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Embedded_game/002_B_Car/len_B_1212.py

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2025-01-02 12:48:11 +08:00
# -*- coding:utf-8 -*-
# @Author len
# @Create 2023/11/23 11:28
import math
import sensor, image, time, lcd
import binascii
from Maix import GPIO
from machine import Timer, PWM, UART, Timer
from fpioa_manager import fm
import KPU as kpu
class Mainlen():
def __init__(self):
'''初始化摄像头和 LCD 显示屏'''
lcd.init() # lcd初始化
self.canera_init() # 摄像头初始化
# sensor.set_auto_gain(0, gain_db=17) # 设置摄像头的自动增益功能
# 映射串口引脚
fm.register(6, fm.fpioa.UART1_RX, force=True)
fm.register(7, fm.fpioa.UART1_TX, force=True)
# 初始化串口
self.uart = UART(UART.UART1, 115200, read_buf_len=4096)
# 循迹
# --------------感光芯片配置 START -------------------
self.IMG_WIDTH = 240
self.IMG_HEIGHT = 320
# 直线灰度图颜色阈值
self.LINE_COLOR_THRESHOLD = [(0, 60)] # 找黑色
self.LINE_COLOR_BAISE = [(60, 255)] # 找白色
self.ROIS = { # 找黑色
# 'left': (0, 0, 320, 50), # 纵向取样-左侧
# 'right': (0, 190, 320, 50), # 纵向取样-右侧
'left': (0, 0, 180, 50), # 纵向取样-左侧
'right': (0, 190, 180, 50), # 纵向取样-右侧
'up': (240, 0, 80, 240), # 横向取样-上方
'middle_up': (160, 0, 80, 240), # 横向取样-中上
'middle_down': (80, 0, 80, 240), # 横向取样-中下
'down': (0, 0, 80, 240), # 横向取样-下方
}
# 红黄绿的阈值
# 颜色识别阈值 (L Min, L Max, A Min, A Max, B Min, B Max) LAB模型
# 下列阈值元组用来识别 红、绿、蓝三种颜色，可通过调整数据阈值完成更多颜色的识别。
# self.thresholds = [(59, 100, 40, 127, 5, 127), # 红色阈值
# (90, 100, -5, 2, -4, 20), # 黄色阈值
# (87, 100, -59, 127, -10, 127),] # 绿色阈值
self.thresholds = [(35, 100, 6, 127, 0, 127, "红色"), # 红色阈值
(25, 100, -6, 127, 5, 43, "黄色"), # 黄色阈值
(52, 100, -128, -5, 5, 127, "绿色")] # 绿色阈值
self.is_need_send_data = False # 是否需要发送数据的信号标志
# 主函数 run
def startMain(self):
Flag_track = False # 循迹标识
Flag_qr = False # 二维码标识
Flag_light = False # 交通灯标识
while True:
data = self.uart.read(8)
# print(data)
# print(len(data))
# if (len(data) >= 8):
if data is not None:
if (data[1] == 0x02) and (data[7] == 0xBB) and self.verify_checksum(data):
# 巡线与控制舵机
if data[2] == 0x91:
if data[3] == 0x01: # 启动循迹
sensor.set_pixformat(sensor.GRAYSCALE) # 设置像素格式为灰色
Flag_track = True
print("开始循迹")
elif data[3] == 0x02: # 停止循迹
sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
Flag_track = False
print("停止循迹")
elif data[3] == 0x03: # 调整舵机
print("调整舵机")
self.Servo(data)
else:
pass
# 识别任务
elif data[2] == 0x92:
if data[3] == 0x01: # 识别二维码
sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
Flag_qr = True
print("开始识别二维码")
elif data[3] == 0x02: # 停止识别二维码
Flag_qr = False
print("停止识别二维码")
elif data[3] == 0x03: # 识别交通灯
sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
Flag_light = True
print("开始识别交通灯")
elif data[3] == 0x04: # 停止识别交通灯
Flag_light = False
print("停止识别交通灯")
elif data[3] == 0x06: # 调整摄像头阈值
