From 2c7a85ec44c183df865bcf5b6d399c8feee1878c Mon Sep 17 00:00:00 2001 From: jcy Date: Tue, 13 May 2025 09:45:31 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=204=EF=BC=8C0/=E6=AF=95?= =?UTF-8?q?=E8=AE=BE.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 4，0/毕设.py | 525 --------------------------------------------------- 1 file changed, 525 deletions(-) delete mode 100644 4，0/毕设.py diff --git a/4，0/毕设.py b/4，0/毕设.py deleted file mode 100644 index c6ac889..0000000 --- a/4，0/毕设.py +++ /dev/null @@ -1,525 +0,0 @@ -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/input.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"] - keyframes = [] - timestamps = [] - prev_frame = None - prev_features = None - - for idx, frame in enumerate(reader): - 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: - # 1. SSIM相似度 - gray_prev = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY) - gray_curr = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - ssim_score = ssim(gray_prev, gray_curr) - - # 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 - 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)) - - # 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 - - keyframes.append(Image.fromarray(frame)) - timestamps.append(curr_time) - prev_frame = frame - - 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 }}
- 页面截图 -
{{ page.text }}
-
- {% endfor %} - - - """) - - 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 = """ - - - - - - - -

PPT视频结构化摘要

- {% for page in pages %} -
-
第 {{ page.num }} 页
-
时间区间：{{ page.time }}
-
- 页面截图 -
-
{{ page.text }}
-
- {% endfor %} - - - """ - - 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()