#%%
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()
#%%