feat: 人员识别与管理模块 - MediaPipe人脸检测、人脸识别、人员库管理
This commit is contained in:
@@ -5,7 +5,7 @@ Local Analyzer - 本地视觉分析（无需大模型）
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功能：
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- 帧间差分：检测运动
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- 背景建模：检测前景物体
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- 人体检测：检测人员进出
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- 人员识别：检测并识别人物（新人员自动入库）
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- 亮度检测：检测光线变化
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- 自动判断是否需要调用大模型
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"""
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@@ -13,6 +13,18 @@ import cv2
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import numpy as np
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from pathlib import Path
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import datetime
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import sys
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import os
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# 添加项目路径
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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try:
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from person_manager import person_manager
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HAS_PERSON_MANAGER = True
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except ImportError:
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HAS_PERSON_MANAGER = False
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print("[LocalAnalyzer] PersonManager not available")
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class LocalAnalyzer:
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@@ -104,40 +116,90 @@ class LocalAnalyzer:
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})
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self.motion_count += 1
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# 2. 人体检测
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human_result = self._detect_human(current_frame)
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metrics['human_count'] = human_result['count']
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# 2. 人员检测与识别
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person_result = {'persons': [], 'total_count': 0, 'new_count': 0, 'known_count': 0}
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# 记录人数变化
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human_count_change = human_result['count'] - self.prev_human_count
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metrics['human_count_change'] = human_count_change
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if human_count_change > 0:
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events.append({
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'event_type': '人物活动',
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'description': f'检测到 {human_count_change} 人进入，当前共 {human_result["count"]} 人',
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'confidence': '高',
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'source': 'local'
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})
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self.human_count += human_count_change
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elif human_count_change < 0:
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events.append({
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'event_type': '人物活动',
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'description': f'检测到 {abs(human_count_change)} 人离开，当前剩 {human_result["count"]} 人',
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'confidence': '高',
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'source': 'local'
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})
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elif human_result['count'] > 0:
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# 人数没变但有人
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events.append({
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'event_type': '人物活动',
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'description': f'检测到 {human_result["count"]} 个人（无变化）',
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'confidence': '低',
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'source': 'local'
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})
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# 更新前一帧人数（在 should_call_model 中更新）
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# self.prev_human_count = human_result['count']
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if HAS_PERSON_MANAGER:
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print(f"[LocalAnalyzer] Using PersonManager for face detection...")
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person_result = person_manager.analyze_image(image_path, save_new_person=True)
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metrics['person_count'] = person_result['total_count']
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metrics['new_persons'] = person_result['new_count']
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metrics['known_persons'] = person_result['known_count']
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# 记录人员变化
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prev_person_count = self.prev_human_count # 用之前的变量名
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person_count_change = person_result['total_count'] - prev_person_count
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metrics['person_count_change'] = person_count_change
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for person in person_result['persons']:
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if person['is_new']:
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events.append({
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'event_type': '人物活动',
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'description': f'新人出现: {person["name"]}，当前共 {person_result["total_count"]} 人',
