from enum import Enum
import time
import numpy as np
import cv2
import matplotlib.pyplot as plt
from yolo_processor import YOLOProcessor
from video_processor import VideoProcessor
from control_stream.servo_stream import DirectionalControl
from control_stream.odrive_stream import ControlFlowSender
from apply_nms import apply_nms
from config import Config
from skimage.morphology import skeletonize
import torch
odrive_control = ControlFlowSender("192.168.2.113", 12345)
directional_control = DirectionalControl("192.168.2.113", 5558, 800, 2000)
class State(Enum):
IDLE = 1 # 车道检测和元素检测
STOP_AND_TURN = 2 # 停车和转向
AVOID_OBSTACLE = 3 # 避障
# 添加全局变量用于存储上一次的平滑中点
last_center_x = None
pulse_width = None
def lane_process(
frame,
results,
lane_class_name,
horizontal_line_y,
target_x,
R,
servo_midpoint,
directional_control,
):
global last_center_x,pulse_width # 引用全局变量
if frame is None:
return frame
filtered_boxes, filtered_scores, filtered_masks, filtered_classes = apply_nms(
results
)
intersection_points = []
# 绘制辅助横线
cv2.line(
frame,
(0, horizontal_line_y),
(frame.shape[1], horizontal_line_y),
(255, 255, 0),
2,
)
for i, box in enumerate(filtered_boxes):
x1, y1, x2, y2 = map(int, box)
class_id = filtered_classes[i]
score = filtered_scores[i]
label = f"{lane_class_name[class_id]}: {score:.2f}"
# 绘制边界框和标签
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(
frame,
label,
(x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
2,
cv2.LINE_AA,
)
if filtered_masks is not None:
mask = filtered_masks[i]
mask_resized = cv2.resize(
mask,
(frame.shape[1], frame.shape[0]),
interpolation=cv2.INTER_NEAREST,
)
binary_mask = mask_resized > 0
skeleton = skeletonize(binary_mask)
# 提取骨架点
points = np.column_stack(np.where(skeleton > 0))
if len(points) > 10:
x = points[:, 1]
y = points[:, 0]
# 拟合多项式
coefficients = np.polyfit(x, y, 3)
polynomial = np.poly1d(coefficients)
# 查找与横线的交点
x_fit = np.linspace(x.min(), x.max(), 1000)
y_fit = polynomial(x_fit)
for xf, yf in zip(x_fit, y_fit):
if abs(yf - horizontal_line_y) < 1:
intersection_points.append((xf, yf))
cv2.circle(frame, (int(xf), int(yf)), 5, (0, 255, 0), -1)
break
# 计算交点中点和舵机控制
if len(intersection_points) == 2:
raw_center_x = int((intersection_points[0][0] + intersection_points[1][0]) / 2)
center_y = int(horizontal_line_y)
# 使用指数加权平均法平滑中点值
if last_center_x is None:
smoothed_center_x = raw_center_x
else:
smoothed_center_x = int(
Config.ALPAH * raw_center_x + (1 - Config.ALPAH) * last_center_x
)
last_center_x = smoothed_center_x # 更新上一次中点
# 计算 smoothed_center_x 与 target_x 的差值
difference = -smoothed_center_x + target_x
# 计算舵机角度
theta = np.arctan(difference / R)
# 映射角度到脉冲宽度（包含中值）
pulse_width = int((200 / 27) * np.degrees(theta) + servo_midpoint)
# 发送舵机控制命令
directional_control.send_protocol_frame_udp(pulse_width)
# 绘制中心点和调试信息
cv2.circle(frame, (smoothed_center_x, center_y), 8, (0, 0, 255), -1)
cv2.putText(
frame,
f"Center: ({smoothed_center_x}, {center_y})",
(smoothed_center_x + 10, center_y - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(0, 255, 255),
2,
cv2.LINE_AA,
