from flask import Flask, render_template, request, redirect, url_for, flash, jsonify, send_from_directory
import os
import shutil
import time
import threading
import json
from datetime import datetime
from pathlib import Path
from flask import Flask, render_template, request, redirect, url_for, flash, jsonify
from werkzeug.utils import secure_filename
from config import config
from batch_detector import BatchVideoDetector

app = Flask(__name__)
app.secret_key = 'road_damage_detection_system'
app.config['MAX_CONTENT_LENGTH'] = 500 * 1024 * 1024  # 500MB max upload size

# 确保必要的文件夹存在
for folder in [config.INPUT_FOLDER, config.OUTPUT_FOLDER, config.LOG_FOLDER, 'models', 'static/css']:
    os.makedirs(folder, exist_ok=True)

# 全局变量用于跟踪检测进度和状态
detection_progress = 0
detection_status = "idle"  # idle, running, completed, error
detection_thread = None
current_detection_info = {}
detection_start_time = None
detection_results = None
current_report_path = None
current_video_name = ""
total_videos = 0
current_video_index = 0
current_frame_count = 0
total_frame_count = 0

# 允许的文件扩展名
ALLOWED_VIDEO_EXTENSIONS = {ext.lstrip('.') for ext in config.SUPPORTED_VIDEO_FORMATS}
ALLOWED_MODEL_EXTENSIONS = {'pt', 'pth', 'weights'}

def allowed_file(filename, allowed_extensions):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in allowed_extensions

# 主页
@app.route('/', methods=['GET', 'POST'])
def index():
    global detection_thread, detection_status, detection_progress, detection_results, current_report_path, detection_start_time
    
    # 处理POST请求（开始检测）
    if request.method == 'POST':
        if detection_status == "running":
            flash('已有检测任务正在运行', 'warning')
        else:
            # 获取选择的模型和置信度阈值
            model_name = request.form.get('model_select')
            confidence_threshold = float(request.form.get('confidence_threshold', 0.25))
            
            # 检查是否有视频文件
            videos = [f for f in os.listdir(config.INPUT_FOLDER) 
                      if allowed_file(f, ALLOWED_VIDEO_EXTENSIONS)]
            if not videos:
                flash('输入文件夹中没有视频文件，请先上传视频', 'danger')
            elif not model_name:
                flash('请选择一个检测模型', 'danger')
            else:
                model_path = os.path.join('models', model_name)
                if not os.path.exists(model_path):
                    flash(f'模型文件 {model_name} 不存在', 'danger')
                else:
                    # 更新配置
                    config.MODEL_PATH = model_path
                    config.CONFIDENCE_THRESHOLD = confidence_threshold
                    
                    # 重置检测状态和进度
                    detection_progress = 0
                    detection_status = "running"
                    detection_results = None
                    current_report_path = None
                    detection_start_time = datetime.now()
                    
                    # 启动检测线程
                    detection_thread = threading.Thread(target=run_detection)
                    detection_thread.daemon = True
                    detection_thread.start()
                    
                    flash('检测已开始，请等待结果', 'info')
    
    # 获取可用的模型列表
    print(f"models文件夹路径: {os.path.abspath('models')}")
    print(f"models文件夹存在: {os.path.exists('models')}")
    if os.path.exists('models'):
        all_files = os.listdir('models')
        print(f"models文件夹中的所有文件: {all_files}")
        for f in all_files:
            print(f"检查文件 {f}: allowed_file结果 = {allowed_file(f, ALLOWED_MODEL_EXTENSIONS)}")
    models = [f for f in os.listdir('models') if allowed_file(f, ALLOWED_MODEL_EXTENSIONS)]
    print(f"找到的模型文件: {models}")
    
    # 获取输入文件夹中的视频文件
    videos = [f for f in os.listdir(config.INPUT_FOLDER) 
              if allowed_file(f, ALLOWED_VIDEO_EXTENSIONS)]
    
    # 获取检测报告列表
    reports = []
    if os.path.exists(config.OUTPUT_FOLDER):
        for report_file in os.listdir(config.OUTPUT_FOLDER):
            if report_file.startswith('batch_detection_report_') and report_file.endswith('.json'):
                report_path = os.path.join(config.OUTPUT_FOLDER, report_file)
                try:
                    with open(report_path, 'r', encoding='utf-8') as f:
                        report_data = json.load(f)
                        reports.append({
                            'id': report_file,
                            'timestamp': report_data.get('timestamp', '未知'),
                            'video_count': report_data.get('statistics', {}).get('processed_videos', 0),
                            'total_frames': report_data.get('statistics', {}).get('total_frames', 0),
                            'detected_frames': report_data.get('statistics', {}).get('detected_frames', 0)
                        })
                except Exception as e:
                    print(f"Error loading report {report_path}: {e}")
    
