from pyb import Pin, Timer inverse_left=False #change it to True to inverse left wheel inverse_right=False #change it to True to inverse right wheel ain1 = Pin('P0', Pin.OUT_PP) ain2 = Pin('P1', Pin.OUT_PP) bin1 = Pin('P2', Pin.OUT_PP) bin2 = Pin('P3', Pin.OUT_PP) ain1.low() ain2.low() bin1.low() bin2.low() pwma = Pin('P7') pwmb = Pin('P8') tim = Timer(4, freq=1000) ch1 = tim.channel(1, Timer.PWM, pin=pwma) ch2 = tim.channel(2, Timer.PWM, pin=pwmb) ch1.pulse_width_percent(0) ch2.pulse_width_percent(0) def run(left_speed, right_speed): if inverse_left==True: left_speed=(-left_speed) if inverse_right==True: right_speed=(-right_speed) if left_speed < 0: ain1.low() ain2.high() else: ain1.high() ain2.low() ch1.pulse_width_percent(int(abs(left_speed))) if right_speed < 0: bin1.low() bin2.high() else: bin1.high() bin2.low() ch2.pulse_width_percent(int(abs(right_speed)))

IIC.zip_IIC_PYB_iic 串口显示_串口at24c02

PYB.rar_Winpy.txt_delphi TXT_十六进制_拼音输入法

拼音处理模块原理及补充说明：原理：根据操作系统拼音输入法的对照表。提取出简单的单字对照表，用于待开发系统的拼音汉字转换。主要用途：输入拼音头的汉字检索拼音对照表（简表）结构： ...

MicroPython部分使用教程 _WeAct工作室1

from pyb import Pin, ExtInt C13 = Pin('PC13', Pin.OUT_PP, Pin.PULL_NONE) C13.high() # 输出高电平 callback = lambda e: print("PA0 Rising Edge Intr") ext = ExtInt(Pin('PA0'), ExtInt.IRQ_RISING, Pin....

from pyb import Pin, Timer p = Pin('P7') # P7 has TIM4, CH1 tim = Timer(4, freq=1000) ch = tim.channel(1, Timer.PWM, pin=p) ch.pulse_width_percent(50) 什么意思

- from pyb import Pin, Timer: 导入Pyboard库中的Pin和Timer模块。 - p = Pin('P7'): 创建一个Pin对象，将引脚P7作为参数传入。这里的P7是Pyboard上的一个引脚，可以用来输出PWM信号。 - tim = Timer(4, freq=...

import sensor, image, time from pyb import UART import json yellow_threshold = (79, 100, -7, 6, 4, 41) sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(10) sensor.set_auto_whitebal(False) clock = time.clock() uart = UART(3, 115200) def find_max(blobs): max_size=0 for blob in blobs: if blob.pixels() > max_size: max_blob=blob max_size = blob.pixels() return max_blob while(True): img = sensor.snapshot() blobs = img.find_blobs([yellow_threshold]) if blobs: max_blob=find_max(blobs) print('sum :', len(blobs)) img.draw_rectangle(max_blob.rect()) img.draw_cross(max_blob.cx(), max_blob.cy()) output_str="[%d,%d]" % (max_blob.cx(),max_blob.cy()) print('you send:',output_str) uart.write(output_str+'\r\n') else: print('not found!')

这段代码是一个基于OpenMV摄像头的颜色识别程序。程序的功能是识别图像中的黄色物体并通过UART串口发送物体的中心坐标。该程序的主要逻辑如下： 1. 导入所需的库和模块。 2. 设置黄色物体的颜色阈值。...

结合以下代码 import sensor import image import time from pyb import UART import struct # 导入struct模块 typecode ='bi' code = '' sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time=2000) clock = time.clock() # 初始化UART uart = UART(3, 115200) # 根据实际情况修改UART的端口和波特率 # 定义一个结构体类型和一个结构体变量 class OpenmvDataStruct: def init(self, shape, num): self.shape = shape self.num = num data = OpenmvDataStruct('N', 0) # 初始值为shape为'N'，num为0 while True: clock.tick() img = sensor.snapshot().lens_corr(1.8) # 检测圆形 for c in img.find_circles(threshold=3500, x_margin=10, y_margin=10, r_margin=10, r_min=2, r_max=100, r_step=2): img.draw_circle(c.x(), c.y(), c.r(), color=(255, 0, 0)) print('圆形') data.shape = 'C' #标识为C data.num = 1 img = sensor.snapshot() # 检测矩形 for r in img.find_rects(threshold=10000): img.draw_rectangle(r.rect(), color=(255, 0, 0)) for p in r.corners(): img.draw_circle(p[0], p[1], 5, color=(0, 255, 0)) print('矩形') data.shape = 'R' #标识为2 data.num = 2 # 检测三角形 sum_theta = 0 count = 0 for l in img.find_line_segments(merge_distance=10, max_theta_diff=10): img.draw_line(l.line(), color=(255, 0, 0)) sum_theta += l.theta() count += 1 avg_theta = sum_theta / count if count > 0 else 0 if 1 < avg_theta < 75: print('三角形') data.shape = 'T' #标识为T data.num = 3 print("FPS %f" % clock.fps()) # 将结构体变量data打包成字节流，并发送给Arduino # 打包data为字节流 packed_data = struct.pack(typecode, ord(data.shape), data.num) print(ord(data.shape)) uart.write(packed_data) # 发送数据

