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毕设.py

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+import os
+import time
+import imageio
+import whisper
+import numpy as np
+from PIL import Image
+from skimage.metrics import structural_similarity as ssim
+import tempfile
+
+# 添加FFmpeg路径（根据你的实际安装路径修改）
+os.environ["PATH"] += os.pathsep + r"D:\ffmpeg\bin"  # 例如：D:\ffmpeg\bin
+# ============================== 配置参数 ==============================
+# 示例：将视频复制到 D:\test\input.mp4
+VIDEO_PATH = "D:/python项目文件/1/input2.mp4"  # 输入视频路径
+MODEL_DIR = "D:/whisper_models"  # 手动下载的模型存放目录
+SSIM_THRESHOLD = 0.85  # 关键帧去重阈值
+FRAME_INTERVAL = 2  # 抽帧间隔（秒）
+OUTPUT_DIR = "output2"  # 输出目录
+
+
+# =====================================================================
+
+def extract_keyframes_with_time(video_path: str) -> tuple:
+    """改进版关键帧提取（返回关键帧图像列表和时间戳列表）"""
+    try:
+        # 初始化视频读取器
+        reader = imageio.get_reader(video_path, 'ffmpeg')
+        fps = reader.get_meta_data().get('fps', 30)
+        print(f"视频帧率: {fps}fps, 总时长: {reader.get_meta_data()['duration']:.1f}秒")
+
+        keyframes = []
+        keyframe_times = []
+        prev_frame = None
+        frame_counter = 0
+
+        for i, frame in enumerate(reader):
+            # 按间隔抽帧（默认每秒抽帧改为每FRAME_INTERVAL秒抽帧）
+            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)
+
+                if score < SSIM_THRESHOLD:
+                    keyframes.append(curr_frame)
+                    keyframe_times.append(current_time)
+                    prev_frame = curr_gray
+
+            frame_counter += 1
+            if frame_counter % 10 == 0:
+                print(f"已处理 {current_time:.1f}秒...")
+
+        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"视频处理失败: {str(e)}")
+        return [], []
+
+
+def align_text_with_keyframes(video_path: str, keyframe_times: list) -> list:
+    try:
+        # 1. 动态添加 FFmpeg 路径
+        ffmpeg_bin = r"D:\ffmpeg\bin"
+        os.environ["PATH"] = ffmpeg_bin + os.pathsep + os.environ["PATH"]
+
+        # 2. 加载模型
+        model = whisper.load_model("tiny", device="cpu")
+
+        # 3. 执行语音识别（不再传递 ffmpeg_path）
+        result = model.transcribe(video_path, fp16=False)
+
+        # 4. 对齐处理
+        alignment = []
+        kf_ptr = 0
+        for seg in result["segments"]:
+            seg_start = seg["start"]
+            seg_end = seg["end"]
+            matched_time = None
+            while kf_ptr < len(keyframe_times):
+                if keyframe_times[kf_ptr] <= seg_end:
+                    matched_time = keyframe_times[kf_ptr]
+                    kf_ptr += 1
+                else:
+                    break
+            if matched_time is not None:
+                alignment.append({
+                    "text": seg["text"].strip(),
+                    "start": seg_start,
+                    "end": seg_end,
+                    "keyframe_time": matched_time
+                })
+        return alignment
+    except Exception as e:
+        print(f"语音处理失败: {str(e)}")
+        return []
+
+def save_results(keyframes, alignment):
+    """保存关键帧和文本对齐结果"""
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+    # 保存关键帧
+    for i, img in enumerate(keyframes):
+        img.save(os.path.join(OUTPUT_DIR, f"frame_{i:04d}.jpg"))
+
+    # 保存对齐文本
+    with open(os.path.join(OUTPUT_DIR, "alignment.txt"), "w", encoding="utf-8") as f:
+        for item in alignment:
+            f.write(
+                f"[{item['keyframe_time']:.1f}s] "
+                f"({item['start']:.1f}-{item['end']:.1f}s): "
+                f"{item['text']}\n"
+            )
+    print(f"结果已保存至{OUTPUT_DIR}目录")
+
+
+
+# 打印临时目录路径并检查可写权限
+temp_dir = tempfile.gettempdir()
+print(f"临时目录: {temp_dir}")
+if not os.access(temp_dir, os.W_OK):
+    print("错误：临时目录不可写！")
+else:
+    print("临时目录可写")
+
+if __name__ == "__main__":
+    # 步骤1: 提取关键帧
+    keyframes, keyframe_times = extract_keyframes_with_time(VIDEO_PATH)
+    if not keyframes:
+        exit()
+
+    # 步骤2: 语音对齐
+    alignment = align_text_with_keyframes(VIDEO_PATH, keyframe_times)
+
+    # 步骤3: 保存结果
+    save_results(keyframes, alignment)