我可以将其修改为以下代码，但我无法计算分数。我可以阅读图像，并将其与示例图像进行比较。我不知道如何使用记分器功能。
from time import time
import numpy, os
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.decomposition import RandomizedPCA
from sklearn.svm import SVC
from PIL import Image
#Path to the root image directory containing sub-directories of images
path="<Path to Folder of Training Images>"
testImage = "<Path to test image>"
#Flat image Feature Vector
X=[]
#Int array of Label Vector
Y=[]
n_sample = 0 #Total number of Images
h = 750 #Height of image in float
w = 250 #Width of image in float
n_features = 187500 #Length of feature vector
target_names = [] #Array to store the names of the persons
label_count = 0
n_classes = 0
for directory in os.listdir(path):
for file in os.listdir(path+directory):
print(path+directory+"/"+file)
img=Image.open(path+directory+"/"+file)
featurevector=numpy.array(img).flatten()
print len(featurevector)
X.append(featurevector)
Y.append(label_count)
n_sample = n_sample + 1
target_names.append(directory)
label_count=label_count+1
print Y
print target_names
n_classes = len(target_names)
###############################################################################
# Split into a training set and a test set using a stratified k fold
# split into a training and teststing set
X_train, X_test, y_train, y_test = train_test_split(
X, Y, test_size=0.25, random_state=42)
###############################################################################
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 10
print("Extracting the top %d eigenfaces from %d faces"
% (n_components, len(X_test)))
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("done in %0.3fs" % (time() - t0))
eigenfaces = pca.components_.reshape((n_components, h, w))
print("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done in %0.3fs" % (time() - t0))
###############################################################################
# Train a SVM classification model
print("Fitting the classifier to the training set")
t0 = time()
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print("done in %0.3fs" % (time() - t0))
print("Best estimator found by grid search:")
print(clf.best_estimator_)
###############################################################################
# Quantitative evaluation of the model quality on the test set
print("Predicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
print clf.score(X_test_pca,y_test)
print("done in %0.3fs" % (time() - t0))
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
###############################################################################
# Prediction of user based on the model
test = []
testImage=Image.open(testImage)
testImageFeatureVector=numpy.array(testImage).flatten()
test.append(testImageFeatureVector)
testImagePCA = pca.transform(test)
testImagePredict=clf.predict(testImagePCA)
#print clf.score(testImagePCA)
#print clf.score(X_train_pca,testImagePCA)
#print clf.best_params_
#print clf.best_score_
#print testImagePredict
print target_names[testImagePredict[0]]