星瞳实验室APP,快速收到回复
  • 我们只解决官方正版的OpenMV的问题(STM32),其他的分支有很多兼容问题,我们无法解决。
  • 如果有产品硬件故障问题,比如无法开机,论坛很难解决。可以直接找售后维修
  • 发帖子之前,请确认看过所有的视频教程,https://singtown.com/learn/ 和所有的上手教程http://book.openmv.cc/
  • 每一个新的提问,单独发一个新帖子
  • 帖子需要目的,你要做什么?
  • 如果涉及代码,需要报错提示全部代码文本,请注意不要贴代码图片
  • 必看:玩转星瞳论坛了解一下图片上传,代码格式等问题。
  • 神经网络中的文件类名怎么打印出来?判断对于识别率为90%时打印出来



    • # Edge Impulse - OpenMV Image Classification Example
      
      import sensor, image, time, os, tf, pyb
      
      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)          # Let the camera adjust.
      
      net = "trained.tflite"
      labels = [line.rstrip('\n') for line in open("labels.txt")]
      
      clock = time.clock()
      while(True):
          clock.tick()
      
          img = sensor.snapshot()
          for i in range(10):
              x = (pyb.rng() % (2*img.width())) - (img.width()//2)
              y = (pyb.rng() % (2*img.height())) - (img.height()//2)
              w = (pyb.rng() % (img.width()//2))
              h = (pyb.rng() % (img.height()//2))
              r = (pyb.rng() % 127) + 128
              g = (pyb.rng() % 127) + 128
              b = (pyb.rng() % 127) + 128
          img.draw_rectangle(x, y, w, h, color = (r, g, b), thickness = 2, fill = False)
          # default settings just do one detection... change them to search the image...
          for obj in tf.classify(net, 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")
          #目标判断及框取
         # img.draw_rectangle(blob.rect())#如果识别到颜色就框起来
          #img.draw_cross(blob.cx(), blob.cy())#draw_cross:在中心画十字架;blob.cx(), blob.cy:色块中心坐标+
      
      


    • predictions_list[i][0]就是名字