
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language="zh",
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task="transcribe",
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verbose=True,
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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。"
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)
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segments = result.get("segments", [])
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# 后处理：专业术语替换
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for seg in segments:
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text = seg["text"]
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for cn, en in PROFESSIONAL_TERMS.items():
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text = text.replace(cn, f"{cn}({en})")
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seg["text"] = text
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return segments
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except Exception as e:
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print(f"[错误] 语音识别失败: {str(e)}")
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return []
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# ---------------------- 业务逻辑模块 ----------------------
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class ContentAligner:
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@staticmethod
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def generate_page_intervals(timestamps: list, duration: float) -> list:
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"""生成页面时间段"""
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intervals = []
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for i in range(len(timestamps)):
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start = timestamps[i]
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end = timestamps[i + 1] if i < len(timestamps) - 1 else duration
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intervals.append((start, end))
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return intervals
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@staticmethod
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def calculate_text_similarity(text1: str, text2: str) -> float:
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"""计算文本相似度"""
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# 使用简单的词重叠度计算
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words1 = set(re.findall(r'\w+', text1.lower()))
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words2 = set(re.findall(r'\w+', text2.lower()))
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if not words1 or not words2:
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return 0.0
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intersection = words1.intersection(words2)
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union = words1.union(words2)
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return len(intersection) / len(union)
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@staticmethod
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def find_best_match(segments: list, intervals: list) -> dict:
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"""为每个语音片段找到最佳匹配的页面"""
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page_texts = defaultdict(list)
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unmatched_segments = []
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for seg in segments:
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seg_start = seg["start"]
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best_match = None
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best_score = 0.0
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# 1. 首先尝试时间戳匹配
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for page_idx, (start, end) in enumerate(intervals):
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if start <= seg_start < end:
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best_match = page_idx
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break
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# 2. 如果时间戳匹配失败，尝试文本相似度匹配
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if best_match is None:
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for page_idx, (start, end) in enumerate(intervals):
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# 获取该页面的所有文本
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page_text = " ".join([s["text"] for s in segments if start <= s["start"] < end])
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similarity = ContentAligner.calculate_text_similarity(seg["text"], page_text)
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if similarity > best_score:
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best_score = similarity
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best_match = page_idx
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# 3. 如果找到匹配，添加到对应页面
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if best_match is not None:
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page_texts[best_match].append(seg)
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else:
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unmatched_segments.append(seg)
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# 4. 处理未匹配的片段
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if unmatched_segments:
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print(f"[警告] 发现{len(unmatched_segments)}个未匹配的语音片段")
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# 将未匹配片段添加到最近的页面
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for seg in unmatched_segments:
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closest_page = min(range(len(intervals)),
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key=lambda i: abs(seg["start"] - (intervals[i][0] + intervals[i][1]) / 2))
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page_texts[closest_page].append(seg)
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return page_texts
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@staticmethod
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def align_content(video_path: str, timestamps: list) -> list:
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"""语音-画面对齐主逻辑（改进版）"""
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try:
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reader = imageio.get_reader(video_path)
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duration = reader.get_meta_data()["duration"]
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reader.close()
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except:
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duration = timestamps[-1] + FRAME_INTERVAL