    # 按时间戳排序，最新的在前面
    reports.sort(key=lambda x: x['timestamp'], reverse=True)
    
    return render_template('index.html', 
                           models=models, 
                           videos=videos, 
                           reports=reports, 
                           detection_status=detection_status,
                           detection_progress=detection_progress,
                           detection_in_progress=(detection_status == "running"),
                           confidence_threshold=config.CONFIDENCE_THRESHOLD,
                           config=config)

# 上传模型
@app.route('/upload_model', methods=['POST'])
def upload_model():
    if 'model_file' not in request.files:
        flash('没有选择文件', 'danger')
        return redirect(url_for('index'))
    
    file = request.files['model_file']
    if file.filename == '':
        flash('没有选择文件', 'danger')
        return redirect(url_for('index'))
    
    if file and allowed_file(file.filename, ALLOWED_MODEL_EXTENSIONS):
        filename = secure_filename(file.filename)
        model_path = os.path.join('models', filename)
        file.save(model_path)
        
        # 立即检测模型信息并缓存
        try:
            print(f"正在分析上传的模型: {filename}")
            from ultralytics import YOLO
            model = YOLO(model_path)
            
            # 保存模型信息到JSON文件
            model_info = {
                'filename': filename,
                'upload_time': datetime.now().isoformat(),
                'classes': dict(model.names) if hasattr(model, 'names') else {},
                'class_count': len(model.names) if hasattr(model, 'names') else 0,
                'model_type': 'YOLO',
                'analyzed': True
            }
            
            # 创建模型信息文件
            info_path = os.path.join('models', f"{os.path.splitext(filename)[0]}_info.json")
            with open(info_path, 'w', encoding='utf-8') as f:
                import json
                json.dump(model_info, f, ensure_ascii=False, indent=2)
            
            # 显示模型分析结果
            if hasattr(model, 'names'):
                scene_types = list(model.names.values())
                flash(f'模型 {filename} 上传成功！检测到 {len(scene_types)} 种场景类型: {", ".join(scene_types)}', 'success')
            else:
                flash(f'模型 {filename} 上传成功！', 'success')
                
        except Exception as e:
            print(f"模型分析失败: {e}")
            flash(f'模型 {filename} 上传成功，但分析模型信息时出错: {str(e)}', 'warning')
    else:
        flash('不支持的文件类型', 'danger')
    
    return redirect(url_for('index'))

# 获取模型列表
@app.route('/get_models', methods=['GET'])
def get_models():
    models = []
    model_files = [f for f in os.listdir('models') if allowed_file(f, ALLOWED_MODEL_EXTENSIONS)]
    
    for model_file in model_files:
        model_info = {
            'filename': model_file,
            'analyzed': False,
            'classes': {},
            'class_count': 0,
            'scene_types': []
        }
        
        # 尝试读取模型信息文件
        info_path = os.path.join('models', f"{os.path.splitext(model_file)[0]}_info.json")
        if os.path.exists(info_path):
            try:
                with open(info_path, 'r', encoding='utf-8') as f:
                    import json
                    cached_info = json.load(f)
                    model_info.update(cached_info)
                    model_info['scene_types'] = list(cached_info.get('classes', {}).values())
            except Exception as e:
                print(f"读取模型信息文件失败: {e}")
        
        models.append(model_info)
    
    return jsonify(models)

# 删除模型
@app.route('/delete_model/<filename>', methods=['POST'])
def delete_model(filename):
    try:
        model_path = os.path.join('models', filename)
        info_path = os.path.join('models', f"{os.path.splitext(filename)[0]}_info.json")
        
        deleted_files = []
        if os.path.exists(model_path):
            os.remove(model_path)
            deleted_files.append('模型文件')
        
        # 同时删除模型信息文件
        if os.path.exists(info_path):
            os.remove(info_path)
            deleted_files.append('信息文件')
        
        if deleted_files:
            flash(f'模型 {filename} 及其{"、".join(deleted_files)}已删除', 'success')
        else:
            flash(f'模型 {filename} 不存在', 'warning')
    except Exception as e:
        flash(f'删除模型时出错: {str(e)}', 'danger')
    
    return redirect(url_for('index'))

