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    csm6

    @csm6

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    csm6 发布的帖子

    • RE: 代码也不报错,连接openmv运行就会自动终止运行,串行终端提示这个是怎么回事啊?

      刚刚又改了一下代码,发现不报错了,但是串行终端没有输出数据,这个代码是哪里出问题了呀?

      发布在 OpenMV Cam
      C
      csm6
    • RE: 代码也不报错,连接openmv运行就会自动终止运行,串行终端提示这个是怎么回事啊?
      import sensor, image, time, os, tf, uos, gc, pyb, machine,math,tf
      from pyb import UART
      uart = UART(3, 9600)
      sensor.reset()
      sensor.set_pixformat(sensor.RGB565)
      sensor.set_framesize(sensor.QVGA)
      sensor.set_windowing((240, 240))
      sensor.skip_frames(time=2000)
      sensor.set_auto_gain(False)
      sensor.set_auto_whitebal(False)
      
      net = None
      labels = None
      
      ROI = (20,20,200,200)
      
      try:
          # load the model, alloc the model file on the heap if we have at least 64K free after loading
          net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
      except Exception as e:
          print(e)
          raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
      
      try:
          labels = [line.rstrip('\n') for line in open("labels.txt")]
      except Exception as e:
          raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
      
      clock = time.clock()
      
      def compare():
      	img1 = sensor.snapshot()
      	img1.draw_rectangle(ROI)
      	statistics = img1.get_statistics(roi = ROI)
      	img2 = sensor.snapshot()
      	img2.draw_rectangle(ROI)
      	statistics2 = img2.get_statistics(roi = ROI)
      	num1 = statistics.mode()
      	num2 = statistics.mode()
      	if(abs(num1-num2)>50):  return 1
          #print("1")return 0
      	
      	
      def rubbish():
          clock.tick()
          
          pyb.delay(1000)
      
          img = sensor.snapshot()
      
          # default settings just do one detection... change them to search the image...
          for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
              print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
              img.draw_rectangle(obj.rect())
              # This combines the labels and confidence values into a list of tuples
              predictions_list = list(zip(labels, obj.output()))
      
              for i in range(len(predictions_list)):
                  print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
      	
          print(clock.fps(), "fps")
          if (sum(predictions_list[i][1])/len(predictions_list[i][1]))>0.8:
              print(prediction_list[i][1])
              uart.write(predictions_list[i][0])
          
      
      while(True):
          res = compare()
          if(res==1):
              print(OK)
              rubbish()
              while(True):
                  if uart.any():
                      notice = uart.readline().decode()
                  if notice =='G':
                      rubbish()
      
      发布在 OpenMV Cam
      C
      csm6
    • RE: 代码也不报错,连接openmv运行就会自动终止运行,串行终端提示这个是怎么回事啊?

      这个代码是哪里有问题吗?怎么运行不了也拍不了照片,传不了值?自动就停止运行了?跟arduino连接也没反应

      发布在 OpenMV Cam
      C
      csm6
    • 代码也不报错,连接openmv运行就会自动终止运行,串行终端提示这个是怎么回事啊?
      import sensor, image, time, os, tf, uos, gc, pyb, machine,math,tf
      from pyb import UART
      uart = UART(3, 9600)
      sensor.reset()
      sensor.set_pixformat(sensor.RGB565)
      sensor.set_framesize(sensor.QVGA)
      sensor.set_windowing((240, 240))
      sensor.skip_frames(time=2000)
      sensor.set_auto_gain(False)
      sensor.set_auto_whitebal(False)
      
      net = None
      labels = None
      
      ROI = (20,20,200,200)
      
      try:
          # load the model, alloc the model file on the heap if we have at least 64K free after loading
          net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
      except Exception as e:
          print(e)
          raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
      
      try:
          labels = [line.rstrip('\n') for line in open("labels.txt")]
      except Exception as e:
          raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
      
      clock = time.clock()
      
      def compare():
      	img1 = sensor.snapshot()
      	img1.draw_rectangle(ROI)
      	statistics = img1.get_statistics(roi = ROI)
      	img2 = sensor.snapshot()
      	img2.draw_rectangle(ROI)
      	statistics2 = img2.get_statistics(roi = ROI)
      	num1 = statistics.mode()
      	num2 = statistics.mode()
      	if(abs(num1-num2)>20):return 1
      	else:return 0
      	
      	
      def rubbish():
          clock.tick()
          
          pyb.delay(1000)
      
          img = sensor.snapshot()
      
