742 lines
29 KiB
Python
742 lines
29 KiB
Python
# -*- coding:utf-8 -*-
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# @Author len
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# @Create 2023/11/23 11:28
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import math
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import sensor, image, time, lcd
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import binascii
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from Maix import GPIO
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from machine import Timer, PWM, UART, Timer
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from fpioa_manager import fm
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import KPU as kpu
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class Mainlen():
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def __init__(self):
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'''初始化摄像头和 LCD 显示屏'''
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lcd.init() # lcd初始化
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self.canera_init() # 摄像头初始化
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# sensor.set_auto_gain(0, gain_db=17) # 设置摄像头的自动增益功能
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# 映射串口引脚
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fm.register(6, fm.fpioa.UART1_RX, force=True)
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fm.register(7, fm.fpioa.UART1_TX, force=True)
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# 初始化串口
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self.uart = UART(UART.UART1, 115200, read_buf_len=4096)
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# 循迹
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# --------------感光芯片配置 START -------------------
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self.IMG_WIDTH = 240
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self.IMG_HEIGHT = 320
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# 直线灰度图颜色阈值
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self.LINE_COLOR_THRESHOLD = [(0, 60)] # 找黑色
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self.LINE_COLOR_BAISE = [(60, 255)] # 找白色
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self.ROIS = { # 找黑色
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# 'left': (0, 0, 320, 50), # 纵向取样-左侧
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# 'right': (0, 190, 320, 50), # 纵向取样-右侧
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'left': (0, 0, 180, 50), # 纵向取样-左侧
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'right': (0, 190, 180, 50), # 纵向取样-右侧
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'up': (240, 0, 80, 240), # 横向取样-上方
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'middle_up': (160, 0, 80, 240), # 横向取样-中上
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'middle_down': (80, 0, 80, 240), # 横向取样-中下
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'down': (0, 0, 80, 240), # 横向取样-下方
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}
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# 红黄绿的阈值
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# 颜色识别阈值 (L Min, L Max, A Min, A Max, B Min, B Max) LAB模型
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# 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。
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# self.thresholds = [(59, 100, 40, 127, 5, 127), # 红色阈值
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# (90, 100, -5, 2, -4, 20), # 黄色阈值
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# (87, 100, -59, 127, -10, 127),] # 绿色阈值
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self.thresholds = [(56, 99, 13, 63, -18, 37, "红色"), # 红色阈值
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(69, 100, -7, 14, 1, 46, "黄色"), # 黄色阈值
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(55, 100, -51, -16, -9, 83, "绿色")] # 绿色阈值
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self.is_need_send_data = False # 是否需要发送数据的信号标志
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self.QRFlag = 0
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# 主函数 run
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def startMain(self):
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Flag_track = False # 循迹标识
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Flag_qr = False # 二维码标识
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Flag_qr_2 = False # 标识B
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Flag_light = False # 交通灯标识
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Flag_Camera_wheel = False # 摄像头转弯
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while True:
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data = self.uart.read(8)
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# print(data)
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# print(len(data))
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# if (len(data) >= 8):
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if data is not None:
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if (data[1] == 0x02) and (data[7] == 0xBB) and self.verify_checksum(data):
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# 巡线与控制舵机
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if data[2] == 0x91:
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if data[3] == 0x01: # 启动循迹
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sensor.set_pixformat(sensor.GRAYSCALE) # 设置像素格式为灰色
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Flag_track = True
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print("开始循迹")
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elif data[3] == 0x02: # 停止循迹
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sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
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Flag_track = False
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print("停止循迹")
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elif data[3] == 0x03: # 调整舵机
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print("调整舵机")
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self.Servo(data)
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elif data[3] == 0x04: # 启动摄像头转弯
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print("摄像头转弯")
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Flag_Camera_wheel = True
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elif data[3] == 0x05: # 停止摄像头转弯
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print("停止摄像头转弯")
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Flag_Camera_wheel = False
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else:
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pass
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# 识别任务
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elif data[2] == 0x92:
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if data[3] == 0x01: # 识别二维码
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sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
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Flag_qr = True
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print("开始识别二维码")
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elif data[3] == 0x02: # 停止识别二维码
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Flag_qr = False
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print("停止识别二维码")
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elif data[3] == 0x03: # 识别交通灯
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sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
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Flag_light = True
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print("开始识别交通灯")