print("调整摄像头阈值")
self.canera_ash()
else:
pass
img = sensor.snapshot() # 获取图像
if Flag_track: # 循迹
print("循迹")
self.tracking(img)
elif Flag_qr: # 二维码
print("识别二维码")
self.discem_QR(img)
# Flag_qr = False
elif Flag_light: # 红绿灯
print("识别红绿灯")
self.discem_light()
# Flag_light = False
lcd.display(img) # 在LCD显示
# 初始化摄像头阈值
def canera_init(self):
# 摄像头模块初始化
sensor.reset() # 复位和初始化摄像头
sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
# sensor.set_pixformat(sensor.GRAYSCALE) # 设置像素格式为灰色
sensor.set_framesize(sensor.QVGA) # 设置帧大小为QVGA（320×240）
sensor.set_vflip(1) # 后置模式
sensor.skip_frames(30) # # 跳过前30帧
# 调整摄像头阈值
def canera_ash(self):
sensor.reset() # 复位和初始化摄像头
sensor.set_vflip(1) # 将摄像头设置成后置方式（所见即所得）
sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
# sensor.set_pixformat(sensor.GRAYSCALE) # 设置像素格式为灰色
sensor.set_framesize(sensor.QVGA) # 设置帧大小为 QVGA (320x240)
# sensor.set_windowing((224, 224)) # 设置摄像头的窗口大小
sensor.set_auto_gain(False)
sensor.set_auto_whitebal(False)
sensor.set_auto_gain(0, 0)
sensor.skip_frames(time=200) # 等待设置生效
# 红绿灯
def discem_light(self):
# 初始化
results = []
start_time = time.time()
while time.time() - start_time < 3: # 循环持续三秒
img = sensor.snapshot()
max_blob = None
max_blob_size = 0
max_blob_color = ""
max_blob_color_index = 0
# 找圆
circles = img.find_circles(threshold=3500, x_margin=10, y_margin=10, r_margin=10,
r_min=2, r_max=100, r_step=2)
for index, threshold in enumerate(self.thresholds):
# 查找每种颜色的色块
blobs = img.find_blobs([threshold[:-1]], pixels_threshold=200, area_threshold=200, merge=True)
if blobs:
# 找到最大的色块
blob = max(blobs, key=lambda b: b.pixels())
if blob.pixels() > max_blob_size:
max_blob = blob
max_blob_size = blob.pixels()
max_blob_color = threshold[-1] # 颜色标签
max_blob_color_index = index
print(circles)
# 画圆
# for c in circles:
# area = (c.x() - c.r(), c.y() - c.r(), 2 * c.r(), 2 * c.r())
# img.draw_rectangle(area, color=(255, 255, 255))
# 绘制最大色块的矩形和中心十字，并输出颜色
if max_blob:
results.append((max_blob_color, max_blob_color_index)) # 存储识别结果
img.draw_rectangle(max_blob[0:4])
img.draw_cross(max_blob[5], max_blob[6])
print("最大色块的颜色是:", max_blob_color, max_blob_color_index)
# 初始化计数字典
color_count = {}
for color, index in results:
if (color, index) in color_count:
color_count[(color, index)] += 1
else:
color_count[(color, index)] = 1
# 找出出现次数最多的颜色和下标
if color_count:
most_common_color, most_common_index = max(color_count, key=color_count.get)
print("出现最多的颜色:", most_common_color, "在下标位置:", most_common_index)
else:
most_common_index = 1
print("没有识别到有效的颜色块")
# 串口发送交通灯信息
# send_data = [
# 0x55,
# 0x02,
# 0x92,
# 0x03,
# most_common_index,
# 0xbb]
# print("红绿灯指令", send_data, "******************", bytes(send_data))
# self.UsartSend(bytes(send_data))
self.uart.write(bytes([0x55]))