          # default settings just do one detection... change them to search the image...
          for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
              print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
              img.draw_rectangle(obj.rect())
              # This combines the labels and confidence values into a list of tuples
              predictions_list = list(zip(labels, obj.output()))
      
              for i in range(len(predictions_list)):
                  print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
      	
          print(clock.fps(), "fps")
          if (sum(predictions_list[i][1])/len(predictions_list[i][1]))>0.8:
              print(prediction_list[i][1])
              uart.write(predictions_list[i][0])
          else:
              rubbish()
      
      if(compare()==1):
      	rubbish()
      	while(True):
      		if uart.any():
      			notice = uart.readline().decode()
      		if notice =='G':
      			rubbish()
      
      

      0_1697433161873_7e1ff1f6-dc6a-4763-a249-7594dacd3c77-image.png

      发布在 OpenMV Cam
      C
      csm6
    • RE: openmv连接超声波模块,提示pix format错误

      @fmcq 在 openmv连接超声波模块,提示pix format错误 中说:

      mport time,utime,pyb
      from pyb import Pin

      wave_echo_pin = Pin('P7', Pin.IN, Pin.PULL_NONE)
      wave_trig_pin = Pin('P8', Pin.OUT_PP, Pin.PULL_DOWN)

      wave_distance = 0
      tim_counter = 0
      flag_wave = 0

      #超声波启动
      def wave_start():
      wave_trig_pin.value(1)
      utime.sleep_us(15)
      wave_trig_pin.value(0)

      #超声波距离计算
      def wave_distance_calculation():
      #全局变量声明
      global tim_counter
      #频率f为0.2MHZ 高电平时间t=计数值1/f
      wave_distance = tim_counter5*0.017
      #输出最终的测量距离(单位cm)
      print('wave_distance',wave_distance)

      #超声波数据处理
      def wave_distance_process():
      global flag_wave
      if(flag_wave == 0):
      wave_start()
      if(flag_wave == 2):
      wave_distance_calculation()
      flag_wave = 0

      #配置定时器
      tim =pyb.Timer(1, prescaler=720, period=65535) #相当于freq=0.2M

      #外部中断配置
      def callback(line):
      global flag_wave,tim_counter
      #上升沿触发处理
      if(wave_echo_pin.value()):
      tim.init(prescaler=720, period=65535)
      flag_wave = 1
      #下降沿
      else:
      tim.deinit()
      tim_counter = tim.counter()
      tim.counter(0)
      extint.disable()
      flag_wave = 2
      #中断配置
      extint = pyb.ExtInt(wave_echo_pin, pyb.ExtInt.IRQ_RISING_FALLING, pyb.Pin.PULL_DOWN, callback)

      while(True):
      wave_distance_process()
      time.sleep(100)
      print('wave_distance',wave_distance)

      想问一下,这个代码最后是怎么实现超声波测距的吗

      发布在 OpenMV Cam
      C
      csm6
    • oenmv能外接一个超声波传感器吗?

      oenmv能外接一个超声波传感器吗?

      发布在 OpenMV Cam
      C
      csm6
    • RE: 需要一个缓冲区协议的对象,这个怎么解决

      前面打印name 一直是'c',我就把代码改成这样了,又提示了这个错误

      发布在 OpenMV Cam
      C
      csm6
    • RE: 需要一个缓冲区协议的对象,这个怎么解决

      0_1692850268256_f7338f32-d1f2-484e-a898-f55540db5394-image.png

      import sensor, image, time, os, tf, uos, gc,pyb,machine
      from pyb import UART
      uart = UART(3,115200)
      
      
      green = (10,100,-50,15,-15,50)
      red = (0,100,-30,50,-30,30)
      blue = (10,100,-20,40,-10,40)
      orange = (10,80,-10,30,-20,35)
      brown = (0,80,-10,20,-20,30)
      yellow = (50,80,-10,10,-20,60)
      white = (10,100,-20,30,-30,20)
      gray = (20,80,-10,15,-10,25)
      
      
      sensor.reset()                         # Reset and initialize the sensor.
      sensor.set_pixformat(sensor.RGB565)    # 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)
      sensor.set_auto_gain(False)
      sensor.set_auto_whitebal(False)
      clock = time.clock()
      
      
      
      while(True):
          clock.tick()
      
      
          def rubbish():
              net = None
              labels = None
      
              try:
                  # load the model, alloc the model file on the heap if we have at least 64K free after loading
                  net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64 * 1024)))
              except Exception as e:
      
                  raise Exception(
                      'Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(
                          e) + ')')
      
              try:
                  labels = [line.rstrip('\n') for line in open("labels.txt")]
              except Exception as e:
                  raise Exception(
                      'Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(
                          e) + ')')
      
              image = sensor.snapshot()
      
              scores = net.classify(image)
      