# 上传视频
@app.route('/upload_video', methods=['POST'])
def upload_video():
    if 'video_files[]' not in request.files:
        flash('没有选择文件', 'danger')
        return redirect(url_for('index'))
    
    files = request.files.getlist('video_files[]')
    if not files or files[0].filename == '':
        flash('没有选择文件', 'danger')
        return redirect(url_for('index'))
    
    success_count = 0
    error_count = 0
    
    for file in files:
        if file and allowed_file(file.filename, ALLOWED_VIDEO_EXTENSIONS):
            filename = secure_filename(file.filename)
            file.save(os.path.join(config.INPUT_FOLDER, filename))
            success_count += 1
        else:
            error_count += 1
    
    if success_count > 0:
        flash(f'成功上传 {success_count} 个视频文件', 'success')
    if error_count > 0:
        flash(f'{error_count} 个文件上传失败（不支持的文件类型）', 'warning')
    
    return redirect(url_for('index'))

# 获取视频列表
@app.route('/get_videos', methods=['GET'])
def get_videos():
    videos = [f for f in os.listdir(config.INPUT_FOLDER) if allowed_file(f, ALLOWED_VIDEO_EXTENSIONS)]
    return jsonify(videos)

# 删除视频
@app.route('/delete_video/<filename>', methods=['POST'])
def delete_video(filename):
    try:
        video_path = os.path.join(config.INPUT_FOLDER, filename)
        if os.path.exists(video_path):
            os.remove(video_path)
            flash(f'视频 {filename} 已删除', 'success')
        else:
            flash(f'视频 {filename} 不存在', 'warning')
    except Exception as e:
        flash(f'删除视频时出错: {str(e)}', 'danger')
    
    return redirect(url_for('index'))

# 清空输入文件夹
@app.route('/clear_input_folder', methods=['POST'])
def clear_input_folder():
    try:
        for filename in os.listdir(config.INPUT_FOLDER):
            file_path = os.path.join(config.INPUT_FOLDER, filename)
            if os.path.isfile(file_path):
                os.unlink(file_path)
        flash('输入文件夹已清空', 'success')
    except Exception as e:
        flash(f'清空文件夹时出错: {str(e)}', 'danger')
    
    return redirect(url_for('index'))

# 开始检测
@app.route('/start_detection', methods=['POST'])
def start_detection():
    global detection_thread, detection_status, detection_progress, detection_results, current_report_path, detection_start_time
    
    print("收到检测请求")
    print(f"当前检测状态: {detection_status}")
    
    if detection_status == "running":
        flash('已有检测任务正在运行', 'warning')
        return redirect(url_for('index'))
    
    # 获取选择的模型和置信度阈值
    model_name = request.form.get('model_select')
    confidence_threshold = float(request.form.get('confidence_threshold', 0.25))
    
    print(f"接收到的模型名称: {model_name}")
    print(f"接收到的置信度阈值: {confidence_threshold}")
    
    # 检查是否有视频文件
    videos = [f for f in os.listdir(config.INPUT_FOLDER) 
              if allowed_file(f, ALLOWED_VIDEO_EXTENSIONS)]
    if not videos:
        flash('输入文件夹中没有视频文件，请先上传视频', 'danger')
        return redirect(url_for('index'))
    
    # 检查模型文件是否存在
    if not model_name:
        flash('请选择一个检测模型', 'danger')
        return redirect(url_for('index'))
    
    model_path = os.path.join('models', model_name)
    if not os.path.exists(model_path):
        flash(f'模型文件 {model_name} 不存在', 'danger')
        return redirect(url_for('index'))
    
    # 更新配置
    config.MODEL_PATH = model_path
    config.CONFIDENCE_THRESHOLD = confidence_threshold
    
    # 重置检测状态和进度
    detection_progress = 0
    detection_status = "running"
    detection_results = None
    current_report_path = None
    detection_start_time = datetime.now()
    
    # 重置进度相关变量
    global current_video_name, total_videos, current_video_index, current_frame_count, total_frame_count
    current_video_name = ""
    total_videos = len(videos)
    current_video_index = 0
    current_frame_count = 0
    total_frame_count = 0
    
    # 启动检测线程
    detection_thread = threading.Thread(target=run_detection)
    detection_thread.daemon = True
    detection_thread.start()
    
    flash('检测已开始，请等待结果', 'info')
    return redirect(url_for('index'))