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segments = VideoProcessor.transcribe_audio(video_path)
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intervals = ContentAligner.generate_page_intervals(timestamps, duration)
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# 使用改进的匹配算法
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page_texts = ContentAligner.find_best_match(segments, intervals)
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# 生成最终的对齐数据
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aligned_data = []
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for idx in range(len(intervals)):
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text = " ".join([seg["text"] for seg in page_texts.get(idx, [])])
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aligned_data.append({
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"page": idx,
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"start_time": intervals[idx][0],
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"end_time": intervals[idx][1],
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"text": text
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})
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return aligned_data
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# ---------------------- 摘要生成模块 ----------------------
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class SummaryGenerator:
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@staticmethod
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def optimize_text(text: str) -> str:
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"""文本浓缩优化"""
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sentences = re.split(r'[。！？]', text)
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filtered = []
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seen = set()
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for sent in sentences:
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sent = sent.strip()
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if (len(sent) >= 10
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and not any(word in sent for word in TRANSITION_WORDS)
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and sent not in seen):
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filtered.append(sent)
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seen.add(sent)
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return '。'.join(filtered) + '。' if filtered else ""
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@staticmethod
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def generate_html(aligned_data: list, keyframes: list, output_dir: str):
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"""生成HTML报告"""
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pages_data = []
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temp_img_dir = os.path.join(output_dir, "_temp_images")
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os.makedirs(temp_img_dir, exist_ok=True)
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try:
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for idx, frame in enumerate(keyframes):
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img_path = os.path.join(temp_img_dir, f"page_{idx}.jpg")
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frame.save(img_path)
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with open(img_path, "rb") as f:
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img_data = base64.b64encode(f.read()).decode("utf-8")
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pages_data.append({
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"num": idx + 1,
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"time": f"{aligned_data[idx]['start_time']:.1f}s - {aligned_data[idx]['end_time']:.1f}s",
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"image": f"data:image/jpeg;base64,{img_data}",
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"text": SummaryGenerator.optimize_text(aligned_data[idx]["text"])
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})
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env = Environment()
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template = env.from_string("""
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="UTF-8">
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<title>PPT视频摘要报告</title>
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<style>
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.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>
|
|||
|
|
""")
|
|||
|
|
|
|||
|
|
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}")
|
|||
|
|
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 generate_pdf(aligned_data: list, keyframes: list, output_dir: str):
|
|||
|
|
"""生成PDF报告（优化版）"""
|
|||
|
|
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)
|
|||
|
|
|
|||
|
|
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 = []
|
|||
|
|
for idx, frame in enumerate(keyframes):
|
|||
|
|
img_filename = f"page_{idx}.jpg"
|
|||
|
|
img_path = os.path.join(abs_temp_img_dir, img_filename)
|
|||
|
|
frame.save(img_path)
|
|||
|
|
pages_data.append({
|
|||
|
|
"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"])
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
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
|
|||
|
|
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)
|
|||
|
|
|
|||
|
|
pdf_path = os.path.join(output_dir, "summary.pdf")
|
|||
|
|
pdfkit.from_file(
|
|||
|
|
temp_html,
|
|||
|
|
pdf_path,
|
|||
|
|
configuration=config,
|
|||
|
|
options=options
|
|||
|
|
)
|
|||
|
|
print(f"[输出] PDF报告已生成: {pdf_path}")
|
|||
|
|
|
|||
|
|
finally:
|
|||
|
|
# 清理临时文件
|
|||
|
|
if os.path.exists(temp_html):
|
|||
|
|
os.remove(temp_html)
|
|||
|
|
if os.path.exists(temp_img_dir):
|
|||
|
|
for f in os.listdir(temp_img_dir):
|
|||
|
|
os.remove(os.path.join(temp_img_dir, f))
|
|||
|
|
os.rmdir(temp_img_dir)
|
|||
|
|
|
|||
|
|
@classmethod
|
|||
|
|
def generate_all(cls, aligned_data: list, keyframes: list, output_dir: str):
|
|||
|
|
"""生成所有格式报告"""
|
|||
|
|
cls.generate_html(aligned_data, keyframes, output_dir)
|
|||
|
|
cls.generate_pdf(aligned_data, keyframes, output_dir)
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ---------------------- 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
|
|||
|
|
|
|||
|
|
# 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
|
|||
|
|
|
|||
|
|
# 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")
|
|||
|
|
|
|||
|
|
# 生成摘要
|
|||
|
|
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
|||
|
|
SummaryGenerator.generate_all(aligned_data, keyframes, OUTPUT_DIR)
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main_process()
|