上传文件至 毕设2.0

毕设2.0/毕设.py

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+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")