这段代码是一个综合示例，使用OpenMV进行图像处理，并通过UART将数据发送给Arduino。在这段代码中，你已经定义了一个结构体类型OpenmvDataStruct，并创建了一个结构体变量data来存储数据。 ...

THRESHOLD = ((0, 10, -128, 6, -128, 10)) import sensor, image, time from pyb import LED import car from pid import PID import time from pyb import UART import math rho_pid = PID(p=0.37, i=0) theta_pid = PID(p=0.001, i=0) LED(1).on() LED(2).on() LED(3).on() uart = UART(3,19200) sensor.reset() sensor.set_vflip(True) sensor.set_hmirror(True) sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQQVGA) sensor.skip_frames(time = 2000) clock = time.clock() while(True): clock.tick() img = sensor.snapshot().binary([THRESHOLD]) line = img.get_regression([(100,100)], robust = True) if (line): rho_err = abs(line.rho())-img.width()/2 if line.theta()>90: theta_err = line.theta()-180 else: theta_err = line.theta() img.draw_line(line.line(), color = 127) print(rho_err,line.magnitude(),rho_err) if line.magnitude()>8: rho_output = rho_pid.get_pid(rho_err,1) theta_output = theta_pid.get_pid(theta_err,1) output = rho_output + theta_output if(output<0): output = abs(output) + 100 OUTPUT = str(round(output)) uart.write(OUTPUT) uart.write('\r\n') print(OUTPUT) pass

1. 导入所需的模块和库，包括OpenMV的sensor、image、time库，pyb库，PID算法库和math库。 2. 设置阈值，用于图像二值化处理，获取黑白图像。 3. 初始化OpenMV摄像头，设置摄像头的参数，如镜像、翻转、像素格式、...

THRESHOLD = (21, 0, -77, 5, -110, 127) import sensor, image, time from pyb import LED import car from pid import PID import time from pyb import UART import math rho_pid = PID(p=0.37, i=0) theta_pid = PID(p=0.001, i=0) LED(1).on() LED(2).on() LED(3).on() uart = UART(3,19200) sensor.reset() sensor.set_vflip(True) sensor.set_hmirror(True) sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQQVGA) sensor.skip_frames(time = 2000) clock = time.clock() while(True): clock.tick() img = sensor.snapshot().binary([THRESHOLD]) line = img.get_regression([(100,100)], robust = True) if (line): rho_err = abs(line.rho())-img.width()/2 if line.theta()>90: theta_err = line.theta()-180 else: theta_err = line.theta() img.draw_line(line.line(), color = 127) print(rho_err,line.magnitude(),rho_err) if line.magnitude()>8: rho_output = rho_pid.get_pid(rho_err,1) theta_output = theta_pid.get_pid(theta_err,1) output = rho_output + theta_output if(output<0): output = abs(output) + 100 OUTPUT = str(round(output)) uart.write(OUTPUT) uart.write('\r\n') print(OUTPUT) pass 帮我分析这段代码

代码中使用了sensor、image、time、pyb等库，并引入了car和pid模块。首先，代码定义了一个阈值THRESHOLD，用于将图像转换为二值图像。然后初始化了传感器和LED灯，并设置了一些参数，如图像翻转、像素格式、帧...

from pyb import Pin, ExtInt callback = lambda e: print("intr") ext = ExtInt(Pin('P7'), ExtInt.IRQ_RISING, Pin.PULL_NONE, callback) 这段代码什么意思

