302 lines
13 KiB
Python
302 lines
13 KiB
Python
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# -*- 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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self.is_need_send_data = False # 是否需要发送数据的信号标志
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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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while True:
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data = self.uart.read(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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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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lcd.display(img) # 在LCD显示
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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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# 循迹
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def tracking(self, img):
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print("循迹")
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roi_blobs_result = self.find_blobs_in_rois(img)
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down_center, state_crossing, deflection_angle = self.state_deflection_angle(roi_blobs_result)
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dsd = self.data_format_wrapper(down_center, state_crossing, deflection_angle)
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self.UsartSend(dsd)
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print("下发指令：", dsd)
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# 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
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def find_blobs_in_rois(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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img.draw_string( x, (y+height)//2, roi_direct, color=(255, 255, 255), scale=1)
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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_baise = canvas.find_blobs(self.LINE_COLOR_BAISE, roi=(0, 0, 60, 240),
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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
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blobs_dixing = canvas.find_blobs(self.LINE_COLOR_BAISE, roi=(125, 0, 60, 240),
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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
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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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if len(blobs_baise) != 0:
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print("*********进入循环第1步*******")
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largest_baise = max(blobs_baise, key=lambda b: b.pixels())
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wx, wy, wwidth, wwheight = largest_baise[:4]
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img.draw_rectangle((wx, wy, wwidth, wwheight), color=(36, 116, 181))
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# img.draw_string(wx, (wy + wwheight) // 2, roi_direct, color=(255, 255, 0), scale=1)
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arc = wwidth * wwheight
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if arc >= 11000:
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print("*********进入循环第2步*******")
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if len(blobs_dixing) != 0:
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print("*********进入循环第3步*******")
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largest_dixing = max(blobs_dixing, key=lambda b: b.pixels())
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wx, wy, wwidth, wwheight = largest_dixing[:4]
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arc = wwidth * wwheight
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if arc >= 11000:
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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。
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self.UsartSend(self.data_format_wrapper(0, 1, 0)) # 地形停止命令
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if len(blobs_zuo) != 0 and len(blobs_you) != 0:
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print("ka十字路口")
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self.UsartSend(self.data_format_wrapper(1, 1, 0)) # 地形停止命令
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else:
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print('dixing')
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print(roi_blobs_result) # 返回的是黑色色块，各区域中心位置
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self.UsartSend(self.data_format_wrapper(0, 1, 0)) # 地形停止命令
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return roi_blobs_result # 返回的是黑色色块，各区域中心位置
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# 计算偏转状态值
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def state_deflection_angle(self, roi_blobs_result):
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'''
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说明：偏转状态值返回
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'''
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# ROI区域权重值
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# ROIS_WEIGHT = [1, 1, 1, 1]
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ROIS_WEIGHT = [1, 0, 0, 1]
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state_crossing = False
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deflection_angle = 0 # 偏转角
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down_center = 0 # 中下值
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center_num = 0 # 中间值
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# print(roi_blobs_result)
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# 偏转值计算，ROI中心区域X值
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centroid_sum = roi_blobs_result['up']['cx'] * ROIS_WEIGHT[0] + roi_blobs_result['middle_up']['cx'] * \
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ROIS_WEIGHT[1] \
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+ roi_blobs_result['middle_down']['cx'] * ROIS_WEIGHT[2] + roi_blobs_result['down']['cx'] * \
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ROIS_WEIGHT[3]
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if roi_blobs_result['up']['blob_flag']:
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center_num += ROIS_WEIGHT[0]
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if roi_blobs_result['middle_up']['blob_flag']:
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center_num += ROIS_WEIGHT[1]
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if roi_blobs_result['middle_down']['blob_flag']:
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center_num += ROIS_WEIGHT[2]
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if roi_blobs_result['down']['blob_flag']:
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center_num += ROIS_WEIGHT[3]
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center_pos = centroid_sum / (ROIS_WEIGHT[0] + ROIS_WEIGHT[1] + ROIS_WEIGHT[2] + ROIS_WEIGHT[3])
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deflection_angle = (self.IMG_WIDTH / 2) - center_pos