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'confidence': '高',
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'source': 'local'
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})
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self.human_count += 1
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else:
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events.append({
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'event_type': '人物活动',
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'description': f'已知人员: {person["name"]}，已访问 {person_manager.persons.get(person["person_id"], {}).get("visit_count", 1)} 次',
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'confidence': '高',
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'source': 'local'
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})
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# 检测人员进出
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if person_count_change > 0:
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events.append({
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'event_type': '人员进出',
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'description': f'检测到 {person_count_change} 人进入，当前共 {person_result["total_count"]} 人',
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'confidence': '高',
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'source': 'local'
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})
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elif person_count_change < 0:
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events.append({
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'event_type': '人员进出',
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'description': f'检测到 {abs(person_count_change)} 人离开，当前剩 {person_result["total_count"]} 人',
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'confidence': '高',
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'source': 'local'
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})
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# 更新前一帧人数
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self.prev_human_count = person_result['total_count']
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else:
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# 使用传统人体检测（备用）
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human_result = self._detect_human(current_frame)
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metrics['human_count'] = human_result['count']
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human_count_change = human_result['count'] - self.prev_human_count
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metrics['human_count_change'] = human_count_change
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if human_count_change > 0:
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events.append({
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'event_type': '人物活动',
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'description': f'检测到 {human_count_change} 人进入，当前共 {human_result["count"]} 人',
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'confidence': '高',
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'source': 'local'
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})
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self.human_count += human_count_change
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elif human_count_change < 0:
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events.append({
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'event_type': '人物活动',
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'description': f'检测到 {abs(human_count_change)} 人离开，当前剩 {human_result["count"]} 人',
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'confidence': '高',
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'source': 'local'
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})
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elif human_result['count'] > 0:
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events.append({
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'event_type': '人物活动',
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'description': f'检测到 {human_result["count"]} 个人（无变化）',
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'confidence': '低',
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'source': 'local'
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})
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self.prev_human_count = human_result['count']
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# 3. 亮度检测
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brightness_result = self._detect_brightness_change(current_gray, prev_image_path)
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@@ -306,23 +368,25 @@ class LocalAnalyzer:
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"""判断是否需要调用大模型"""
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# 条件1：人数变化（最重要）
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current_human_count = metrics.get('human_count', 0)
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human_count_change = abs(current_human_count - self.prev_human_count)
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current_person_count = metrics.get('person_count', metrics.get('human_count', 0))
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person_count_change = metrics.get('person_count_change', metrics.get('human_count_change', 0))
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# 更新前一帧人数
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self.prev_human_count = current_human_count
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if human_count_change >= self.config['human_count_change_threshold']:
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print(f"[LocalAnalyzer] Human count changed: {self.prev_human_count} -> {current_human_count}, triggering model")
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# 更新前一帧人数（如果还没更新）
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if abs(person_count_change) >= self.config['human_count_change_threshold']:
