From 26527e7775f5cf316acafd9347aba69d60fcddda Mon Sep 17 00:00:00 2001
From: Havoc <2993167370@qq.com>
Date: Sun, 18 May 2025 16:59:56 +0800
Subject: [PATCH] =?UTF-8?q?=E2=9C=A8=20=E5=9C=A8=20move=5Fbase=5Fhori=5Fli?=
 =?UTF-8?q?ne.py=20=E4=B8=AD=E6=96=B0=E5=A2=9E=20follow=5Fleft=5Fside=5Ftr?=
 =?UTF-8?q?ack=20=E5=87=BD=E6=95=B0=EF=BC=8C=E5=AE=9E=E7=8E=B0=E6=9C=BA?=
 =?UTF-8?q?=E5=99=A8=E7=8B=97=E6=B2=BF=E5=B7=A6=E4=BE=A7=E9=BB=84=E8=89=B2?=
 =?UTF-8?q?=E8=BD=A8=E8=BF=B9=E7=BA=BF=E7=A7=BB=E5=8A=A8=E7=9A=84=E6=8E=A7?=
 =?UTF-8?q?=E5=88=B6=E9=80=BB=E8=BE=91=E3=80=82=E5=90=8C=E6=97=B6=E5=9C=A8?=
 =?UTF-8?q?=20detect=5Ftrack.py=20=E4=B8=AD=E6=B7=BB=E5=8A=A0=20detect=5Fl?=
 =?UTF-8?q?eft=5Fside=5Ftrack=20=E5=87=BD=E6=95=B0=EF=BC=8C=E7=94=A8?=
 =?UTF-8?q?=E4=BA=8E=E6=A3=80=E6=B5=8B=E5=B7=A6=E4=BE=A7=E8=BD=A8=E8=BF=B9?=
 =?UTF-8?q?=E7=BA=BF=EF=BC=8C=E5=A2=9E=E5=BC=BA=E4=BA=86=E6=9C=BA=E5=99=A8?=
 =?UTF-8?q?=E7=8B=97=E7=9A=84=E8=BD=A8=E8=BF=B9=E8=B7=9F=E9=9A=8F=E8=83=BD?=
 =?UTF-8?q?=E5=8A=9B=E5=92=8C=E7=A8=B3=E5=AE=9A=E6=80=A7=E3=80=82=E6=9B=B4?=
 =?UTF-8?q?=E6=96=B0=E4=BA=86=E7=9B=B8=E5=85=B3=E5=8F=82=E6=95=B0=E5=92=8C?=
 =?UTF-8?q?=E6=97=A5=E5=BF=97=E8=AE=B0=E5=BD=95=E5=8A=9F=E8=83=BD=EF=BC=8C?=
 =?UTF-8?q?=E6=8F=90=E5=8D=87=E4=BA=86=E8=B0=83=E8=AF=95=E4=BF=A1=E6=81=AF?=
 =?UTF-8?q?=E7=9A=84=E5=8F=AF=E8=AF=BB=E6=80=A7=E3=80=82?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 base_move/move_base_hori_line.py              | 253 +++++++++++++++++-
 logs/robot_2025-05-18.log                     |  25 ++
 .../result_image_20250513_162556.png          | Bin 0 -> 47555 bytes
 test/task-path-track/left_track_demo.py       | 231 ++++++++++++++++
 utils/detect_track.py                         | 237 ++++++++++++++++
 5 files changed, 745 insertions(+), 1 deletion(-)
 create mode 100644 logs/robot_2025-05-18.log
 create mode 100644 res/path/test/left_track_results/result_image_20250513_162556.png
 create mode 100644 test/task-path-track/left_track_demo.py

diff --git a/base_move/move_base_hori_line.py b/base_move/move_base_hori_line.py
index c94fc12..c8725c0 100644
--- a/base_move/move_base_hori_line.py
+++ b/base_move/move_base_hori_line.py
@@ -7,7 +7,7 @@ import sys
 import os
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from utils.detect_track import detect_horizontal_track_edge, detect_dual_track_lines
+from utils.detect_track import detect_horizontal_track_edge, detect_dual_track_lines, detect_left_side_track
 from base_move.turn_degree import turn_degree
 from base_move.go_straight import go_straight
 from utils.log_helper import LogHelper, get_logger, section, info, debug, warning, error, success, timing
@@ -1123,6 +1123,255 @@ def follow_dual_tracks(ctrl, msg, speed=0.5, max_time=30, target_distance=None,
     
     return True
 
+def follow_left_side_track(ctrl, msg, target_distance=150, speed=0.3, max_time=30, observe=False):
+    """
+    控制机器狗向左侧移动并靠近左侧的黄色轨迹线
+    
+    参数:
+    ctrl: Robot_Ctrl 对象，包含里程计信息
+    msg: robot_control_cmd_lcmt 对象，用于发送命令
+    target_distance: 目标与左侧线的像素距离，默认为150像素（保持一定间距）
+    speed: 移动速度(米/秒)，默认为0.3米/秒
+    max_time: 最大执行时间(秒)，默认为30秒
+    observe: 是否输出中间状态信息和可视化结果，默认为False
+    
+    返回:
+    bool: 是否成功达到目标位置
+    """
+    section("开始左侧轨迹线跟随", "左侧跟踪")
+    
+    # 设置移动命令基本参数
+    msg.mode = 11  # Locomotion模式
+    msg.gait_id = 26  # 自变频步态
+    msg.duration = 0  # wait next cmd
+    msg.step_height = [0.06, 0.06]  # 抬腿高度
+    
+    # 记录起始时间
+    start_time = time.time()
+    
+    # 记录起始位置
+    start_position = list(ctrl.odo_msg.xyz)
+    if observe:
+        debug(f"起始位置: {start_position}", "位置")
+        # 在起点放置绿色标记
+        if hasattr(ctrl, 'place_marker'):
+            ctrl.place_marker(start_position[0], start_position[1], start_position[2] if len(start_position) > 2 else 0.0, 'green', observe=True)
+    
+    # PID控制参数 - 侧向移动的PID参数
+    kp_side = 0.002  # 比例系数
+    ki_side = 0.0001  # 积分系数
+    kd_side = 0.0005  # 微分系数
+    
+    # 记录目标到达状态和稳定计数
+    target_reached = False
+    stable_count = 0
+    required_stable_count = 10  # 需要连续稳定的检测次数
+    
+    # PID控制变量
+    previous_error = 0
+    integral = 0
+    
+    # 最大侧向速度限制
+    max_side_velocity = 0.3  # m/s
+    
+    # 检测成功计数器
+    detection_success_count = 0
+    detection_total_count = 0
+    
+    # 保存上一次有效的检测结果，用于检测失败时的平滑过渡
+    last_valid_track_info = None
+    
+    # 滤波队列
+    filter_size = 5
+    distance_queue = []
+    
+    # 开始跟踪循环
+    while time.time() - start_time < max_time:
+        # 获取当前图像
+        image = ctrl.image_processor.get_current_image()
+        
+        # 检测左侧轨迹线
+        detection_total_count += 1
+        track_info, tracking_point = detect_left_side_track(image, observe=observe, delay=500 if observe else 0, save_log=True)
+        
+        if track_info is not None:
+            detection_success_count += 1
+            
+            # 保存有效的轨迹信息
+            last_valid_track_info = track_info
+            
+            # 获取当前与左侧线的距离
+            current_distance = track_info["distance_to_left"]
+            
+            # 添加到滤波队列
+            distance_queue.append(current_distance)
+            if len(distance_queue) > filter_size:
+                distance_queue.pop(0)
+            
+            # 计算滤波后的距离值 (去除最大和最小值后的平均)
+            if len(distance_queue) >= 3:
+                filtered_distances = sorted(distance_queue)[1:-1] if len(distance_queue) > 2 else distance_queue
+                filtered_distance = sum(filtered_distances) / len(filtered_distances)
+            else:
+                filtered_distance = current_distance
+            
+            if observe and time.time() % 0.5 < 0.02:
+                debug(f"原始左侧距离: {current_distance:.1f}px, 滤波后: {filtered_distance:.1f}px, 目标: {target_distance}px", "距离")
+            
+            # 计算误差 - 正误差表示需要向左移动，负误差表示需要向右移动
+            error = target_distance - filtered_distance
+            
+            # 检查是否达到目标位置
+            if abs(error) < 20:  # 误差小于20像素视为达到目标
+                stable_count += 1
+                if stable_count >= required_stable_count:
+                    if not target_reached:
+                        target_reached = True
+                        if observe:
+                            success(f"已达到目标位置，误差: {abs(error):.1f}px", "成功")
+            else:
+                stable_count = 0
+                target_reached = False
+            
+            # 比例项
+            p_control = kp_side * error
+            
+            # 积分项 (添加积分限制以防止积分饱和)
+            integral += error
+            integral = max(-500, min(500, integral))  # 限制积分范围
+            i_control = ki_side * integral
+            
+            # 微分项
+            derivative = error - previous_error
+            d_control = kd_side * derivative
+            previous_error = error
+            
+            # 计算侧向速度
+            side_velocity = p_control + i_control + d_control
+            
+            # 限制侧向速度范围
+            side_velocity = max(-max_side_velocity, min(max_side_velocity, side_velocity))
+            
+            # 根据目标状态调整速度
+            forward_speed = speed
+            if target_reached:
+                # 如果已达到目标，降低侧向速度以减少振荡
+                side_velocity *= 0.5
+                # 降低前进速度让侧向移动更平稳
+                forward_speed *= 0.7
+            
+            if observe and time.time() % 0.5 < 0.02:
+                debug(f"误差: {error:.1f}px, P: {p_control:.4f}, I: {i_control:.4f}, D: {d_control:.4f}", "控制")
+                debug(f"控制指令: 前进={forward_speed:.2f}m/s, 侧向={side_velocity:.2f}m/s", "速度")
+            
+            # 设置速度命令 - [前进速度, 侧向速度, 角速度]
+            # 侧向速度为正表示向左移动，为负表示向右移动
+            msg.vel_des = [forward_speed, side_velocity, 0]
+            
+            # 使用轨迹线斜率进行小角度调整，确保机器人方向与轨迹线平行
+            # 斜率大于0表示向右倾斜，需要顺时针旋转(负角速度)
+            # 斜率小于0表示向左倾斜，需要逆时针旋转(正角速度)
+            if not track_info["is_vertical"]:  # 对于非垂直线，进行角度校正
+                slope = track_info["slope"]
+                # 计算旋转角度 - 垂直线的ideal_angle应该是0
+                angle_correction = -math.atan(1/slope) if abs(slope) > 0.01 else 0
+                # 将角度校正应用为小的角速度
+                angular_velocity = angle_correction * 0.2  # 角速度控制系数
+                # 限制角速度范围
+                angular_velocity = max(-0.2, min(0.2, angular_velocity))
+                
+                if abs(angular_velocity) > 0.02:  # 只在需要明显校正时应用
+                    msg.vel_des[2] = angular_velocity
+                    if observe and time.time() % 0.5 < 0.02:
+                        debug(f"应用角度校正，斜率: {slope:.2f}, 角速度: {angular_velocity:.3f}", "校正")
+            
+        else:
+            warning("未检测到左侧轨迹线", "警告")
+            
+            # 如果之前有有效检测结果，使用上一次的控制值但降低强度
+            if last_valid_track_info is not None and len(distance_queue) > 0:
+                # 使用上一次的距离，但降低侧向速度
+                reduced_speed = speed * 0.5  # 降低前进速度
