339 lines
11 KiB
Plaintext
339 lines
11 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": "2025-04-14T09:34:18.359212Z",
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"start_time": "2025-04-14T09:34:18.356046Z"
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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 numpy as np\n",
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"import io\n",
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"from numpy.linalg import norm\n",
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"from collections import defaultdict\n",
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"import os"
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],
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"id": "6917288db44004ea",
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"outputs": [],
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"execution_count": 59
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T09:34:18.371739Z",
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"start_time": "2025-04-14T09:34:18.367219Z"
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}
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},
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"cell_type": "code",
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"source": [
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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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"\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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"\n",
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"# 相似度函数 (余弦相似度)\n",
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"def cosine_similarity(a, b):\n",
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" return np.dot(a, b) / (norm(a) * norm(b))\n",
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"\n"
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],
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"id": "dd9b84fa98c77e5a",
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"outputs": [],
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"execution_count": 60
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T09:34:18.388488Z",
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"start_time": "2025-04-14T09:34:18.385501Z"
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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 get_video_id(path):\n",
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" filename = os.path.basename(path)\n",
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" name = os.path.splitext(filename)[0] # 去掉扩展名\n",
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" return name"
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],
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"id": "884f8c27d67a93d5",
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"outputs": [],
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"execution_count": 61
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T09:34:18.451821Z",
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"start_time": "2025-04-14T09:34:18.443936Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"# 计算指标函数\n",
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"def compute_metrics(org_feats, query_feats, sim_feats, K=5):\n",
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" precision_top1 = []\n",
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" recall_at_k = []\n",
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" AP_list = []\n",
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"\n",
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" # 构建搜索库 (原始视频 + 相似视频)\n",
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" search_db = org_feats + sim_feats\n",
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"\n",
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" for query in query_feats:\n",
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" similarities = []\n",
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" query_feature = query['feature']\n",
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"\n",
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" # 计算query与所有库视频的相似度\n",
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" for target in search_db:\n",
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" sim = cosine_similarity(query_feature, target['feature'])\n",
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" label = 1 if target in org_feats else 0 # 1代表匹配，0代表不匹配\n",
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" if label == 1:\n",
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" query_id = get_video_id(query['path'])\n",
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" target_id = get_video_id(target['path'])\n",
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" label = 1 if target_id == query_id else 0\n",
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"\n",
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" similarities.append((sim, label))\n",
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"\n",
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" # 相似度降序排序\n",
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" similarities.sort(key=lambda x: x[0], reverse=True)\n",
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" labels_sorted = [label for _, label in similarities]\n",
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"\n",
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" # Top-1 precision\n",
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" precision_top1.append(labels_sorted[0])\n",
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"\n",
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" # Recall@K\n",
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" recall = int(1 in labels_sorted[:K])\n",
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" recall_at_k.append(recall)\n",
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"\n",
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" # Average Precision (AP)\n",
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" hits, sum_precisions = 0, 0\n",
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" for idx, label in enumerate(labels_sorted, start=1):\n",
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" if label == 1:\n",
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" hits += 1\n",
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" sum_precisions += hits / idx\n",
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" AP = sum_precisions / len(org_feats) if len(org_feats) else 0\n",
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" AP_list.append(AP)\n",
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"\n",
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" # 返回指标均值\n",
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" return {\n",
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" 'Top-1 Precision': np.mean(precision_top1),\n",