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print(f"[LocalAnalyzer] Person count changed: {current_person_count - person_count_change} -> {current_person_count}, triggering model")
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return True
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# 条件2：运动面积超过阈值（排除有人但不动的情况）
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# 只有在没有人变化时才用这个条件
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# 条件2：检测到新人
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if metrics.get('new_persons', 0) > 0:
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print(f"[LocalAnalyzer] New person detected: {metrics.get('new_persons', 0)}, triggering model")
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return True
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# 条件3：运动面积超过阈值（排除有人但不动的情况）
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if metrics.get('motion_ratio', 0) > self.config['trigger_model_threshold'] * 2:
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print(f"[LocalAnalyzer] Large motion detected: {metrics.get('motion_ratio', 0):.2%}")
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return True
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# 条件3：亮度大幅变化（灯开关等）
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# 条件4：亮度大幅变化（灯开关等）
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if abs(metrics.get('brightness_change', 0)) > self.config['brightness_change_threshold'] * 2:
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print(f"[LocalAnalyzer] Brightness changed: {metrics.get('brightness_change', 0)}")
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return True
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448
person_manager.py
Normal file
448
person_manager.py
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@@ -0,0 +1,448 @@
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"""
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Person Manager - 人员识别与管理模块
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功能：
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- 人脸检测（MediaPipe）
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- 人脸识别，判断是否为同一个人
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- 人员库管理，新人自动添加
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- 追踪人员进出记录
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"""
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import cv2
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import numpy as np
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import json
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import datetime
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from pathlib import Path
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from config import DATA_DIR
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try:
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import mediapipe as mp
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HAS_MEDIAPIPE = True
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except ImportError:
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HAS_MEDIAPIPE = False
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print("[PersonManager] MediaPipe not installed, using basic detection")
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try:
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import face_recognition
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HAS_FACE_REC = True
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except ImportError:
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HAS_FACE_REC = False
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print("[PersonManager] face_recognition not installed, using basic matching")
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class PersonManager:
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"""人员识别与管理器"""
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def __init__(self):
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self.persons_db_path = DATA_DIR / "persons.json"
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self.faces_dir = DATA_DIR / "faces"
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# 创建目录
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self.faces_dir.mkdir(parents=True, exist_ok=True)
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# 加载人员库
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self.persons = self._load_persons_db()
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# 初始化检测器
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self._init_detectors()
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# 配置
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self.config = {
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'face_match_threshold': 0.6, # 人脸匹配阈值
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'unknown_person_id': 'unknown', # 未知人员ID
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'max_persons': 100, # 最大人员数量
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}
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# 统计
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self.total_detections = 0
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self.known_persons_detected = 0
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self.new_persons_added = 0
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def _load_persons_db(self):
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"""加载人员数据库"""
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if self.persons_db_path.exists():
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try:
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with open(self.persons_db_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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except:
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return {}
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return {}
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def _save_persons_db(self):
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"""保存人员数据库"""
