init

slowFast.py

@@ -0,0 +1,159 @@
+#%%
+import torch
+import numpy as np
+import cv2
+from decord import VideoReader, cpu
+#%%
+print(torch.__version__)
+print(torch.cuda.is_available())
+#%%
+def load_first_480_frames(video_path, resize=(224, 224)):
+    vr = VideoReader(video_path, ctx=cpu(0))
+    total_frames = len(vr)
+
+    if total_frames < 480:
+        raise ValueError(f"{video_path},视频帧数不足 480 帧")
+
+    indices = list(range(480))
+    frames = vr.get_batch(indices).asnumpy()
+
+    if resize:
+        frames = np.array([cv2.resize(f, resize) for f in frames])
+    return frames
+#%%
+def preprocess_for_slowfast(frames, num_frames=32, alpha=4):
+    total = len(frames)
+    indices = np.linspace(0, total - 1, num=num_frames, dtype=int)
+    frames = frames[indices]
+
+    frames = frames / 255.0
+    frames = (frames - [0.45, 0.45, 0.45]) / [0.225, 0.225, 0.225]
+    frames = frames.astype(np.float32)
+
+    frames = torch.from_numpy(frames).permute(3, 0, 1, 2).unsqueeze(0)
+
+    fast_pathway = frames
+    slow_pathway = frames[:, :, ::alpha, :, :]
+
+    return [slow_pathway, fast_pathway]
+#%%
+# 定义特征提取模型 (SlowFast Backbone)
+class SlowFastFeatureExtractor(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        model = torch.hub.load("facebookresearch/pytorchvideo", "slowfast_r50", pretrained=True)
+        # 移除分类头，仅保留backbone部分
+        self.blocks = model.blocks[:-1]  # 去掉最后的分类器 head
+        self.pool = torch.nn.AdaptiveAvgPool3d(1)  # 全局池化
+
+    def forward(self, x):
+        for block in self.blocks:
+            x = block(x)
+        x = self.pool(x)
+        x = torch.flatten(x, 1)  # [B, C]
+        x = torch.nn.functional.normalize(x, dim=1)  # 特征归一化
+        return x
+#%%
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print("Loading SlowFast backbone for feature extraction...")
+model = SlowFastFeatureExtractor().to(device).eval()
+
+
+def extract_video_feature(video_path):
+
+
+    frames = load_first_480_frames(video_path)
+    inputs = preprocess_for_slowfast(frames, num_frames=32, alpha=4)
+
+    with torch.no_grad():
+        inputs = [x.to(device) for x in inputs]
+        features = model(inputs)
+
+    features = features.cpu().numpy().squeeze()
+    return features
+#%%
+def getFeature(video_path = r"D:\DESKTOP\2025\44\a1\dataset\org\1.mp4"):
+
+    feature = extract_video_feature(video_path)
+    # print("Video feature shape (for copyright):", feature.shape)
+    return feature
+#%%
+import sqlite3
+import io
+import os
+import tqdm
+
+# 注册适配器与转换器：numpy数组 <-> BLOB
+def adapt_array(arr):
+    out = io.BytesIO()
+    np.save(out, arr)
+    out.seek(0)
+    return sqlite3.Binary(out.read())
+
+def convert_array(text):
+    out = io.BytesIO(text)
+    out.seek(0)
+    return np.load(out)
+
+# 注册自定义类型处理
+sqlite3.register_adapter(np.ndarray, adapt_array)
+sqlite3.register_converter("array", convert_array)
+
+# 创建数据库连接（带类型检测）
+conn = sqlite3.connect("slowfast.db", detect_types=sqlite3.PARSE_DECLTYPES)
+cursor = conn.cursor()
+
+# 创建表
+cursor.execute("""
+CREATE TABLE IF NOT EXISTS data (
+    id INTEGER PRIMARY KEY,
+    array BLOB,
+    group_path TEXT,
+    full_path TEXT
+)
+""")
+
+
+def add_to_db(array_to_store,group_path,full_path):
+    cursor.execute("INSERT INTO data (array,group_path,full_path) VALUES (?,?,?)", (array_to_store,group_path,full_path,))
+    conn.commit()
+
+# # 读取数组
+# cursor.execute("SELECT array FROM data WHERE id=1")
+# fetched_array = cursor.fetchone()[0]
+#
+# print("原始数组:\n", array_to_store)
+# print("读取的数组:\n", fetched_array)
+
+
+#%%
+folder_path = r"D:\DESKTOP\2025\44\a1\dataset"
+
+all_files = []
+names = [str(x)+".mp4" for x in range(10)]
+print(names)
+
+
+# 遍历文件夹
+for group_path in os.listdir(folder_path):
+    full_path = os.path.join(folder_path, group_path)
+    if os.path.isdir(full_path):
+        # 遍历子文件夹
+        for video_path in os.listdir(full_path):
+            if os.path.basename(video_path) in names:
+                video_full_path = os.path.join(full_path, video_path)
+                if os.path.isfile(video_full_path):
+                    # 处理视频文件
+                    all_files.append((group_path,video_full_path))
+print(len(all_files))
+#%%
+for group_path, video_full_path in tqdm.tqdm(all_files):
+    # 读取视频特征
+    feature = getFeature(video_full_path)
+    # 将特征存储到数据库
+    add_to_db(feature,group_path,video_full_path)
+    # print(f"已处理并存储: {video_full_path}")
+#%%
+conn.close()
+#%%