可以把这段代码精简吗import sensor, image, time from pyb import UART sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time = 2000) sensor.set_auto_gain(False) # must be turned off for color tracking sensor.set_auto_whitebal(False) # must be turned off for color tracking clock = time.clock() uart = UART(3, 19200, timeout_char=2000) while(True): clock.tick() img = sensor.snapshot().lens_corr(1.8) for c in img.find_circles(threshold = 3500, x_margin = 10, y_margin = 10, r_margin = 10, r_min = 2, r_max = 100, r_step = 2): area = (c.x()-c.r(), c.y()-c.r(), 2c.r(), 2c.r()) #area为识别到的圆的区域，即圆的外接矩形框 statistics = img.get_statistics(roi=area)#像素颜色统计 print(statistics) #(0,100,0,120,0,120)是红色的阈值，所以当区域内的众数（也就是最多的颜色），范围在这个阈值内，就说明是红色的圆。 #l_mode()，a_mode()，b_mode()是L通道，A通道，B通道的众数。 if 0<statistics.l_mode()<100 and 0<statistics.a_mode()<127 and 0<statistics.b_mode()<127:#if the circle is red img.draw_circle(c.x(), c.y(), c.r(), color = (255, 0, 0))#识别到的红色圆形用红色的圆框出来 uart.write("Hello esp32!\r") time.sleep_ms(2000) else: img.draw_rectangle(area, color = (255, 255, 255)) #将非红色的圆用白色的矩形框出来 print("FPS %f" % clock.fps())

from pyb import UART sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time=2000) sensor.set_auto_gain(False) sensor.set_auto_whitebal(False) ...

可以把这段代码精简吗import sensor, image, time, lcd from pyb import UART sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time = 2000) lcd.init() sensor.set_auto_gain(False) sensor.set_auto_whitebal(False) clock = time.clock() uart = UART(3, 19200, timeout_char=2000) while(True): clock.tick() img = sensor.snapshot().lens_corr(1.8) lcd.display(img) for c in img.find_circles(threshold = 3500, x_margin = 10, y_margin = 10, r_margin = 10, r_min = 2, r_max = 100, r_step = 2): area = (c.x()-c.r(), c.y()-c.r(), 2c.r(), 2c.r()) statistics = img.get_statistics(roi=area) print(statistics) if 0<statistics.l_mode()<100 and 0<statistics.a_mode()<127 and 0<statistics.b_mode()<127: img.draw_circle(c.x(), c.y(), c.r(), color = (255, 0, 0)) uart.write("find red cirl!\n") time.sleep_ms(2000) else: img.draw_rectangle(area, color = (255, 255, 255)) uart.write("nothing!\n") time.sleep_ms(2000) print("FPS %f" % clock.fps())

from pyb import UART sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time=2000) lcd.init() sensor.set_auto_gain(False) sensor.set_auto_...

import sensor import image import time from pyb import UART sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time=2000) clock = time.clock() # 初始化UART uart = UART(3, 115200) # 根据实际情况修改UART的端口和波特率 while True: clock.tick() img = sensor.snapshot().lens_corr(1.8) # 检测圆形 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 c in circles: img.draw_circle(c.x(), c.y(), c.r(), color=(255, 0, 0)) uart.write("1") # 发送圆形标识为1 print('圆形') img = sensor.snapshot() # 检测矩形 rects = img.find_rects(threshold=10000) for r in rects: img.draw_rectangle(r.rect(), color=(255, 0, 0)) for p in r.corners(): img.draw_circle(p[0], p[1], 5, color=(0, 255, 0)) uart.write("2") # 发送矩形标识为2 print('矩形') # 检测三角形 sum_theta = 0 count = 0 lines = img.find_line_segments(merge_distance=10, max_theta_diff=10) for l in lines: img.draw_line(l.line(), color=(255, 0, 0)) sum_theta += l.theta() count += 1 avg_theta = sum_theta / count if count > 0 else 0 if 1 < avg_theta < 75: uart.write("3") # 发送三角形标识为3 print('三角形') print("FPS %f" % clock.fps())

这段代码是一个基于OpenMV的图像处理程序。它使用了OpenMV的库来进行图像处理和形状检测，并通过UART与其他设备进行通信。代码的主要逻辑如下： 1. 初始化摄像头和串口。 2. 进入无限循环，每次循环读取一帧图像。...

