{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-14T06:58:32.199713Z",
     "start_time": "2025-04-14T06:58:32.134049Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import sqlite3\n",
    "import numpy as np\n",
    "import io\n",
    "from numpy.linalg import norm\n",
    "from collections import defaultdict"
   ],
   "id": "6917288db44004ea",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-14T06:58:34.129528Z",
     "start_time": "2025-04-14T06:58:34.126022Z"
    }
   },
   "cell_type": "code",
   "source": [
    "currdb = \"i3d.db\"\n",
    "#currdb = \"LBPTOP.db\"\n",
    "currdb = \"slowfast.db\""
   ],
   "id": "dd9b84fa98c77e5a",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-14T06:58:36.801611Z",
     "start_time": "2025-04-14T06:58:36.798114Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 注册适配器与转换器：numpy数组 <-> BLOB\n",
    "def adapt_array(arr):\n",
    "    out = io.BytesIO()\n",
    "    np.save(out, arr)\n",
    "    out.seek(0)\n",
    "    return sqlite3.Binary(out.read())\n",
    "\n",
    "def convert_array(text):\n",
    "    out = io.BytesIO(text)\n",
    "    out.seek(0)\n",
    "    return np.load(out)"
   ],
   "id": "fa2ab158cbe0aa98",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-14T07:01:06.063185Z",
     "start_time": "2025-04-14T07:01:06.060008Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "sqlite3.register_adapter(np.ndarray, adapt_array)\n",
    "sqlite3.register_converter(\"BLOB\", convert_array)\n",
    "\n",
    "# 连接SQLite数据库\n",
    "conn = sqlite3.connect(currdb, detect_types=sqlite3.PARSE_DECLTYPES)\n",
    "cursor = conn.cursor()"
   ],
   "id": "629eb156f332c0bc",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-14T07:01:06.372809Z",
     "start_time": "2025-04-14T07:01:06.362911Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 读取数据库所有数据\n",
    "cursor.execute(\"SELECT id, array, group_path, full_path FROM data\")\n",
    "all_data = cursor.fetchall()"
   ],
   "id": "ba7902156f1b6117",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-04-14T07:01:06.786016Z",
     "start_time": "2025-04-14T07:01:06.779812Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 整理特征\n",
    "data_by_group = defaultdict(list)\n",
    "for vid, arr, group, path in all_data:\n",
    "    data_by_group[group].append({'id': vid, 'feature': arr, 'path': path})\n",
    "\n",
    "# 相似度函数 (余弦相似度)\n",
    "def cosine_similarity(a, b):\n",
    "    return np.dot(a, b) / (norm(a) * norm(b))\n",
    "\n",
    "# 计算指标函数\n",
    "def compute_metrics(org_feats, query_feats, sim_feats, K=5):\n",
    "    precision_top1 = []\n",
    "    recall_at_k = []\n",
    "    AP_list = []\n",
    "\n",
    "    # 构建搜索库 (原始视频 + 相似视频)\n",
    "    search_db = org_feats + sim_feats\n",
    "\n",
    "    for query in query_feats:\n",
    "        similarities = []\n",
    "        query_feature = query['feature']\n",
    "\n",
    "        # 计算query与所有库视频的相似度\n",
    "        for target in search_db:\n",
    "            sim = cosine_similarity(query_feature, target['feature'])\n",
    "            label = 1 if target in org_feats else 0  # 1代表匹配，0代表不匹配\n",
    "            similarities.append((sim, label))\n",
    "\n",
    "        # 相似度降序排序\n",
    "        similarities.sort(key=lambda x: x[0], reverse=True)\n",
    "        labels_sorted = [label for _, label in similarities]\n",
    "\n",
    "        # Top-1 precision\n",
    "        precision_top1.append(labels_sorted[0])\n",
    "\n",
    "        # Recall@K\n",
    "        recall = sum(labels_sorted[:K]) / len(org_feats)\n",
    "        recall_at_k.append(recall)\n",
    "\n",
    "        # Average Precision (AP)\n",
    "        hits, sum_precisions = 0, 0\n",
    "        for idx, label in enumerate(labels_sorted, start=1):\n",
    "            if label == 1:\n",
    "                hits += 1\n",
    "                sum_precisions += hits / idx\n",
    "        AP = sum_precisions / len(org_feats) if len(org_feats) else 0\n",
    "        AP_list.append(AP)\n",
    "\n",
    "    # 返回指标均值\n",
    "    return {\n",
    "        'Top-1 Precision': np.mean(precision_top1),\n",
    "        f'Recall@{K}': np.mean(recall_at_k),\n",
    "        'MAP': np.mean(AP_list)\n",
    "    }"
   ],
   "id": "initial_id",
   "outputs": [],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-14T07:01:07.745386Z",
     "start_time": "2025-04-14T07:01:07.740942Z"
    }
   },
   "cell_type": "code",
   "source": "data_by_group['org'][0]['feature'].shape",
   "id": "55e57dc7ec56b732",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2304,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-14T06:00:12.392784Z",
     "start_time": "2025-04-14T06:00:12.382725Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 主函数，计算所有变形组的指标\n",
    "def evaluate_all_variants(K=5):\n",
    "    org_feats = data_by_group['org']\n",
    "    sim_feats = data_by_group['sim']\n",
    "\n",
    "    results = {}\n",
    "\n",
    "    # 排除 'org' 和 'sim'，其余都是变形组\n",
    "    variant_groups = [group for group in data_by_group if group not in ['org', 'sim']]\n",
    "\n",
    "    for variant in variant_groups:\n",
    "        variant_feats = data_by_group[variant]\n",
    "        metrics = compute_metrics(org_feats, variant_feats, sim_feats, K)\n",
    "        results[variant] = metrics\n",
    "\n",
    "    return results\n",
    "\n",
    "# 执行评估\n",
    "results = evaluate_all_variants(K=3)\n",
    "\n",
    "# 输出结果\n",
    "for variant, metrics in results.items():\n",
    "    print(f\"变形组: {variant}\")\n",
    "    for metric_name, value in metrics.items():\n",
    "        print(f\"  {metric_name}: {value:.4f}\")\n",
    "    print(\"-\" * 40)\n",
    "\n",
    "# 关闭数据库连接\n",
    "conn.close()"
   ],
   "id": "d9a0103dbc3f1bef",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "变形组: 动态水印2\n",
      "  Top-1 Precision: 1.0000\n",
      "  Recall@3: 0.1500\n",
      "  MAP: 0.5815\n",
      "----------------------------------------\n",
      "变形组: 水印1+2\n",
      "  Top-1 Precision: 0.8000\n",
      "  Recall@3: 0.1600\n",
      "  MAP: 0.5751\n",
      "----------------------------------------\n",
      "变形组: 水印1+2+滤镜\n",
      "  Top-1 Precision: 0.8000\n",
      "  Recall@3: 0.1500\n",
      "  MAP: 0.5685\n",
      "----------------------------------------\n",
      "变形组: 滤镜\n",
      "  Top-1 Precision: 0.9000\n",
      "  Recall@3: 0.1800\n",
      "  MAP: 0.6100\n",
      "----------------------------------------\n",
      "变形组: 静态水印1\n",
      "  Top-1 Precision: 0.9000\n",
      "  Recall@3: 0.1700\n",
      "  MAP: 0.6058\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "execution_count": 25
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}