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elif data[3] == 0x04: # 停止识别交通灯
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Flag_light = False
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print("停止识别交通灯")
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elif data[3] == 0x06: # 调整摄像头阈值
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print("调整摄像头阈值")
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self.canera_ash()
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elif data[3] == 0x09:
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print("开始识别第二个二维码")
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Flag_qr_2 = True
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elif data[3] == 0x10:
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print("停止识别第二个二维码")
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Flag_qr_2 = False
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else:
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pass
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img = sensor.snapshot() # 获取图像
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if Flag_track: # 循迹
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print("循迹")
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self.tracking(img)
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elif Flag_Camera_wheel: # 摄像头转弯
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print("摄像头转弯")
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self.Camera_wheel(img)
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elif Flag_qr and (self.QRFlag <3): # 二维码
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print("识别二维码")
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self.discem_QR(img)
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Flag_qr = False
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elif Flag_light: # 红绿灯
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print("识别红绿灯")
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self.discem_light()
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# Flag_light = False
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elif Flag_qr_2:
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print("识别第二个二维码")
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self.discem_QR_2(img)
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Flag_qr_2 = False
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lcd.display(img) # 在LCD显示
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# 摄像头转弯
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def Camera_wheel(self, img):
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canvas = img.copy()
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roi_blobs_result = {} # 在各个ROI中寻找色块的结果记录
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for roi_direct in self.ROIS.keys():
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roi_blobs_result[roi_direct] = {
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'cx': 0,
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'cy': 0,
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'w': 0,
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'blob_flag': False
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}
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for roi_direct, roi in self.ROIS.items():
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blobs = canvas.find_blobs(self.LINE_COLOR_THRESHOLD, roi=roi, merge=True)
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if len(blobs) != 0:
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largest_blob = max(blobs, key=lambda b: b.pixels())
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if largest_blob.area() > 1000:
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roi_blobs_result[roi_direct]['cx'] = largest_blob.cy()
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roi_blobs_result[roi_direct]['cy'] = largest_blob.cx()
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roi_blobs_result[roi_direct]['w'] = largest_blob.h()
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roi_blobs_result[roi_direct]['blob_flag'] = True
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x, y, width, height = largest_blob[:4]
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img.draw_rectangle((x, y, width, height), color=(255))
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else:
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# blobs=canvas.find_blobs(LINE_COLOR_THRESHOLD, roi=roi, merge=True, pixels_area=10)
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continue
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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
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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
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print(blobs_zuo)
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print(blobs_you)
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if len(blobs_zuo) != 0:
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zuo = blobs_zuo[0][4]
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else:
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zuo = 0
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if len(blobs_you) != 0:
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you = blobs_you[0][4]
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else:
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you = 0
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print(zuo, you)
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if zuo >= 8000 or you >= 8000:
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print("黑线")
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wire = 0x01
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else:
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print("白线")
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wire = 0x00
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# print()
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self.uart.write(bytes([0x55]))
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self.uart.write(bytes([0x02]))
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self.uart.write(bytes([0x93]))
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self.uart.write(bytes([0x03])) # 线
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self.uart.write(bytes([wire])) # 黑白线
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self.uart.write(bytes([0x03]))
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self.uart.write(bytes([0x03]))
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self.uart.write(bytes([0xbb]))
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# 初始化摄像头阈值
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def canera_init(self):
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# 摄像头模块初始化
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sensor.reset() # 复位和初始化摄像头
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sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
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# sensor.set_pixformat(sensor.GRAYSCALE) # 设置像素格式为灰色
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sensor.set_framesize(sensor.QVGA) # 设置帧大小为QVGA（320×240）
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sensor.set_vflip(1) # 后置模式
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sensor.skip_frames(30) # # 跳过前30帧
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# 调整摄像头阈值
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def canera_ash(self):
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sensor.reset() # 复位和初始化摄像头
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sensor.set_vflip(1) # 将摄像头设置成后置方式（所见即所得）