+                
+                # 如果上次距离已经接近目标，维持当前位置
+                last_error = target_distance - distance_queue[-1]
+                if abs(last_error) < 50:  # 如果接近目标距离
+                    side_velocity = 0  # 停止侧向移动
+                    if observe:
+                        info("接近目标位置，保持当前侧向位置", "保持")
+                else:
+                    # 向目标方向缓慢移动
+                    side_velocity = 0.05 * (1 if last_error > 0 else -1)
+                    if observe:
+                        info(f"距离目标较远，缓慢向{'左' if side_velocity > 0 else '右'}移动", "调整")
+                
+                msg.vel_des = [reduced_speed, side_velocity, 0]
+                if observe:
+                    warning(f"使用降低强度的控制: 前进={reduced_speed:.2f}m/s, 侧向={side_velocity:.2f}m/s", "恢复")
+            else:
+                # 如果从未检测到轨迹线，降低速度直接向前
+                reduced_speed = speed * 0.3
+                msg.vel_des = [reduced_speed, 0, 0]
+                if observe:
+                    warning(f"无法检测到左侧轨迹线，降低速度前进: {reduced_speed:.2f}m/s", "警告")
+        
+        # 发送命令
+        msg.life_count += 1
+        ctrl.Send_cmd(msg)
+        
+        # 短暂延时
+        time.sleep(0.05)
+        
+        # 计算已移动距离(仅用于记录)
+        current_position = ctrl.odo_msg.xyz
+        dx = current_position[0] - start_position[0]
+        dy = current_position[1] - start_position[1]
+        distance_moved = math.sqrt(dx*dx + dy*dy)
+        
+        if observe and time.time() % 2.0 < 0.02:  # 每2秒左右打印一次
+            info(f"已移动: {distance_moved:.3f}米, 已用时间: {time.time() - start_time:.1f}秒", "移动")
+            # 显示检测成功率
+            if detection_total_count > 0:
+                detection_rate = (detection_success_count / detection_total_count) * 100
+                info(f"左侧轨迹检测成功率: {detection_rate:.1f}% ({detection_success_count}/{detection_total_count})", "统计")
+    
+    # 平滑停止
+    if observe:
+        info("开始平滑停止", "停止")
+    
+    # 先降低速度再停止，实现平滑停止
+    slowdown_steps = 5
+    for i in range(slowdown_steps, 0, -1):
+        slowdown_factor = i / slowdown_steps
+        msg.vel_des = [speed * slowdown_factor, 0, 0]
+        msg.life_count += 1
+        ctrl.Send_cmd(msg)
+        time.sleep(0.1)
+    
+    # 最后完全停止
+    ctrl.base_msg.stop()
+    
+    # 计算最终移动距离
+    final_position = ctrl.odo_msg.xyz
+    dx = final_position[0] - start_position[0]
+    dy = final_position[1] - start_position[1]
+    final_distance = math.sqrt(dx*dx + dy*dy)
+    
+    if observe:
+        # 在终点放置红色标记
+        end_position = list(final_position)
+        if hasattr(ctrl, 'place_marker'):
+            ctrl.place_marker(end_position[0], end_position[1], end_position[2] if len(end_position) > 2 else 0.0, 'red', observe=True)
+        
+        success(f"左侧轨迹跟随完成，总移动距离: {final_distance:.3f}米", "完成")
+        
+        # 显示检测成功率
+        if detection_total_count > 0:
+            detection_rate = (detection_success_count / detection_total_count) * 100
+            info(f"左侧轨迹检测成功率: {detection_rate:.1f}% ({detection_success_count}/{detection_total_count})", "统计")
+    
+    return target_reached
+
 # 用法示例
 if __name__ == "__main__":
     move_to_hori_line(None, None, observe=True)
@@ -1132,4 +1381,6 @@ if __name__ == "__main__":
     arc_turn_around_hori_line(None, None, angle_deg=-90, observe=True)
     # 双轨道跟随
     follow_dual_tracks(None, None, observe=True)
+    # 左侧轨迹跟随
+    follow_left_side_track(None, None, observe=True)
     
\ No newline at end of file
diff --git a/logs/robot_2025-05-18.log b/logs/robot_2025-05-18.log
new file mode 100644
index 0000000..b008187
--- /dev/null
+++ b/logs/robot_2025-05-18.log
@@ -0,0 +1,25 @@
+2025-05-18 16:58:17 | ERROR    | utils.log_helper - ❌ 左侧区域未检测到垂直线
+2025-05-18 16:58:45 | DEBUG    | utils.log_helper - 🐞 步骤1: 原始图像已加载
+2025-05-18 16:58:46 | DEBUG    | utils.log_helper - 🐞 步骤2: 创建黄色掩码
+2025-05-18 16:58:47 | DEBUG    | utils.log_helper - 🐞 步骤3: 左侧区域掩码
+2025-05-18 16:58:48 | DEBUG    | utils.log_helper - 🐞 步骤4: 边缘检测
+2025-05-18 16:58:49 | DEBUG    | utils.log_helper - 🐞 步骤5: 检测到 17 条直线
+2025-05-18 16:58:50 | ERROR    | utils.log_helper - ❌ 左侧区域未检测到垂直线
+2025-05-18 16:59:14 | DEBUG    | utils.log_helper - 🐞 步骤1: 原始图像已加载
+2025-05-18 16:59:15 | DEBUG    | utils.log_helper - 🐞 步骤2: 创建黄色掩码
+2025-05-18 16:59:16 | DEBUG    | utils.log_helper - 🐞 步骤3: 左侧区域掩码
+2025-05-18 16:59:17 | DEBUG    | utils.log_helper - 🐞 步骤4: 边缘检测
+2025-05-18 16:59:18 | DEBUG    | utils.log_helper - 🐞 步骤5: 检测到 26 条直线
+2025-05-18 16:59:19 | DEBUG    | utils.log_helper - 🐞 步骤6: 左侧区域找到 2 条垂直线
+2025-05-18 16:59:20 | DEBUG    | utils.log_helper - 🐞 步骤7: 左侧最佳跟踪线和点
+2025-05-18 16:59:21 | INFO     | utils.log_helper - ℹ️ 保存左侧轨迹线检测结果图像到: logs/image/left_track_20250518_165921_631983.jpg
+2025-05-18 16:59:21 | INFO     | utils.log_helper - ℹ️ 左侧轨迹线检测结果: {'timestamp': '20250518_165921_631983', 'tracking_point': (95, 1077), 'ground_intersection': (91, 1080), 'distance_to_left': 216.5, 'slope': -0.7530864197530864, 'line_mid_x': 216.5}
+2025-05-18 16:59:34 | DEBUG    | utils.log_helper - 🐞 步骤1: 原始图像已加载
+2025-05-18 16:59:35 | DEBUG    | utils.log_helper - 🐞 步骤2: 创建黄色掩码
+2025-05-18 16:59:36 | DEBUG    | utils.log_helper - 🐞 步骤3: 左侧区域掩码
+2025-05-18 16:59:37 | DEBUG    | utils.log_helper - 🐞 步骤4: 边缘检测
+2025-05-18 16:59:38 | DEBUG    | utils.log_helper - 🐞 步骤5: 检测到 26 条直线
+2025-05-18 16:59:39 | DEBUG    | utils.log_helper - 🐞 步骤6: 左侧区域找到 2 条垂直线
+2025-05-18 16:59:40 | DEBUG    | utils.log_helper - 🐞 步骤7: 左侧最佳跟踪线和点
+2025-05-18 16:59:41 | INFO     | utils.log_helper - ℹ️ 保存左侧轨迹线检测结果图像到: logs/image/left_track_20250518_165941_364168.jpg
+2025-05-18 16:59:41 | INFO     | utils.log_helper - ℹ️ 左侧轨迹线检测结果: {'timestamp': '20250518_165941_364168', 'tracking_point': (95, 1077), 'ground_intersection': (91, 1080), 'distance_to_left': 216.5, 'slope': -0.7530864197530864, 'line_mid_x': 216.5}
diff --git a/res/path/test/left_track_results/result_image_20250513_162556.png b/res/path/test/left_track_results/result_image_20250513_162556.png
new file mode 100644
index 0000000000000000000000000000000000000000..bb43db2d9eb27492de0f1ac839b1f09fd2276b6d
GIT binary patch
literal 47555
zcmeIbcUTnJ*DhKhm=!U>m_R^LNoEWr6-7)43J3_85Hki4L`h>pQIQ~sN*Kfh0xBp9
z5=KQ-RDuE~f&l?hG7`J{uDy3v)78!IJLlZzJkPz~_xQ(1P208iUTeMUUGG{|%$!)c
z!eBt(5q&9&8eq6s--M!ic~X?btlr}AiT!P}p%f)g8S2kl?IQN`U$HnvtG%l8;w3k$
z&fM$OnIYEuR^F@VkNI1;i*sJ7yNosTSE_PI^ieYOk5ZO-cou#(X?|GfuVk1X(9cFM
zWiebOpX+acFI@Nj+i!~u++mpH|CwAEsk?~%Qb3ECO&vupo=a*(Si?F>#Y>J|#uwTT
z$|T7aR?m0y_wwBD2j6m3`Rfe6X!`cIOs+bt?Z0c><2-y3wzf{3oF#VCtpC$PHS`Fn
z`^PFR`-oI=!@}{0DuXnpeqNAtD7cSYa=Fj4)?78-jj5kwt+<QEcj_e_dUix38LX}f
zS7IF}Y3gU6U2Uf059O)J;mfmqY#p=XyDmNam$gP(g>8D|>y+)+Ckd}1r^s5P)b3I2
zt^?UeYLm;=hU{|6wi<t^NX;Q+cZ}7UW%mozcz;d(Y?h?;{786Jkm8WE>fsOx4}X@u
zaA=3(y_+5#-{rN;r8+1H{h>IdB)R<Ak)&O-krgHA4|(*Fn!x7^k~(|wzrsB@^>ZcM
zJByrAgLOvRO^>XQ6Ov$uS;^(*A-f&>{Z}7NgY9(C7dm-CaJGiAXxH>f<N#-OL15dn
zmI_8cTLzZQc7P)ce|F@umHW9q21$D3M-?|*g#9GZ$!zT&<*A*8k2}W?IkRgTd{Vjz
zKH;UK&(oj7P23ptJt<fS;F_;%!vb7dc9a!Y3GSRdwTj$nFuu4!8SUz4NY+Guql*@j
z57^Zffb3Ko5@E$nMXQp%#)Z@sLTKt1CIRj_jY@(zEU!feF+yLqLaX8h%)o@bDzGcW
zH)&U!<_lz705T#N9Ud@ahL;LpFQs9J+>N1_f`VC#!bhNLJ^mqr2>g-06#WS<1CW}a
z%j3&hy|8QT7qeXJYX`_^QyAi2payZompYE%@VXfEDO|=+6-zEJodCy#e}_Mol32ip
z=Ez{kIbMHOVzz~#1OedUQ$l>LQ345^QsZ17kIR5x@SzX-!vSOAG+g2&8p{HgE^-I}
z{Ib<9<U+`)${-w=Sahqc+7PY{!NNiW2u<|Y>4AVMpp_=6vx3|c9FA6jY@=6U2ckh&
zeh7C_(!(ymEJAK8&4zn`R=8p;(rVZk;+UPYDPK+ZEZ{>Az%mU!kb)0*2l$BaP(=RH
zK)Z^r5XoCj9ElF>hk|Z6UJw28&<O)nj|EV7RX|<HvgGn!07JQFQC9Bb;R7%DAO-#@
zRY$qAfP|TLDqt;^pb4RI8XH3PG-LpE>L|v}D3Um0pI#s+E=NnHn<J0n3=KlOr-{xB
zz`!8?2b=U4+bIlb3NOo`iog+4#tiuxJ~c%<SdZ7L!n}2g#ao@r&>@(U!I!a-81Yu<
z5RlcdPf|8;FPIJp!yEwkNWm~yHe|s(2Zh}TLD>-PsRX&(g#-=sIWHW+*2ABKq8K6!
z6#&KU0L#e#UzV%?>4(xiivXB{0wh3EfDNe8&rprV6r+J_w4cydF~E7}(Y-iXF|2G~
zkdn<=#gYr<W~f-FqrXI$V(PX)2^Y8&uIBN$um|ItQxG}9c_SfFp=?7&w;Qko4FDTz
zI8@#g#Qs(&&q@(>fBFH%nN{bzz5_*^(E_&0Q0$Q?!ORchz!2_)Bx*8Z$*tg8^o=vf
z_OqzcqGX0utdd5d3^G6ohN=aw){v3>&kvzOn?&jomI!C~ssTv^D7mz1pg-3k1s^Zb
zse(2jmy`Hq1y&)vjmRyYl~EwD;2g(c2f^z8pFf((kR2Qw2McQQfAP^pMq+dc1zul7
zWe_hU2<k4<g$>CETxcNa-PvvgN7GdvYm7<2>saa_N)$W=(EW0tm(A#0SSz3cfJjc9
z;d((6frpTQc?I<Wmwha0VsKd-%iP!yv#iyWlm$32;(k(1gbrBCsFO2L4lq$8-GZoY
zFVrM_7W6kpV8?K%9{<N$2UQ$Yh#{<bY91O;dyt(FIrsxtV-K7z9{z?&SUH%YS~dwt
z7NYHOkIn0;$U=NHtbGvSLe-(`J33*v5(#|`qn0;^)FFsq_@+nBsPaN4fu4j*x>(`r
z&ppge?f;c*B5Z<{7+q-~YZw&+?1OnG9;Xpwc8ry~{E#q5lD4$4#`=}_o{ex^OMt0t
zolpT~NNeHv&S;vySeK;TFz@O-;1UDE93>^xK>$_2G8ZI1`s5;+h?5ax8P#SQlF*L`
zsa&8UR;_1iNZj6FwMNP=Zhu&noc9=Qg=H9&N%$&0lmp!)&;=Ndqlv^9_bc1|AUOnu
z8`8c|h0pAIfW-WN)OWa8>#Cx2W4(vrLFD!T1OqIbP&&4q?URlPFnu0dIH7*v!2bcp
zFch&g;02(1f{%vtg+RU(F~BIacRaEPM28iK^eEmq;fVhSeW)8i3&tLT3UV1JNZpCZ
zOesWaM1`QEw}E?Ut!z$jx&ZGtfVH0jCkOYckPA31X#Uw{Cr<yX0rEHImIVlPke0lj
zY}I}n_`#l0m#E2M<YxGDNPhGIlAHW*ApNNh|A4%O`n!?dm+k}GKow)Ez#<KEKUQZB
zA$7|C0UWrHvEYVtkLXd+goHY#f=D0hK>!3alUGogBbr+Q{T080L4mzY8ctl5fJOd+
znuyCy64Hvo+4{edvA`5qEq7^B*qG=Nd{9lafxGGu_Xw2Tmr-wq^#rcAP~f2nMeRG?
