openmv循迹&脱机调阈值代码与实现_openmv脱机调阈值

实验用具：

                                                                 openmv4  h7  R2

                                                  立创自己打印的openmv  lcd扩展板 

                                                         1.8寸tft  spi  屏幕芯片st7735s 

                                                        大夏龙雀BT24蓝牙模块

                                                接线：P5接BT24的TX 用来接收蓝牙发来的数据 

   成果展示：                        

openmv阈值脱机调试

 代码：

原理：利用蓝牙，你发送a b会使阈值number1  number2加加，c  d会减减

import sensor, image, time, math, pybfrom machine import UARTimport display#用来调binary值NUMBER1=51NUMBER2=255#这两变量是用来你调阈值，是为了适应在不同场合光线下刚好把黑色与白色分开 ，借助按键调整这个阈值就可以完成现场调试turnGRAYSCALE_THRESHOLD = [(NUMBER1, NUMBER2)]GRAYSCALE_THRESHOLD =[(0,0)]# 每个roi为(x, y, w, h)，线检测算法将尝试找到每个roi中最大的blob的质心。# 然后用不同的权重对质心的x位置求平均值，其中最大的权重分配给靠近图像底部的roi，# 较小的权重分配给下一个roi，以此类推。rois = [(0, 100, 160, 20), (0, 50, 160, 20), (0, 0, 160, 20)]# roi代表三个取样区域，（x,y,w,h,weight），代表左上顶点（x,y）宽高分别为w和h的矩形，# weight为当前矩形的权值。注意本例程采用的QQVGA图像大小为160x120，roi即把图像横分成三个矩形。# 三个矩形的阈值要根据实际情况进行调整，离机器人视野最近的矩形权值要最大，# 如上图的最下方的矩形，即(0, 100, 160, 20, 0.7)# 初始化sensorsensor.reset()# 设置图像色彩格式，有RGB565色彩图和GRAYSCALE灰度图两种sensor.set_pixformat(sensor.GRAYSCALE) # use grayscale.# 设置图像像素大小sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.# 让新的设置生效。sensor.skip_frames(time=2000) # Let new settings take effect.# 颜色跟踪必须关闭自动增益sensor.set_auto_gain(False) # must be turned off for color tracking# 颜色跟踪必须关闭白平衡sensor.set_auto_whitebal(False) # must be turned off for color tracking# 跟踪FPS帧率lcd = display.SPIDisplay() # Initialize the lcd screen.kk=\'y\'sensor.set_vflip(1)sensor.set_hmirror(1)clock = time.clock() # Tracks FPS.largest2_blob=0deflection_angle = 0 # Initialize deflection_angle outside of conditional blocksuart = UART(3,9600,timeout_char=1000)while True: turnGRAYSCALE_THRESHOLD = [(NUMBER1, NUMBER2)] clock.tick() # Track elapsed milliseconds between snapshots. img = sensor.snapshot() # Capture an image. img.binary(turnGRAYSCALE_THRESHOLD) largest_blob = None #largest2_blob = None #largest3_blob = None # Track lines in each defined ROI. #blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=rois[0], merge=True) #if blobs: # largest_blob = max(blobs, key=lambda b: b.pixels()) blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=rois[1], merge=True) if blobs: largest2_blob = max(blobs, key=lambda b: b.pixels()) # #blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=rois[2], merge=True) #if blobs: # largest3_blob = max(blobs, key=lambda b: b.pixels()) # Calculate deflection_angle based on largest2_blob.cx() - 79 #通过计算图像中心点x与块中心点x的差判断偏移 pianyi = 0 if largest2_blob: pianyi = largest2_blob.cx() - 79 img.draw_rectangle(largest2_blob.rect(),color=(0)); if -5 <= pianyi < 5: deflection_angle = 0 elif -15 <= pianyi < -5: deflection_angle = -2 elif 5 <= pianyi < 15: deflection_angle = 2 elif -30 <= pianyi < -15: deflection_angle = -3 elif 15 <= pianyi < 30: deflection_angle = 3 elif -50 <= pianyi < -30: deflection_angle = -5 elif 30 <= pianyi 255: NUMBER1=255 if NUMBER2>255: NUMBER2=255 if NUMBER1<0: NUMBER1=0 if NUMBER2<0: NUMBER2=0 lcd.write(img) # Take a picture and display the image. byte=uart.read(1) if byte : print(byte) if byte ==b\'a\': NUMBER1=NUMBER1+5 if byte ==b\'b\': NUMBER2=NUMBER2+5 if byte ==b\'c\': NUMBER1=NUMBER1-5 if byte ==b\'d\': NUMBER2=NUMBER2-5 

