Python 我对现有代码有一些问题。我正在使用Facenet进行人脸识别

在这段代码中，它从我的测试文件夹中随机选取一幅图像，并通过给出与准确度分数匹配的人的姓名来预测输出，但我不想随机选取图像，我想给出我自己的图像路径进行预测，请帮我解决这个问题

下面的代码从本地存储中获取所有图像，并使用mtcnn提取人脸

# face detection for the 5 Celebrity Faces Dataset
from os import listdir
from os.path import isdir
from PIL import Image
from matplotlib import pyplot
from numpy import savez_compressed
from numpy import asarray
from mtcnn.mtcnn import MTCNN

# extract a single face from a given photograph
def extract_face(filename, required_size=(160, 160)):
    # load image from file
    image = Image.open(filename)
    # convert to RGB, if needed
    image = image.convert('RGB')
    # convert to array
    pixels = asarray(image)
    # create the detector, using default weights
    detector = MTCNN()
    # detect faces in the image
    results = detector.detect_faces(pixels)
    # extract the bounding box from the first face
    x1, y1, width, height = results[0]['box']
    # bug fix
    x1, y1 = abs(x1), abs(y1)
    x2, y2 = x1 + width, y1 + height
    # extract the face
    face = pixels[y1:y2, x1:x2]
    # resize pixels to the model size
    image = Image.fromarray(face)
    image = image.resize(required_size)
    face_array = asarray(image)
    return face_array

# load images and extract faces for all images in a directory
def load_faces(directory):
    faces = list()
    # enumerate files
    for filename in listdir(directory):
        # path
        path = directory + filename
        # get face
        face = extract_face(path)
        # store
        faces.append(face)
    return faces

# load a dataset that contains one subdir for each class that in turn contains images
def load_dataset(directory):
    X, y = list(), list()
    # enumerate folders, on per class
    for subdir in listdir(directory):
        # path
        path = directory + subdir + '/'
        # skip any files that might be in the dir
        if not isdir(path):
            continue
        # load all faces in the subdirectory
        faces = load_faces(path)
        # create labels
        labels = [subdir for _ in range(len(faces))]
        # summarize progress
        print('>loaded %d examples for class: %s' % (len(faces), subdir))
        # store
        X.extend(faces)
        y.extend(labels)
    return asarray(X), asarray(y)

load train dataset
trainX, trainy = load_dataset('5-celebrity-faces-dataset/train/')
print(trainX.shape, trainy.shape)
# load test dataset
testX, testy = load_dataset('5-celebrity-faces-dataset/val/')
# save arrays to one file in compressed format
savez_compressed('5-celebrity-faces-dataset.npz', trainX, trainy, testX, testy)

下面的代码使用facenet计算数据集中每个人脸的人脸嵌入

    # calculate a face embedding for each face in the dataset using facenet
from numpy import load
from numpy import expand_dims
from numpy import asarray
from numpy import savez_compressed
from keras.models import load_model

# get the face embedding for one face
def get_embedding(model, face_pixels):
    # scale pixel values
    face_pixels = face_pixels.astype('float32')
    # standardize pixel values across channels (global)
    mean, std = face_pixels.mean(), face_pixels.std()
    face_pixels = (face_pixels - mean) / std
    # transform face into one sample
    samples = expand_dims(face_pixels, axis=0)
    # make prediction to get embedding
    yhat = model.predict(samples)
    return yhat[0]

# load the face dataset
data = load('5-celebrity-faces-dataset.npz')
trainX, trainy, testX, testy = data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3']
print('Loaded: ', trainX.shape, trainy.shape, testX.shape, testy.shape)
# load the facenet model
model = load_model('facenet_keras.h5')
print('Loaded Model')
# convert each face in the train set to an embedding
newTrainX = list()
for face_pixels in trainX:
    embedding = get_embedding(model, face_pixels)
    newTrainX.append(embedding)
newTrainX = asarray(newTrainX)
print(newTrainX.shape)
# convert each face in the test set to an embedding
newTestX = list()
for face_pixels in testX:
    embedding = get_embedding(model, face_pixels)
    newTestX.append(embedding)
newTestX = asarray(newTestX)
print(newTestX.shape)
# save arrays to one file in compressed format
savez_compressed('5-celebrity-faces-embeddings.npz', newTrainX, trainy, newTestX, testy)

下面的代码用于制作分类器，该分类器从数据集中获取随机图像并对其进行预测

   # develop a classifier for the 5 Celebrity Faces Dataset
from random import choice
from numpy import load
from numpy import expand_dims
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVC
from matplotlib import pyplot
# load faces
data = load('5-celebrity-faces-dataset.npz')
testX_faces = data['arr_2']
# load face embeddings
data = load('5-celebrity-faces-embeddings.npz')
trainX, trainy, testX, testy = data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3']
# normalize input vectors
in_encoder = Normalizer(norm='l2')
trainX = in_encoder.transform(trainX)
testX = in_encoder.transform(testX)
# label encode targets
out_encoder = LabelEncoder()
out_encoder.fit(trainy)
trainy = out_encoder.transform(trainy)
testy = out_encoder.transform(testy)
# fit model
model = SVC(kernel='linear', probability=True)
model.fit(trainX, trainy)

下面的代码表示测试数据集中的一个随机示例上的测试模型，所以它的基本功能是从我的测试文件夹中获取一个随机图像并预测其输出。但我不希望随机选取图像，而是希望指定所选图像的路径，以便对其进行预测

# test model on a random example from the test dataset
selection = choice([i for i in range(testX.shape[0])])
random_face_pixels = testX_faces[selection]
random_face_emb = testX[selection]
random_face_class = testy[selection]
random_face_name = out_encoder.inverse_transform([random_face_class])
# prediction for the face
samples = expand_dims(random_face_emb, axis=0)
yhat_class = model.predict(samples)
yhat_prob = model.predict_proba(samples)
# get name
class_index = yhat_class[0]
class_probability = yhat_prob[0,class_index] * 100
predict_names = out_encoder.inverse_transform(yhat_class)
print('Predicted: %s (%.3f)' % (predict_names[0], class_probability))
print('Expected: %s' % random_face_name[0])
# plot for fun
pyplot.imshow(random_face_pixels)
title = '%s (%.3f)' % (predict_names[0], class_probability)
pyplot.title(title)
pyplot.show()

请帮助我，如何给我自己的图像路径识别

---

请帮助

路径就在那里：

trainX，trainy=load_dataset（'5-名人面孔-dataset/train/'）

以上路径是数据集的路径，我想给出一个外部图像路径，我可以预测该路径的标签。请您的问题澄清您的具体问题，或添加额外的细节，以突出显示您需要的内容。请参阅页面以获取澄清此问题的帮助。