这段代码哪里有问题 import pyb import sensor, image, time, math from machine import UART from pyb import LED import os, tf void main(){ uart_num = uart.any() # 获取当前串口数据数量 uart_str = None while(1): if(uart_num): uart_str = uart.read(uart_num).strip() # 读取串口数据 print(str(uart_str)) SendArray[i] += 128 print(" 发送数据：", SendArray) uart.write(bytearray(SendArray)) SendModel = 0 SendArray = [0,0,0,0,0,0] # 数组归零 FinalNumResult = [] }

from pyb import LED import os, tf def main(): uart = UART(1) # 初始化 UART 对象 SendArray = [0, 0, 0, 0, 0, 0] # 初始化 SendArray SendModel = 0 while True: uart_num = uart.any() # 获取当前...

import sensor, image, time,math,pyb from pyb import UART,LED import json import ustruct sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA) sensor.skip_frames(time = 2000) sensor.set_auto_gain(False) # must be turned off for color tracking sensor.set_auto_whitebal(False) # must be turned off for color tracking red_threshold_01=(10, 100, 127, 32, -43, 67) clock = time.clock() uart = UART(3,115200) #定义串口3变量 uart.init(115200, bits=8, parity=None, stop=1) # init with given parameters def find_max(blobs): #定义寻找色块面积最大的函数 max_size=0 for blob in blobs: if blob.pixels() > max_size: max_blob=blob max_size = blob.pixels() return max_blob def sending_data(cx,cy,cw,ch): global uart; #frame=[0x2C,18,cx%0xff,int(cx/0xff),cy%0xff,int(cy/0xff),0x5B]; #data = bytearray(frame) data = ustruct.pack("<bbhhhhb", #格式为俩个字符俩个短整型(2字节) 0x2C, #帧头1 0x12, #帧头2 int(cx), # up sample by 4 #数据1 int(cy), # up sample by 4 #数据2 int(cw), # up sample by 4 #数据1 int(ch), # up sample by 4 #数据2 0x5B) uart.write(data); #必须要传入一个字节数组 while(True): clock.tick() img = sensor.snapshot() blobs = img.find_blobs([red_threshold_01]) cx=0;cy=0; if blobs: max_b = find_max(blobs) #如果找到了目标颜色 cx=max_b[5] cy=max_b[6] cw=max_b[2] ch=max_b[3] img.draw_rectangle(max_b[0:4]) # rect img.draw_cross(max_b[5], max_b[6]) # cx, cy FH = bytearray([0x2C,0x12,cx,cy,cw,ch,0x5B]) #sending_data(cx,cy,cw,ch) uart.write(FH)

while(True): clock.tick() img = sensor.snapshot() blobs = img.find_blobs([red_threshold_01]) cx=0;cy=0; if blobs: max_b = find_max(blobs) # 如果找到了目标颜色 cx=max_b[5] cy=max_b[6] cw=max_...

import time from pyb import UART import ustruct # 初始化串口 uart = UART(3, 115200) # 根据实际情况选择串口号和波特率 def send_serial_data(data1, data2): frame_start = bytes([0x02]) # 帧头 frame_end = bytes([0x03]) # 帧尾 payload = bytes([ord(data1), data2]) # 字符和数字作为有效载荷 frame = frame_start + payload + frame_end uart.write(frame) time.sleep(0.01) # 等待传输完成 # 调用函数发送数据 send_serial_data('A', 123) 完善该代码

from pyb import UART # 初始化串口 uart = UART(3, 115200) # 根据实际情况选择串口号和波特率 def send_serial_data(data1, data2): frame_start = bytes([0x02]) # 帧头 frame_end = bytes([0x03]) # 帧尾 ...

openMV用UART库将以下代码通过串口发送给arduino import sensor import image import time sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time=2000) clock = time.clock() while True: clock.tick() img = sensor.snapshot().lens_corr(1.8) # 检测圆形 for c in img.find_circles(threshold=3500, x_margin=10, y_margin=10, r_margin=10, r_min=2, r_max=100, r_step=2): img.draw_circle(c.x(), c.y(), c.r(), color=(255, 0, 0)) print(c) img = sensor.snapshot() # 检测矩形 for r in img.find_rects(threshold=10000): img.draw_rectangle(r.rect(), color=(255, 0, 0)) for p in r.corners(): img.draw_circle(p[0], p[1], 5, color=(0, 255, 0)) print(r) # 检测三角形 sum_theta = 0 count = 0 for l in img.find_line_segments(merge_distance=10, max_theta_diff=10): img.draw_line(l.line(), color=(255, 0, 0)) sum_theta += l.theta() count += 1 avg_theta = sum_theta / count if count > 0 else 0 if 1 < avg_theta < 110: print('三角形') print("FPS %f" % clock.fps())

from pyb import UART uart = UART(3, 115200) # 根据你的实际情况选择正确的串口和波特率 # 你的代码 code = ''' import sensor import image import time sensor.reset() sensor.set_pixformat(sensor.RGB565) ...