              # default settings just do one detection... change them to search the image...
              for obj in net.classify(image, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
      
                  image.draw_rectangle(obj.rect())
                  # This combines the labels and confidence values into a list of tuples
                  predictions_list = list(zip(labels, obj.output()))
                  name = predictions_list[0][0]
                  for i in range(len(predictions_list)):
      
                      max = 0.8
                      if predictions_list[i][1] > max:
                          max = predictions_list[i][1]
                          name = predictions_list[i][0]
      
                  if name == 'KHSLJ':
                      name = 'k'
                  elif name == 'YHLJ':
                      name = 'y'
                  elif name == 'CYLJ':
                      name = 'c'
                  elif name == 'QTLJ':
                      name = 'q'
      
              return name
              
          name = rubbish()
      
          img = sensor.snapshot()
          blobs1 = img.find_blobs([green])
          blobs2 = img.find_blobs([red])
          blobs3 = img.find_blobs([blue])
          blobs4 = img.find_blobs([orange])
          blobs5 = img.find_blobs([brown])
          blobs6 = img.find_blobs([yellow])
          blobs7 = img.find_blobs([white])
          blobs8 = img.find_blobs([gray])
      
          if blobs1:
              rubbish()
              uart.write(name)
              print(name)
      
          if blobs2:
              rubbish()
              uart.write(name)
      
          if blobs3:
              rubbish()
              uart.write(name)
          if blobs4:
              rubbish()
              uart.write(name)
      
          if blobs5:
              rubbish()
              uart.write(name)
      
          if blobs6:
              rubbish()
              uart.write(name)
      
          if blobs7:
              rubbish()
              uart.write(name)
      
          if blobs8:
              rubbish()
              uart.write(name)
      
      发布在 OpenMV Cam
      C
      csm6
    • 需要一个缓冲区协议的对象,这个怎么解决

      ![0_1692785268446_报错.jpg](正在上传 72%)

      # Edge Impulse - OpenMV Image Classification Example
      
      import sensor, image, time, os, tf, uos, gc,pyb,machine
      from pyb import UART
      uart = UART(3,115200)
      
      
      green = (10,100,-50,15,-15,50)
      red = (0,100,-30,50,-30,30)
      blue = (10,100,-20,40,-10,40)
      orange = (10,80,-10,30,-20,35)
      brown = (0,80,-10,20,-20,30)
      yellow = (50,80,-10,10,-20,60)
      white = (10,100,-20,30,-30,20)
      gray = (20,80,-10,15,-10,25)
      
      
      sensor.reset()                         # Reset and initialize the sensor.
      sensor.set_pixformat(sensor.RGB565)    # 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)
      sensor.set_auto_gain(False)
      sensor.set_auto_whitebal(False)
      clock = time.clock()
      
      name = 'c'
      
      while(True):
          clock.tick()
      
      
          def rubbish():
              net = None
              labels = None
      
              try:
                  # load the model, alloc the model file on the heap if we have at least 64K free after loading
                  net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64 * 1024)))
              except Exception as e:
      
                  raise Exception(
                      'Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(
                          e) + ')')
      
              try:
                  labels = [line.rstrip('\n') for line in open("labels.txt")]
              except Exception as e:
                  raise Exception(
                      'Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(
                          e) + ')')
      
              image = sensor.snapshot()
      
              scores = net.classify(image)
      
              # default settings just do one detection... change them to search the image...
              for obj in net.classify(image, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
      
                  image.draw_rectangle(obj.rect())
                  # This combines the labels and confidence values into a list of tuples
                  predictions_list = list(zip(labels, obj.output()))
                  name = predictions_list[0][0]
                  for i in range(len(predictions_list)):
      
                      max = 0.8
                      if predictions_list[i][1] > max:
                          max = predictions_list[i][1]
                          name = predictions_list[i][0]
      
                  if name == 'KHSLJ':
                      name = 'k'
                  elif name == 'YHLJ':
                      name = 'y'
                  elif name == 'CYLJ':
                      name = 'c'
                  elif name == 'QTLJ':
                      name = 'q'
      
              return name
          img = sensor.snapshot()
          blobs1 = img.find_blobs([green])
          blobs2 = img.find_blobs([red])
          blobs3 = img.find_blobs([blue])
          blobs4 = img.find_blobs([orange])
          blobs5 = img.find_blobs([brown])
          blobs6 = img.find_blobs([yellow])
          blobs7 = img.find_blobs([white])
          blobs8 = img.find_blobs([gray])
      
          if blobs1:
              rubbish()
              uart.write(name)
      
          if blobs2:
              rubbish()
              uart.write(name)
      
          if blobs3:
              rubbish()
              uart.write(name)
          if blobs4:
              rubbish()
              uart.write(name)
      
          if blobs5:
              rubbish()
              uart.write(name)
      
          if blobs6:
              rubbish()
              uart.write(name)
      
          if blobs7:
              rubbish()
              uart.write(name)
      
          if blobs8:
              rubbish()
              uart.write(name)
      
      
      
      
      
      
      发布在 OpenMV Cam
      C
      csm6