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with open(self.persons_db_path, 'w', encoding='utf-8') as f:
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json.dump(self.persons, f, ensure_ascii=False, indent=2)
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def _init_detectors(self):
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"""初始化检测器"""
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# MediaPipe 人脸检测
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if HAS_MEDIAPIPE:
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self.mp_face_detection = mp.solutions.face_detection
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self.face_detector = self.mp_face_detection.FaceDetection(
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model_selection=0, # 0: 短距离，1: 远距离
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min_detection_confidence=0.5
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)
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print("[PersonManager] MediaPipe face detector initialized")
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else:
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# OpenCV DNN 人脸检测
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try:
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model_path = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
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self.cv_face_detector = cv2.CascadeClassifier(model_path)
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print("[PersonManager] OpenCV face detector initialized")
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except:
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self.cv_face_detector = None
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print("[PersonManager] No face detector available")
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def detect_faces(self, image):
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"""检测人脸
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Args:
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image: 图片（numpy array 或路径）
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Returns:
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list: [{'bbox': [x,y,w,h], 'confidence': float}]
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"""
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if isinstance(image, str):
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image = cv2.imread(image)
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if image is None:
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return []
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faces = []
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# MediaPipe 检测
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if HAS_MEDIAPIPE and hasattr(self, 'face_detector'):
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rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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results = self.face_detector.process(rgb_image)
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if results.detections:
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for detection in results.detections:
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bboxC = detection.location_data.relative_bounding_box
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h, w, _ = image.shape
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x = int(bboxC.xmin * w)
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y = int(bboxC.ymin * h)
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width = int(bboxC.width * w)
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height = int(bboxC.height * h)
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faces.append({
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'bbox': [x, y, width, height],
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'confidence': detection.score[0],
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'source': 'mediapipe'
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})
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# OpenCV 检测（备用）
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elif self.cv_face_detector is not None:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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detections = self.cv_face_detector.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30, 30)
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)
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for (x, y, w, h) in detections:
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faces.append({
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'bbox': [x, y, w, h],
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'confidence': 0.8,
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'source': 'opencv'
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})
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return faces
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def extract_face_encoding(self, image, face_bbox):
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"""提取人脸特征
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Args:
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image: 图片
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face_bbox: [x, y, w, h]
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Returns:
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numpy array: 人脸特征向量
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"""
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if isinstance(image, str):
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image = cv2.imread(image)
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if image is None:
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return None
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x, y, w, h = face_bbox
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# 确保坐标有效
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h_img, w_img = image.shape[:2]
