diff --git a/analy.ipynb b/analy.ipynb index 55695df..0c81f7f 100644 --- a/analy.ipynb +++ b/analy.ipynb @@ -3,8 +3,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-04-14T06:58:32.199713Z", - "start_time": "2025-04-14T06:58:32.134049Z" + "end_time": "2025-04-14T09:34:18.359212Z", + "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", - "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" + "end_time": "2025-04-14T09:34:18.371739Z", + "start_time": "2025-04-14T09:34:18.367219Z" } }, "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)" - ], - "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": [ + " return np.load(out)\n", "\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", - " }" - ], - "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": [ + " }\n", + "\n", + "\n", "# 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", - "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", + "def results_to_latex(currdb, results):\n", + " metric_names = list(next(iter(results.values())).keys()) # 获取所有指标名\n", + " header = \"Deformation & \" + \" & \".join(metric_names) + \" \\\\\\\\ \\\\hline\\n\"\n", "\n", - "# 关闭数据库连接\n", - "conn.close()" + " rows = \"\"\n", + " 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", - " 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" + "\\begin{table}[htbp]\n", + "\\centering\n", + "\\caption{Top-1 Precision, Recall@K and MAP for LBPTOP}\n", + "\\begin{tabular}{lccc}\n", + "\\hline\n", + "Deformation & Top-1 Precision & Recall@3 & MAP \\\\ \\hline\n", + "watermark1 & 0.5000 & 0.6000 & 0.0608 \\\\\n", + "watermark2 & 0.1000 & 0.5000 & 0.0322 \\\\\n", + "watermark1\\&2 & 0.0000 & 0.3000 & 0.0259 \\\\\n", + "filters & 0.3000 & 0.4000 & 0.0461 \\\\\n", + "filters\\&watermark1\\&2 & 0.1000 & 0.3000 & 0.0310 \\\\\n", + "avg & 0.2000 & 0.4200 & 0.0392 \\\\\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 i3d}\n", + "\\begin{tabular}{lccc}\n", + "\\hline\n", + "Deformation & Top-1 Precision & Recall@3 & MAP \\\\ \\hline\n", + "watermark1 & 0.7000 & 0.9000 & 0.0798 \\\\\n", + "watermark2 & 0.6000 & 0.6000 & 0.0662 \\\\\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": {