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# 判断两侧ROI区域检测到黑色线
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if roi_blobs_result['left']['blob_flag'] and roi_blobs_result['right']['blob_flag']:
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# 判断两侧ROI区域检测到黑色线处于图像下方1/3处
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if roi_blobs_result['left']['cy'] <= ((self.IMG_HEIGHT / 3)) or roi_blobs_result['right']['cy'] <= (
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(self.IMG_HEIGHT / 3)):
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# 当最下方ROI区域的黑线宽度大于140像素（检测到路口）
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if roi_blobs_result['down']['w'] > 140:
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down_center = 1 # 自行修改处 判断识别到十字路口
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print("输出了十字路口标识")
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return down_center, state_crossing, deflection_angle
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# 控制舵机
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def Tise_servo(self, angle):
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# 判断舵机控制方向
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if angle < 0:
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# 限制舵机角度，防止过大损坏舵机
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if angle > 80:
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angle = 80
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angle = -angle
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elif angle > 0:
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# 限制舵机角度，防止过大损坏舵机
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if angle > 35:
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angle = 35
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angle = angle
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# PWM通过定时器配置，接到IO17引脚
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tim_pwm = Timer(Timer.TIMER0, Timer.CHANNEL0, mode=Timer.MODE_PWM)
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S1 = PWM(tim_pwm, freq=50, duty=0, pin=17)
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S1.duty((angle + 90) / 180 * 10 + 2.5)
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def Servo(self, data):
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'''
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功能：180度舵机：angle:-90至90 表示相应的角度
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360连续旋转度舵机：angle:-90至90 旋转方向和速度值。
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【duty】占空比值：0-100
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'''
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angle = data[5]
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# 判断舵机控制方向
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if data[4] == ord('-'):
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# 限制舵机角度，防止过大损坏舵机
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if angle > 80:
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angle = 80
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angle = -angle
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elif data[4] == ord('+'):
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# 限制舵机角度，防止过大损坏舵机
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if angle > 35:
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angle = 35
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angle = angle
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# PWM通过定时器配置，接到IO17引脚
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tim_pwm = Timer(Timer.TIMER0, Timer.CHANNEL0, mode=Timer.MODE_PWM)
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S1 = PWM(tim_pwm, freq=50, duty=0, pin=17)
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S1.duty((angle + 90) / 180 * 10 + 2.5)
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# 检验校验和
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def verify_checksum(self, data):
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if len(data) != 8:
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return False
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# 计算校验和：data[2]、data[3]、data[4] 和 data[5] 的和，然后对256取模
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calculated_checksum = (data[2] + data[3] + data[4] + data[5]) % 256
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# 比较计算出的校验和与data[6]是否相等
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if calculated_checksum == data[6]:
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return True
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else:
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return False
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# 串口发送
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def UsartSend(self, str_data):
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'''
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串口发送函数
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'''
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print(str_data)
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self.uart.write(str_data)
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# 判断符号
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def get_symbol(self, num):
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'''
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根据数值正负，返回数值对应的符号
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正数： ‘+’， 负数‘-’ 主要为了方便C语言解析待符号的数值。
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'''
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print("num = ", num)
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if num >= 0:
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return ord('+')
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else:
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return ord('-')
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# 封装数据
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def data_format_wrapper(self, down_center, state_crossing, deflection_angle):
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'''
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根据通信协议封装循迹数据
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TODO 重新编写通信协议 与配套C解析代码
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'''
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send_data = [
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0x55,
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0x02,
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0x91,
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down_center, # 底部色块中心是否在中点附近#底部色块十字路口
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1 if state_crossing else 0, # 是否越障 无用
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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
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abs(int(deflection_angle)), # 偏航角
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0xbb]
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return bytes(send_data)
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# 运行程序
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myMain = Mainlen()
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myMain.startMain()
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