zjH9Y8rz+g@beo7^l-YkRim}h22ZnD#SCvBtA3l|#+ialeB19QjcF+|bx!_+GoDip?
zCe@Rqj@EMt!Q4fS3Qmi#O#m-`D~m-nHW)fitt#mFs)E9?NJk3+W1&Jeeh3$rl3Y+(
z5|zmIa5a|g{#+Y?Dk6}0)(pTWDm@r752ac)>#Pb@xVHwn<0Z2S&EochU<Hz1>FWQR
zTk!msp&pdegViqKf!6<vA=3YJRsMf(-1f&)|E;H`f5P`CeE+v6@P9D%2UCAA1rNgh
zKY9N3C;R?eXI}&F-Qmk4`|RB@*Yo(9p)2KfuE?Ken6~ypfW?-G(f?55?Sn5IQhD_<
zE%Zchidq-bqO!CAE_UbpheU0Yps060I4?|Qzysk+iJQjX7$HVc_gvHOtUe7-h?Uhs
z45JrOlxJ*7ysD`pMP(Q+-RvJN{b!l~X}-Dj+b)B77vN9u|0KqrZ2!ZAf2zcv()ULe
z{Lwo9)3nYXee*}({Lwdm^vxfA^GDzO(Kmne&3{9E(^+0q@jU%Ynr@p$O1LubWJlY#
zsBzN_5u@EI=s0%;=6asIO)ZVz(tiqmr#439OS&zMo2ubE#T>2o$>Bz=&fA|a!_&YW
z&F*%nMzrQQ>l)pw=?Ew-&IyR^XglI@FX*Kb9tN7#ng_u!&{Ovjy1CZK9e=Z8NNdYh
zp+&graNa^UkA{?v19zNs!!vYEg$9XrSr5XMty(zGYtc73hr~PQ3XLN(N?+64>_G>r
z7*ZB~O~@Rs1Z_<wv4-MW3QaIOmt!lq-<-iY@K2u%UEz_Y#|3o8((uk2_plwsIm%S$
zILoO4cphq%YHI=WP;MdH8mdkzmpx3aQ--hzT0?o<VSBAit_&^TqMVMu+1Q8bjJ?aA
zzj~ab5AuaRNJjWow!vIO`XD~UtrX?4ifwRRQ8GD*2JKd=PI(I}IG@8SgbtEXx|AIp
z-as};D5~`<J2;)Iy4<>{+FeJCDyxlTo%LI(@HSMZG|R6~Ia+PST@Zm`vqu{3vpTtQ
z`i%FHHI2`C<rN*T8(cjkDQXsjHC`m!UA$MFhe|DMYU2e4*{xEJY%ue_iY%1D3t$7F
zv!$-l&Hg}Yx<IjL^>tz`B;g-kJ2$(UGmN6j=uFJu9cQmoSNmIJ9d08yu2cX9oe3P_
zQ+MST1N_nokyCrfu%Yki5iN3}&OJi7?JRdst5uC}F^3U<0P%Ha>L--uI6-hETaUN3
zr6oKpv4sZ%D@efV41~~~VOuGDpgL!^rzJc+P6;0<BmAon^g720A1I^r13T-f)?Tf`
z2lBYX4n36-{!{osJW2?}p6T(z2kIOr<RGeZw(x;EHwrn3YMt9Ibf7vJI_nrRqcY@v
z2_2|GY9AXxlxKRd(1D(%KNd0`GLMaFZWgc8wk;F#hR*>RrI|S`q2<k7__aUul;<11
zy4B1AAA$1@HrPnASm~BJ2NiXw(2sk_RpFl{|GWB<t9h_4;luq8R9&0$nFUQ#4_#Xv
zqi|U7#NEoc!1X~#)!Xw-t4tl=m&{M9C>&hsHN2-+YT%uNl4dLCt-H1O%gak`!OPrp
z4#Ru$QAJ9V#q3NK(eH3Z?k}3G5;FoW4dV^hiNA<m1XuYBf5ojgGn;|l6|)J*kO(wA
zjc)V#VEy>!&24b0lsew4&jfOt%-xh@78V8Q*G65Jiw{{zZd2dpJRsptH2QtdrVA3|
zBgk#mZ)-+urgxsvXPF+kE%|!!jF<Gz+XtWbB)2&lr))oej_myBz<)dpNcsbbKS_uA
z=?_c%Z}9N!tS7^ncL6Y!|M%czOG}xFOS`KL?W=D31S+3YD1VvUTQ$}vu-l?gHDC?g
zzR+*)W^0KsxJ=jYt|X;BPF{Q<a)Qb0n?4!&aB1FM)zOJ|^1HKo=)|Sf3bzu~O?1M#
zDPvL2Tdbp0hb?>&;w0^|!8)qaw!0$Qezb7miy`DtL4^xXy2o_4RD6QENf0ql>$bW{
z6L*|wm5!2H$KP)aI4a+;8}nQrv$tzn(A`Sm8~H1tlP`8K>Siev45OjgJiJ}FifG-B
z(tVb^*kaPjz-iSAbx)EP`y~$j>Nl;M2&N9GR<OIl_qt!xC0Sq9%vpOk%btNB4R^Y6
zN?HypP{OwabYqy#EfO~S!i8lgoct83B&UqAIJxKB%gLkAk5@}YRZi-HYK8I>k&9<1
zsEb3i7v^Vg(7H49>$8ycv(S&Mv!W8H{Vw>}#5f9oh!Eu0bVt@#OZw}x=aWYrP<Sfs
z7Ak5?zPS}2ng|6^MNeEdO?>J%?eT8-A<daD>ar#=7UYK-#-FHvW^zi={@7>qqunjp
zebbHVMXwz>ic76^$BW=4|KmLtrj*={3c5V9^jft+Q1Y&CQPpx5Kid7|#)?MQ{Q}>e
z>$U<++UE$mQ$O9P$XC^=_*qDt2b{#9<d~@4u7EC-BKbINhZ@?dJyl#f<;<nOj-elw
zEmiG)SK>y_e$xyKah`=Mh@2rm+>Q;^_HFDln6l2`=r0nks+~Xu0yy?hTX@PK%zMwb
zO|oYoYToWKao~VtziHOIXC_6ym06-$+*C{ge#t_{OBqGa0Z@KL*5Gyj$LL2>3Y^`f
z==7zkcJQdXzJq9DHr)o&x*~wGBp<<Gy7nN!T#?KLf5_#@DT_G5HJqK;Q6A%1u5_*z
zbftHbDEBpGcb&oLrq57A-jY)WK^sQRp4K1z$Qzm43`o-4dldshx=MMmNVcSX5K76>
zg301t;>mL5Vh0luTG2VihZM=sxvWG;9bm0{=dF{XJ#mB|ccn;TxHfhL;|j|0jwdeP
zPRJUpfV>z2nA%y%azvDjNW)Wg%|ZwfRxC$c%p;D-Z}!!KOMiFKCPv#{2$j27sB~CV
z1h+aVDicgCcKs+)WVotAydyQ3g{jSsEKKExs|!P-@)ap}cM@sl5?UdMy+SwLT$3VD
zj36qdhOy2dyn=h0aE3P`TzA#wp@zDvS*{%UhJ`6ZS1Dmfyh^#trYX+KDPw>}8EQ<O
z!bU`tuLw~+9CF}Zdpb9VH|2<m61u&M^7S2PjxsC)TtO_O;)8@4qU3~@m0Mfq#O(QD
zy_8<@0o|Z~O3`&Ai)0lH+mDKzf3BZ>j7GSwn8ol0E%{WBZg-~{QpvD(iEu_7P;S`K
zT;8a>8Q;&*Vc!pgB*3srhH}gP)BP*%sD3-3*KW=~KU6R8k|=gU`Zt~aPv_q={)nk@
z=V_hutRj&VO^U=a+I>o#y}}9koztqzZQ*$-pZuabcI`hc@=q0wo-l<yl39RM8EHfR
zl4~Lua^JBlPBRHOI@4qR{rS2UECg8<uo2|16D_hp+_(2XvMY?V{Orc&^l+WWzaLm$
z-+^5qV8D@nP6PoLF#=8~BOjJsK3V^+#V-JO{TQ3q;r9%6M67pfZ)jT;7Q5!fowF5Q
zzR}eiN)4JCh3`wX7rC!k@II-V_`W+r(Teg_6HfYv_7^W!zuNi2q<2hY>qRNmryEPN
zWDU6bBuN;)@mF$767kaOodRzD(xW;VtW1ikHVCUs_6r~-9Qy1MPmvqBtQ$At8|D5H
z0*0Z-5s@1W-hEo@uxnM=GwhpKurW)>LZz)D>Bt>QEF6LoqV99?&W?^>Jz&7pE+W)l
z$!*%VFZIFl0}3d0C7eHU-8($NgfwH}Bxy?!M9AMMJ3Z3f^kB_W5y8>9hooaz@Aalb
zJubg5zL#FABM$?xc#&Bcccrb4$BTqvOUK^4qk3fb(RCiOHAx)qWb|#3{^WvgQE*Op
z!;Z`$3+uxN#pV9o^|91J4*mmd`3ps$;d}4FgOX2I37qFV`1rf!V=pvI`&-1p*FJVR
zSS<7t<0{t`A`YNtY8GAm7>E(BXJ2!V!}%=+vph9gL_3r~VPE?DxR_zSK|Y?-HBO<k
za+AfgM$aJr9igP0R`mcuYx~axle9<4y$^*t`87=WDb*wXy_|w=#co-b*T@a+A!KN%
zbL!Qr=I=AhPYg0!Sg&cB%osGC$npGxx71C-R^eu=?vj&Vq|`<a*)<>J6qX);oRN<f
za$SMPfv*hi5XVSAnv^}ixA~ST#)<89gw~oalX7IY6*@p@siKnBcP+KUL-uTx?rO4B
zuK>*2^_L=BjmS!J10UnqHF*)AR6zrQT0mve0xE^Rm+QH?W5Ks?yJcs=rBW4V3m$W}
zxzLCS!0hvdf2Ql?Ox%^E^I1{2kcjB=-^aZIIAizjoz?#(@_2hO?WSlbBjxW;k{IRi
z<4!l9QJOo5=6Igm8-(aYf9{r!#~C|3yf2L!*yEON|Hb)6Z%E|4P&Lajqv`N91L3BB
zN$5E9L3u<?Fo6S=Z30!FpMFOdo#ffi-LjsX^?Lqk3y6=h1aav4FMpl&^g33s&NAbu
zm$1%DXoIk`Jvn2i&K=bga^Wm9GuC-17=BlNN;Qd*bwa@y<%NDztz^8Qi(Kf>)$e#r
z{gm>1|9LXi@z^s;DxQppZ?S@3DaqtJBaPfBV%O$sh23}5O_U5}eo292^vj4@`B}Nx
zJ(u5q|L(f)VgreMrCrxDq`yr?W`TR?Wtzl5URFPu)i4b7Ww^rf_}0E-Z{DA9L?ue*
zr_^KappelRH&wpG`IiHTv9l3-MFazTJzoC~&;q|6ua6VG@^qKE0RZ+EMO-iMOz&?H
zHstFwqrzrI+iTT}O73ad?lBIFZJj-T&7Seg<Ja_EpBVzYA-B^5f;3kAIC4=B|B_fT
z!w-E=hyY_Sxss2;uh_+xZJ`aj1g-oIYYB+@N%thuu;xR<y5f{#X&fm6GSEuG-Udy-
zfTtE7_${OHtZ|q<T$}fU&8Mesn0REEJyhu0T<&BNAAE$v<OpizqzEe`GH$xSaBP(n
zX~|GGKYQZIp=)HkklD5qo+{cy??D>DLzm;^4n$Tj3If?*;mGmUxu<2dXM7HK!^9l(
zOV1OA*^WIjEMa=;7bW-sPWX6Fw|@qAkk{k&6iOfmL-MpB>{&tqkgUs!prcw+l{2~m
zOISV?@&}yI7v?_ClNaY14Z>O%wGAP%25Iw)d@ohuLilaAwXQ<ylmqv^EDPgghG^M}
z!KK~Uo9C}o4O506M|U4B@#jEP6?V_+j{sVFjQ~`m6-xmZ1+1jf9`O@~OmiBIft&&&
z7kfELpO5t!>K0Cv*3|w6uMxbG-M=j(JtVaB-c6r@2`pfL!?5`Ca4B=-)`p;~=9l2d
z@JGYiNB=&)-orcNGsK~XzxzKV0;xYi1i@K>XDx<9oPdpwamJS5GUJ+q*{E#@`wS_v
zzL!qy*)+4i!HNx|4lE~X6SOikfd)#PLkI3{2(tIs<6oh&8P=5?do#c3WWXRZka@OS
zJY;_#?>&lu|Jqheg?S4}>E1`AVEpc+U$se6@N_8E3S<AFCQzZ-(1{7O2SNpRe{j9p
zU`6t-O@(+C;2otqKP=W$tHZ}4EZ$Sgs$Rdhiv05U^G9!qBaQeTAy9hOsAIra`BJ1}
zT`nROE9W`L4KcS+J_1KCL5=KpHAuXK$i*QrwE-klM#AP#W=$P=Oi_8tnYGXxC|hez
zt!_6!-dECA&N0&i@2lpR6Nl<uu$j_ty(E-*_rC>W?-4^{Z@ZpI5rVyHD8)?Pmz=|8
z+x;IWzr7|ytAM<r_f7?s4BKW*9yR;Ko^O)Yx_k3Uw`f@83q&4duObiV52}D4W?E40
zmdDTPvwj!^eve?_(@0%Bo%WihG}V3w#^jo(Fa@^6mu!9A$#1F(PQCX4e3Mfs*dYTR
z`&pb))PQa|AZ}!c69??qRWPj87xen=p=fJxdf(4|Ir1HkZ|OcEW~D~ZX73a<+lsG1
zYUg1EQVP8D@}z<!ls*_nO^2C>;VH$0^|SU4{`#yR06waEQAD-e?5cKY!}i4uJ0!oo
zx77=a2gU(;J3Uo={u(iWT5W=E|BeL`HGrf!G|4*LCbdABWLhRa)4t={y>GMNwmJ`S
zq{Sca{-*TQ4@mBrVbSTAz%&7Ia(+r~p;}gAz;$OUVi4yX8Fu!nxnA!WU>WE8AAge!
