就是模型里和最小置信度比较的那个值存在那个变量里？

生成了，而且也都是英文标签

还有别的方法吗，因为我们要满足识别成熟果子抓取的条件，置信度不能太低

# Edge Impulse - OpenMV Object Detection Example

import sensor, image, time, os, tf, math, uos, gc
import pyb
import sensor, image, time
import json
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.

def modified_data(data):
   data = int(data)# 将data转化为整数型变量
   str_data = ''
   if data < 10:                                  # 目标色块的中心点的横坐标、纵坐标或面积的开方<10
       str_data = str_data + '000' + str(data)   # 运用字符串的拼接把色块参数全部转化为长度为四个字符的字符串，如8->“0008”
   elif data >= 10 and data < 100:               # 10<目标色块的中心点的横坐标、纵坐标或面积的开方<100
       str_data = str_data + '00' + str(data)    # 运用字符串的拼接把色块参数全部转化为长度为四个字符的字符串，如88->“0088”
   elif data >=100 and data <1000:               # 100<目标色块的中心点的横坐标、纵坐标或面积的开方<1000
       str_data = str_data + '0' + str(data)     # 运用字符串的拼接把色块参数全部转化为长度为四个字符的字符串，如888->“0888”
   else:                                         # 1000<目标色块的中心点的横坐标、纵坐标或面积的开方
       str_data = str_data + str(data)           # 运用字符串的拼接把色块参数全部转化为长度为四个字符的字符串，如8888->“8888”
   return str_data.encode('utf-8')               # ******将字符串中的每一个字母转化为UTF-8码值(与ASCII码值基本一样)*******
                                                 # ******十进制0对应ASCII码值的48，可进行换算

net = None
labels = None
min_confidence = 0.70
uart = UART(3, 115200)

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:
    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) + ')')

colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
    (255,   0,   0),
    (  0, 255,   0),
    (255, 255,   0),
    (  0,   0, 255),
    (255,   0, 255),
    (  0, 255, 255),
    (255, 255, 255),
]

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()

    # detect() returns all objects found in the image (splitted out per class already)
    # we skip class index 0, as that is the background, and then draw circles of the center
    # of our objects

    for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
        if (i == 0): continue # background class
        if (len(detection_list) == 0): continue # no detections for this class?

        print("********** %s **********" % labels[i])
        for d in detection_list:
            [x, y, w, h] = d.rect()
            center_x = math.floor(x + (w / 2))
            center_y = math.floor(y + (h / 2))
            print('x %d\ty %d' % (center_x, center_y))
            img.draw_rectangle(d.rect())
            print(w*h)
            print(detection_list[0][4])
            s = detection_list[0][4]
            print(s)
            print(clock.fps(), "fps", end="\n\n")
        if s > 0.75:
            t = 1
            x = modified_data(center_x)
            y = modified_data(center_y)
            p = modified_data(math.sqrt(w*h))
            uart.write('st')                                    # 向单片机发送’st’(应该是作为一个发送的起始标志)
            uart.write(x)                                       # 向单片机发送目标色块的中心点横坐标(经过处理后)
            uart.write(y)                                       # 向单片机发送目标色块的中心点纵坐标(经过处理后)
            uart.write(p)
            time.sleep(0.01)
            print(t)
        if s < 0.75 and t == 0:
            uart.write('wz')
            time.sleep(0.01)