)
cv2.putText(
frame,
f"Diff: {difference}, Theta: {np.degrees(theta):.2f}°",
(smoothed_center_x + 10, center_y + 20),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 0),
2,
cv2.LINE_AA,
)
cv2.putText(
frame,
f"Pulse: {pulse_width}",
(smoothed_center_x + 10, center_y + 40),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 0, 255),
2,
cv2.LINE_AA,
)
return frame
def elements_process(
frame,
results_elements,
elements_class_name,
cone_count,
cone_detection_start_time,
last_cone_count_time,
avoid_obstacle_done,
zebra_or_turn_detection_start_time, # 新增参数：斑马线/转向标志检测开始时间
stop_and_turn_done, # 新增参数：确保STOP_AND_TURN只执行一次
):
"""处理元素检测并返回相关标志和计数信息"""
detected_target_element = False
detected_zebra_or_turn = False # 用于标记是否检测到斑马线或转向标志
filtered_boxes, filtered_scores, filtered_masks, filtered_classes = apply_nms(
results_elements
)
for i, box in enumerate(filtered_boxes):
x1, y1, x2, y2 = map(int, box)
elements_class_id = filtered_classes[i]
label = f"{elements_class_name[elements_class_id]}: {filtered_scores[i]:.2f}"
# 获取检测到的元素名称
class_name = elements_class_name[elements_class_id]
# 检查是否检测到斑马线或者转向标志
if (
class_name in ["zebra", "turn_sign"]
and filtered_scores[i] >= Config.TURN_SIGN_CT
):
if stop_and_turn_done:
continue
if zebra_or_turn_detection_start_time is None:
zebra_or_turn_detection_start_time = time.time() # 记录开始时间
else:
# 计算检测持续时间
elapsed_time = time.time() - zebra_or_turn_detection_start_time
print(f"检测斑马线/转向标志，检测时间: {elapsed_time:.2f}秒")
if elapsed_time >= Config.ZEBRA_OR_TURN_CONFIRMATION_DURATION:
detected_zebra_or_turn = True # 确认为已检测到
print("检测到斑马线或转向标志，确认完成!")
else:
zebra_or_turn_detection_start_time = None
# 检查是否检测到锥桶
if class_name == "cone" and filtered_scores[i] >= Config.CONE_CT:
if avoid_obstacle_done:
# 如果避障任务已完成，则不再处理锥桶
continue
# 如果最后一次锥桶计数时间为空，或已超过冷却时间，则允许增加计数
if (
last_cone_count_time is None
or time.time() - last_cone_count_time >= Config.CONE_DET_COOLING_TIME
):
if cone_detection_start_time is None: # 锥桶检测开始时间
cone_detection_start_time = time.time()
else:
elapsed_time = time.time() - cone_detection_start_time
if (
elapsed_time >= Config.CONE_CONFIRMATION_DURATION
): # 锥桶检测确定时间
if cone_count < 3: # 限制锥桶计数只增加到 3
cone_count += 1
last_cone_count_time = time.time() # 更新最后一次计数时间
cone_detection_start_time = None # 重置计时器
print(f"锥桶检测计数增加！当前锥桶计数: {cone_count}")
else:
# 如果检测到的锥桶置信度低于 0.9，重置计时器
cone_detection_start_time = None
# 绘制目标框
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.putText(
frame,
label,
(x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
2,
cv2.LINE_AA,
)
return (
frame,
detected_target_element,
detected_zebra_or_turn,
cone_count,
cone_detection_start_time,
last_cone_count_time,
zebra_or_turn_detection_start_time, # 返回更新后的时间戳
stop_and_turn_done, # 返回任务标志
)
def process_idle(frame, *args, **kwargs):
"""处理车道检测和元素检测逻辑"""
yolo_processor_lane = kwargs.get("yolo_processor_lane")
yolo_processor_elements = kwargs.get("yolo_processor_elements")
lane_class_name = kwargs.get("lane_class_name")
elements_class_name = kwargs.get("elements_class_name")