self.uart.write(bytes([0x02]))
self.uart.write(bytes([0x92]))
self.uart.write(bytes([0x03])) # 红绿灯
self.uart.write(bytes([most_common_index])) # 红绿灯结果
self.uart.write(bytes([0x03]))
self.uart.write(bytes([0x03]))
self.uart.write(bytes([0xbb]))
self.canera_init() # 恢复摄像头
# 二维码
def discem_QR(self, img):
# self.Tise_servo(10)
res_QR = img.find_qrcodes() # 寻找二维码
for i in range(9):
img = sensor.snapshot() # 获取图像
TS_QR = img.find_qrcodes() # 再次寻找二维码
for qr in TS_QR:
if all(qr.payload() != existing_qr.payload() for existing_qr in res_QR):
res_QR.append(qr)
print(len(res_QR))
if len(res_QR): # 识别到
for res in range(len(res_QR)):
count = 0
if_end = 0x01
result = res_QR[res].payload()
print(result)
# result = self.remove_chinese_chars(result) # 剔除中文
# print(result)
if self.if_plate(result):
count = 1
elif self.if_formula(result):
count = 2
if res == len(res_QR)-1:
if_end = 0x00
print("count: ", count)
print("if_end: ", if_end)
# 串口发送二维码信息
self.uart.write(bytes([0x55]))
self.uart.write(bytes([0x02]))
self.uart.write(bytes([0x92]))
self.uart.write(bytes([0x06]))
self.uart.write(bytes([count]))
self.uart.write(bytes([if_end]))
self.uart.write(bytes([len(result)]))
for qr_data in result:
self.uart.write(bytes([ord(qr_data)]))
print(bytes([ord(qr_data)]))
self.uart.write(bytes([0xbb]))
# time.sleep(1)
else: # 未识别
self.uart.write(bytes([0x55]))
self.uart.write(bytes([0x02]))
self.uart.write(bytes([0x92]))
self.uart.write(bytes([0x06]))
self.uart.write(bytes([0xff]))
self.uart.write(bytes([0x00]))
self.uart.write(bytes([0x00]))
self.uart.write(bytes([0xbb]))
# time.sleep(1)
def if_plate(self, text):
if len(text) == 6:
allowed_chars = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
for char in text:
if char not in allowed_chars:
return False
return True
else:
return False
def if_formula(self, text):
# 允许的字符：数字、运算符和括号
allowed_chars = "0123456789+-*/%^(). abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
# 检查字符是否合法
for char in text:
if char not in allowed_chars:
return False
# 括号匹配检查
bracket_count = 0
for char in text:
if char == '(':
bracket_count += 1
elif char == ')':
bracket_count -= 1
if bracket_count < 0: # 右括号多于左括号
return False
if bracket_count != 0: # 左括号和右括号数量不匹配
return False
return True
# 移除字符串内的中文
def remove_chinese_chars(self, text):
"""移除字符串中的中文字符，并返回新的字符串"""
# 定义中文字符的Unicode范围
chinese_char_ranges = [
('\u4e00', '\u9fff'), # 基本汉字
('\u3400', '\u4dbf'), # 扩展A
('\u20000', '\u2a6df'), # 扩展B
# 可以根据需要添加更多的中文范围
]
# 移除中文字符
return ''.join(char for char in text if not any(start <= char <= end for start, end in chinese_char_ranges))
# 循迹
def tracking(self, img):
print("循迹")
roi_blobs_result = self.find_blobs_in_rois(img)
down_center, state_crossing, deflection_angle = self.state_deflection_angle(roi_blobs_result)