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sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
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# sensor.set_pixformat(sensor.GRAYSCALE) # 设置像素格式为灰色
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sensor.set_framesize(sensor.QVGA) # 设置帧大小为 QVGA (320x240)
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# sensor.set_windowing((224, 224)) # 设置摄像头的窗口大小
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sensor.set_auto_gain(False)
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sensor.set_auto_whitebal(False)
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sensor.set_auto_gain(0, 0)
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sensor.skip_frames(time=200) # 等待设置生效
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# 红绿灯
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def discem_light(self):
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# 初始化
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results = []
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start_time = time.time()
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while time.time() - start_time < 3: # 循环持续三秒
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img = sensor.snapshot()
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img = img.crop((160, 0, 80, 224))
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# print(img.width()) # 320
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# print(img.height()) #240
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# img = img.crop((0, 0, 160, 224))
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max_blob = None
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max_blob_size = 0
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max_blob_color = ""
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max_blob_color_index = 0
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# 找圆
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# circles = img.find_circles(threshold=3500, x_margin=10, y_margin=10, r_margin=10,
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# r_min=2, r_max=100, r_step=2)
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for index, threshold in enumerate(self.thresholds):
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# 查找每种颜色的色块
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blobs = img.find_blobs([threshold[:-1]], pixels_threshold=150, area_threshold=150, merge=True)
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if blobs:
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# 找到最大的色块
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blob = max(blobs, key=lambda b: b.pixels())
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if blob.pixels() > max_blob_size:
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max_blob = blob
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max_blob_size = blob.pixels()
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max_blob_color = threshold[-1] # 颜色标签
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max_blob_color_index = index
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# print(circles)
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# # 画圆
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# for c in circles:
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# area = (c.x() - c.r(), c.y() - c.r(), 2 * c.r(), 2 * c.r())
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# img.draw_rectangle(area, color=(255, 255, 255))
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# 绘制最大色块的矩形和中心十字，并输出颜色
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if max_blob:
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results.append((max_blob_color, max_blob_color_index)) # 存储识别结果
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img.draw_rectangle(max_blob[0:4])
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img.draw_cross(max_blob[5], max_blob[6])
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print("最大色块的颜色是:", max_blob_color, max_blob_color_index)
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# 初始化计数字典
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color_count = {}
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for color, index in results:
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if (color, index) in color_count:
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color_count[(color, index)] += 1
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else:
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color_count[(color, index)] = 1
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# 找出出现次数最多的颜色和下标
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if color_count:
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most_common_color, most_common_index = max(color_count, key=color_count.get)
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print("出现最多的颜色:", most_common_color, "在下标位置:", most_common_index)
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else:
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most_common_index = 1
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print("没有识别到有效的颜色块")
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# 串口发送交通灯信息
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# send_data = [
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# 0x55,
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# 0x02,
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# 0x92,
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# 0x03,
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# most_common_index,
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# 0xbb]
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# print("红绿灯指令", send_data, "******************", bytes(send_data))
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# self.UsartSend(bytes(send_data))
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self.uart.write(bytes([0x55]))
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self.uart.write(bytes([0x02]))
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self.uart.write(bytes([0x92]))
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self.uart.write(bytes([0x03])) # 红绿灯
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self.uart.write(bytes([most_common_index])) # 红绿灯结果
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self.uart.write(bytes([0x03]))
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self.uart.write(bytes([0x03]))
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self.uart.write(bytes([0xbb]))
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self.canera_init() # 恢复摄像头
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# 二维码
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def discem_QR(self, img):
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# self.Tise_servo(10)
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res_QR = img.find_qrcodes() # 寻找二维码
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timeflag = 0
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while timeflag < 30:
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img = sensor.snapshot() # 获取图像
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TS_QR = img.find_qrcodes() # 再次寻找二维码
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for qr in TS_QR:
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if all(qr.payload() != existing_qr.payload() for existing_qr in res_QR):
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res_QR.append(qr)
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timeflag += 1
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time.sleep(0.001)
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# print(timeflag)
