4. 为什么自己训练的模型用的是net.classify，历程是tf.？net和labels都是none？
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    • 为什么自己训练的模型用的是net.classify，历程是tf.？net和labels都是none？

    • H
       

      # 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.

uart = UART(3, 115200, timeout_char=1000)                         # i使用给定波特率初始化
uart.init(115200, bits=8, parity=None, stop=1, timeout_char=1000) # 使用给定参数初始化

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())
        print(obj.output())
        # This combines the labels and confidence values into a list of tuples
        predictions_list = list(zip(labels, obj.output()))
        print(predictions_list)
        for i in range(len(predictions_list)):
            print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
            if predictions_list[0][1]>predictions_list[1][1]:
                uart.write('1\r\n')
            if predictions_list[0][1]<predictions_list[1][1]:
                uart.write('2\r\n')

    print(clock.fps(), "fps")
      0 1 条回复

    • 写代码的不是一个人，所以有点差别，但是差不多。具体函数看文档。。

      0

    • @hqpz “net和labels都是none？”这句话我看不懂。

      0
      H
       1 条回复
    • H

      @kidswong999

      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) + ')')
      0 1 条回复

    • @hqpz 代码没什么问题。写法不一样罢了，net和labels都在后面赋值，最前面的net = None
      labels = None，你删了问题也不大。

      不需要死扣这点事情，没意义。

      0