4. 脱机运行识别率太低，有六种图像通过edge impulse训练之后只能识别两种，图像2识别成图像3
  • 免费好用的星瞳AI云服务上线！简单标注，云端训练，支持OpenMV H7和OpenMV H7 Plus。可以替代edge impulse。 https://forum.singtown.com/topic/9519
  • 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。
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  • 必看：玩转星瞳论坛了解一下图片上传，代码格式等问题。

    • 脱机运行识别率太低，有六种图像通过edge impulse训练之后只能识别两种，图像2识别成图像3

    • W
       

      # Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, uos, gc

from pyb import UART
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()
    uart = UART(3,9600)
    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()))
        predictions_list=sorted(predictions_list,key=lambda x:x[1],reverse=True)
        highest_prediction=predictions_list[0]
        print("The highest prediction is:%s=%f"%(highest_prediction[0],highest_prediction[1]))
        uart.write(highest_prediction[0])

        uart.write("\r\n")
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    • 增加图像数量，每个分类1000张，不同的角度，位置和光线都要。

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