z%g-t#rNbeRRt;Fmv@haQ%t(;2ow~eQ9#Cx5p><Qngk;WQZ>R@Jm5&-wTZJ4m*e^lL
z7SehePALn#U(!dEu48aUxoMH7u<iSywQXn{qA}ql53rY(iEv+!Wp4h(K-jY}$D{2{
zrpu<`U!U!eJ@XQhctw%#OOi&H5R$82Wha=1aU{RB^?jBA^t}dHECHu*Jg_M3S2<Gg
zPjk;{S?$~v-?$@lA8sd0`IW@=An=C=w@srK+B1NU*ZNsgefA)#M99r;hCSW&{hJJ!
z%_ilTUFx&`9OFxbN!pik(F8iBp9x!$O6lOt=ZbNCmSyPjI)gP;pDHHE8myB&19{qT
zDNZiLskk?kD2&s5ld#x^pBnNwKb4M{Ua%1xdlQ(!)*kVa3nu2KUNkMWv=L$^$@!7r
zQaCIUA5xd2%PZG}(W>fGX<5t#T2KNvTWgBL>;`3{f0H8ciWd$SaNkx}$s;Ho+>R0&
zfJVZ#f(W>#Vj{9Duw>?*;}g&Tn51#rD$+Y8ri_7nmw=L^`V<v9ghT)a&<=FQ?jRjz
z5Eu?dY3GTfRD@NA6wtlw*%F%QIlXC~oxf&SW@%+9Us0on3uWOR-0XB1CP8*j?r*Sp
z${7)ZR`3m)W-**><5P1_dpEL0m$v0FO{RTtALLQZmlSR^5H!3QpFxnqfJQ6?Y~ilG
zcyT)k&axm$`6H&i&5>Xmb03<i)B`lO_Yil<kiMT+$j{=rpjvpPyq0vFL<rmaU=CB*
zg)^mBaO=yNNfV(S=Qs6*1|<xWgcdFct^u?S3rORtf%;D|juqxF(oCkCLF7GnrQtw)
z&u(HCf-7&-Wj5?MXYWzo1^|AElEoC~=03(fOX;OA6SebD9;=<vOnVe%JHe|@Yd9~M
z2Lo|v?u<Y}_iQ+H&R%6RI_V_rh!ZJ0r9MGbF7YIFn<GIdIX<UbI3G8`%mkKW3GQ6C
z@8Vv6JF(uV4p@HZ;tI}q+Mlx<cHDiC`Z{44T>-lms$d>`aCEvBLS91@2c2M55qV`J
zFQ=)z@fuXyON8Kd@d+*m$OgX(;@Sf{NDP|E0M&kaVZ9O)5sf^Az;XuD2YH@$h#P3H
z5;o*f^aSoA-7X=tk0^eaGMv}4Dt~W#APxFwU?WH>7+x(UyERQCZ<SP5knYxI<xpN9
zNVp`vU-*;h)b$7My+}wGcJQ}dAA3O092HJisq`nLKK_mCqc2~UaG2?Gl=Fst`v!c&
zLC3^LcgR)8X}`pi&XW$eBIG3JUMZIK?q}()Jb!NDg9oo0O`(1wgU(f;hWua|aCMVn
zB7RM#Qsx6z={9Fkr`SG^4;ucnm!>d4Y$JjTq|61I@&kIxv`^e<+-+w;dKN!EEzDDk
zR1fSD$c+RXp6Q5rQHX76GSo=IwUajGE5BryGIQ0YUe8g*xcgvvh@AxmT@2l^xm~8=
zI*^7d8Ij2S;8O#CLgNt$RCbG5K%PNaHSGt@=R~Mm7<VckQE6JhC*)Gd9{g%ilL5rO
z2Vl$s*dwP0n%H1UXWjyFM<Z7xi!s%@U*|)^ib0spF~K9$eg1`-HKay0NZX!Q`<p<W
z2E~4+{<txZ@A7*kK=Kkt9vCx-4rU^Gjp?%HI--2$;by}CynGzwQGX<MnfIfbjQP>b
z76%5RJLpi_3p>elj7CErJ{o+;+yI8XB^b8iz|mS#y~*GAp>eR8&@)YKqxd-J$J~5@
z00lrWWr_D>=3RgTV_NHIDwDG!T~moa=~zMCpZwy`w2u#n90pQVFY!61W}`8lRPC;h
zcvKSMOBZqn^4AS_@F=CS0-{X@Rzb{JTp_zck-ox1a%QI2ewMc7H*E`AUyL)IZwvo1
zng)0Zz%Ufg5zzr)be7wT)`FTa>VQGq7dzZ>hks|#c!7C7?3nlc`-pd+uIiyk9daSd
z>ztx{CQ};BWb<Xr=FCJ1D#9FGZ2N9PxlnhTV6~m1&wh{0TIg(=*46Ll_hKfS`AH<u
z@(2W8zQ4%v<zk5paws8l!G4%`botYft}%C7v4H#ex6{tA{g<XTLfCB#ikj+~L}+s0
zBLSkHf%^xDPg54n2Xl#*et2^NcL+2X&-p9R<02S5y5T}SwD<aRc4LpI=-eXpD_1Ux
zm{6NV&Tt3QYYwC2qNEh*Y>tv6-H{jiLhC!S17{-b!Z=p=2lFF94Q}PVKO8P&a7V(Z
z&em^l)0h(Js@ml_Cfu7JGn5h@=fA(Dy7%_+=+~=Ui=NkO&v4sT`S|SFTW(G1^N#;@
ze%xivoB^+9_W#-HY8&+KP0i|}q5l8g(LVlS@vAsJ`F&HBziJ=Qx6^6#pFLvo<duV}
z#>DmJjSWZ(&k@&?9Vf4#ppZOwPs%XoBZ14WL<P=$x9jlR0dM}g&K>_%ddd1g&mZG=
zC4D)Q(H!V+xit5%Y}Bsvul4lx^)JSWsZvLb#o_kIxx<pI_83qrmuyqJ-Q4rW$g9&<
zhAw}q82D&{Si#CqyD~?6RZkpuHz?xx)WyY<L%qDcy=R*DSVQ%`ECCDYx5%RnsEEL~
zmK#zAm}oAukN9xml);3f#^TY+u@_Qn)Z?zf-~B!2g}jqRkWs=YxF_+%hz$Pe<<RLR
zs4G#0gFo)PQ2zMs@`O>V7X;MzzA|}<-rXyv2}Q-3%~3l>G!AX@#HSl1EuHstE7gwz
z^WZR*I+?%BK1ox^!W~KTejKanzgm;NeMV7r&9;Ik&8H41ln<;F!^Z=cMGnZPhErgl
zG%Tg<^D*|mA~gX_WSnQlI4{1axFGrjF~8SzzWEEg%$HK=GT&!;!lCoYTZ0a7+c0&^
zKSLMw*`oLa9qoRYiOKdpu4`~);KbLtUFN$^u$=LA#nPCo@Rbw4X5uY=tfOyV#k&2C
zYWQY%5<aKGuJb=W*>(O6c#AE+=<w}4d%yVPT=Ak+5ZZZCyUr%BpVI?OE-(`~|B9H+
zpSEr-SdF4zydvlIHH(3k8)^4l947#tjePLTc-ytR;t7%6t(n~~k=+CCY<;;Z7j9S2
zZUE<i1GZi1LSNQp(`hw{iQ6V2|I_DJV^)a7D`-yrej;#gMAl>`06#7Z+&>(lS0H*@
zUM(Zw-8linZ`Y=mLnb=w`EKHyKaZHdTFy(_Wyc%UUI+x<$P57pC9a?`Ff-Wfm5sBE
zrU~+4*CN_CGkU8|Srjo;_QW_Z>Fd)yB|QPIwJMXv9i}WQTkhp|-*}zG%*^T8^;frk
zuU~aXHOn8{e$NJF)IS2*a(BvKzAO7)mPo8}Ew+xmK!Zh#0ZV17#G-X4rih=*SiD+X
zu}+LY=iO8|yU!HyrQ(Y9K^eVsA79u!d&!%Zm;LSF2eN1<cK-Z{H|{<u9Er1jJA<lz
zP01;iGxlhsjB9eaYN7M06Q}i-YDv7)m5jxzeNJWc*oIRq;<dQqttYZxChL@fBo=wj
zESjEed|~sLbE@daYyh*_m;EX=z0qP;4%o%o_nx~PF_9e?@oNwYO_}Qw!`N|^#!T{k
zkoS^av*DE9#L0u<^tVcgL2E_|eyZ1GbJb$tx(f#=i=ZCo(KcH+I2X`T_e!Q`8|A#7
z=evzglkIex1TNng_V<{N@=lK(dqR@m9V+WJ?rHBpamCw8lf^F*%>8|Eh^*JiDdNOP
zDsSV(&9gnXem8z`=g@_6d4)&=OokXvt~dmh^$eT>1i@%9g9c+|7|Uyg+p*WyIRPkS
z6)A~Dk%MK$B9~8j)qpn{l`%bgp1wX|4FSf@RSY4V`C%%iLk6@vY0&1yN-l~3r;Wo-
z(^Da_0h~!s_)BqR-&6V%CyVQ^zOcD%dLwlQcmh0D=`P?g>=W1ibY6c{#(8}j7ljc_
zUVl`cEWY%V^aRO88w}S;RB~qkrg>Ek-2PWGdJoF$b)L2O8G*&C-nW5OQoHDIs9-+6
z?}tdwvImXh>)*u{S3<rFmOZg#ox~P3w75HOkiMf70-%<sYBms$f<ilmYZ)TLSZCM$
zI<NKuv}w<nS<X0EZ2f65U|J&fI3{{1C1cp|oDaEO*&;PhUuwgJ@;5JgJfq`kDtg{6
z^m%=@7W{N_-lK>hoH(W{A8IVuNlbd$8?w&0&naqWOwU_XZmrzk-eVq5&%PGYYw|LC
zz>Adtacm6=4+}j!CU=w8{E4Rq=N48wIp5v(#noiy=>vT&N79a{bY%hEaqfV1ViBdf
z64KiDwgwqZlUj!vz6cj3!bkM7FUYck#8PYG%$9$8c3#<}#<TD#L3`w{K^WGACG;E}
zP*$aT^j5G`MrA{XX5Ny)U1a%2Rabj%FKus<tn!oSs7*17`Xm;6ns%=fWJ^~L>2F8G
z+thJfKGJChLUaOw+I76FY@_~*(R2lN<k9p={;F&dO581>{gFP9S8&zB*|D<EDgAMP
zTU^cBISU3EkM3{MOP-u{OZC=*&L$bTk&Q!XUMWtXk7|jxqt|>zui3ti<=4dJj0d7G
z&ig^9#Jx4~DId4$=2>p2CQ7AaI^kY@2V6vV6AWyoWkxC_)v%2clf3+{1M_+BLS+vh