# 检测线程函数
def run_detection():
    global detection_progress, detection_status, detection_results, current_report_path, current_video_name, current_video_index, current_frame_count
    
    try:
        print(f"开始检测，模型路径: {config.MODEL_PATH}")
        print(f"置信度阈值: {config.CONFIDENCE_THRESHOLD}")
        print(f"输入文件夹: {config.INPUT_FOLDER}")
        
        # 获取视频列表
        videos = [f for f in os.listdir(config.INPUT_FOLDER) 
                if any(f.endswith(ext) for ext in config.SUPPORTED_VIDEO_FORMATS)]
        print(f"找到视频文件: {videos}")
        
        # 创建检测报告基本信息
        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        
        # 创建检测器实例
        print("正在创建检测器实例...")
        detector = BatchVideoDetector(
            model_path=config.MODEL_PATH,
            confidence=config.CONFIDENCE_THRESHOLD
        )
        print("检测器实例创建成功")
        
        # 显示模型检测能力信息
        print("\n=== 网页端模型检测分析 ===")
        if hasattr(detector.model, 'names'):
            model_classes = list(detector.model.names.values())
            print(f"当前模型可检测场景: {model_classes}")
            print(f"使用的置信度阈值: {config.CONFIDENCE_THRESHOLD}")
        print("=" * 40)
        
        # 设置进度回调
        def progress_callback(status, progress, video_name=None, video_index=None, frame_count=None):
            global detection_status, detection_progress, current_video_name, current_video_index, current_frame_count
            detection_status = status
            detection_progress = progress
            
            # 更新详细进度信息
            if video_name:
                current_video_name = video_name
            if video_index is not None:
                current_video_index = video_index
            if frame_count is not None:
                current_frame_count = frame_count
                
            print(f"进度更新: {status} - {progress}%")
            if video_name:
                print(f"当前视频: {video_name} ({video_index}/{total_videos})")
            if frame_count is not None:
                print(f"已处理帧数: {frame_count}")
        
        detector.set_progress_callback(progress_callback)
        
        # 执行检测
        detection_status = "开始检测视频..."
        report_path = detector.process_all_videos(config.INPUT_FOLDER, config.OUTPUT_FOLDER)
        
        # 读取检测结果
        if report_path and os.path.exists(report_path):
            with open(report_path, 'r', encoding='utf-8') as f:
                detection_results = json.load(f)
            
            current_report_path = report_path
            detection_status = "检测完成"
            detection_progress = 100
        else:
            detection_status = "检测完成，但未生成报告"
            detection_progress = 100
    except Exception as e:
        detection_status = f"检测失败: {str(e)}"
        print(f"检测异常: {str(e)}")
        import traceback
        traceback.print_exc()
    finally:
        # 如果状态仍为running，则更新为失败
        if detection_status == "running":
            detection_status = "失败"
        # 确保进度显示为100%
        if detection_progress < 100:
            detection_progress = 100

# 获取检测进度
@app.route('/detection_progress')
def get_detection_progress():
    # 计算检测时长
    elapsed_time = ""
    if detection_start_time and detection_status == "running":
        elapsed_seconds = (datetime.now() - detection_start_time).total_seconds()
        minutes, seconds = divmod(elapsed_seconds, 60)
        elapsed_time = f"{int(minutes)}分{int(seconds)}秒"
    
    # 构建详细状态信息
    detailed_status = detection_status
    if detection_status == "running" and current_video_name:
        detailed_status = f"正在处理: {current_video_name} ({current_video_index}/{total_videos})"
        if current_frame_count > 0:
            detailed_status += f" - 已处理帧数: {current_frame_count}"
    
    return jsonify({
        'in_progress': detection_status == "running",
        'progress': detection_progress,
        'status': detailed_status,
        'elapsed_time': elapsed_time,
        'current_video': current_video_name,
        'current_video_index': current_video_index,
        'total_videos': total_videos,
        'current_frame_count': current_frame_count,
        'total_frame_count': total_frame_count
    })

# 查看检测报告
@app.route('/report/<report_filename>')
def view_report(report_filename):
    report_path = os.path.join(config.OUTPUT_FOLDER, report_filename)
    if not os.path.exists(report_path):
        flash('报告文件不存在', 'danger')
        return redirect(url_for('index'))
    
    try:
        with open(report_path, 'r', encoding='utf-8') as f:
            report_data = json.load(f)
        
