From 71c468026f355c6c9a79ba4b9a6d65cb3626b751 Mon Sep 17 00:00:00 2001 From: jcy Date: Sat, 19 Apr 2025 13:15:50 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?= =?UTF-8?q?=20=E6=AF=95=E8=AE=BE2.0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 毕设2.0/毕设.py | 171 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 毕设2.0/毕设.py diff --git a/毕设2.0/毕设.py b/毕设2.0/毕设.py new file mode 100644 index 0000000..9c98c97 --- /dev/null +++ b/毕设2.0/毕设.py @@ -0,0 +1,171 @@ +import os +import warnings +import imageio +import whisper +import numpy as np +from PIL import Image +from skimage.metrics import structural_similarity as ssim +from collections import defaultdict +import subprocess + +# ======================== 配置参数 ======================== +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（需修改为实际路径） +MODEL_DIR = "D:/whisper_models" # Whisper模型目录 +FFMPEG_BIN = r"D:\Program Files\ffmpeg\bin" # FFmpeg安装目录 +SSIM_THRESHOLD = 0.85 # 关键帧去重阈值（值越小保留越多帧） +FRAME_INTERVAL = 2 # 抽帧间隔（秒） +OUTPUT_DIR = "output_summary" # 输出目录 +# ======================================================== + +# 动态添加FFmpeg到环境变量（修复核心问题） +os.environ["PATH"] = FFMPEG_BIN + os.pathsep + os.environ["PATH"] + +def check_ffmpeg(): + """验证FFmpeg可访问性""" + try: + subprocess.run(["ffmpeg", "-version"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + print("FFmpeg验证成功！") + return True + except Exception as e: + print(f"FFmpeg验证失败: {str(e)}") + print(f"请确认FFmpeg路径是否正确: {FFMPEG_BIN}") + return False + +def extract_keyframes_with_time(video_path: str) -> tuple: + """提取关键帧及其时间戳""" + try: + # 初始化视频读取器 + reader = imageio.get_reader(video_path, 'ffmpeg') + fps = reader.get_meta_data().get('fps', 30) + keyframes = [] + keyframe_times = [] + prev_frame = None + + for i, frame in enumerate(reader): + # 按固定间隔抽帧（每FRAME_INTERVAL秒抽1帧） + if i % int(fps * FRAME_INTERVAL) != 0: + continue + + current_time = i / fps + # 降采样至320x240加速处理 + curr_frame = Image.fromarray(frame).resize((320, 240)) + + if prev_frame is None: + # 首帧强制保留 + keyframes.append(curr_frame) + keyframe_times.append(current_time) + prev_frame = np.array(curr_frame.convert('L')) + else: + # 计算灰度图SSIM + curr_gray = np.array(curr_frame.convert('L')) + score = ssim(prev_frame, curr_gray, data_range=255) + + # 判定为关键帧的条件 + if score < SSIM_THRESHOLD: + keyframes.append(curr_frame) + keyframe_times.append(current_time) + prev_frame = curr_gray + + reader.close() + print(f"关键帧提取完成，共{len(keyframes)}帧") + return keyframes, keyframe_times + except Exception as e: + print(f"关键帧提取失败: {str(e)}") + return [], [] + + +def generate_page_intervals(keyframe_times, video_duration): + """生成每个关键帧对应的时间段: [start, end)""" + intervals = [] + n = len(keyframe_times) + for i in range(n): + start = keyframe_times[i] + end = keyframe_times[i + 1] if i < n - 1 else video_duration + intervals.append((start, end)) + return intervals + + +def align_text_with_keyframes(video_path: str, keyframe_times: list, video_duration: float) -> list: + """语音-关键帧对齐（修复版）""" + try: + # 加载模型（不再传递ffmpeg_path） + model = whisper.load_model("tiny", device="cpu", download_root=MODEL_DIR) + result = model.transcribe(video_path, fp16=False) # 移除ffmpeg_path参数 + segments = result["segments"] + + # 生成时间段并聚合文本 + page_intervals = generate_page_intervals(keyframe_times, video_duration) + page_texts = defaultdict(list) + + # 将语音段落分配到对应页面（按起始时间判断） + for seg in segments: + seg_start = seg["start"] + seg_end = seg["end"] + for page_idx, (page_start, page_end) in enumerate(page_intervals): + if page_start <= seg_start < page_end: + page_texts[page_idx].append(seg["text"].strip()) + break # 每个段落只属于一个页面 + + # 合并页面文本 + merged = [] + for page_idx in sorted(page_texts.keys()): + full_text = " ".join(page_texts[page_idx]) + merged.append({ + "page": page_idx, + "start_time": page_intervals[page_idx][0], + "end_time": page_intervals[page_idx][1], + "text": full_text + }) + return merged + except Exception as e: + print(f"语音处理失败: {str(e)}") + return [] + + +def save_summary(merged_texts, keyframes, output_dir): + """保存关键帧和对应的完整语音文本""" + os.makedirs(output_dir, exist_ok=True) + + for idx, item in enumerate(merged_texts): + # 保存关键帧图像 + img_path = os.path.join(output_dir, f"page_{idx}.jpg") + keyframes[idx].save(img_path) + + # 保存文本 + txt_path = os.path.join(output_dir, f"page_{idx}.txt") + with open(txt_path, "w", encoding="utf-8") as f: + f.write(f"页面时间段: {item['start_time']:.1f}s - {item['end_time']:.1f}s\n\n") + f.write("完整讲解内容:\n") + f.write(item["text"]) + print(f"已保存: {txt_path}") + +if __name__ == "__main__": + # 步骤0: 验证FFmpeg + if not check_ffmpeg(): + exit() + + # 步骤1: 检查视频文件存在性 + if not os.path.exists(VIDEO_PATH): + print(f"错误：视频文件 {VIDEO_PATH} 不存在！") + exit() + + # 步骤2: 提取关键帧 + keyframes, keyframe_times = extract_keyframes_with_time(VIDEO_PATH) + if not keyframes: + exit() + + # 步骤3: 获取视频总时长 + reader = imageio.get_reader(VIDEO_PATH) + video_duration = reader.get_meta_data()["duration"] + reader.close() + + # 步骤4: 语音对齐与聚合 + merged_texts = align_text_with_keyframes(VIDEO_PATH, keyframe_times, video_duration) + + # 步骤5: 保存结果 + if merged_texts: + save_summary(merged_texts, keyframes, OUTPUT_DIR) + print(f"\n处理完成！结果已保存至目录: {os.path.abspath(OUTPUT_DIR)}") + else: + print("无有效语音内容可保存") \ No newline at end of file