zQyU8Xg*H@7^UU(2-&fb{pEE+@QdlUc8%GD)@Jcn3$WciA6y}gHeOSplq`q@B<If8t
zqXuRm8s3uq<X7Q5^Q~(0GDu#vY$Q1Kc3*<U*hs_o)4xyL!db?gAvQIm_v%y9a7M&6
z3a&dCMD0A;W%*ML-Hvw{GM;Y|8le=`{7d7>dO^(Q_!J-KDY(csPb0iGX5~7GAX!8$
zPy8YMjf+u*gM_up=$LKx+0AR5$;>@6fRsGWMZ^n;aU<0tOdH?W9@_D8)u73*8r~%A
z%hf(layJr!2{~4;{#46vZ?D84;^4d=BzKprQ<~%@Egz|tOB}2<skXw!r$w?PuwviC
z=N;lB=e-yo)cf|~q{Z)_42Dwf*zfRLG1;rr21)Nsv~g|;&^3lk@FZzc^MN)ib!mJ`
zjOh?kx3_P_JepV7?^MR%jS@?|{MH~>MJCZ!$$?ctF1G6o!*c4!*Y?wrukL&E(FgIQ
z2&W-(dNJc`0t1&*^M6Jt*bNy6`%BP}`>@h-hAt;F>#3IHxvLiLuKNcjuc8^&u^-7&
zLRK*(n=UE{60Em(4NX?JpWxjiz}(djHFg`^RH+-jy`Spsn!XOV<1%{YMV}aF>2^eO
zYw?r8u@~0(@z3H3qUaZRdh7S&RsBKJUoR^Dl^DK7ZQd2UpQAGa#r?QM)699B9HyGZ
z_TJuO765a$UQBP3d#F*Dc2i~L?Ry!6tK;g%SNF};&W%3dZzlIol+%!Lpio9nyht5+
zCnj&V>c!~X@gqo2k+>&u+wbI*&@(+viHb~CWPGt*FcaS4Oo;4Y6=!|@8F>(UZbABw
z4JVu=ClwB#VBvgpT;Z^Y<h<xUdNIB$^OE7m_+3hNtGq{3CNsqrKj;yiPLmXpsRNUj
zZ0|E^c|vE?&ryyBwBcKZk)4(7-kiS{(M;5J<$iu80_7&J@L?t7OIdnHEzb%}EqsIb
zqqY=FM3@dKI(uj0lYvW2W?uOuR**|m?SO{t9G#gsTk=VYI<Kcw)NCkLJc~?bp7TfS
z{BC;o@f)Bpwqz3#N2?BLX;Y$l<deh9mdEPMH>|#DwtVe^iGB25T!o;v(<M1k`pf=m
zBi*(hplc@Oq>VUS^N2=wtU9jXDVTF}U5UbT8ZKA^^!4Q;h)j+gwzc5x55>U5UC;|z
z1^QD_JC;1?Vd-%AtsT_fy@R9+Y~xcpo`+8WY!m1-{mVcn71vD{8C)EH<Gjrs(VLY;
z3a)zKLHSdI2NfT^Uo0Nsv?^wN2@@!ATr61=VtkF^hn3|`KS6IrNKK>pw{|HZA!DSn
zw-X&%e3!**3YHGcHZx)`Sh`8*A*Vewd~tQqTq-dr!ijEAuYY$fE?&`bqHr+MLsjo*
zS3af{TwbqSMAl!dQC(dLwFWh-+l3;tGYIPZphbPWYeG<Hh&e%v-w)kj0CGl(uHrwI
zq4R#M+QPSFGHr?6P24k%#4Ym$9LQUee8hqF0WDjN)DN@^O}7%aZEKeHysvmmf8s=(
zPM7I)T5X?Nv+Ej1pAYILF(xGHM87Leya>co@C2dqm?a2y0b)~#9L8+n@@0QNy-(6>
zA_FBN?n-Dw7lK5J_x0D@tjiwjU#?;dXqiD9;2cE^Fnqsy3BV-!1kN&FkkA6P(3{9Z
zupzN0vca!#dF+Mj6UK=F`$+A&P=3w6LS7*{_I`1Df^Fwgge$UdNf8a;;!v!nYd7HL
zYzFA#^*^p!oRMPd4SoIUYcz&u3`w%G)V#Ybk~Sn_Ymn3`O^n{NX0N_p4mX6ek{aA(
z!?<b%7BvLDtL1R)%>FTL%+c*w5IYVU(I4^j>P{g0sV`^r79w$}9YIQ@_w{2soVF6>
z@Cmv8_O_?z)!NZ%USo+Xu`@>NU5vZW`!W5Q%sl!o&+>0*i(b<iZh3G|illr^??5cJ
z7tGe4i`Ds$?{C6u-5O4atNn%fGv*>)DG!pSiC{DqWAwfxZe*7r4TZYr82Zo`a-(k)
z80;~Kz#5~bl(a?qt2HfW`1-n!FCVz&IpHT2T0%#5NMn8?ix-o{{cwM~6deyHD+Mb_
zbFA+K{^l?Jo~)z4TBU1gvKxQtgO~B7CS@^FC)bZ*rdV4W9PV~133XF2b%{PlXV#C2
zh`jU+f$p~VNtPo*RNA=sP<vylk&bbMrgq1+{+4+RQpgmkWZ?^#k-Qkal0G&PG_txl
zUeeOWZgc~_+8#4Lc2%W2frGyO-G-{17M(VIuM$VnQ%qt261KJA)LgaL3obGn!%#{2
z2ZP<At=wMjEACqnNEjMz@0t`v93%<{xbq}~K1)JCW^_NVFBORsiJ)Z0^u^Xe<!@jN
zVC(*SN(p`LJXg{#dTt@39z4X#wVum>WI@dL`n(*FS!yAe%C-!nYiiR<JS?D*ZG@3M
zDZTUO#!r8vwhOx78T5!KuV>eUCSoqAcF<*T>KE5^OFO;|k#v2N5dSp7lwrsoSO?ds
zBdA@!hl5DrWiQan-lb<hd7Ja1|8lmpoN*2f7DMMSC%#-%tX;{~jL-}va(Fv);9FRO
zJoG{9M8_gG|3u&zhIkUdVL6Q^_nl<s<B7hPtjJ))ZA*ccPJ0N{#&P7MVn*j_-zP+(
zfNT7mgu=NMw-r#ef;nS#6rCvpx<;JyoWJ4P1N7vZ&W+La2R*t86IO&3lY?1w4o2;m
zG!9k%kh>+befcMqVos_adtuMDE2eL-tgd-P%Y*h=Iv=D?QK?n$%{S)8(vz$_GAG;J
z4-4Sc_Jyt$T4s1I3XS@|2XqZGJ0j_`E{B<A&qcD0Lmg#^OqMRls?T$Wj(j<=Dhzju
zU(=bPJ%$Xp<{{E5xI`cF3M%dZ0I9E>QNwEc1vVTybFxJ;4(ThHc;*eSbaI~iOBsUJ
zi>L<u)pA(|9bVEAnzVYF%Y^JYrnLK#$eXM86}D@IAGBS!kY=HhZ-UEXDT^k;R(hx~
zZpW-Hx0y^YjNL}b{pz*Ivt&By3VtzY+xUiHsGi`Bd$alVftxFkH88-MUR!b9#g-Pu
zb!9Z2ei*nXrTJQTr`Hstd2*YXLgV}OG6$w$R<!aw$~1~nScMbNedvvy9u*jPXJYw4
zU}&9(;g`R-d6|Ue7}uv|@H25cl-JPTLttdpV;Po{`uffFnHAEN*XRzfAMKySnRE`~
zLcCLAd+NS1F?iIfukV}HF?&*p%!Kkb6oVaSY393k43TXS222S|($E+S{BL_`jF<Zh
z@~=sZkf>H75^pi7!}RfK%+z?dpk86+i$=7~J2M87Fr#XJW5mq>x!c0e^(>iigNX2{
z#QAEB^Dkgtpb?oU{SbmX3sPs|#rZ#>i0kX?W(t~+t41?yq8(EcJBYjjg8b@}qnJB`
zINvpxalS4Q-x!@c`wDm`akQZ4sp7kgcTSnTO7Ha1?dj%;$}R1fxIKikQ<#HWl3u+Z
z9w{7NQytp4CKR(%<cJ1$g!Lr{bReI^o31mM(edDzdnmyZ05?=w6e8POzcluCJWWdy
zrm7d3;;L?i#&YvUKCuk1*jL-{=3=BTQEAkkDF{ftW+tJ2w^gUND}uzSEpM*QPVeY|
zR*V3~6-o9I{_^K>p-n<hO8%bdgR-@NkYDol5(GiLi8%=Io-<d|CHP|n2$je88zq`-
zY|_&@Q_^$v^j8n@l7@7RsHXY#GUIIrG9CLH4?$<15nSF+5G(THe6NIa57h=34XsSQ
z6rRp4<+jv<%m35z2St}}iA0uo-#fut-qM(VdL!O9Deg}h-r&UKqIiwtRX>w7+$E%q
zmrE=HJ~$kwNdS48uEp2KlliwKuEZZ^2~gyZmx53nRD6vAj4utC*qScaOUv##lqi*)
zhw9o$ntqMlh-N03`RbzElD7NxMkh>VTXwo2`W3&Th3G&m@-9EHdC_gb9`#uf1)N5Y
zJG{@~>fnBOW`Nv3l6HS8?f&R<xC+t1^+VpL-BWX%<(_E%*>mPO#}}DK3`=!)o4w3F
z-K|~`GD&grm)0$Ej+e0vapbRlQE>IZuGmI?tY;9PHMq9lpq<H#XZWihYWu3&nc@JU
zRq0inYGdxTi)P_w#_z|-bN~y74=ZV!C%(Do&p$kjM?AfQ7!q(xYt<5utqJD^aHM#S
zFiEkTU0Ktd+*gA5QV%$_t;JqrUJ+ALUFoF7!_W_FQE*GJ7Km(bWSNlHfoI$Ir|$kn
zQSy|b{=C(Jo?Eg<Ikvc3OIqet7X?3d{QXfz_iuReFfjRGYTTnmWC2ecKQAlzC9j-*
zxqR#H(bkc?xX|T0OI?1}t%0~JxDH}SG~{rNO?-@=ifda#mPe~|DQvY1Z#7>VS88!&
z9+ZsZx8wAlfB2%y;{``lGe`?1bJWoU#e#+&{LZhK<4R=YBb(pQB{`@K=qNF=l*j#H
z&THa0*mm|-h)gC9-Tt&=_7Z*}IV4-%H}Iy~$~!fEhrzJoBoo5#q*9N@U3wlWqYrV%
zt{+p!a<1_!x-Cq!!vU;>v{^r=<Cl*d;6oGR<MTYhbnt>-<@Y*yS8_?A^*<Y|#ggUd
z+PB(+@WB~GydSod<fLj`!ZeKM7ivP5?xY{L=FKEVC7#UA{!*ST$K$*QVlBwUnb10!
