293 lines
8.0 KiB
Plaintext
293 lines
8.0 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": "2025-04-14T06:58:32.199713Z",
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"start_time": "2025-04-14T06:58:32.134049Z"
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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"
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],
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"id": "6917288db44004ea",
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"outputs": [],
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"execution_count": 1
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T06:58:34.129528Z",
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"start_time": "2025-04-14T06:58:34.126022Z"
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}
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},
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"cell_type": "code",
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"source": [
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"currdb = \"i3d.db\"\n",
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"#currdb = \"LBPTOP.db\"\n",
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"currdb = \"slowfast.db\""
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],
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"id": "dd9b84fa98c77e5a",
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"outputs": [],
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T06:58:36.801611Z",
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"start_time": "2025-04-14T06:58:36.798114Z"
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}
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},
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"cell_type": "code",
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"source": [
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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)"
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],
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"id": "fa2ab158cbe0aa98",
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"outputs": [],
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T07:01:06.063185Z",
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"start_time": "2025-04-14T07:01:06.060008Z"
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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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"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, detect_types=sqlite3.PARSE_DECLTYPES)\n",
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"cursor = conn.cursor()"
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],
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"id": "629eb156f332c0bc",
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"outputs": [],
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"execution_count": 12
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T07:01:06.372809Z",
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"start_time": "2025-04-14T07:01:06.362911Z"
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}
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},
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"cell_type": "code",
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"source": [
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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()"
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],
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"id": "ba7902156f1b6117",
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"outputs": [],
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"execution_count": 13
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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": "2025-04-14T07:01:06.786016Z",
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"start_time": "2025-04-14T07:01:06.779812Z"
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}
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},
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"cell_type": "code",
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"source": [
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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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"# 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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"# 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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" 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 = sum(labels_sorted[:K]) / len(org_feats)\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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" }"
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],
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"id": "initial_id",
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"outputs": [],
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"execution_count": 14
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T07:01:07.745386Z",
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"start_time": "2025-04-14T07:01:07.740942Z"
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}
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},
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"cell_type": "code",
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"source": "data_by_group['org'][0]['feature'].shape",
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"id": "55e57dc7ec56b732",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(2304,)"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 15
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-14T06:00:12.392784Z",
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"start_time": "2025-04-14T06:00:12.382725Z"
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}
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},
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"cell_type": "code",
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"source": [
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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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"# 执行评估\n",
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"results = evaluate_all_variants(K=3)\n",
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"\n",
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"# 输出结果\n",
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"for variant, metrics in results.items():\n",
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" print(f\"变形组: {variant}\")\n",
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" for metric_name, value in metrics.items():\n",
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" print(f\" {metric_name}: {value:.4f}\")\n",
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" print(\"-\" * 40)\n",
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"\n",
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"# 关闭数据库连接\n",
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"conn.close()"
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],
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"id": "d9a0103dbc3f1bef",
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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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"变形组: 动态水印2\n",
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" Top-1 Precision: 1.0000\n",
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" Recall@3: 0.1500\n",
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" MAP: 0.5815\n",
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"----------------------------------------\n",
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"变形组: 水印1+2\n",
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" Top-1 Precision: 0.8000\n",
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" Recall@3: 0.1600\n",
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" MAP: 0.5751\n",
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"----------------------------------------\n",
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"变形组: 水印1+2+滤镜\n",
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" Top-1 Precision: 0.8000\n",
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" Recall@3: 0.1500\n",
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" MAP: 0.5685\n",
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"----------------------------------------\n",
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"变形组: 滤镜\n",
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" Top-1 Precision: 0.9000\n",
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" Recall@3: 0.1800\n",
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" MAP: 0.6100\n",
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"----------------------------------------\n",
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"变形组: 静态水印1\n",
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" Top-1 Precision: 0.9000\n",
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" Recall@3: 0.1700\n",
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" MAP: 0.6058\n",
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"----------------------------------------\n"
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]
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}
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],
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"execution_count": 25
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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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