4. 神经网络模型导入openmv后运行帧数很低是为什么应该怎么解决？
  • OpenMV VSCode 扩展发布了，在插件市场直接搜索OpenMV就可以安装
  • 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，需要报错提示与全部代码文本，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。

    • 神经网络模型导入openmv后运行帧数很低是为什么应该怎么解决？

    • L

      我将edgeimpluse网站中训练的神经网络模型导出到openmv后不修改任何代码帧率只有2帧多，加上输出高低电平信号更是只有零点几帧了，换了高速快门模块也没有任何改善，想请问下是什么原因要怎么办呢？

      # Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, uos, gc, pyb
from pyb import LED, Pin

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.WVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.

net = None
labels = None
fish = ""
gl = 0

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())
        # This combines the labels and confidence values into a list of tuples
        predictions_list = list(zip(labels, obj.output()))
        fish = predictions_list[0][0]
        gl = predictions_list[0][1]
        for i in range(len(predictions_list)):
            print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
            if predictions_list[i][1]>gl:
                fish = predictions_list[i][0]
                gl = predictions_list[i][1]
            else:
                fish = fish
                gl = gl
        print("result:%s" % fish)
        if fish == predictions_list[0][0]:
            led = LED(1) # 红led
            led.toggle()
            led.on()#亮
            p_out = Pin('P5', Pin.OUT_PP)#设置p_out为输出引脚
            p_out.high()#设置p_out引脚为高
            pyb.delay(1000)
            p_out.low()#设置p_out引脚为低
            led.off()#灭
        elif fish == predictions_list[1][0]:
            led = LED(2) # 绿led
            led.toggle()
            led.on()#亮
            p_out = Pin('P6', Pin.OUT_PP)#设置p_out为输出引脚
            p_out.high()#设置p_out引脚为高
            pyb.delay(1000)
            p_out.low()#设置p_out引脚为低
            led.off()#灭
        elif fish == predictions_list[2][0]:
            led = LED(3) # 蓝led
            led.toggle()
            led.on()#亮
            p_out = Pin('P7', Pin.OUT_PP)#设置p_out为输出引脚
            p_out.high()#设置p_out引脚为高
            pyb.delay(1000)
            p_out.low()#设置p_out引脚为低
            led.off()#灭
        else:
            led = LED(4) # 红外led
            led.toggle()
            led.on()#亮
            p_out = Pin('P8', Pin.OUT_PP)#设置p_out为输出引脚
            p_out.high()#设置p_out引脚为高
            pyb.delay(1000)
            p_out.low()#设置p_out引脚为低
            led.off()#灭
    print(clock.fps(), "fps")
      0

    • 把pyb.delay删掉。

      0