zACCzKY|9?6F^|}=)63t<e1e6!=a`SQ=Gl4D#@Re(e4H|j;?VQ%h~@UN&ac0wq8=k+
zz!#Q>6((7rHN+VGzH@JGakbO1EyGGZc#nA<Iq~V-r_c!>JLCJihvM!5nBJ*pxT3aB
zE*y4!ja9^^;$cf*3Uz^*W6tS!N-tAZI!Th6cySFpQ{LiZ0rkg41J@#Jf({^(jVD?p
zy}jX1dW}hi!|vPAyl6#oJn^J?yt*yWdO^Ic8*lGGg~$=@jXB(vG2>|<OKrrSr>f&t
z-j1Vo1~Ejh%$l9Y+ycQ{ZxiT`XH?p~`87(lK8=z#&P)~`w!L5A&6N3Y<@^sVF=K%t
z<z&T@@6tzT&-AtR_zDm(B2&u?cmzk0yAsba&5{oPNf$g5(R1W=;jGE#A-0*Wy!!e}
z@c>cpd9uoCm}cH$O3pw+L1t4z)7tr)TH#G8>@4aQM)n|U2mVBj^|;RR4s9+}<TB2v
zrF;n_uVA@B0I6R5_GftSQy|ED#eOM>$%ZOq#H&X=^t}>-r`)cIFq-jiS%u_;iw0@q
zH$b64v`R~8Z|{;#f?J$M<7zCaiQ+H*5Mv%3Z)@w$`H~f%UJp&p2Uj{>E>6JH6PP>W
zT~fM0QyA8=4?@M%e(y>(LD0OL%5YLL;iQ=JD{S5P>nqTYbgvI2119n?T0YX06gZL<
zE^A71V8YDQHyC=aF<*D$t|SdCYi0y;dN?kemYFT3yxj*5IJNs~fC|*VLU0$61rM0W
zlOXgoOuASX3A$Qn%8@!cMAcRRiPBn{lFl;%YRvo~K8}F5_$XbLW_}=@OrcF`@*z%p
zL$!>qJLfb!u9jv%R<jsCbfX{I>L&(ufo!P|#)@tU?xB5?*`tUQR;z=}KN^7F;%z%y
zn>ELPDXZyctjUX*XB+tOQ{qIthsMN<z{(2A$DAaC)>zR%%pJir=nEvVYQfOumUo(`
zyGz2$GnP}y)a)Y}kI@14zIPGojFdv;N0u9Q9$}97mhA>uteKMy&uqQ7JSVt(Yr%4Z
zz6!$qJpGR^1oJu3j>#*1y0rfyAWwI%|5{Y)f?kxlNRoG5D1Xd}a#x=u@1VS%<ba+|
z=4*i7Beyg4eGjg8;;|d%xHUKX$YG)c{L|L9s4{RSk5TS!0uDaxU3iW^x<Bb8Ngi*Y
zp73IML`qThq})l|Y>&3jE$QiVI9b8yHJosIJ(a$QRlVrvKnfp*mgIysq7r#rFsH>L
zj~CDx*ORFuwP_un8}77j{zjkBojfIlM^^O98F=7IhoilVV}PIZ65|;64W((RzSvoJ
zt<7+aw0ez22rwTfF?L?>%;Ej!ExLgo)Jhj46(OY%&96|`*O+^5QDl54P5bUkO0E0-
zhpIM*zS^GBg4oUkXp={32zE*P_cn~Wf*yhEC*tNvf8;B{;ObjlrP;cDE|yk_CyB8x
z^`||iG@y~mK~f3GBf3w%0Ra^gn&H{3LSzBr7x`$A2&?&GVNl)-+T7>|G?%p2q{yjj
z_{fyD$|y=fI8chis}bZG@~tr|R)k%CiynA;mjok&BeO_FqP2iRazC1!3f_~9Gd<K&
zEEOD_>EqDp<A>g=8O@(c1MNkGbuFmli5Hw}C%~tmz}7Ge7Sao@UHdKG#@d~?ms?%>
zuqUi|T9D}8i7PAT82>;|`}O5sTGn3bm3LaO?&#@Pqvxyenp>pn9NLFIKAn&wwYIN4
zI2Vh+RloQHXGCkM<=?)|Uc%vyg{h3Xi>~wQgSMXcx*kX&RP*D!dI61^J$wApU7gxE
z*=_M2jUynO69o?$A+?VmXh&znG%H_WS%Rbu0*?2+Wz5KRAFWShWZIyVl%`+ewQI(M
zaWfoqi~HesJeqEGIk5)A&~ln!!2teRM6Y(2Q9Ht`HN$EkgDU+lUD7h35#0l^xnmC7
zdyX;;Vtg6I{Qg*K4GFTdp2VYo4wS*(E*l+?jkzb;OEobPAo>Iu)x?$`0KeJV45z}}
zW=jmZw{eiPu{NRDG0&er-xr7;w8MK@c%IkHUW}i(C<9P8W(qiwk(uootl{I)YRi*b
z{b+xGm^c5E;r;cpTdv~l*$2*;pqC*Ol08u4G4qTd#lur%hL;OJcHs`KXKy>O{_3sK
z@XFe@3Aiz9THNLSF((1TzIW|e5RO!1MEwXRYXNaz6nfSD%pO}Z^go%|<4$2k+ouk3
zsn;wD(04pdSB$~}Trohpx_6{xXNzYfXdGWN7%vwp>b1kjYkOp{*Qj;H!$5ul)KeI}
z(%y$iB840upp?;qW~z&tc><XUnt@j17_#1Dm{$n5f2X-!UA@7_XB0Y@%c^&00JP0A
zSWmbrbtS29g)Yw{9v)VYtL_WIO(t(Kd2R`%ySvQHh3<R&dbs>9AG8#Q@WW6NU;%tJ
zy`!h1dHm9>4Fd&7Q+TV0_Rg30!3!Qv+>r$~4;U<Ri0bw9J5{k)80TitVgE*BGAC`+
zm*wl{0^#fo62TDvO`a_rn(>>iiz^OHhFXz$QcEMPqsAFw`iVaV&P?NVPwMk@1d}Z`
z1b6p96f5Zba@C@##1T?r?Kgq#uZwQ4%YST}=emWw1zS0V@xVez_RNJ`KG^cU_r<Z+
z_V20=PRy%og?cnqNY#)a{z&8eb_N3HgDdZ9Katz%$LR3JfGKl3Bu7LH$O@GgjbFYf
zq(4;cvj3i*sQsz5{Y@^6{S#>WgDWzfOF1=N@q^cz&@@L7L^Gz)Z{FcI7LdMZ(V+KF
zf|g)dtxqX0t(8+7FK`G$kwX+@p_jPi&`TtFk7*oMhK<Hk25~%|9BcQcL)^jd&WR;q
zG6@#k0-pZ{Lkw)I@ghm`gPwFtYe&$Ss#(77$vkstnBE-F?ImA65Ck&yP$O!`7d4Zb
zvi%JY@85sMdzH@hrIQ8Ty+eZ^L4GOhn!PM3wBgvMUw~Jy$1I_J-trZPY{#hD3X__M
zZ=AI+Mt?f|UNs$-{r#TF9y8JG@e~}rW~i+(@1HLRFN90cZ{$X%Ko^i}3Qq%C8h$0E
zXE%Ov2p@1;3Fnd;X%GZsA;%ibeVSN(a$b86sGa}Nnc;YiXra_#Ee#+DkH&bOB4NS2
z{i4X)&YC<JY_OiP*n^Um)(&KRx|sOXvm|>*5afwR#}4mtI8CM5FyU=KSQr0g@kJi5
z%{rpY=H9#?%fI=Rxlj1##i;0BSu<5;ju=0+RpMAL-G4Pc>KvQ;E@bG-iSK&V+0ArZ
zw`O_S+Na`n=K_E9RBLXFOqux6uJ7BQIx$;sT;rw1ZC9sU?0>XJtgtq?`*Prqw@z<;
z%xk`srt=n1p4a>rpH>RrEI#J(s~vTF_N!frnecn_o(F5Hyu+7CQhM99_J-@a4GMey
z>zC%;hC{H>5Y|E?O@tRxpEl;f#rMrF-;N}l&m8pAqb-~#Ra~LGN{q_=%lA`-k=zmc
zy$3s%$7+T5qik*^>s((|WUQ8Oc#hNPX7zI^tu3{@K~&idB@@Nx*Frq|eR|nb&c5i;
zgXkCYZ<TMjqUb&q{`2uIrwwWvuSq?c_8^sW)hsMWk}`^mlLEU|Xa7V-elDHlTwhzt
z8%t%ZGK^Z}Z!}HJ9`=6q(^YHlqX#E9I1CT=Id`V@`D%*l45)wR#%-GCGCnD(^zDnr
z2UMBg+va(p$!E5OjDCBgdV-e!UKt*jBPUL2ozvf@)HZ!c-EPf<f3x1--L0OmW@rZ-
zs-tt$B#Ppm6IrP=x*FM4Fm*06(q(VCI+xR-D?zzTI`MXK!`AU-W1lKIPfHo&duexE
z^qoOF7_Vp4jui4#M#}1<UQR#eMBD9Oc`fd8-5pL=>E9G(G|q2?q0TFb&hz!pieJWM
zDJrjx5DZ^N`iwCTa>8|b=Y0G9`=aFm9T`u}D3+&UTYb|d#i+0)3N!o<UFg#)M%yOF
zvTcQM<T8BBS?A$)vd?4n)h{K)+Sg#keLYi)-uKqtbbMlTRh%lzIwcgjvJjTH%UXmC
zOS|~K?CVd<`<@p4|9$yo@9lhP-H^VNr`$|oy9`VP59FCuC+E>mAZGFO#&$0^82^Om
z*E9y)pePsk9b0zvJ3R#vDcQNKTs?YKka0e**@Jhj%i*6i8IWhMK6i9ocIFZT41zJc
zgz}|qbc`SdGK|b>BInrsHSS!>T&EY-L#Z;!#RQ-RyEY&8P1yc<>gFjD(|q^~zMd*%
zXx(?V!<1E=jYIF8+Qwh7nO-oK4~eI$!!z9hTRPZv`-GF{;b12>1S-qT2w{C&+g@0&
zj#0gASzfaubgzsg<>E8=hTpe0%RHOf-W;!g)<-60QonzLm;_q8S=eHW0>S(#s~`vT
ze^DGdr(C@+RhKpn=XPCMG0E-Bmm7L-?#jruJA@ZhzRq%CjyZN=!6awrAAu<oD9T-r
zfVZ~5<=d{!MPL1fsBa$bvmqT&7mi3QW23B4I<>B6nd~x{MA_s)%BpLh7K%}I*NMq4
z!E{unu%n#g@=eF1U0y5T<41>W<!x(SS_DaPXq^(c@EUPpEOKGSG(tn4L-{l$WVKeb
z5EAPavE67Gm~wrE#iF~68$E;7aPA**-l(-#Gocy+<NHsvs)UugS#l1}wcA30K5BkN
zF!jZZu6$bP^kV7~z8_0Y5`rr^PLj?=$<%J_JATfE!$z!H@Q`AAaT4)j>|W<<D<1ej
zwujPI#{!)Z^4&PuXY6^4wd!VbzMzUw_eO{hj)q_D?u^l<H8pQ1Ivd0PImDS%hPtaX
zdK7OEG?IjFeeT{dV;H|$X|1%D0}{U;^d2#~c-YDZK0gBO0$1}_+dzo;q48J0j?SvX
zbuO$rWAr7-cGc&QPbKcFRB|fj$FPrf9lKB9Xmru4`wm8J-mg7o_Gdq+fG<_mWmHDj
zW4f7?&biQ+;o_o;W-Q85UM+;{x(3?Tdvq22%CgmEK5I1<R)z4qX38cS%H<V}X%(jE
z)VZ)qq1J;^_vJ^K;#{4{&bl{~*gQkI^rF`r0;w<=jI9?kb|C9`Mnc9y5?k8+)s@7a
zigYLd%&5B7U8h4qs%zMDhxDubg6|Z>Vri`hdu0kHW>2{BsrbgKnn?Jc*}NvI=1~mP
zKO#m9Ootv!xX-7A#<pKYtNBI$44YG*pJV45Rc5~gv-D@D<d6Y6uj7sy*bQGW881ib
z+5^t-?US@lkW-n2@oKkmf_V+4i=(S2l+7lYYsl~^RX3H6>jyqij7>=uX=y{E<ROJz
zC&LOr`q%2B+HG!aQ-{>O6Ji&t?gPD`D}TY;!!k<{@QCuNo7xHI>kJU|n$zug+w_>`
z^6X_9zK~jiHH69-c@C?>M=E=!jXZS8x}WbWs3A7_d@cz72APpMQZWC2^l{^~TgPhH
zZhn+P7@A+?sEm|gvYrpWZf2R84*y$?f51m@z-V)pic4K=o9=F2ogExfw@5f3l<s)K
ze!;&$2m<4~{~h`6VkTdV*@OX>n-v5p^tyQs<3XEq7%$h&7`~^<Zqr4{geFa+bx=Vx
zw(=F((&mrxH^NwS;;NbE&WS4w)|mF$ZUS~7zs6Bijxv2ddxo8L=g~@t)TDmDVL^3P
zQq&po7ql17Hp=E9A!nQOgq&OZFlCdLGVg);3Tj!Hxt~!KUs}-KU^iZCEoQ#eG5idv
zJ4Rz>1B02=9;SA@w(vc(9_Fu_;<NB}a3Wu}&1jY5cJP+?V;X%F(AwUdVriT9@`$2P
z-K5H_=|r(>?W}t2@~vqgCWSdgBL~JeDX}uz=0tku0}ZVJ28FeH!bkKhrBSml1!rBe
z4ywZ2X5+;ILTGBG5$@Vqp5yj81Rl>|47$D&QZW>zb&Pm045~Ml32)}ELs@+N%)Zpm
zbxU*uEL2aXM)8}B_*b^vPcmW8X%}7%24%o1msHtS8Z^s@N=TK0dU?KPU!3MBo2qL!