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x = max(0, min(x, w_img - 1))
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y = max(0, min(y, h_img - 1))
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w = max(1, min(w, w_img - x))
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h = max(1, min(h, h_img - y))
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# 提取人脸区域
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face_image = image[y:y+h, x:x+w]
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if HAS_FACE_REC:
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# 使用 face_recognition 库
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rgb_face = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
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encodings = face_recognition.face_encodings(rgb_face)
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if len(encodings) > 0:
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return encodings[0]
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# 简单特征：使用颜色直方图作为特征
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# 将人脸缩放到固定大小
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face_resized = cv2.resize(face_image, (64, 64))
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# 计算 HSV 直方图
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hsv = cv2.cvtColor(face_resized, cv2.COLOR_BGR2HSV)
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hist_h = cv2.calcHist([hsv], [0], None, [16], [0, 180])
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hist_s = cv2.calcHist([hsv], [1], None, [16], [0, 256])
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hist_v = cv2.calcHist([hsv], [2], None, [16], [0, 256])
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# 合并特征
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feature = np.concatenate([
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cv2.normalize(hist_h, hist_h).flatten(),
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cv2.normalize(hist_s, hist_s).flatten(),
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cv2.normalize(hist_v, hist_v).flatten()
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])
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return feature
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def match_face(self, face_encoding, threshold=None):
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"""匹配人脸，找出对应的已知人员
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Args:
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face_encoding: 人脸特征向量
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threshold: 匹配阈值
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Returns:
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dict: {'person_id': str, 'name': str, 'is_new': bool}
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"""
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if threshold is None:
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threshold = self.config['face_match_threshold']
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if face_encoding is None:
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return {'person_id': 'unknown', 'name': 'Unknown', 'is_new': False}
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best_match = None
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best_distance = float('inf')
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for person_id, person_data in self.persons.items():
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if 'face_encoding' in person_data:
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stored_encoding = np.array(person_data['face_encoding'])
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if HAS_FACE_REC:
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# face_recognition 距离计算
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distance = face_recognition.face_distance([stored_encoding], face_encoding)[0]
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else:
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# 简单特征距离
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distance = np.linalg.norm(stored_encoding - face_encoding)
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if distance < best_distance:
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best_distance = distance
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best_match = person_data
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if best_match and best_distance < threshold:
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self.known_persons_detected += 1
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return {
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'person_id': best_match.get('person_id'),
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'name': best_match.get('name', 'Unknown'),
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'is_new': False,
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'confidence': 1 - best_distance
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}
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# 未匹配到，是新人员
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return {
|
||||
'person_id': 'unknown',
|
||||
'name': 'Unknown',
|
||||
'is_new': True,
|
||||
'confidence': 0
|
||||
}
|
||||
|
||||
def add_new_person(self, image, face_bbox, name=None):
|
||||
"""添加新人员到库
|
||||
|
||||
Args:
|
||||
image: 图片
|
||||
face_bbox: 人脸位置
|
||||
name: 人员名称（可选）
|
||||
|
||||
Returns:
|
||||
dict: 新人员信息
|
||||
"""
|
||||
if isinstance(image, str):
|
||||
image = cv2.imread(image)
|
||||
|
||||
# 提取特征
|
||||
face_encoding = self.extract_face_encoding(image, face_bbox)
|
||||
|
||||
if face_encoding is None:
|
||||
return None
|
||||
|
||||
# 生成人员ID
|
||||
person_id = f"person_{len(self.persons) + 1}"
|
||||
if name is None:
|
||||
name = f"Person #{len(self.persons) + 1}"
|
||||
|
||||
# 保存人脸图片
|
||||
x, y, w, h = face_bbox
|
||||
face_image = image[y:y+h, x:x+w]
|
||||
face_path = self.faces_dir / f"{person_id}.jpg"
|
||||
cv2.imwrite(str(face_path), face_image)
|
||||
|
||||
# 记录到数据库
|
||||
person_data = {
|
||||
'person_id': person_id,
|
||||