        # 计算检测率
        total_frames = report_data.get('statistics', {}).get('total_frames', 0)
        detected_frames = report_data.get('statistics', {}).get('detected_frames', 0)
        detection_rate = 0
        if total_frames > 0:
            detection_rate = (detected_frames / total_frames) * 100
        
        # 为每个视频计算检测率
        for video in report_data.get('videos', []):
            video_total_frames = video.get('total_frames', 0)
            video_detected_frames = video.get('detected_frames', 0)
            if video_total_frames > 0:
                video['detection_rate'] = (video_detected_frames / video_total_frames) * 100
            else:
                video['detection_rate'] = 0
        
        return render_template('report.html', 
                               report=report_data, 
                               report_filename=report_filename,
                               detection_rate=detection_rate)
    
    except Exception as e:
        flash(f'读取报告时出错: {str(e)}', 'danger')
        return redirect(url_for('index'))

# 查看视频检测结果
@app.route('/video_results/<report_filename>/<video_name>')
def view_video_results(report_filename, video_name):
    report_path = os.path.join(config.OUTPUT_FOLDER, report_filename)
    if not os.path.exists(report_path):
        flash('报告文件不存在', 'danger')
        return redirect(url_for('index'))
    
    try:
        with open(report_path, 'r', encoding='utf-8') as f:
            report_data = json.load(f)
        
        # 查找特定视频的结果
        video_data = None
        for video in report_data['videos']:
            if video['video_name'] == video_name:
                video_data = video
                break
        
        if not video_data:
            flash('视频结果不存在', 'danger')
            return redirect(url_for('view_report', report_filename=report_filename))
        
        # 获取检测到的帧列表
        video_output_dir = os.path.join(config.OUTPUT_FOLDER, os.path.splitext(video_name)[0])
        detected_frames = []
        
        if os.path.exists(video_output_dir):
            for frame_file in os.listdir(video_output_dir):
                if frame_file.startswith('detected_') and frame_file.endswith('.jpg'):
                    try:
                        frame_number = int(frame_file.split('_')[1].split('.')[0])
                        frame_path = os.path.join(os.path.basename(video_output_dir), frame_file)
                        
                        # 获取对应的JSON文件以读取检测信息
                        json_file = frame_file.replace('.jpg', '.json')
                        json_path = os.path.join(video_output_dir, json_file)
                        detections = []
                        
                        if os.path.exists(json_path):
                            with open(json_path, 'r', encoding='utf-8') as f:
                                frame_data = json.load(f)
                                detections = frame_data.get('detections', [])
                        
                        detected_frames.append({
                            'frame_number': frame_number,
                            'filename': frame_file,
                            'path': frame_path,
                            'timestamp': frame_number / video_data.get('fps', 30),  # 计算时间戳
                            'detections': detections,
                            'detection_count': len(detections)
                        })
                    except Exception as e:
                        print(f"Error processing frame {frame_file}: {e}")
        
        # 按帧号排序
        detected_frames.sort(key=lambda x: x['frame_number'])
        
        # 计算检测率
        total_frames = video_data.get('total_frames', 0)
        detected_count = video_data.get('detected_frames', 0)
        detection_rate = 0
        if total_frames > 0:
            detection_rate = (detected_count / total_frames) * 100
        
        # 获取时间线数据
        timeline_data = []
        for frame in detected_frames:
            timeline_data.append({
                'timestamp': frame['timestamp'],
                'frame_number': frame['frame_number'],
                'detection_count': frame['detection_count']
            })
        
        return render_template('video_results.html', 
                               report_filename=report_filename,
                               report=report_data,
                               video=video_data,
                               detected_frames=detected_frames,
                               detection_rate=detection_rate,
                               timeline_data=json.dumps(timeline_data))
    
    except Exception as e:
        flash(f'读取视频结果时出错: {str(e)}', 'danger')
        return redirect(url_for('view_report', report_filename=report_filename))

# 获取检测到的帧图像
@app.route('/frame/<path:frame_path>')
def get_frame(frame_path):
    directory, filename = os.path.split(frame_path)
    return send_from_directory(os.path.join(config.OUTPUT_FOLDER, directory), filename)

# 删除报告
@app.route('/delete_report/<report_filename>', methods=['POST'])
def delete_report(report_filename):
    report_path = os.path.join(config.OUTPUT_FOLDER, report_filename)
    if not os.path.exists(report_path):
        flash('报告文件不存在', 'danger')
        return redirect(url_for('index'))
    
    # 读取报告以获取视频文件夹列表
    try:
        with open(report_path, 'r', encoding='utf-8') as f:
            report_data = json.load(f)
        