zl8V=5QPf{6(7OPFHOl2Ex1l1fZW$A7Z4oq58YV>1=$5*Cd;8bvOHrT<Q%5emt?ARQ
z{JQzrV-=1BBNJ)HSmDvO&(>u;lQHT-GLxd}UXa7?4yu24W7>$>cb{I?t$YR%N(;%1
zd{^x}3w!k)lE>p9E$M>wihWehnXbf%K|+b5Y8R2ChO(DEBDV_iyQ+l*i_PUW4}24J
zxmC%^S|E<baU6Knyc>lc9i^NifdtBM!}Z{*=1#4>KTa)L5nEp=HD@Z68DkvyAAz>+
ze^v79D-D8nw@aLQqay9kek{&)+LU4uQ~qWd9G6-_G)d~ruYRlSr|;c%+Q1{%55!YW
z6o{uczHQ~fUSk$OrjGB*eXIICzq53$TaI{u%>V(W<Vz(L-z<PO4lR3)WRs1jkQ;(u
zsUBOm*;w~yux-!eH7o`g*+7<Ug0@#c+WoNqu&Zzz^qH`#=PFWTb+ZT#EWhT-Ic|a`
z*2)-p*UP=lE}|b(c4;5nJ4Aets>Vvr)czk|S*k&!56kW*-Cou4(*`R-^Pb(9F@`hG
z0RDq%52s;Nxyo0#KK^{}wJGdjXvx=V!)fQH_GIZiS5tUSWeH6vEL;|dQ|wJTbS)R(
z-?ao{uA7S>)x4XNHnRUE;UyQ*OKOLA;R@0$)mZebX-qp)76?qgQ2Wzvm-4kn_ETs*
zNo(F;?Xx<*hQ}Rj??Y;26w#utEh1-J#V6)jnK(pJ-_|++^`S|_WJ7-cRgz4wdQFaG
z6eCzV&;QyS3%~e?QeYP8!j+(OVA&_88-cL&xVdB3<lS@3jeow1?4p(0jI8%^Jjdby
zP<sLbNb8=+dW2NObTgRa#g{t2Zgw1=Ap3VnSEcg|-@yhqycjCkc^<|I%cP2GQ~u4U
zT8mhz>i`)6@7VZ)-`^6uhC%e>Gf+!~tMHLCR=pR!gJ%X{zIuFLTv6>@xW$L*&)x8Z
zCS!;Yh|wnL@i3b({!gd+*v%d%5hag#{lF%@_EL4#T10^U{LYigS>KK20od{i5=(~7
zNNP}80YXlOfynMB#J%17Dxn)QLg^hyIN}f*yYS*!agR+3@OGrQ5(?Qu?X~H(J$qc+
zhxm3Wq!ocI<sv48P25i+6l!1Em+D?RyXcJ&COu^~iWq*hB^EoE7%qu~eHV>WHvXc9
zm#Dyq=V0iSQs={7ZypGF>fvOe4203&!=(sFUC|JNl~1CG;g9BS#0B<vbxZNOGDmgn
z1_56MZ|lYm%)=H0h`~%rCQX2+j40NRw){&1NL$WMUr!ka(Jh>DdRnx*Y0AzD&Bk~0
zl`T~^Lo_`;$Euu6wRrn@c2RXp=ss<@hZe&$*i~+en*Pxa1Y~?d6XJkmGz9A^C{$ha
z4$bX%VcZ7fkueeK9Lyc-7NHhkPBx~sim8a!0{H@ra@pApNcLd`*t{Dn0&8Sc0J;O~
zORBE#PSHK?AptdKDaK(xbS;og@rC;n%;4byoQb*w?NbyCXdRBCw2thGIj#?aH@)xX
zrum2&ge}LzC#vk6XqtaMHw{_)mc(4dSL3_!)dj3sR9`o@yH%_;67`l!H{QC7a>6Od
zy>vj&6*AXBkSX!I59;CAZs0zj6vJ5+uAH=eao73{g^p?ozZ_BzXLobYOq3L_zhL%}
z#NiRh!jeeQEVPlrNkONfB!ga}>L`b|qVA2?$L^g)uxO3uy~YCYUEYm@Gie87#9Nbk
zB5+G=H@DcKA)n)L#&!lPq}QNIyt=XE7GlEC%m*dAU9My^&SFSTuA7MXA<}V*BN3Sl
z9f$dLrr)CK!nz521BR~;&5FX#s(mTyER7Qwz_0UhC7j9>5XOw~Zp=7^=)GdZz<8)Q
z;+t<z_`QuUhpCd7Zf>AR1Zl&CJ#}03jxZ|}!F^aBBrjv+0Znl&J4Ff09?dSUComz;
zK=c#KZ&awLZnijqnDcc3F`Hp(vIUjcO)Lgt;LaFj1LdX*xOCl&ND$?>H<+T{T3o6g
zaNIok6xz;`ubQY&Ledzj;G-eY2vQ{6!Dx~tkbq_3-Go~SCy)~#gjl1X45UGok$y+?
zTsh`Q8L!21Q+*@i;6i3<K~d@5RD<xFD1+#LPZe@ir<G`ucTPvBm=nyvke}wXx5@%V
z<Kcb=Cr1n}wst7X?{?{!)+wT!Xw76$MYNB|!ET9eGR73+3b$O2fANbg%_T10-P}Q^
zhX76lf~fULG@<Z}`OSk%)y*BRAB3r+CJ(g;Ohhb1p>_WhNkMuKeg&a(aCe;OvDDG<
zCB9$(<L01uLr^{npk2KiFyd!=PU^UAQ8z=$A`o<$Zs_eZrXDX$J;At#z{Qr7PS31c
zqPXgT%QF*n;SjoLiH5LqG6^9GdL}AA1zqK%%($ESyb&S8dflvtd*iNUi@<$jH--!)
zdt_tQ^+9J8psTul-DH&m`ckqU)&M?eN*H~Yne$K?M%_5K7K<N#$oV-%#1G|uRWyLy
zVMtqAM_{;T3d6lk6eO+6DDl)MWBw6wm7%B^v}~`~i-#>67@lTQ@P_CexrlNcNyzad
zthWNe*eV(fc`|2MON$H{<b~B;1<F%dl;T`laQtYq5yViyncUbXDlAhSP(@14z@ks&
zP;H_{5YcX(L3c#tVWD8eZ;JZ1)&$W)KcYm>Gepg>!Y&cu#yLO5nizbGdpkU~xGIoP
z8jnSIp;{YzSBzxfW~}-J?|3SDhZ}wr^j4CHl9x<PqifVdrEboJb-N_PJ!pV@Ljb$k
zqS$qPLrMr8bu5DfV<@v#q}Vp^$J%l#3_$j2Ti*mxy$S`L$X80EK-7@alTdR2$AUuW
z+8^OqfI}bd5Vb=y3;S255_d&Ya0UyM1DATy7&<J)F=P~g@Ui0m6+Kq0pntWG39+b+
zuSnJwU0jJuCvEvld`1D@wK*qxhl)LpbU!jYlw}f~ruMoIq5!r@KnDq~LZ&y#3Ui{9
zXuF+iiBrUruD0Z{sUVLQbaT&q#0SaCFvn#ft1wPzb}tfbBAY)T*_Qkg=b`UENDv5G
zo-S&6^?Jr~+!9)gxU1xd=uIkkYBv>Y6Fg+ZSw-_`$w|>0eE9ywyJ#OnDnbw{R-#bR
z!0Iv^r@L!^Ty6yk;(IqS!OGowGcG$K?n>Gsnj;D%M`&v7au}1m!t=X^Lvg{^&`it@
zXS;S!R34nV6p+iCZtNi1M7?Y+J!nZU;r{Y?!MlkEPl#L?$0c{;IO1vC8Hbj~CwaWl
zZTsc%&{!aqYcoW1Mv~+VeacU^4!o-#Dq9_28o|3O@DNM1&3D~}>(*Ct#-`mKsho>k
z-P3`UE#ppeJ@6=uDiNJJ6(wNKAp4;6l{RT8wJRH5<^`h{7AQ~W&7w|UhBeFW&vh>z
zgpH|zdmQIScDNf}Lr3fAhWoIjSO1E8Q46kX$8uezJU`ofc<qI%<BFmnEC}l~^*3Z_
z2$o!x^RIlSQ3VeqDHjjX02y7!5vA$)b#u{22Ukyh1NuaByE)Ndj7B;yvpbuAUmPQE
zToLRDZ%<JVb=gGe8b3mNapa1~MQHadW>&(JdY(7Eilbt#0UuL_3s>mJ&0qKT{{xww
Bj6wha

literal 0
HcmV?d00001

diff --git a/test/task-path-track/left_track_demo.py b/test/task-path-track/left_track_demo.py
new file mode 100644
index 0000000..dacc369
--- /dev/null
+++ b/test/task-path-track/left_track_demo.py
@@ -0,0 +1,231 @@
+import cv2
+import os
+import sys
+import time
+import argparse
+
+# 添加父目录到系统路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.dirname(os.path.dirname(current_dir))
+sys.path.append(project_root)
+
+from utils.detect_track import detect_left_side_track
+
+def process_image(image_path, save_dir=None, show_steps=False):
+    """处理单张图像"""
+    print(f"处理图像: {image_path}")
+    
+    # 检测左侧轨迹线
+    start_time = time.time()
+    track_info, tracking_point = detect_left_side_track(image_path, observe=show_steps, save_log=True)
+    processing_time = time.time() - start_time
+    
+    # 输出结果
+    if track_info is not None and tracking_point is not None:
+        print(f"处理时间: {processing_time:.3f}秒")
+        print(f"最佳跟踪点: ({tracking_point[0]}, {tracking_point[1]})")
+        print(f"距左边界: {track_info['distance_to_left']:.1f}像素")
+        print(f"线段斜率: {track_info['slope']:.4f}")
+        print(f"是否垂直: {track_info['is_vertical']}")
+        print(f"线段中点: ({track_info['mid_x']:.1f}, {track_info['mid_y']:.1f})")
+        print(f"地面交点: ({track_info['ground_intersection'][0]}, {track_info['ground_intersection'][1]})")
+        
+        # 提取线段坐标
+        x1, y1, x2, y2 = track_info['line']
+        print(f"线段端点: ({x1}, {y1}) - ({x2}, {y2})")
+        print("-" * 30)
+        
+        # 如果指定了保存目录，加载原始图像并绘制检测结果
+        if save_dir:
+            if not os.path.exists(save_dir):
+                os.makedirs(save_dir)
+                
+            # 构建输出文件路径
+            base_name = os.path.basename(image_path)
+            out_path = os.path.join(save_dir, f"result_{base_name}")
+            
+            # 加载原始图像
+            img = cv2.imread(image_path)
+            if img is not None:
+                # 绘制检测结果
+                height, width = img.shape[:2]
+                center_x = width // 2
+                
+                # 绘制左侧区域范围
+                cv2.rectangle(img, (0, 0), (center_x, height), (255, 0, 0), 2)
+                
+                # 绘制检测到的线段
+                cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                
+                # 绘制跟踪点
+                cv2.circle(img, tracking_point, 8, (0, 0, 255), -1)
+                
+                # 绘制地面交点
+                cv2.circle(img, track_info['ground_intersection'], 8, (255, 0, 255), -1)
+                
+                # 添加文本信息
+                cv2.putText(img, f"斜率: {track_info['slope']:.2f}", (10, 30), 
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+                cv2.putText(img, f"距左边界: {track_info['distance_to_left']:.1f}px", (10, 70), 
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+                
+                # 保存结果图像
+                cv2.imwrite(out_path, img)
+                print(f"结果已保存至: {out_path}")
+    else:
+        print("未能检测到左侧黄色轨迹线")
+    
+    return track_info, tracking_point
+
+def process_video(video_path, save_dir=None, show_steps=False):
+    """处理视频"""
+    print(f"处理视频: {video_path}")
+    
+    # 打开视频文件
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        print("错误：无法打开视频文件")
+        return
+    
+    # 获取视频信息
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    
+    print(f"视频信息: {width}x{height}, {fps}fps, 总帧数: {frame_count}")
+    
+    # 如果需要保存结果视频
+    video_writer = None
+    if save_dir:
+        if not os.path.exists(save_dir):
+            os.makedirs(save_dir)
+        
+        # 构建输出文件路径
+        base_name = os.path.basename(video_path)
+        name, ext = os.path.splitext(base_name)
+        out_path = os.path.join(save_dir, f"result_{name}{ext}")
+        
+        # 创建视频写入器
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 可以根据需要修改编码器
+        video_writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
+    
+    # 帧计数器
+    frame_idx = 0
+    detect_success_count = 0
+    processing_times = []
+    
+    # 处理每一帧
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        frame_idx += 1
+        print(f"\r处理帧 {frame_idx}/{frame_count}", end="")
+        
+        # 检测左侧轨迹线
+        start_time = time.time()
+        track_info, tracking_point = detect_left_side_track(frame, observe=False, save_log=False)
+        processing_time = time.time() - start_time
+        processing_times.append(processing_time)
+        
+        # 如果检测成功
+        if track_info is not None and tracking_point is not None:
+            detect_success_count += 1
+            
+            # 提取线段坐标
+            x1, y1, x2, y2 = track_info['line']
+            
+            # 在帧上绘制检测结果
+            center_x = width // 2
+            
+            # 绘制左侧区域范围
+            cv2.rectangle(frame, (0, 0), (center_x, height), (255, 0, 0), 2)
+            
+            # 绘制检测到的线段
+            cv2.line(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            
+            # 绘制跟踪点
+            cv2.circle(frame, tracking_point, 8, (0, 0, 255), -1)
+            
+            # 绘制地面交点
+            cv2.circle(frame, track_info['ground_intersection'], 8, (255, 0, 255), -1)
+            
+            # 添加文本信息
+            cv2.putText(frame, f"斜率: {track_info['slope']:.2f}", (10, 30), 
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+            cv2.putText(frame, f"距左边界: {track_info['distance_to_left']:.1f}px", (10, 70), 
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+            cv2.putText(frame, f"帧: {frame_idx}/{frame_count}", (10, height-30), 
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+        else:
+            # 如果检测失败，显示错误消息
+            cv2.putText(frame, "未检测到轨迹线", (10, 50), 
+                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+        
+        # 如果需要显示
+        if show_steps:
+            cv2.imshow('Left Track Detection', frame)
+            key = cv2.waitKey(1)
+            if key == 27:  # ESC键退出
+                break
+        
+        # 如果需要保存
+        if video_writer is not None:
+            video_writer.write(frame)
+    
+    # 清理资源
+    cap.release()
