我该怎么在openMV上使用我自己训练过的facenet模型呢？主要是tensorflow等模块的环境问题？

[4up6]

import numpy as np
from PIL import Image

from facenet import Facenet

if __name__ == "__main__":
    model = Facenet()

    image_001 = Image.open('img/1.jpg')
    image_002 = Image.open('img/2.jpg')
    image_003 = Image.open('img/3.jpg')
    image_004 = Image.open('img/4.jpg')

    while True:
        image_x = input('Input image_x filename:')
        try:
            image_x = Image.open(image_x)
        except:
            print('Image_1 Open Error! Try again!')
            continue

        probability_001 = model.detect_image(image_001, image_x)
        probability_002 = model.detect_image(image_002, image_x)
        probability_003 = model.detect_image(image_003, image_x)
        probability_004 = model.detect_image(image_004, image_x)

        x = min(probability_001,probability_002,probability_003,probability_004)

        if(x==probability_001):
            print(1)
            print(x)
        elif(x==probability_002):
            print(2)
            print(x)
        elif(x==probability_003):
            print(3)
            print(x)
        elif(x==probability_004):
            print(4)
            print(x)

[4up6]

import colorsys
import os

import keras
import matplotlib.pyplot as plt
import numpy as np
from keras import backend as K
from keras.applications.imagenet_utils import preprocess_input
from PIL import Image, ImageDraw, ImageFont

from nets.facenet import facenet

#--------------------------------------------#
#   使用自己训练好的模型预测需要修改2个参数
#   model_path和backbone需要修改！
#--------------------------------------------#
class Facenet(object):
    _defaults = {
        "model_path"    : "model_data/facenet_mobilenet.h5",
        "input_shape"   : [160,160,3],
        "backbone"      : "mobilenet"
    }

    @classmethod
    def get_defaults(cls, n):
        if n in cls._defaults:
            return cls._defaults[n]
        else:
            return "Unrecognized attribute name '" + n + "'"

    #---------------------------------------------------#
    #   初始化Facenet
    #---------------------------------------------------#
    def __init__(self, **kwargs):
        self.__dict__.update(self._defaults)
        self.sess = K.get_session()
        self.generate()
        
    def generate(self):
        model_path = os.path.expanduser(self.model_path)
        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
        
        self.model = facenet(self.input_shape, backbone=self.backbone, mode="predict")
        self.model.load_weights(self.model_path, by_name=True)
        
        self.model.summary()
        print('{} model, anchors, and classes loaded.'.format(model_path))
    
    def letterbox_image(self, image, size):
        if self.input_shape[-1]==1:
            image = image.convert("RGB")
        iw, ih = image.size
        w, h = size
        scale = min(w/iw, h/ih)
        nw = int(iw*scale)
        nh = int(ih*scale)

        image = image.resize((nw,nh), Image.BICUBIC)
        new_image = Image.new('RGB', size, (128,128,128))
        new_image.paste(image, ((w-nw)//2, (h-nh)//2))
        if self.input_shape[-1]==1:
            new_image = new_image.convert("L")
        return new_image

    #---------------------------------------------------#
    #   检测图片
    #---------------------------------------------------#
    def detect_image(self, image_1, image_2):
        #---------------------------------------------------#
        #   对输入图片进行不失真的resize
        #---------------------------------------------------#
        image_1 = self.letterbox_image(image_1,[self.input_shape[1],self.input_shape[0]])
        image_2 = self.letterbox_image(image_2,[self.input_shape[1],self.input_shape[0]])
        
        #---------------------------------------------------#
        #   进行图片的归一化
        #---------------------------------------------------#
        image_1 = np.asarray(image_1).astype(np.float64)/255
        image_2 = np.asarray(image_2).astype(np.float64)/255

        photo1 = np.expand_dims(image_1,0)
        photo2 = np.expand_dims(image_2,0)
        #---------------------------------------------------#
        #   图片传入网络进行预测
        #---------------------------------------------------#
        output1 = self.model.predict(photo1)
        output2 = self.model.predict(photo2)
        
        #---------------------------------------------------#
        #   计算二者之间的距离
        #---------------------------------------------------#
        l1 = np.sqrt(np.sum(np.square(output1 - output2), axis=-1))
        #
        # plt.subplot(1, 2, 1)
        # plt.imshow(np.array(image_1))
        #
        # plt.subplot(1, 2, 2)
        # plt.imshow(np.array(image_2))
        # plt.text(-12, -12, 'Distance:%.3f' % l1, ha='center', va= 'bottom',fontsize=11)
        # plt.show()
        return l1

    def close_session(self):
        self.sess.close()

[kidswong999]

你需要先把模型导出到tflite。