280 lines
7.8 KiB
Plaintext
280 lines
7.8 KiB
Plaintext
{
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"cells": [
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T03:58:08.619189Z",
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"start_time": "2025-04-14T03:58:08.616451Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import torch\n",
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"import torch.nn.functional as F\n",
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"import torchvision.transforms as transforms\n",
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"from pytorchvideo.models.hub import i3d_r50\n",
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"import cv2\n",
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"import numpy as np\n",
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"from PIL import Image"
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],
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"id": "79af3e0bb61c3290",
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"outputs": [],
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"execution_count": 5
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},
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{
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2025-04-14T03:58:09.075199Z",
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"start_time": "2025-04-14T03:58:08.635303Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 初始化预训练I3D模型\n",
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"model = i3d_r50(pretrained=True).eval()\n",
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"feature_extractor = torch.nn.Sequential(*model.blocks[:-1])"
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],
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"id": "initial_id",
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"outputs": [],
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"execution_count": 6
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T03:58:09.118627Z",
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"start_time": "2025-04-14T03:58:09.113363Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def preprocess_video(video_path, num_frames=32, size=224):\n",
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" cap = cv2.VideoCapture(video_path)\n",
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" frames = []\n",
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" transform = transforms.Compose([\n",
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" transforms.Resize((size, size)),\n",
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" transforms.ToTensor(),\n",
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" transforms.Normalize(mean=[0.45, 0.45, 0.45], std=[0.225, 0.225, 0.225])\n",
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" ])\n",
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"\n",
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" total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
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" frame_indices = np.linspace(0, total_frames - 1, num_frames).astype(int)\n",
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"\n",
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" for i in frame_indices:\n",
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" cap.set(cv2.CAP_PROP_POS_FRAMES, i)\n",
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" ret, frame = cap.read()\n",
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" if not ret:\n",
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" break\n",
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" frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
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" frame = Image.fromarray(frame)\n",
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" frame = transform(frame)\n",
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" frames.append(frame)\n",
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"\n",
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" cap.release()\n",
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" video_tensor = torch.stack(frames).permute(1, 0, 2, 3).unsqueeze(0)\n",
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" return video_tensor\n"
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],
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"id": "5fd2f80d5947967e",
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"outputs": [],
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"execution_count": 7
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T03:58:09.128752Z",
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"start_time": "2025-04-14T03:58:09.125636Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def extract_features(video_tensor):\n",
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" with torch.no_grad():\n",
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" features = feature_extractor(video_tensor)\n",
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" features = F.adaptive_avg_pool3d(features, 1)\n",
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" features = features.flatten()\n",
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" return features.numpy()"
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],
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"id": "728a0b9ece5bdc06",
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"outputs": [],
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"execution_count": 8
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T03:58:09.142606Z",
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"start_time": "2025-04-14T03:58:09.138924Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def video_features(video_path):\n",
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" video_tensor = preprocess_video(video_path)\n",
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" features = extract_features(video_tensor)\n",
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" return features"
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],
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"id": "60ca6ade121d00af",
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"outputs": [],
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"execution_count": 9
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T03:58:09.156458Z",
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"start_time": "2025-04-14T03:58:09.152546Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def getFeature(video_path = r\"D:\\DESKTOP\\2025\\44\\a1\\dataset\\org\\1.mp4\"):\n",
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"\n",
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" feature = video_features(video_path)\n",
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" # print(\"Video feature shape (for copyright):\", feature.shape)\n",
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" return feature"
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],
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"id": "5c3f6ede68d0f22f",
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"outputs": [],
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"execution_count": 10
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T03:58:09.206798Z",
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"start_time": "2025-04-14T03:58:09.166673Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import sqlite3\n",
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"import io\n",
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"import os\n",
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"import tqdm\n",
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"\n",
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"# 注册适配器与转换器：numpy数组 <-> BLOB\n",
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"def adapt_array(arr):\n",
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" out = io.BytesIO()\n",
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" np.save(out, arr)\n",
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" out.seek(0)\n",
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" return sqlite3.Binary(out.read())\n",
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"\n",
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"def convert_array(text):\n",
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" out = io.BytesIO(text)\n",
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" out.seek(0)\n",
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" return np.load(out)\n",
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"\n",
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"# 注册自定义类型处理\n",
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"sqlite3.register_adapter(np.ndarray, adapt_array)\n",
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"sqlite3.register_converter(\"array\", convert_array)\n",
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"\n",
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"# 创建数据库连接（带类型检测）\n",
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"conn = sqlite3.connect(\"i3d.db\", detect_types=sqlite3.PARSE_DECLTYPES)\n",
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"cursor = conn.cursor()\n",
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"\n",
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"# 创建表\n",
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"cursor.execute(\"\"\"\n",
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"CREATE TABLE IF NOT EXISTS data (\n",
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" id INTEGER PRIMARY KEY,\n",
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" array BLOB,\n",
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" group_path TEXT,\n",
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" full_path TEXT\n",
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")\n",
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"\"\"\")\n",
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"\n",
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"\n",
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"def add_to_db(array_to_store,group_path,full_path):\n",
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" cursor.execute(\"INSERT INTO data (array,group_path,full_path) VALUES (?,?,?)\", (array_to_store,group_path,full_path,))\n",
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" conn.commit()\n",
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"\n",
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"# # 读取数组\n",
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"# cursor.execute(\"SELECT array FROM data WHERE id=1\")\n",
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"# fetched_array = cursor.fetchone()[0]\n",
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"#\n",
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"# print(\"原始数组:\\n\", array_to_store)\n",
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"# print(\"读取的数组:\\n\", fetched_array)\n",
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"\n",
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"folder_path = r\"D:\\DESKTOP\\2025\\44\\a1\\dataset\"\n",
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"\n",
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"all_files = []\n",
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"names = [str(x)+\".mp4\" for x in range(10)]\n",
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"print(names)\n",
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"\n",
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"\n",
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"# 遍历文件夹\n",
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"for group_path in os.listdir(folder_path):\n",
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" full_path = os.path.join(folder_path, group_path)\n",
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" if os.path.isdir(full_path):\n",
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" # 遍历子文件夹\n",
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" for video_path in os.listdir(full_path):\n",
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" if os.path.basename(video_path) in names:\n",
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" video_full_path = os.path.join(full_path, video_path)\n",
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" if os.path.isfile(video_full_path):\n",
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" # 处理视频文件\n",
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" all_files.append((group_path,video_full_path))\n",
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"print(len(all_files))\n"
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],
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"id": "517f4d3e7e8d4402",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['0.mp4', '1.mp4', '2.mp4', '3.mp4', '4.mp4', '5.mp4', '6.mp4', '7.mp4', '8.mp4', '9.mp4']\n",
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"70\n"
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]
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}
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],
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"execution_count": 11
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T03:59:48.339352Z",
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"start_time": "2025-04-14T03:58:09.217915Z"
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}
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},
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"cell_type": "code",
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"source": [
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"for group_path, video_full_path in tqdm.tqdm(all_files):\n",
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" # 读取视频特征\n",
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" feature = getFeature(video_full_path)\n",
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" # 将特征存储到数据库\n",
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" add_to_db(feature,group_path,video_full_path)\n",
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" # print(f\"已处理并存储: {video_full_path}\")\n",
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"\n",
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"conn.close()\n"
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],
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"id": "aa7bdb735dbc1e1e",
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 70/70 [01:39<00:00, 1.42s/it]\n"
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]
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}
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],
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"execution_count": 12
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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