4. 在Edge Impuse上生成的神经网络模型只能全屏识别物品。怎么样可以像模板识别一样用小一点的矩形框选出识别到的物品
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    • 在Edge Impuse上生成的神经网络模型只能全屏识别物品。怎么样可以像模板识别一样用小一点的矩形框选出识别到的物品

    • D

      在Edge Impuse上生成的神经网络模型只能全屏识别物品。怎么样可以像模板识别一样用小一点的矩形框选出识别到的物品
      这里附上生成的代码：# Edge Impulse - OpenMV Image Classification Example

      import sensor, image, time, os, tf, uos, gc
      flag=0
      a=0
      b=0
      pre=0
      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.
      from pyb import UART
      net = None
      labels = None
      uart = UART(3, 115200)
      while(True):
      #a=uart.read()
      #if a==b'o' :
      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()
    while(True):
        clock.tick()

        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()) #, roi=(10, 0, 60, 60))
            # 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]))

        if predictions_list[0][1]>0.85:
            flag=1

        elif predictions_list[1][1]>0.85:
            flag=2

        elif predictions_list[0][1]<0.85 and predictions_list[1][1]<0.85:
            flag=3
        if pre!=flag:
            b=0
            pre=flag
        if flag==1:
            if b<=2:
                uart.write("z1")
                b+=1
            
        if flag==2:
            if b<=2:
                uart.write("z2")
                b+=1
            
        if flag==3:
            if b<=2:
                uart.write("g")
                b+=1
       print(clock.fps(), "fps")

      求大佬解答

      0

    • 最近会发布edge impulse使用FOMO目标点检测的视频。

      0