import sensor, image, time, os, tf, pyb from pyb import UART sensor.reset() # Reset and initialize the sensor. sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE) sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) sensor.set_windowing((240, 240)) # Set 240x240 window. sensor.skip_frames(time=2000) # Let the camera adjust. uart = UART(3, 15200) clock = time.clock() while(True): clock.tick() img = sensor.snapshot().binary([(0,64)]) img.lens_corr(1.8) for obj in tf.classify("shuzishibie.tflite", img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0): output = obj.output() number = output.index(max(output)) if number==1: uart.write('1') print(number) elif number==2: uart.write('2') print(number) elif number==3: uart.write('3') print(number) elif number==4: uart.write('4') print(number) elif number==5: uart.write('5') print(number) elif number==6: uart.write('6') print(number) elif number==7: uart.write('7') print(number) else: uart.write('8') print(number)

这段代码是用于图像识别的，它使用了OpenMV摄像头和TensorFlow Lite模型。代码的功能是从摄像头获取图像并将其传递给TensorFlow模型进行物体识别。识别结果通过UART发送给外部设备。在代码中，首先进行了一些初始...

基于Flask，mysql slope one的图书推荐系统全部资料+详细文档.zip

【资源说明】基于Flask，mysql slope one的图书推荐系统全部资料+详细文档.zip 【备注】 1、该项目是个人高分项目源码，已获导师指导认可通过，答辩评审分达到95分 2、该资源内项目代码都经过测试运行成功，功能ok的情况下才上传的，请放心下载使用！ 3、本项目适合计算机相关专业(人工智能、通信工程、自动化、电子信息、物联网等)的在校学生、老师或者企业员工下载使用，也可作为毕业设计、课程设计、作业、项目初期立项演示等，当然也适合小白学习进阶。 4、如果基础还行，可以在此代码基础上进行修改，以实现其他功能，也可直接用于毕设、课设、作业等。欢迎下载，沟通交流，互相学习，共同进步！

from pyb import Pin, ADC adc = ADC('P6') adc.read() # read value, 0-4095 from pyb import Pin, DAC dac = DAC('P6') dac.write(120) # output between 0 and 255 什么意思

相关推荐

from pyb import Pin, ADC adc = ADC('P6') adc.read() # read value, 0-4095 from pyb import Pin, DAC dac = DAC('P6') dac.write(120) # output between 0 and 255 什么意思

相关推荐

IIC.zip_IIC_PYB_iic 串口显示_串口at24c02

PYB.rar_Winpy.txt_delphi TXT_十六进制_拼音输入法

MicroPython部分使用教程 _WeAct工作室1

from pyb import Pin, Timer p = Pin('P7') # P7 has TIM4, CH1 tim = Timer(4, freq=1000) ch = tim.channel(1, Timer.PWM, pin=p) ch.pulse_width_percent(50) 什么意思

from pyb import Pin, ExtInt callback = lambda e: print("intr") ext = ExtInt(Pin('P7'), ExtInt.IRQ_RISING, Pin.PULL_NONE, callback) 这段代码什么意思

基于Flask，mysql slope one的图书推荐系统全部资料+详细文档.zip

最新推荐

基于Flask，mysql slope one的图书推荐系统全部资料+详细文档.zip

舰艇2 glb模型文件，航空母舰glb模型（亲测可用） 效果图见描述

WordPress作为新闻管理面板的实现指南

管理建模和仿真的文件

函数与模块化编程宝典：J750编程高效之路

用C语言求有4个圆塔，圆心分别为（2，2)，(2，-2)，(-2，2)，(-2，-2)圆半径为1， 这4个塔的高度为10m 塔以外无建筑物接输入任意点的坐标 求该点的建筑高度（塔外的高度为零)的程序

NPC_Generator：使用Ruby打造的游戏角色生成器

"互动学习：行动中的多样性与论文攻读经历"

流程控制与循环结构详解：J750编程逻辑构建指南

python实现生成一个窗口，其窗口题目为“二冷配水模型模型”，窗口中包含八个输入栏，三个按钮，每个按钮点击后会产生一个不同的页面

舰艇2 glb模型文件，航空母舰glb模型（亲测可用）效果图见描述

用C语言求有4个圆塔，圆心分别为（2，2)，(2，-2)，(-2，2)，(-2，-2)圆半径为1，这4个塔的高度为10m 塔以外无建筑物接输入任意点的坐标求该点的建筑高度（塔外的高度为零)的程序