dsd = self.data_format_wrapper(down_center, state_crossing, deflection_angle)
self.UsartSend(dsd)
print("下发指令：", dsd)
# 寻找色块 在ROIS中寻找色块，获取ROI中色块的中心区域与是否有色块的信息
def find_blobs_in_rois(self, img):
canvas = img.copy()
roi_blobs_result = {} # 在各个ROI中寻找色块的结果记录
for roi_direct in self.ROIS.keys():
roi_blobs_result[roi_direct] = {
'cx': 0,
'cy': 0,
'w': 0,
'blob_flag': False
}
for roi_direct, roi in self.ROIS.items():
blobs = canvas.find_blobs(self.LINE_COLOR_THRESHOLD, roi=roi, merge=True)
if len(blobs) != 0:
largest_blob = max(blobs, key=lambda b: b.pixels())
if largest_blob.area() > 1000:
roi_blobs_result[roi_direct]['cx'] = largest_blob.cy()
roi_blobs_result[roi_direct]['cy'] = largest_blob.cx()
roi_blobs_result[roi_direct]['w'] = largest_blob.h()
roi_blobs_result[roi_direct]['blob_flag'] = True
else:
# blobs=canvas.find_blobs(LINE_COLOR_THRESHOLD, roi=roi, merge=True, pixels_area=10)
continue
blobs_baise = canvas.find_blobs(self.LINE_COLOR_BAISE, roi=(0, 0, 60, 240),
merge=True) # 车载摄像头屏幕下部找白色#宽度200修改为240#用途寻卡，y示例中40修改为0
blobs_dixing = canvas.find_blobs(self.LINE_COLOR_BAISE, roi=(125, 0, 60, 240),
merge=True) # 车载摄像头屏幕中间找白色 #宽度200修改为240，y示例中40修改为0
blobs_zuo = canvas.find_blobs(self.LINE_COLOR_THRESHOLD, roi=(0, 0, 180, 50), merge=True) # 车载摄像头屏幕左下部找黑色
blobs_you = canvas.find_blobs(self.LINE_COLOR_THRESHOLD, roi=(0, 190, 180, 50), merge=True) # 车载摄像头屏幕右下部找黑色
if len(blobs_baise) != 0:
print("*********进入循环第1步*******")
largest_baise = max(blobs_baise, key=lambda b: b.pixels())
wx, wy, wwidth, wwheight = largest_baise[:4]
arc = wwidth * wwheight
if arc >= 11000:
print("*********进入循环第2步*******")
if len(blobs_dixing) != 0:
print("*********进入循环第3步*******")
largest_dixing = max(blobs_dixing, key=lambda b: b.pixels())
wx, wy, wwidth, wwheight = largest_dixing[:4]
arc = wwidth * wwheight
if arc >= 11000:
print('kapian') # 首先中部区域识别到白色进入判断地形还是卡片，接着判断下部，如果为白色判断为卡片。
self.UsartSend(self.data_format_wrapper(0, 1, 0)) # 地形停止命令
if len(blobs_zuo) != 0 and len(blobs_you) != 0:
print("ka十字路口")
self.UsartSend(self.data_format_wrapper(1, 1, 0)) # 地形停止命令
else:
print('dixing')
print(roi_blobs_result) # 返回的是黑色色块，各区域中心位置
self.UsartSend(self.data_format_wrapper(0, 1, 0)) # 地形停止命令
return roi_blobs_result # 返回的是黑色色块，各区域中心位置
# 计算偏转状态值
def state_deflection_angle(self, roi_blobs_result):
'''
说明：偏转状态值返回
'''
# ROI区域权重值
# ROIS_WEIGHT = [1, 1, 1, 1]
ROIS_WEIGHT = [1, 0, 0, 1]
state_crossing = False
deflection_angle = 0 # 偏转角
down_center = 0 # 中下值
center_num = 0 # 中间值
# print(roi_blobs_result)
# 偏转值计算，ROI中心区域X值
centroid_sum = roi_blobs_result['up']['cx'] * ROIS_WEIGHT[0] + roi_blobs_result['middle_up']['cx'] * \
ROIS_WEIGHT[1] \
+ roi_blobs_result['middle_down']['cx'] * ROIS_WEIGHT[2] + roi_blobs_result['down']['cx'] * \
ROIS_WEIGHT[3]
