• 星瞳AI VISION软件内测!可以离线标注,训练,并生成OpenMV的模型。可以替代edge impulse https://forum.singtown.com/topic/8206
  • 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。
  • 串行终端,只显示最高得分



    • # Edge Impulse - OpenMV Image Classification Example
             
             
             
             import sensor, image, time, os, tf, uos, gc
             
             
             
             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 = 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:
             
                 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()))
             
             
             
                     for i in range(len(predictions_list)):
             
                         print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
             
             
             
                 print(clock.fps(), "fps")


    • 添加什么代码,让其只在川行输出最高分?