horizontal_line_y = kwargs.get("horizontal_line_y")
target_x = kwargs.get("target_x")
R = kwargs.get("R")
servo_midpoint = kwargs.get("servo_midpoint")
directional_control = kwargs.get("directional_control")
cone_count = kwargs.get("cone_count", 0) # 锥桶计数器
cone_detection_start_time = kwargs.get(
"cone_detection_start_time", None
) # 锥桶检测计时器
last_cone_count_time = kwargs.get("last_cone_count_time", None) # 上次锥桶计数时间
avoid_obstacle_done = kwargs.get("avoid_obstacle_done", False) # 避障任务是否完成
zebra_or_turn_detection_start_time = kwargs.get(
"zebra_or_turn_detection_start_time", None
)
stop_and_turn_done = kwargs.get("stop_and_turn_done", False)
# CORE 运行 yolo 进行推理
results_lane = yolo_processor_lane.infer(frame)
results_elements = yolo_processor_elements.infer(frame)
# core 处理车道检测结果
frame = lane_process(
frame,
results_lane,
lane_class_name,
horizontal_line_y,
target_x,
R,
servo_midpoint,
directional_control,
)
# core 处理元素检测结果
(
frame,
detected_target_element,
detected_zebra_or_turn,
cone_count,
cone_detection_start_time,
last_cone_count_time,
zebra_or_turn_detection_start_time, # 返回更新后的时间戳
stop_and_turn_done, # 返回任务标志
) = elements_process(
frame,
results_elements,
elements_class_name,
cone_count,
cone_detection_start_time,
last_cone_count_time,
avoid_obstacle_done,
zebra_or_turn_detection_start_time,
stop_and_turn_done,
)
# 返回处理后的结果
return (
frame,
detected_target_element,
cone_count,
cone_detection_start_time,
last_cone_count_time,
detected_zebra_or_turn,
avoid_obstacle_done,
zebra_or_turn_detection_start_time, # 返回更新后的时间戳
stop_and_turn_done, # 返回任务标志
)
def process_stop_and_turn(frame, *args, **kwargs):
"""处理停车和转向逻辑"""
# 注意由于并未使用多线程，进入该状态的车将会关闭循迹
print("♻执行停车和转向任务")
# 1. 停车
odrive_control.motor_velocity(1, 0)
print("完成停车，执行转向")
# 2. 停车后等待车身稳定
time.sleep(Config.STABILIZATION_TIME) # 等待车停稳
# 3. 舵机回中值
directional_control.send_protocol_frame_udp(Config.SERVO_MIDPOINT)
# 4. 左变道设定的变道角度
directional_control.send_protocol_frame_udp(
Config.SERVO_MIDPOINT + Config.LANE_CHANGE_ANGLE
)
# 5. 开始停车
time.sleep(Config.PARKING_TIME - Config.STABILIZATION_TIME)
# 6. 前进变道距离，使用变道速度
odrive_control.motor_velocity(1, Config.LANE_CHANGE_SPEED)
time.sleep(Config.LANE_CHANGE_TIME)
return frame
def process_avoid_obstacle(frame, *args, **kwargs):
"""处理避障逻辑"""
print("♻执行避障任务")
# 速度降低准备避障
odrive_control.motor_velocity(1, Config.AVOID_SPEED)
odrive_control.motor_velocity(1, Config.AVOID_SPEED)
# 持续向左打方向之后再持续向右打方向
# 舵机回中值
# directional_control.send_protocol_frame_udp(Config.SERVO_MIDPOINT)
# 向左打方向 200 个脉冲
# BUG 存在问题
for i in range(50):
# 发送脉冲，向左打方向
directional_control.send_protocol_frame_udp(
pulse_width - i * 2
) # 每次发送2个脉冲
if i == 49:
record_last_pulse = pulse_width - i * 2
time.sleep(0.02) # 等待 20 毫秒
# 向右打方向 200 个脉冲
for i in range(50):
# 发送脉冲，向右打方向
directional_control.send_protocol_frame_udp(
# BUG 存在问题
record_last_pulse
+ i * 2
) # 每次发送2个脉冲
time.sleep(0.02) # 等待 20 毫秒
# 向右打方向 200 个脉冲
for i in range(50):
# 发送脉冲，向左打方向
directional_control.send_protocol_frame_udp(
pulse_width + i * 2
) # 每次发送2个脉冲
if i == 49:
record_last_pulse = pulse_width + i * 2