'name': name,
|
||||
'face_encoding': face_encoding.tolist() if isinstance(face_encoding, np.ndarray) else face_encoding,
|
||||
'face_path': str(face_path),
|
||||
'first_seen': datetime.datetime.now().isoformat(),
|
||||
'last_seen': datetime.datetime.now().isoformat(),
|
||||
'visit_count': 1
|
||||
}
|
||||
|
||||
self.persons[person_id] = person_data
|
||||
self._save_persons_db()
|
||||
|
||||
self.new_persons_added += 1
|
||||
|
||||
print(f"[PersonManager] New person added: {person_id} ({name})")
|
||||
|
||||
return person_data
|
||||
|
||||
def update_person_visit(self, person_id):
|
||||
"""更新人员访问记录"""
|
||||
if person_id in self.persons:
|
||||
self.persons[person_id]['last_seen'] = datetime.datetime.now().isoformat()
|
||||
self.persons[person_id]['visit_count'] += 1
|
||||
self._save_persons_db()
|
||||
|
||||
def analyze_image(self, image_path, save_new_person=True):
|
||||
"""分析图片中的人员
|
||||
|
||||
Args:
|
||||
image_path: 图片路径
|
||||
save_new_person: 是否保存新人员
|
||||
|
||||
Returns:
|
||||
dict: {
|
||||
'faces': list, # 检测到的人脸
|
||||
'persons': list, # 识别的人员
|
||||
'new_count': int, # 新人员数量
|
||||
'known_count': int, # 已知人员数量
|
||||
}
|
||||
"""
|
||||
image = cv2.imread(image_path)
|
||||
if image is None:
|
||||
return {'faces': [], 'persons': [], 'error': 'Cannot load image'}
|
||||
|
||||
self.total_detections += 1
|
||||
|
||||
# 检测人脸
|
||||
faces = self.detect_faces(image)
|
||||
|
||||
persons = []
|
||||
new_count = 0
|
||||
known_count = 0
|
||||
|
||||
for face in faces:
|
||||
bbox = face['bbox']
|
||||
|
||||
# 提取特征
|
||||
encoding = self.extract_face_encoding(image, bbox)
|
||||
|
||||
# 匹配
|
||||
match_result = self.match_face(encoding)
|
||||
|
||||
if match_result['is_new']:
|
||||
# 新人员
|
||||
new_count += 1
|
||||
|
||||
if save_new_person and len(self.persons) < self.config['max_persons']:
|
||||
new_person = self.add_new_person(image, bbox)
|
||||
if new_person:
|
||||
persons.append({
|
||||
'person_id': new_person['person_id'],
|
||||
'name': new_person['name'],
|
||||
'bbox': bbox,
|
||||
'is_new': True,
|
||||
'confidence': face['confidence']
|
||||
})
|
||||
else:
|
||||
persons.append({
|
||||
'person_id': 'unknown',
|
||||
'name': 'Unknown (new)',
|
||||
'bbox': bbox,
|
||||
'is_new': True,
|
||||
'confidence': face['confidence']
|
||||
})
|
||||
else:
|
||||
# 已知人员
|
||||
known_count += 1
|
||||
self.update_person_visit(match_result['person_id'])
|
||||
|
||||
persons.append({
|
||||
'person_id': match_result['person_id'],
|
||||
'name': match_result['name'],
|
||||
'bbox': bbox,
|
||||
'is_new': False,
|
||||
'confidence': match_result['confidence']
|
||||
})
|
||||
|
||||
return {
|
||||
'faces': faces,
|
||||
'persons': persons,
|
||||
'new_count': new_count,
|
||||
'known_count': known_count,
|
||||
'total_count': len(persons)
|
||||
}
|
||||
|
||||
def get_persons_list(self):
|
||||
"""获取人员列表"""
|
||||
return [
|
||||
{
|
||||
'person_id': p['person_id'],
|
||||
'name': p['name'],
|
||||
'visit_count': p['visit_count'],
|
||||
'first_seen': p['first_seen'],
|
||||
'last_seen': p['last_seen']
|
||||
}
|
||||
for p in self.persons.values()
|
||||
]
|
||||
|
||||
def get_stats(self):
|
||||
"""获取统计信息"""
|
||||
return {
|
||||
'total_persons': len(self.persons),
|
||||
'total_detections': self.total_detections,
|
||||
'known_persons_detected': self.known_persons_detected,
|
||||
'new_persons_added': self.new_persons_added,
|
||||
'recognition_rate': self.known_persons_detected / max(self.total_detections, 1)
|
||||
}
|
||||
|
||||
def reset(self):
|
||||
"""重置统计"""
|
||||
self.total_detections = 0
|
||||
self.known_persons_detected = 0
|
||||
self.new_persons_added = 0
|
||||
|
||||
|
||||
# 全局实例
|
||||
person_manager = PersonManager()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试
|
||||
import sys
|
||||
|
||||
if len(sys.argv) >= 2:
|
||||
test_image = sys.argv[1]
|
||||
|
||||
print(f"[Test] Analyzing: {test_image}")
|
||||
result = person_manager.analyze_image(test_image)
|
||||
|
||||
print(f"[Test] Faces detected: {len(result['faces'])}")
|
||||
print(f"[Test] Persons: {result['total_count']}")
|
||||
print(f"[Test] New: {result['new_count']}, Known: {result['known_count']}")
|
||||
|
||||
for person in result['persons']:
|
||||
status = "NEW" if person['is_new'] else "KNOWN"
|
||||
print(f" - [{status}] {person['name']} (confidence: {person['confidence']:.2f})")
|
||||
else:
|
||||
print("Usage: python person_manager.py <image_path>")
|
||||
@@ -1,4 +1,9 @@
|
||||
opencv-python>=4.8.0
|
||||
fastapi>=0.100.0
|
||||
uvicorn>=0.23.0
|
||||
requests>=2.31.0
|
||||
requests>=2.31.0
|
||||
numpy>=1.20.0
|
||||
|
||||
# Optional: More accurate face detection and recognition
|
||||
# mediapipe>=0.10.0
|
||||
# face-recognition>=1.7.0 (requires dlib, may need manual install on Windows)
|
||||
29
web/app.py
29
web/app.py
@@ -165,6 +165,35 @@ async def analyze_unanalyzed():
|
||||
return {"results": results}
|
||||
|
||||
|
||||
@app.get("/api/persons")
|
||||
async def get_persons():
|
||||
"""获取人员列表"""
|
||||
from person_manager import person_manager
|
||||
return {"persons": person_manager.get_persons_list(), "stats": person_manager.get_stats()}
|
||||
|
||||
|
||||
@app.delete("/api/persons/{person_id}")
|
||||
async def delete_person(person_id: str):
|
||||
"""删除人员"""
|
||||
from person_manager import person_manager
|
||||
if person_id in person_manager.persons:
|
||||
del person_manager.persons[person_id]
|
||||
person_manager._save_persons_db()
|
||||
return {"success": True}
|
||||
raise HTTPException(status_code=404, detail="人员不存在")
|
||||
|
||||
|
||||
@app.post("/api/persons/{person_id}/rename")
|
||||
async def rename_person(person_id: str, name: str):
|
||||
"""重命名人员"""
|
||||
from person_manager import person_manager
|
||||
if person_id in person_manager.persons:
|
||||
person_manager.persons[person_id]['name'] = name
|
||||
person_manager._save_persons_db()
|
||||
return {"success": True, "name": name}
|
||||
raise HTTPException(status_code=404, detail="人员不存在")
|
||||
|
||||
|
||||
# ============== 图片 API ==============
|
||||
|
||||
@app.get("/api/images")
|
||||
|
||||
@@ -405,6 +405,96 @@ function closeSettingsModal() {
