机器学习之SVM建模实现人脸识别—Jason niu

Posted 2020-10-20 一个处女座的IT

from __future__ import print_function
from time import time          
import logging                  
import matplotlib.pyplot as plt  

from sklearn.cross_validation import train_test_split
from sklearn.datasets import fetch_lfw_people
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.decomposition import RandomizedPCA
from sklearn.svm import SVC    


print(__doc__)

logging.basicConfig(level=logging.INFO, format=‘%(asctime)s %(message)s‘) 


###############################################################################

lfw_people = fetch_lfw_people(min_faces_per_person=99, resize=0.4)  

n_samples, h, w = lfw_people.images.shape 

X = lfw_people.data     
n_features = X.shape[1] 


y = lfw_people.target  
target_names = lfw_people.target_names  
n_classes = target_names.shape[0]      

print("Total dataset size:")
print("n_samples: %d" % n_samples)   
print("n_features: %d" % n_features)
print("n_classes: %d" % n_classes)  


###############################################################################

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)



###############################################################################
n_components = 150  

print("Extracting the top %d eigenfaces from %d faces"
      % (n_components, X_train.shape[0]))
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))


###############################################################################
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)  #auto改为balanced

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_) 


###############################################################################
print("Predicting people‘s names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)  
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)))


def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
    """Helper function to plot a gallery of portraits"""    
    plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))                 
    plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
    
    for i in range(n_row * n_col):
        plt.subplot(n_row, n_col, i + 1)
        plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)
        plt.title(titles[i], size=12)
        plt.xticks(())
        plt.yticks(())
        
def title(y_pred, y_test, target_names, i):  
    pred_name = target_names[y_pred[i]].rsplit(‘ ‘, 1)[-1]
    true_name = target_names[y_test[i]].rsplit(‘ ‘, 1)[-1]
    return ‘predicted: %s\ntrue:      %s‘ % (pred_name, true_name)

prediction_titles = [title(y_pred, y_test, target_names, i)  
                     for i in range(y_pred.shape[0])]

plot_gallery(X_test, prediction_titles, h, w)  

eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)  
plt.show()