time.sleep(0.02) # 等待 20 毫秒
# 向左打方向 200 个脉冲
# BUG 存在问题
for i in range(50):
# 发送脉冲，向左打方向
directional_control.send_protocol_frame_udp(
record_last_pulse - i * 2
) # 每次发送2个脉冲
time.sleep(0.02) # 等待 20 毫秒
# 恢复行驶速度 # TODO 需要调试速度
odrive_control.motor_velocity(1, Config.CAR_SPEED)
print("避障完成")
return frame
def send_pulses(total_pulses):
# 计算脉冲的步长，正负脉冲的情况
step = 2 if total_pulses > 0 else -2
abs_pulses = abs(total_pulses) # 取脉冲数的绝对值
for i in range(abs_pulses):
directional_control.send_protocol_frame_udp(
Config.SERVO_MIDPOINT - i * step
) # 每次发送2个脉冲
print(i * step)
time.sleep(0.02) # 等待 20 毫秒
def main():
# 初始化模块
device = "cuda" if torch.cuda.is_available() else "cpu"
# 第一个 YOLO 模型（车道检测）
yolo_processor_lane = YOLOProcessor(
Config.LANE_MODEL, Config.CONF_THRESH, Config.IMG_SIZE, device
)
# 第二个 YOLO 模型（目标检测）
yolo_processor_elements = YOLOProcessor(
Config.ELEMENTS_MODEL,
Config.CONF_THRESH,
Config.IMG_SIZE,
device,
)
video_processor = VideoProcessor(Config.INPUT_SOURCE)
# 配置参数
lane_class_name = Config.LANE_CLASS_NAME
elements_class_name = Config.ELEMENTS_CLASS_NAME
horizontal_line_y = Config.HORIZONTAL_LINE_Y
target_x = Config.TARGET_X
R = Config.R
servo_midpoint = Config.SERVO_MIDPOINT
prev_time = time.time()
fps_list = []
# 状态初始化
current_state = State.IDLE
# 初始化检测计时器
detection_start_time = None
cone_count = 0 # 锥桶计数器
cone_detection_start_time = None # 锥桶检测计时器
last_cone_count_time = None # 最后一次锥桶计数时间
stop_and_turn_done = False # 添加标志，确保只执行一次停车和转向任务
avoid_obstacle_done = False # 避障任务是否完成
zebra_or_turn_detection_start_time = None
cone_to_avoid_timer_start = None
zebra_or_turn_timer_start = None
# CORE 等待视频流准备好
start_time = time.time()
while True:
ret, frame = video_processor.read_frame()
if ret:
break
if time.time() - start_time > 10: # 如果 10 秒后仍未获取到帧，退出
print("无法连接到视频流")
return
while True:
ret, frame = video_processor.read_frame()
if not ret:
break
current_time = time.time()
if current_state == State.IDLE:
# 执行车道检测和元素检测
(
frame,
detected_target_element,
cone_count,
cone_detection_start_time,
last_cone_count_time,
detected_zebra_or_turn,
avoid_obstacle_done,
zebra_or_turn_detection_start_time,
stop_and_turn_done,
) = process_idle(
frame,
yolo_processor_lane=yolo_processor_lane,
yolo_processor_elements=yolo_processor_elements,
lane_class_name=lane_class_name,
elements_class_name=elements_class_name,
horizontal_line_y=horizontal_line_y,
target_x=target_x,
R=R,
servo_midpoint=servo_midpoint,
directional_control=directional_control,
cone_count=cone_count,
cone_detection_start_time=cone_detection_start_time, # 传递计时器
last_cone_count_time=last_cone_count_time, # 传递最后一次计数时间
avoid_obstacle_done=avoid_obstacle_done, # 传递是否完成避障任务的标志
zebra_or_turn_detection_start_time=zebra_or_turn_detection_start_time,
stop_and_turn_done=stop_and_turn_done,
)
# 如果检测到斑马线或转向标志且置信度 >= 0.9，切换到 STOP_AND_TURN 状态
# TODO 需要更新距离判定条件，到达特定的距离阈值才开始停车
if detected_zebra_or_turn and not stop_and_turn_done:
if zebra_or_turn_timer_start is None:
# 开始计时
zebra_or_turn_timer_start = current_time
print("检测到斑马线或转向标志，开始计时...")
