init

analy.ipynb

@@ -3,8 +3,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-14T06:58:32.199713Z",
+     "end_time": "2025-04-14T09:34:18.359212Z",
-     "start_time": "2025-04-14T06:58:32.134049Z"
+     "start_time": "2025-04-14T09:34:18.356046Z"
     }
    },
    "cell_type": "code",
@@ -13,38 +13,23 @@
     "import numpy as np\n",
     "import io\n",
     "from numpy.linalg import norm\n",
-    "from collections import defaultdict"
+    "from collections import defaultdict\n",
+    "import os"
    ],
    "id": "6917288db44004ea",
    "outputs": [],
-   "execution_count": 1
+   "execution_count": 59
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-14T06:58:34.129528Z",
+     "end_time": "2025-04-14T09:34:18.371739Z",
-     "start_time": "2025-04-14T06:58:34.126022Z"
+     "start_time": "2025-04-14T09:34:18.367219Z"
-    }
-   },
-   "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": [
+    "\n",
     "# 注册适配器与转换器：numpy数组 <-> BLOB\n",
     "def adapt_array(arr):\n",
     "    out = io.BytesIO()\n",
@@ -52,71 +37,49 @@
     "    out.seek(0)\n",
     "    return sqlite3.Binary(out.read())\n",
     "\n",
+    "\n",
     "def convert_array(text):\n",
     "    out = io.BytesIO(text)\n",
     "    out.seek(0)\n",
-    "    return np.load(out)"
+    "    return np.load(out)\n",
-   ],
-   "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"
+   ],
+   "id": "dd9b84fa98c77e5a",
+   "outputs": [],
+   "execution_count": 60
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-14T09:34:18.388488Z",
+     "start_time": "2025-04-14T09:34:18.385501Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def get_video_id(path):\n",
+    "    filename = os.path.basename(path)\n",
+    "    name = os.path.splitext(filename)[0]  # 去掉扩展名\n",
+    "    return name"
+   ],
+   "id": "884f8c27d67a93d5",
+   "outputs": [],
+   "execution_count": 61
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-14T09:34:18.451821Z",
+     "start_time": "2025-04-14T09:34:18.443936Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
     "\n",
     "# 计算指标函数\n",
     "def compute_metrics(org_feats, query_feats, sim_feats, K=5):\n",
@@ -135,6 +98,11 @@
     "        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",
+    "            if label == 1:\n",
+    "                query_id = get_video_id(query['path'])\n",
+    "                target_id = get_video_id(target['path'])\n",
+    "                label = 1 if target_id == query_id else 0\n",
+    "\n",
     "            similarities.append((sim, label))\n",
     "\n",
     "        # 相似度降序排序\n",
@@ -145,7 +113,7 @@
     "        precision_top1.append(labels_sorted[0])\n",
     "\n",
     "        # Recall@K\n",
-    "        recall = sum(labels_sorted[:K]) / len(org_feats)\n",
+    "        recall = int(1 in labels_sorted[:K])\n",
     "        recall_at_k.append(recall)\n",
     "\n",
     "        # Average Precision (AP)\n",
@@ -162,45 +130,9 @@
     "        'Top-1 Precision': np.mean(precision_top1),\n",
     "        f'Recall@{K}': np.mean(recall_at_k),\n",
     "        'MAP': np.mean(AP_list)\n",
-    "    }"
+    "    }\n",
-   ],
+    "\n",
-   "id": "initial_id",
+    "\n",
-   "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",
@@ -218,54 +150,168 @@
     "\n",
     "    return results\n",
     "\n",