if roi_blobs_result['up']['blob_flag']:
center_num += ROIS_WEIGHT[0]
if roi_blobs_result['middle_up']['blob_flag']:
center_num += ROIS_WEIGHT[1]
if roi_blobs_result['middle_down']['blob_flag']:
center_num += ROIS_WEIGHT[2]
if roi_blobs_result['down']['blob_flag']:
center_num += ROIS_WEIGHT[3]
center_pos = centroid_sum / (ROIS_WEIGHT[0] + ROIS_WEIGHT[1] + ROIS_WEIGHT[2] + ROIS_WEIGHT[3])
deflection_angle = (self.IMG_WIDTH / 2) - center_pos
# 判断两侧ROI区域检测到黑色线
if roi_blobs_result['left']['blob_flag'] and roi_blobs_result['right']['blob_flag']:
# 判断两侧ROI区域检测到黑色线处于图像下方1/3处
if roi_blobs_result['left']['cy'] <= ((self.IMG_HEIGHT / 3)) or roi_blobs_result['right']['cy'] <= (
(self.IMG_HEIGHT / 3)):
# 当最下方ROI区域的黑线宽度大于140像素（检测到路口）
if roi_blobs_result['down']['w'] > 140:
down_center = 1 # 自行修改处 判断识别到十字路口
print("输出了十字路口标识")
return down_center, state_crossing, deflection_angle
# 控制舵机
def Tise_servo(self, angle):
# 判断舵机控制方向
if angle < 0:
# 限制舵机角度，防止过大损坏舵机
if angle > 80:
angle = 80
angle = -angle
elif angle > 0:
# 限制舵机角度，防止过大损坏舵机
if angle > 35:
angle = 35
angle = angle
# PWM通过定时器配置，接到IO17引脚
tim_pwm = Timer(Timer.TIMER0, Timer.CHANNEL0, mode=Timer.MODE_PWM)
S1 = PWM(tim_pwm, freq=50, duty=0, pin=17)
S1.duty((angle + 90) / 180 * 10 + 2.5)
def Servo(self, data):
'''
功能：180度舵机：angle:-90至90 表示相应的角度
360连续旋转度舵机：angle:-90至90 旋转方向和速度值。
【duty】占空比值：0-100
'''
angle = data[5]
# 判断舵机控制方向
if data[4] == ord('-'):
# 限制舵机角度，防止过大损坏舵机
if angle > 80:
angle = 80
angle = -angle
elif data[4] == ord('+'):
# 限制舵机角度，防止过大损坏舵机
if angle > 35:
angle = 35
angle = angle
# PWM通过定时器配置，接到IO17引脚
tim_pwm = Timer(Timer.TIMER0, Timer.CHANNEL0, mode=Timer.MODE_PWM)
S1 = PWM(tim_pwm, freq=50, duty=0, pin=17)
S1.duty((angle + 90) / 180 * 10 + 2.5)
# 检验校验和
def verify_checksum(self, data):
if len(data) != 8:
return False
# 计算校验和：data[2]、data[3]、data[4] 和 data[5] 的和，然后对256取模
calculated_checksum = (data[2] + data[3] + data[4] + data[5]) % 256
# 比较计算出的校验和与data[6]是否相等
if calculated_checksum == data[6]:
return True
else:
return False
# 串口发送
def UsartSend(self, str_data):
'''
串口发送函数
'''
print(str_data)
self.uart.write(str_data)
# 判断符号
def get_symbol(self, num):
'''
根据数值正负，返回数值对应的符号
正数： ‘+’， 负数‘-’ 主要为了方便C语言解析待符号的数值。
'''
print("num = ", num)
if num >= 0:
return ord('+')
else:
return ord('-')
# 封装数据
def data_format_wrapper(self, down_center, state_crossing, deflection_angle):
'''
根据通信协议封装循迹数据
TODO 重新编写通信协议 与配套C解析代码
'''
send_data = [
0x55,
0x02,
0x91,
down_center, # 底部色块中心是否在中点附近#底部色块十字路口
1 if state_crossing else 0, # 是否越障 无用
self.get_symbol(deflection_angle), # 偏航角符号 print输出是43 + 45- +向左转调整 -向右转调整
abs(int(deflection_angle)), # 偏航角
0xbb]
return bytes(send_data)
# 运行程序
myMain = Mainlen()
myMain.startMain()
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