原理：利用按键，你按下按键1  按键2会使阈值number1  number2加加减减，支持长久按

P3 P4 P5 P6分别接按键模块KEY1  KEY2  KEY3  KEY4，用if判断是否按下

openmv阈值脱机调试

import sensor, image, time, math, pybfrom machine import UARTimport displayfrom pyb import Pin#用来调binary值NUMBER1=51NUMBER2=255#这两变量是用来你调阈值，是为了适应在不同场合光线下刚好把黑色与白色分开 ，借助按键调整这个阈值就可以完成现场调试turnGRAYSCALE_THRESHOLD = [(NUMBER1, NUMBER2)]GRAYSCALE_THRESHOLD =[(0,0)]# 每个roi为(x, y, w, h)，线检测算法将尝试找到每个roi中最大的blob的质心。# 然后用不同的权重对质心的x位置求平均值，其中最大的权重分配给靠近图像底部的roi，# 较小的权重分配给下一个roi，以此类推。rois = [(0, 100, 160, 20), (0, 50, 160, 20), (0, 0, 160, 20)]# roi代表三个取样区域，（x,y,w,h,weight），代表左上顶点（x,y）宽高分别为w和h的矩形，# weight为当前矩形的权值。注意本例程采用的QQVGA图像大小为160x120，roi即把图像横分成三个矩形。# 三个矩形的阈值要根据实际情况进行调整，离机器人视野最近的矩形权值要最大，# 如上图的最下方的矩形，即(0, 100, 160, 20, 0.7)# 初始化sensorsensor.reset()# 设置图像色彩格式，有RGB565色彩图和GRAYSCALE灰度图两种sensor.set_pixformat(sensor.GRAYSCALE) # use grayscale.# 设置图像像素大小sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.# 让新的设置生效。sensor.skip_frames(time=2000) # Let new settings take effect.# 颜色跟踪必须关闭自动增益sensor.set_auto_gain(False) # must be turned off for color tracking# 颜色跟踪必须关闭白平衡sensor.set_auto_whitebal(False) # must be turned off for color tracking# 跟踪FPS帧率lcd = display.SPIDisplay() # Initialize the lcd screen.kk=\'y\'#p_in4 = Pin(\'P7\', Pin.IN, Pin.PULL_UP)#设置p_in为输入引脚，并开启上拉电阻p_in4 = Pin(\'P4\', Pin.IN, Pin.PULL_UP)#设置p_in为输入引脚，并开启上拉电阻p_in6 = Pin(\'P6\', Pin.IN, Pin.PULL_UP)#设置p_in为输入引脚，并开启上拉电阻#p_in7 = Pin(\'P7\', Pin.IN, Pin.PULL_UP)#设置p_in为输入引脚，并开启上拉电阻p_in5 = Pin(\'P5\', Pin.IN, Pin.PULL_UP)#设置p_in为输入引脚，并开启上拉电阻p_in3 = Pin(\'P3\', Pin.IN, Pin.PULL_UP)#设置p_in为输入引脚，并开启上拉电阻sensor.set_vflip(1)sensor.set_hmirror(1)clock = time.clock() # Tracks FPS.largest2_blob=0deflection_angle = 0 # Initialize deflection_angle outside of conditional blocksuart = UART(3,9600,timeout_char=1000)while True: turnGRAYSCALE_THRESHOLD = [(NUMBER1, NUMBER2)] clock.tick() # Track elapsed milliseconds between snapshots. img = sensor.snapshot() # Capture an image. img.binary(turnGRAYSCALE_THRESHOLD) largest_blob = None #largest2_blob = None #largest3_blob = None # Track lines in each defined ROI. #blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=rois[0], merge=True) #if blobs: # largest_blob = max(blobs, key=lambda b: b.pixels()) blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=rois[1], merge=True) if blobs: largest2_blob = max(blobs, key=lambda b: b.pixels()) # #blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=rois[2], merge=True) #if blobs: # largest3_blob = max(blobs, key=lambda b: b.pixels()) # Calculate deflection_angle based on largest2_blob.cx() - 79 #通过计算图像中心点x与块中心点x的差判断偏移 pianyi = 0 if largest2_blob: pianyi = largest2_blob.cx() - 79 img.draw_rectangle(largest2_blob.rect(),color=(0)); if -5 <= pianyi < 5: deflection_angle = 0 elif -15 <= pianyi < -5: deflection_angle = -2 elif 5 <= pianyi < 15: deflection_angle = 2 elif -30 <= pianyi < -15: deflection_angle = -3 elif 15 <= pianyi < 30: deflection_angle = 3 elif -50 <= pianyi < -30: deflection_angle = -5 elif 30 <= pianyi 255: NUMBER1=255 if NUMBER2>255: NUMBER2=255 if NUMBER1<0: NUMBER1=0 if NUMBER2<0: NUMBER2=0 lcd.write(img) # Take a picture and display the image. byte=uart.read(1) if byte : print(byte) if byte ==b\'a\': NUMBER1=NUMBER1+5 if byte ==b\'b\': NUMBER2=NUMBER2+5 if byte ==b\'c\': NUMBER1=NUMBER1-5 if byte ==b\'d\': NUMBER2=NUMBER2-5