-    "# 执行评估\n",
-    "results = evaluate_all_variants(K=3)\n",
     "\n",
-    "# 输出结果\n",
+    "def results_to_latex(currdb, results):\n",
-    "for variant, metrics in results.items():\n",
+    "    metric_names = list(next(iter(results.values())).keys())  # 获取所有指标名\n",
-    "    print(f\"变形组: {variant}\")\n",
+    "    header = \"Deformation & \" + \" & \".join(metric_names) + \" \\\\\\\\ \\\\hline\\n\"\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",
+    "    rows = \"\"\n",
-    "conn.close()"
+    "    metric_sums = {metric: 0.0 for metric in metric_names}\n",
+    "    count = 0\n",
+    "\n",
+    "    for variant_id, variant_display in variant_order:\n",
+    "        if variant_id not in results:\n",
+    "            continue  # 忽略不存在的组\n",
+    "        metrics = results[variant_id]\n",
+    "        count += 1\n",
+    "        row = variant_display\n",
+    "        for metric in metric_names:\n",
+    "            val = metrics[metric]\n",
+    "            metric_sums[metric] += val\n",
+    "            row += f\" & {val:.4f}\"\n",
+    "        rows += row + \" \\\\\\\\\\n\"\n",
+    "\n",
+    "    # 添加平均值行\n",
+    "    avg_row = \"avg\"\n",
+    "    for metric in metric_names:\n",
+    "        avg_val = metric_sums[metric] / count if count else 0.0\n",
+    "        avg_row += f\" & {avg_val:.4f}\"\n",
+    "    rows += avg_row + \" \\\\\\\\\\n\"\n",
+    "\n",
+    "    latex_table = (\n",
+    "        \"\\\\begin{table}[htbp]\\n\"\n",
+    "        \"\\\\centering\\n\"\n",
+    "        f\"\\\\caption{{Top-1 Precision, Recall@K and MAP for {currdb}}}\\n\"\n",
+    "        \"\\\\begin{tabular}{l\" + \"c\" * len(metric_names) + \"}\\n\"\n",
+    "        \"\\\\hline\\n\"\n",
+    "        + header +\n",
+    "        rows +\n",
+    "        \"\\\\hline\\n\"\n",
+    "        \"\\\\end{tabular}\\n\"\n",
+    "        \"\\\\end{table}\\n\"\n",
+    "    )\n",
+    "    return latex_table"
    ],
-   "id": "d9a0103dbc3f1bef",
+   "id": "282bfd74f5982041",
+   "outputs": [],
+   "execution_count": 62
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-14T09:34:18.460011Z",
+     "start_time": "2025-04-14T09:34:18.454827Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "variant_order = [\n",
+    "    (r\"静态水印1\", r\"watermark1\"),\n",
+    "    (r\"动态水印2\", r\"watermark2\"),\n",
+    "    (r\"水印1+2\", r\"watermark1\\&2\"),\n",
+    "    (r\"滤镜\", r\"filters\"),\n",
+    "    (r\"水印1+2+滤镜\", r\"filters\\&watermark1\\&2\"),\n",
+    "    (r\"watermark1\", r\"watermark1\"),\n",
+    "    (r\"watermark2\", r\"watermark2\"),\n",
+    "    (r\"watermark1+2\", r\"watermark11\\&2\"),\n",
+    "    (r\"filters\", r\"filters\"),\n",
+    "    (r\"filters+watermark1+2\", r\"filters\\&watermark1\\&2\"),\n",
+    "]"
+   ],
+   "id": "12e194a6bf64b8ac",
+   "outputs": [],
+   "execution_count": 63
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-14T09:34:18.512805Z",
+     "start_time": "2025-04-14T09:34:18.473552Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "for currdb in [\"LBPTOP\", \"i3d\", \"slowfast\"]:\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 + \".db\", detect_types=sqlite3.PARSE_DECLTYPES)\n",
+    "    cursor = conn.cursor()\n",
+    "    # 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()\n",