elif (
current_time - zebra_or_turn_timer_start
>= Config.ZEBRA_OR_TURN_IDLE_TIME
):
# 两秒计时完成后，切换到 STOP_AND_TURN 状态
current_state = State.STOP_AND_TURN
stop_and_turn_done = True # 设置为已完成，避免重复执行
zebra_or_turn_timer_start = None # 重置计时器
print("计时结束，切换到 STOP_AND_TURN 状态！")
else:
# 如果计时器未完成或检测条件不再满足，重置计时器
zebra_or_turn_timer_start = None
# 如果锥桶计数达到 CONE_TO_AVOID_INDEX，切换到 AVOID_OBSTACLE 状态
if cone_count >= Config.CONE_TO_AVOID_INDEX and not avoid_obstacle_done:
if cone_to_avoid_timer_start is None:
# 开始计时
cone_to_avoid_timer_start = current_time
print("检测到第三个锥桶，开始计时...")
elif current_time - cone_to_avoid_timer_start >= Config.CONE_IDLE_TIME:
# 两秒计时完成后，切换到避障状态
current_state = State.AVOID_OBSTACLE
avoid_obstacle_done = True # 设置为已完成，避免重复执行
print(
f"锥桶计数达到 {Config.CONE_TO_AVOID_INDEX}，切换到 AVOID_OBSTACLE 状态！"
)
else:
# 如果计时器未完成，保持在 IDLE 状态
cone_to_avoid_timer_start = None
elif current_state == State.AVOID_OBSTACLE:
# 执行避障任务
process_avoid_obstacle(frame)
current_state = State.IDLE # 避障任务完成后，切换回 IDLE 状态
elif current_state == State.STOP_AND_TURN:
# 执行停车和转向任务
process_stop_and_turn(frame)
current_state = State.IDLE # 停车和转向任务完成后，切换回 IDLE 状态
# 计算帧率
fps = 1 / (current_time - prev_time) if current_time != prev_time else 0
prev_time = current_time
fps_list.append(fps)
# 显示计时器
if cone_to_avoid_timer_start is not None:
remaining_time = (current_time - cone_to_avoid_timer_start)
cv2.putText(
frame,
f"Obstacle Timer: {remaining_time:.2f}s",
(10, 150),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(255, 255, 0),
2,
)
if zebra_or_turn_timer_start is not None:
remaining_time = (current_time - zebra_or_turn_timer_start)
cv2.putText(
frame,
f"Zebra/Turn Timer: {remaining_time:.2f}s",
(10, 190),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0, 255, 255),
2,
)
# 显示帧率
cv2.putText(
frame,
f"FPS: {fps:.2f}",
(10, 30),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0, 255, 0),
2,
)
# 显示当前状态
cv2.putText(
frame,
f"State: {current_state.name}",
(10, 70),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(255, 0, 0),
2,
)
# 显示锥桶计数
cv2.putText(
frame,
f"Cone Count: {cone_count}",
(10, 110),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(255, 255, 255),
2,
)
# 显示结果帧
cv2.imshow("State Machine with YOLO", frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# 计算平均帧率
avg_fps = sum(fps_list) / len(fps_list) if fps_list else 0
print(f"平均帧率: {avg_fps:.2f}")
# 绘制帧率变化图
plt.plot(fps_list)
plt.axhline(avg_fps, color="r", linestyle="--", label=f"Average FPS:{avg_fps:.2f}")
plt.title("FPS over Time")
plt.xlabel("Frame Index")
plt.ylabel("FPS")
plt.legend()
plt.show()
# 释放资源
video_processor.release()
if __name__ == "__main__":
main()
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