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" f'Recall@{K}': np.mean(recall_at_k),\n",
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" 'MAP': np.mean(AP_list)\n",
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" }\n",
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"\n",
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"\n",
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"# 主函数，计算所有变形组的指标\n",
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"def evaluate_all_variants(K=5):\n",
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" org_feats = data_by_group['org']\n",
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" sim_feats = data_by_group['sim']\n",
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"\n",
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" results = {}\n",
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"\n",
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" # 排除 'org' 和 'sim'，其余都是变形组\n",
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" variant_groups = [group for group in data_by_group if group not in ['org', 'sim']]\n",
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"\n",
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" for variant in variant_groups:\n",
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" variant_feats = data_by_group[variant]\n",
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" metrics = compute_metrics(org_feats, variant_feats, sim_feats, K)\n",
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" results[variant] = metrics\n",
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"\n",
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" return results\n",
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"\n",
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"\n",
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"def results_to_latex(currdb, results):\n",
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" metric_names = list(next(iter(results.values())).keys()) # 获取所有指标名\n",
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" header = \"Deformation & \" + \" & \".join(metric_names) + \" \\\\\\\\ \\\\hline\\n\"\n",
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"\n",
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" rows = \"\"\n",
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" metric_sums = {metric: 0.0 for metric in metric_names}\n",
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" count = 0\n",
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"\n",
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" for variant_id, variant_display in variant_order:\n",
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" if variant_id not in results:\n",
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" continue # 忽略不存在的组\n",
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" metrics = results[variant_id]\n",
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" count += 1\n",
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" row = variant_display\n",
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" for metric in metric_names:\n",
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" val = metrics[metric]\n",
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" metric_sums[metric] += val\n",
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" row += f\" & {val:.4f}\"\n",
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" rows += row + \" \\\\\\\\\\n\"\n",
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"\n",
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" # 添加平均值行\n",
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" avg_row = \"avg\"\n",
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" for metric in metric_names:\n",
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" avg_val = metric_sums[metric] / count if count else 0.0\n",
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" avg_row += f\" & {avg_val:.4f}\"\n",
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" rows += avg_row + \" \\\\\\\\\\n\"\n",
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"\n",
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" latex_table = (\n",
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" \"\\\\begin{table}[htbp]\\n\"\n",
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" \"\\\\centering\\n\"\n",
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" f\"\\\\caption{{Top-1 Precision, Recall@K and MAP for {currdb}}}\\n\"\n",
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" \"\\\\begin{tabular}{l\" + \"c\" * len(metric_names) + \"}\\n\"\n",
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" \"\\\\hline\\n\"\n",
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" + header +\n",
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" rows +\n",
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" \"\\\\hline\\n\"\n",
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" \"\\\\end{tabular}\\n\"\n",
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" \"\\\\end{table}\\n\"\n",
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" )\n",
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" return latex_table"
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],
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"id": "282bfd74f5982041",
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"outputs": [],
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"execution_count": 62
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T09:34:18.460011Z",
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"start_time": "2025-04-14T09:34:18.454827Z"
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}
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},
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"cell_type": "code",
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"source": [
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"variant_order = [\n",
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" (r\"静态水印1\", r\"watermark1\"),\n",
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" (r\"动态水印2\", r\"watermark2\"),\n",
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" (r\"水印1+2\", r\"watermark1\\&2\"),\n",
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" (r\"滤镜\", r\"filters\"),\n",
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" (r\"水印1+2+滤镜\", r\"filters\\&watermark1\\&2\"),\n",
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" (r\"watermark1\", r\"watermark1\"),\n",
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" (r\"watermark2\", r\"watermark2\"),\n",
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" (r\"watermark1+2\", r\"watermark11\\&2\"),\n",
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" (r\"filters\", r\"filters\"),\n",
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" (r\"filters+watermark1+2\", r\"filters\\&watermark1\\&2\"),\n",
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"]"
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],