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print(len(res_QR))
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if len(res_QR): # 识别到
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for res in range(len(res_QR)):
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count = 0
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if_end = 0x01
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result = res_QR[res].payload()
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print(result)
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if len(result) == 8: # 车牌
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result = self.extract_numbers_and_letters_combined(result)
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count = 1
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elif self.if_formula(result): # 公式
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count = 2
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# continue
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if res == len(res_QR) - 1:
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if_end = 0x00
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# if_end = 0x00
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print("count: ", count)
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print("if_end: ", if_end)
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# 串口发送二维码信息
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self.uart.write(bytes([0x55]))
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self.uart.write(bytes([0x02]))
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self.uart.write(bytes([0x92]))
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self.uart.write(bytes([0x06]))
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self.uart.write(bytes([count]))
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self.uart.write(bytes([if_end]))
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self.uart.write(bytes([len(result)]))
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for qr_data in result:
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self.uart.write(bytes([ord(qr_data)]))
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print(bytes([ord(qr_data)]))
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self.uart.write(bytes([0xbb]))
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time.sleep(1)
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else: # 未识别
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self.uart.write(bytes([0x55]))
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self.uart.write(bytes([0x02]))
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self.uart.write(bytes([0x92]))
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self.uart.write(bytes([0x06]))
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self.uart.write(bytes([0xff]))
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self.uart.write(bytes([0x00]))
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self.uart.write(bytes([0x00]))
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self.uart.write(bytes([0xbb]))
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# time.sleep(1)
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def discem_QR_2(self, img):
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# self.Tise_servo(10)
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res_QR = img.find_qrcodes() # 寻找二维码
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timeflag = 0
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while timeflag < 30:
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img = sensor.snapshot() # 获取图像
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TS_QR = img.find_qrcodes() # 再次寻找二维码
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for qr in TS_QR:
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if all(qr.payload() != existing_qr.payload() for existing_qr in res_QR):
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res_QR.append(qr)
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timeflag += 1
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time.sleep(0.001)
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# print(timeflag)
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print(len(res_QR))
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if len(res_QR) == 2:
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qr_txt = [res_QR[0].payload(), res_QR[1].payload()]
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# 调用函数处理示例列表
|
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result_list = self.extract_differences(qr_txt)
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# 串口发送二维码信息
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self.uart.write(bytes([0x55]))
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self.uart.write(bytes([0x02]))
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self.uart.write(bytes([0x92]))
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self.uart.write(bytes([0x07]))
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self.uart.write(bytes([0x01]))
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# self.uart.write(bytes([if_end]))
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#
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# self.uart.write(bytes([len(result_list)]))
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for qr_data in result_list:
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self.uart.write(bytes([ord(qr_data)]))
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print(bytes([ord(qr_data)]))
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|
||
self.uart.write(bytes([0xbb]))
|
||
|
||
time.sleep(1)
|
||
|
||
# for res in range(len(res_QR)):
|
||
# count = 0
|
||
# if_end = 0x01
|
||
# result = res_QR[res].payload()
|
||
# print(result)
|
||
#
|
||
# if len(result) == 8: # 车牌
|
||
# result = self.extract_numbers_and_letters_combined(result)
|
||
# count = 1
|
||
#
|
||
# elif self.if_formula(result): # 公式
|
||
# count = 2
|
||
# # continue
|
||
#
|
||
# if res == len(res_QR) - 1:
|
||
# if_end = 0x00
|
||
# # if_end = 0x00
|
||
# print("count: ", count)
|
||
# print("if_end: ", if_end)
|
||
|
||
else: # 未识别
|
||
self.uart.write(bytes([0x55]))
|
||
self.uart.write(bytes([0x02]))
|
||
self.uart.write(bytes([0x92]))
|
||
|
||
self.uart.write(bytes([0x07]))
|
||
|
||
self.uart.write(bytes([0xff]))
|
||
self.uart.write(bytes([0x00]))
|
||
self.uart.write(bytes([0x00]))
|
||
self.uart.write(bytes([0xbb]))
|
||
|
||
# time.sleep(1)
|
||
|
||
# 提取字符和数字
|
||
def extract_numbers_and_letters_combined(self, text):
|
||
combined_str = ''
|
||
for char in text:
|
||
# 检查字符是否是数字（0-9）或字母（A-Z 或 a-z）
|
||
if ('0' <= char <= '9') or ('A' <= char <= 'Z') or ('a' <= char <= 'z'):
|
||
combined_str += char
|
||
return combined_str
|
||
|
||
def extract_differences(self, QR_txt):
|
||
"""
|
||
该函数接收一个包含两个二维码字符串的列表，提取尖括号内的有效信息，
|
||
并根据给定规则返回两个二维码之间的差异数据。
|
||
"""
|
||
# 提取两个二维码中尖括号内的数据
|
||
qr_data = [qr[qr.find("<") + 1:qr.find(">")] for qr in QR_txt]
|
||
|
||
# # 检查提取的数据是否确切地为15个字节长
|
||
# if not all(len(data) == 15 for data in qr_data):
|
||
# return "错误：尖括号内的数据不是15个字节长。"
|
||
|
||
# 对比数据并找出差异
|
||
differences = [[], []] # differences[0] 用于存储 QR1 != QR2 的差异，反之亦然
|
||
for a, b in zip(qr_data[0], qr_data[1]):
|
||
if a != b:
|
||
differences[0].append(a)
|
||
differences[1].append(b)
|
||
|
||
# 按要求返回合并后的差异数据
|
||
char_list = differences[0] + differences[1]
|
||
return ''.join(char_list)
|
||
|
||
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
|
||
x, y, width, height = largest_blob[:4]
|
||
img.draw_rectangle((x, y, width, height), color=(255))
|
||
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()
|