值错误：加载预先训练的模型facenet_keras.h5时，元帅数据不正确

编程问答 2022-07-21

问题描述

这是我的代码，用于在加载预训练的模型facenet_keras.h5的地方准备数据 错误发生在我正在加载模型的第16行。

import os
import pickle
import numpy as np
import cv2
import mtcnn

from keras.models import load_model
from utils import get_face,get_encode,l2_normalizer,normalize
encoder_model = r'C:\Users\hussa\PycharmProjects\FCN\_03_facenet_keras\data\model\facenet_keras.h5'
people_dir = 'C:/Users/hussa/PycharmProjects/FCN/_03_facenet_keras/data/people'
encodings_path = 'C:/Users/hussa/PycharmProjects/FCN/_03_facenet_keras/data/encodings/encodings.pkl'

required_size = (160,160)
face_detector = mtcnn.MTCNN()
face_encoder = load_model(encoder_model)
encoding_dict = dict()

for person_name in os.listdir(people_dir):
person_dir = os.path.join(people_dir,person_name)
encodes = []
for img_name in os.listdir(person_dir):
    img_path = os.path.join(person_dir,img_name)
    img = cv2.imread(img_path)
    img_rgb = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    results = face_detector.detect_faces(img_rgb)
    if results:
        res = max(results,key=lambda b: b['Box'][2] * b['Box'][3])
        face,_,_ = get_face(img_rgb,res['Box'])
        face = normalize(face)
        face = cv2.resize(face,required_size)
        encode = face_encoder.predict(np.expand_dims(face,axis=0))[0]
        encodes.append(encode)
if encodes:
    encode = np.sum(encodes,axis=0)
    encode = l2_normalizer.transform(np.expand_dims(encode,axis=0))[0]
    encoding_dict[person_name] = encode
for key in encoding_dict.keys():
    print(key)
with open(encodings_path,'bw') as file:
    pickle.dump(encoding_dict,file)

错误消息显示：

"Traceback (most recent call last):

  File "C:/Users/hussa/PycharmProjects/FCN/_03_facenet_keras/01_prepare_data.py",line 16,in <module>
    face_encoder = load_model(encoder_model)

  File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\saving\save.py",line 182,in load_model
    return hdf5_format.load_model_from_hdf5(filepath,custom_objects,compile)
 
 File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\saving\hdf5_format.py",line 177,in load_model_from_hdf5
    model = model_config_lib.model_from_config(model_config,File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\saving\model_config.py",line 55,in model_from_config
    return deserialize(config,custom_objects=custom_objects)

  File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\layers\serialization.py",line 171,in deserialize
    return generic_utils.deserialize_keras_object(

  File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\utils\generic_utils.py",line 354,in deserialize_keras_object
    return cls.from_config(

  File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\engine\training.py",line 2238,in from_config
    return functional.Functional.from_config(

  File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\engine\functional.py",line 616,in from_config
    input_tensors,output_tensors,created_layers = reconstruct_from_config(

  File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\engine\functional.py",line 1204,in reconstruct_from_config
    process_layer(layer_data)

  File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\engine\functional.py",line 1186,in process_layer
    layer = deserialize_layer(layer_data,in deserialize_keras_object
    return cls.from_config(

  File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\layers\core.py",line 1005,in from_config
    function = cls._parse_function_from_config(

  File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\layers\core.py",line 1057,in _parse_function_from_config
    function = generic_utils.func_load(

  File "C:\Users\hussa\AppData\Roaming\Python\python38\site-packages\tensorflow\python\keras\utils\generic_utils.py",line 457,in func_load
    code = marshal.loads(raw_code)

**ValueError: bad marshal data (unkNown type code)**

Process finished with exit code 1"

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