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"id": "12e194a6bf64b8ac",
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"outputs": [],
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"execution_count": 63
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T09:34:18.512805Z",
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"start_time": "2025-04-14T09:34:18.473552Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"for currdb in [\"LBPTOP\", \"i3d\", \"slowfast\"]:\n",
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" sqlite3.register_adapter(np.ndarray, adapt_array)\n",
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" sqlite3.register_converter(\"BLOB\", convert_array)\n",
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"\n",
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" # 连接SQLite数据库\n",
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" conn = sqlite3.connect(currdb + \".db\", detect_types=sqlite3.PARSE_DECLTYPES)\n",
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" cursor = conn.cursor()\n",
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" # 读取数据库所有数据\n",
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" cursor.execute(\"SELECT id, array, group_path, full_path FROM data\")\n",
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" all_data = cursor.fetchall()\n",
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" # 整理特征\n",
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" data_by_group = defaultdict(list)\n",
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" for vid, arr, group, path in all_data:\n",
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" data_by_group[group].append({'id': vid, 'feature': arr, 'path': path})\n",
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"\n",
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" # 执行评估\n",
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" results = evaluate_all_variants(K=3)\n",
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" # 关闭数据库连接\n",
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" conn.close()\n",
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" # 打印 LaTeX 表格\n",
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" print(results_to_latex(currdb, results))\n"
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],
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"id": "97f972940a003aa4",
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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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"\\begin{table}[htbp]\n",
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"\\centering\n",
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"\\caption{Top-1 Precision, Recall@K and MAP for LBPTOP}\n",
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"\\begin{tabular}{lccc}\n",
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"\\hline\n",
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"Deformation & Top-1 Precision & Recall@3 & MAP \\\\ \\hline\n",
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"watermark1 & 0.5000 & 0.6000 & 0.0608 \\\\\n",
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"watermark2 & 0.1000 & 0.5000 & 0.0322 \\\\\n",
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"watermark1\\&2 & 0.0000 & 0.3000 & 0.0259 \\\\\n",
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"filters & 0.3000 & 0.4000 & 0.0461 \\\\\n",
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"filters\\&watermark1\\&2 & 0.1000 & 0.3000 & 0.0310 \\\\\n",
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"avg & 0.2000 & 0.4200 & 0.0392 \\\\\n",
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"\\hline\n",
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"\\end{tabular}\n",
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"\\end{table}\n",
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"\n",
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"\\begin{table}[htbp]\n",
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"\\centering\n",
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"\\caption{Top-1 Precision, Recall@K and MAP for i3d}\n",
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"\\begin{tabular}{lccc}\n",
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"\\hline\n",
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"Deformation & Top-1 Precision & Recall@3 & MAP \\\\ \\hline\n",
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"watermark1 & 0.7000 & 0.9000 & 0.0798 \\\\\n",
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"watermark2 & 0.6000 & 0.6000 & 0.0662 \\\\\n",
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"watermark1\\&2 & 0.5000 & 0.8000 & 0.0634 \\\\\n",
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"filters & 0.8000 & 0.8000 & 0.0850 \\\\\n",
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"filters\\&watermark1\\&2 & 0.5000 & 0.8000 & 0.0634 \\\\\n",
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"avg & 0.6200 & 0.7800 & 0.0715 \\\\\n",
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"\\hline\n",
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"\\end{tabular}\n",
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"\\end{table}\n",
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"\n",
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"\\begin{table}[htbp]\n",
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"\\centering\n",
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"\\caption{Top-1 Precision, Recall@K and MAP for slowfast}\n",
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"\\begin{tabular}{lccc}\n",
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"\\hline\n",
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"Deformation & Top-1 Precision & Recall@3 & MAP \\\\ \\hline\n",
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"watermark1 & 0.7000 & 0.9000 & 0.0817 \\\\\n",
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"watermark2 & 0.7000 & 1.0000 & 0.0800 \\\\\n",
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"watermark1\\&2 & 0.3000 & 0.9000 & 0.0583 \\\\\n",
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"filters & 0.6000 & 0.9000 & 0.0758 \\\\\n",
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"filters\\&watermark1\\&2 & 0.3000 & 0.8000 & 0.0558 \\\\\n",
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"avg & 0.5200 & 0.9000 & 0.0703 \\\\\n",
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"\\hline\n",
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"\\end{tabular}\n",
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"\\end{table}\n",
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"\n"
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]
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}
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],
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"execution_count": 64
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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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