+    if video_writer is not None:
+        video_writer.release()
+    cv2.destroyAllWindows()
+    
+    # 打印统计信息
+    print(f"\n视频处理完成")
+    print(f"总帧数: {frame_count}")
+    print(f"成功检测帧数: {detect_success_count}")
+    print(f"检测成功率: {detect_success_count/frame_count*100:.2f}%")
+    if processing_times:
+        avg_time = sum(processing_times) / len(processing_times)
+        print(f"平均处理时间: {avg_time*1000:.2f}ms")
+        print(f"处理帧率: {1/avg_time:.2f}fps")
+    
+    if save_dir:
+        print(f"结果已保存至: {out_path}")
+
+def main():
+    parser = argparse.ArgumentParser(description='左侧黄色轨迹线检测演示程序')
+    parser.add_argument('--input', type=str, default='res/path/image_20250513_162556.png', help='输入图像或视频的路径')
+    parser.add_argument('--output', type=str, default='res/path/test/left_track_results/', help='输出结果的保存目录')
+    parser.add_argument('--type', type=str, choices=['image', 'video'], help='输入类型，不指定会自动检测')
+    parser.add_argument('--show', default=True, help='显示处理步骤')
+    
+    args = parser.parse_args()
+    
+    # 检查输入路径
+    if not os.path.exists(args.input):
+        print(f"错误：文件 '{args.input}' 不存在")
+        return
+    
+    # 如果未指定类型，根据文件扩展名判断
+    if args.type is None:
+        ext = os.path.splitext(args.input)[1].lower()
+        if ext in ['.jpg', '.jpeg', '.png', '.bmp']:
+            args.type = 'image'
+        elif ext in ['.mp4', '.avi', '.mov']:
+            args.type = 'video'
+        else:
+            print(f"错误：无法确定文件类型 '{ext}'")
+            return
+    
+    # 根据类型处理
+    if args.type == 'image':
+        process_image(args.input, args.output, args.show)
+    elif args.type == 'video':
+        process_video(args.input, args.output, args.show)
+    else:
+        print("错误：不支持的类型")
+
+if __name__ == "__main__":
+    main() 
\ No newline at end of file
diff --git a/utils/detect_track.py b/utils/detect_track.py
index 37ab7a5..5ec3b64 100644
--- a/utils/detect_track.py
+++ b/utils/detect_track.py
@@ -610,3 +610,240 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True):
     }
     
     return center_info, left_track_info, right_track_info
+
+def detect_left_side_track(image, observe=False, delay=1000, save_log=True):
+    """
+    检测视野左侧黄色轨道线，用于机器狗左侧靠线移动
+    
+    参数:
+    image: 输入图像，可以是文件路径或者已加载的图像数组
+    observe: 是否输出中间状态信息和可视化结果，默认为False
+    delay: 展示每个步骤的等待时间(毫秒)
+    save_log: 是否保存日志和图像
+    
+    返回:
+    tuple: (线信息字典, 最佳跟踪点)
+    """
+    # 如果输入是字符串（文件路径），则加载图像
+    if isinstance(image, str):
+        img = cv2.imread(image)
+    else:
+        img = image.copy()
+    
+    if img is None:
+        error("无法加载图像", "失败")
+        return None, None
+    
+    # 获取图像尺寸
+    height, width = img.shape[:2]
+    
+    # 计算图像中间和左侧区域的范围
+    center_x = width // 2
+    # 主要关注视野的左半部分
+    left_region_width = center_x
+    left_region_height = height
+    left_bound = 0
+    right_bound = center_x
+    bottom_bound = height
+    top_bound = 0
+    
+    if observe:
+        debug("步骤1: 原始图像已加载", "加载")
+        region_img = img.copy()
+        # 绘制左侧搜索区域
+        cv2.rectangle(region_img, (left_bound, top_bound), (right_bound, bottom_bound), (255, 0, 0), 2)
+        cv2.line(region_img, (center_x, 0), (center_x, height), (0, 0, 255), 2)  # 中线
+        cv2.imshow("左侧搜索区域", region_img)
+        cv2.waitKey(delay)
+    
+    # 转换到HSV颜色空间以便更容易提取黄色
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    
+    # 黄色的HSV范围 - 扩大范围以更好地捕捉不同光照条件下的黄色
+    lower_yellow = np.array([15, 80, 80])  # 更宽松的黄色下限
+    upper_yellow = np.array([35, 255, 255])  # 更宽松的黄色上限
+    
+    # 创建黄色的掩码
+    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+    
+    # 形态学操作以改善掩码
+    kernel = np.ones((5, 5), np.uint8)  # 增大kernel尺寸
+    mask = cv2.dilate(mask, kernel, iterations=1)
+    mask = cv2.erode(mask, np.ones((3, 3), np.uint8), iterations=1)  # 添加腐蚀操作去除噪点
+    
+    if observe:
+        debug("步骤2: 创建黄色掩码", "处理")
+        cv2.imshow("黄色掩码", mask)
+        cv2.waitKey(delay)
+    
+    # 裁剪左侧区域
+    left_region_mask = mask[:, left_bound:right_bound]
+    
+    if observe:
+        debug("步骤3: 左侧区域掩码", "处理")
+        cv2.imshow("左侧区域掩码", left_region_mask)
+        cv2.waitKey(delay)
+    
+    # 边缘检测
+    edges = cv2.Canny(mask, 50, 150, apertureSize=3)
+    
+    if observe:
+        debug("步骤4: 边缘检测", "处理")
+        cv2.imshow("边缘检测", edges)
+        cv2.waitKey(delay)
+    
+    # 霍夫变换检测直线
+    lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=25, 
+                           minLineLength=height*0.15, maxLineGap=50)  # 调整参数以检测更长的线段
+    
+    if lines is None or len(lines) == 0:
+        error("未检测到直线", "失败")
+        return None, None
+    
+    if observe:
+        debug(f"步骤5: 检测到 {len(lines)} 条直线", "处理")
+        lines_img = img.copy()
+        for line in lines:
+            x1, y1, x2, y2 = line[0]
+            cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.imshow("检测到的直线", lines_img)
+        cv2.waitKey(delay)
+    
+    # 筛选左侧区域内的近似垂直线
+    left_vertical_lines = []
+    for line in lines:
+        x1, y1, x2, y2 = line[0]
+        
+        # 确保线在左侧区域内
+        if not (max(x1, x2) <= right_bound):
+            continue
+        
+        # 计算斜率 (避免除零错误)
+        if abs(x2 - x1) < 5:  # 几乎垂直的线
+            slope = 100  # 设置一个较大的值表示接近垂直
+        else:
+            slope = (y2 - y1) / (x2 - x1)
+        
+        # 筛选接近垂直的线 (斜率较大)
+        if abs(slope) > 0.7:  # 设置较宽松的垂直线斜率阈值
+            line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
+            # 计算线的中点坐标
+            mid_x = (x1 + x2) / 2
+            mid_y = (y1 + y2) / 2
+            
+            # 保存线段、其坐标、斜率和长度
+            left_vertical_lines.append((line[0], mid_x, mid_y, slope, line_length))
+    
+    if len(left_vertical_lines) == 0:
+        error("左侧区域未检测到垂直线", "失败")
+        return None, None
+    
+    if observe:
+        debug(f"步骤6: 左侧区域找到 {len(left_vertical_lines)} 条垂直线", "处理")
+        left_lines_img = img.copy()
+        for line_info in left_vertical_lines:
+            line, _, _, slope, _ = line_info
+            x1, y1, x2, y2 = line
+            cv2.line(left_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
+            # 显示斜率
+            cv2.putText(left_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2), 
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
+        cv2.imshow("左侧垂直线", left_lines_img)
+        cv2.waitKey(delay)
+    
+    # 按线段长度和位置进行评分，优先选择更长且更靠近图像左边的线
+    def score_left_line(line_info):
+        _, mid_x, _, _, length = line_info
+        # 线段越长分数越高
+        length_score = min(1.0, length / (height * 0.3))
+        # 越靠近左边分数越高
+        position_score = 1.0 - (mid_x / center_x)
+        # 综合评分
+        return length_score * 0.7 + position_score * 0.3
+    
+    # 对线段进行评分并排序
+    left_vertical_lines = sorted(left_vertical_lines, key=score_left_line, reverse=True)
+    
+    # 选择最佳的左侧线段
+    best_left_line = left_vertical_lines[0]
+    line, mid_x, mid_y, slope, length = best_left_line
+    x1, y1, x2, y2 = line
+    
+    # 确保线段的顺序是从上到下
+    if y1 > y2:
+        x1, x2 = x2, x1
+        y1, y2 = y2, y1
+    
+    # 计算最佳跟踪点 - 选择线段底部较靠近机器人的点
+    tracking_point = (x2, y2) if y2 > y1 else (x1, y1)
+    
+    # 计算线与地面的交点
+    # 使用线段的方程: (y - y1) = slope * (x - x1)
+    # 地面对应图像底部: y = height
+    # 解这个方程得到交点的x坐标
+    if abs(slope) < 0.01:  # 几乎垂直
+        ground_intersection_x = x1
+    else:
+        ground_intersection_x = x1 + (height - y1) / slope
+    ground_intersection = (int(ground_intersection_x), height)
+    
+    # 计算线与图像左边界的距离（以像素为单位）
+    distance_to_left = mid_x
+    
+    result_img = None
+    if observe or save_log:
+        result_img = img.copy()
+        # 绘制检测到的最佳左侧线
+        cv2.line(result_img, (x1, y1), (x2, y2), (255, 0, 0), 2)
+        # 绘制图像中线
+        cv2.line(result_img, (center_x, 0), (center_x, height), (0, 0, 255), 1)
+        # 标记最佳跟踪点和地面交点
+        cv2.circle(result_img, tracking_point, 10, (0, 255, 0), -1)
+        cv2.circle(result_img, ground_intersection, 10, (0, 0, 255), -1)
+        
+        # 显示信息
+        cv2.putText(result_img, f"斜率: {slope:.2f}", (10, 30), 
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.putText(result_img, f"距左边界: {distance_to_left:.1f}px", (10, 70), 
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.putText(result_img, f"地面交点: ({ground_intersection[0]}, {ground_intersection[1]})", (10, 110), 
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        
+        if observe:
+            debug("步骤7: 左侧最佳跟踪线和点", "显示")
+            cv2.imshow("左侧最佳跟踪线和点", result_img)
+            cv2.waitKey(delay)
+        
+    # 保存日志图像
+    if save_log and result_img is not None:
+        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+        log_dir = "logs/image"
+        os.makedirs(log_dir, exist_ok=True)
+        img_path = os.path.join(log_dir, f"left_track_{timestamp}.jpg")
+        cv2.imwrite(img_path, result_img)
+        info(f"保存左侧轨迹线检测结果图像到: {img_path}", "日志")
+        
+        # 保存文本日志信息
+        log_info = {
+            "timestamp": timestamp,
+            "tracking_point": tracking_point,
+            "ground_intersection": ground_intersection,
+            "distance_to_left": distance_to_left,
+            "slope": slope,
+            "line_mid_x": mid_x
+        }
+        info(f"左侧轨迹线检测结果: {log_info}", "日志")
+    
+    # 创建线段信息字典
+    track_info = {
+        "line": line,
+        "slope": slope,
+        "tracking_point": tracking_point,
+        "ground_intersection": ground_intersection,
+        "distance_to_left": distance_to_left,
+        "mid_x": mid_x,
+        "mid_y": mid_y,
+        "is_vertical": abs(slope) > 5.0  # 判断是否接近垂直
+    }
+    
+    return track_info, tracking_point