549 lines
21 KiB
Python
549 lines
21 KiB
Python
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("""
|
||
<!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>
|
||
""")
|
||
|
||
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()
|