|
||||
document.getElementById('settings-modal').classList.remove('active');
|
||||
}
|
||||
|
||||
// Persons Management
|
||||
function openPersonsModal() {
|
||||
loadPersonsList();
|
||||
document.getElementById('persons-modal').classList.add('active');
|
||||
}
|
||||
|
||||
function closePersonsModal() {
|
||||
document.getElementById('persons-modal').classList.remove('active');
|
||||
}
|
||||
|
||||
function loadPersonsList() {
|
||||
fetch(API_BASE + '/api/persons')
|
||||
.then(function(res) { return res.json(); })
|
||||
.then(function(data) {
|
||||
var statsDiv = document.getElementById('persons-stats');
|
||||
var listDiv = document.getElementById('persons-list');
|
||||
|
||||
// 统计信息
|
||||
statsDiv.innerHTML = '<div class="stats-summary">' +
|
||||
'<span>Total: ' + data.stats.total_persons + '</span>' +
|
||||
'<span>Detected: ' + data.stats.total_detections + '</span>' +
|
||||
'<span>Known: ' + data.stats.known_persons_detected + '</span>' +
|
||||
'<span>New: ' + data.stats.new_persons_added + '</span>' +
|
||||
'</div>';
|
||||
|
||||
// 人员列表
|
||||
if (data.persons.length === 0) {
|
||||
listDiv.innerHTML = '<p style="color:#888;text-align:center;">No persons recorded yet</p>';
|
||||
return;
|
||||
}
|
||||
|
||||
listDiv.innerHTML = '';
|
||||
data.persons.forEach(function(person) {
|
||||
var item = document.createElement('div');
|
||||
item.className = 'person-item';
|
||||
|
||||
var firstSeen = new Date(person.first_seen).toLocaleDateString();
|
||||
var lastSeen = new Date(person.last_seen).toLocaleDateString();
|
||||
|
||||
item.innerHTML = '<div class="person-info">' +
|
||||
'<span class="person-name">' + person.name + '</span>' +
|
||||
'<span class="person-id">' + person.person_id + '</span>' +
|
||||
'</div>' +
|
||||
'<div class="person-stats">' +
|
||||
'<span>Visits: ' + person.visit_count + '</span>' +
|
||||
'<span>First: ' + firstSeen + '</span>' +
|
||||
'<span>Last: ' + lastSeen + '</span>' +
|
||||
'</div>' +
|
||||
'<div class="person-actions">' +
|
||||
'<button onclick="renamePerson(\'' + person.person_id + '\')" class="btn-small">Rename</button>' +
|
||||
'<button onclick="deletePerson(\'' + person.person_id + '\')" class="btn-small btn-danger">Delete</button>' +
|
||||
'</div>';
|
||||
|
||||
listDiv.appendChild(item);
|
||||
});
|
||||
})
|
||||
.catch(function(e) { console.error('Load persons failed:', e); });
|
||||
}
|
||||
|
||||
function renamePerson(personId) {
|
||||
var newName = prompt('Enter new name:');
|
||||
if (!newName) return;
|
||||
|
||||
fetch(API_BASE + '/api/persons/' + personId + '/rename?name=' + encodeURIComponent(newName), {
|
||||
method: 'POST'
|
||||
})
|
||||
.then(function(res) { return res.json(); })
|
||||
.then(function(data) {
|
||||
if (data.success) {
|
||||
showToast('Renamed to ' + newName, 1500);
|
||||
loadPersonsList();
|
||||
}
|
||||
})
|
||||
.catch(function(e) { showToast('Error: ' + e.message, 3000); });
|
||||
}
|
||||
|
||||
function deletePerson(personId) {
|
||||
if (!confirm('Delete this person?')) return;
|
||||
|
||||
fetch(API_BASE + '/api/persons/' + personId, {method: 'DELETE'})
|
||||
.then(function(res) { return res.json(); })
|
||||
.then(function(data) {
|
||||
if (data.success) {
|
||||
showToast('Person deleted', 1500);
|
||||
loadPersonsList();
|
||||
}
|
||||
})
|
||||
.catch(function(e) { showToast('Error: ' + e.message, 3000); });
|
||||
}
|
||||
|
||||
function loadSettingsForm() {
|
||||
fetch(API_BASE + '/api/config')
|
||||
.then(function(res) { return res.json(); })
|
||||
|
||||
@@ -29,6 +29,11 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="panel-section">
|
||||
<h3>👥 人员库</h3>
|
||||
<button onclick="openPersonsModal()" class="btn-settings">查看人员库</button>
|
||||
</div>
|
||||
|
||||
<div class="panel-section">
|
||||
<h3>⚙️ 系统设置</h3>
|
||||
<button onclick="openSettingsModal()" class="btn-settings">🔧 设置参数</button>
|
||||
@@ -155,6 +160,22 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 人员库模态框 -->
|
||||
<div class="modal" id="persons-modal" onclick="closeModalOnBackground(event, 'persons-modal')">
|
||||
<div class="modal-content settings-modal-content" onclick="event.stopPropagation()">
|
||||
<span class="modal-close" onclick="closePersonsModal()">×</span>
|
||||
<h2>👥 人员库管理</h2>
|
||||
|
||||
<div class="persons-stats" id="persons-stats">
|
||||
<!-- 统计信息 -->
|
||||
</div>
|
||||
|
||||
<div class="persons-list" id="persons-list">
|
||||
<!-- 人员列表 -->
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 图片详情模态框 -->
|
||||
<div class="modal" id="image-modal" onclick="closeModalOnBackground(event, 'image-modal')">
|
||||
<div class="modal-content" onclick="event.stopPropagation()">
|
||||
|
||||
@@ -582,6 +582,73 @@ button {
|
||||
}
|
||||
|
||||
/* 响应式 */
|
||||
.settings-actions {
|
||||
margin-top: 20px;
|
||||
display: flex;
|
||||
gap: 10px;
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
/* 人员库样式 */
|
||||
.persons-stats {
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
|
||||
.stats-summary {
|
||||
display: flex;
|
||||
gap: 20px;
|
||||
justify-content: center;
|
||||
padding: 15px;
|
||||
background: #f5f5f5;
|
||||
border-radius: 8px;
|
||||
}
|
||||
|
||||
.stats-summary span {
|
||||
color: #555;
|
||||
}
|
||||
|
||||
.persons-list {
|
||||
max-height: 400px;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.person-item {
|
||||
background: #f9f9f9;
|
||||
padding: 15px;
|
||||
margin-bottom: 10px;
|
||||
border-radius: 8px;
|
||||
border-left: 4px solid #2196F3;
|
||||
}
|
||||
|
||||
.person-info {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
|
||||
.person-name {
|
||||
font-weight: bold;
|
||||
font-size: 16px;
|
||||
}
|
||||
|
||||
.person-id {
|
||||
color: #888;
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.person-stats {
|
||||
display: flex;
|
||||
gap: 15px;
|
||||
color: #555;
|
||||
font-size: 13px;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
.person-actions {
|
||||
display: flex;
|
||||
gap: 10px;
|
||||
}
|
||||
|
||||
@media (max-width: 768px) {
|
||||
.control-panel {
|
||||
flex-direction: column;
|
||||
|
||||
Reference in New Issue
Block a user