+    "    # 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",
+    "    # 执行评估\n",
+    "    results = evaluate_all_variants(K=3)\n",
+    "    # 关闭数据库连接\n",
+    "    conn.close()\n",
+    "    # 打印 LaTeX 表格\n",
+    "    print(results_to_latex(currdb, results))\n"
+   ],
+   "id": "97f972940a003aa4",
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "变形组: 动态水印2\n",
+      "\\begin{table}[htbp]\n",
-      "  Top-1 Precision: 1.0000\n",
+      "\\centering\n",
-      "  Recall@3: 0.1500\n",
+      "\\caption{Top-1 Precision, Recall@K and MAP for LBPTOP}\n",
-      "  MAP: 0.5815\n",
+      "\\begin{tabular}{lccc}\n",
-      "----------------------------------------\n",
+      "\\hline\n",
-      "变形组: 水印1+2\n",
+      "Deformation & Top-1 Precision & Recall@3 & MAP \\\\ \\hline\n",
-      "  Top-1 Precision: 0.8000\n",
+      "watermark1 & 0.5000 & 0.6000 & 0.0608 \\\\\n",
-      "  Recall@3: 0.1600\n",
+      "watermark2 & 0.1000 & 0.5000 & 0.0322 \\\\\n",
-      "  MAP: 0.5751\n",
+      "watermark1\\&2 & 0.0000 & 0.3000 & 0.0259 \\\\\n",
-      "----------------------------------------\n",
+      "filters & 0.3000 & 0.4000 & 0.0461 \\\\\n",
-      "变形组: 水印1+2+滤镜\n",
+      "filters\\&watermark1\\&2 & 0.1000 & 0.3000 & 0.0310 \\\\\n",
-      "  Top-1 Precision: 0.8000\n",
+      "avg & 0.2000 & 0.4200 & 0.0392 \\\\\n",
-      "  Recall@3: 0.1500\n",
+      "\\hline\n",
-      "  MAP: 0.5685\n",
+      "\\end{tabular}\n",
-      "----------------------------------------\n",
+      "\\end{table}\n",
-      "变形组: 滤镜\n",
+      "\n",
-      "  Top-1 Precision: 0.9000\n",
+      "\\begin{table}[htbp]\n",
-      "  Recall@3: 0.1800\n",
+      "\\centering\n",
-      "  MAP: 0.6100\n",
+      "\\caption{Top-1 Precision, Recall@K and MAP for i3d}\n",
-      "----------------------------------------\n",
+      "\\begin{tabular}{lccc}\n",
-      "变形组: 静态水印1\n",
+      "\\hline\n",
-      "  Top-1 Precision: 0.9000\n",
+      "Deformation & Top-1 Precision & Recall@3 & MAP \\\\ \\hline\n",
-      "  Recall@3: 0.1700\n",
+      "watermark1 & 0.7000 & 0.9000 & 0.0798 \\\\\n",
-      "  MAP: 0.6058\n",
+      "watermark2 & 0.6000 & 0.6000 & 0.0662 \\\\\n",
-      "----------------------------------------\n"
+      "watermark1\\&2 & 0.5000 & 0.8000 & 0.0634 \\\\\n",
+      "filters & 0.8000 & 0.8000 & 0.0850 \\\\\n",
+      "filters\\&watermark1\\&2 & 0.5000 & 0.8000 & 0.0634 \\\\\n",
+      "avg & 0.6200 & 0.7800 & 0.0715 \\\\\n",
+      "\\hline\n",
+      "\\end{tabular}\n",
+      "\\end{table}\n",
+      "\n",
+      "\\begin{table}[htbp]\n",
+      "\\centering\n",
+      "\\caption{Top-1 Precision, Recall@K and MAP for slowfast}\n",
+      "\\begin{tabular}{lccc}\n",
+      "\\hline\n",
+      "Deformation & Top-1 Precision & Recall@3 & MAP \\\\ \\hline\n",
+      "watermark1 & 0.7000 & 0.9000 & 0.0817 \\\\\n",
+      "watermark2 & 0.7000 & 1.0000 & 0.0800 \\\\\n",
+      "watermark1\\&2 & 0.3000 & 0.9000 & 0.0583 \\\\\n",
+      "filters & 0.6000 & 0.9000 & 0.0758 \\\\\n",
+      "filters\\&watermark1\\&2 & 0.3000 & 0.8000 & 0.0558 \\\\\n",
+      "avg & 0.5200 & 0.9000 & 0.0703 \\\\\n",
+      "\\hline\n",
+      "\\end{tabular}\n",
+      "\\end{table}\n",
+      "\n"
      ]
     }
    ],
-   "execution_count": 25
+   "execution_count": 64
   }
  ],
  "metadata": {