        # 删除每个视频的输出文件夹
        for video in report_data['videos']:
            video_output_folder = os.path.join(config.OUTPUT_FOLDER, os.path.splitext(video['video_name'])[0])
            if os.path.exists(video_output_folder) and os.path.isdir(video_output_folder):
                shutil.rmtree(video_output_folder)
        
        # 删除报告文件
        os.remove(report_path)
        flash(f'报告 {report_filename} 已删除', 'success')
    except Exception as e:
        flash(f'删除报告失败: {str(e)}', 'danger')
    
    return redirect(url_for('index'))

# 添加BatchVideoDetector的进度回调方法
def add_progress_callback_to_detector():
    # 检查BatchVideoDetector类是否已有set_progress_callback方法
    if not hasattr(BatchVideoDetector, 'set_progress_callback'):
        def set_progress_callback(self, callback):
            self.progress_callback = callback
        
        def update_progress(self, status, progress, video_name=None, video_index=None, frame_count=None):
            if hasattr(self, 'progress_callback') and self.progress_callback:
                self.progress_callback(status, progress, video_name, video_index, frame_count)
        
        # 添加方法到BatchVideoDetector类
        BatchVideoDetector.set_progress_callback = set_progress_callback
        BatchVideoDetector.update_progress = update_progress
        
        # 修改process_all_videos方法以支持进度更新
        original_process_all_videos = BatchVideoDetector.process_all_videos
        
        def process_all_videos_with_progress(self, input_folder="input_videos", output_folder="output_frames"):
            self.update_progress("获取视频文件列表", 0)
            video_files = self.get_video_files(input_folder)
            total_videos = len(video_files)
            
            if total_videos == 0:
                self.update_progress("没有找到视频文件", 100)
                return None
            
            self.update_progress(f"开始处理 {total_videos} 个视频文件", 5)
            
            # 处理每个视频文件
            for i, video_path in enumerate(video_files, 1):
                progress = int(5 + (i / total_videos) * 90)
                video_name = video_path.name
                self.update_progress(f"处理视频 {i}/{total_videos}: {video_name}", progress, video_name, i)
                self.process_video(video_path, output_folder)
            
            self.update_progress("生成检测报告", 95)
            self.stats['end_time'] = datetime.now()
            report_path = self.save_final_report(output_folder)
            self.print_summary()
            self.update_progress("检测完成", 100)
            return report_path
        
        # 修改save_final_report方法以返回报告路径
        original_save_final_report = BatchVideoDetector.save_final_report
        
        def save_final_report_with_return(self, output_folder):
            timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
            report_path = Path(output_folder) / f"batch_detection_report_{timestamp}.json"
            
            # 计算总处理时间
            total_seconds = 0
            if self.stats['start_time'] and self.stats['end_time']:
                total_seconds = (self.stats['end_time'] - self.stats['start_time']).total_seconds()
            
            # 创建报告数据
            report_data = {
                'timestamp': timestamp,
                'model_path': self.model_path,
                'confidence_threshold': self.confidence,
                'frame_interval_threshold': config.MIN_FRAME_INTERVAL,
                'filter_strategy': '相同场景在指定时间间隔内只保存一次',
                'statistics': {
                    'total_videos': self.stats['total_videos'],
                    'processed_videos': self.stats['processed_videos'],
                    'total_frames': self.stats['total_frames'],
                    'detected_frames': self.stats['detected_frames'],
                    'filtered_frames': self.stats['filtered_frames'],
                    'saved_images': self.stats['saved_images'],
                    'processing_time_seconds': total_seconds
                },
                'errors': self.stats['errors'],
                'videos': []
            }
            
            # 保存为JSON
            with open(report_path, 'w', encoding='utf-8') as f:
                json.dump(report_data, f, ensure_ascii=False, indent=2)
            
            print(f"\n批处理报告已保存: {report_path}")
            return str(report_path)
        
        BatchVideoDetector.process_all_videos = process_all_videos_with_progress
        BatchVideoDetector.save_final_report = save_final_report_with_return

# 在应用启动前添加进度回调方法
add_progress_callback_to_detector()

if __name__ == '__main__':
    # 确保必要的文件夹存在
    for folder in [config.INPUT_FOLDER, config.OUTPUT_FOLDER, config.LOG_FOLDER, 'models', 'static/css']:
        os.makedirs(folder, exist_ok=True)
    
    app.run(debug=True, host='0.0.0.0', port=5000)