ML之SVM：調用(sklearn的lfw_people函數在線下載55個外國人圖片文件夾數據集)來精確實現人臉識別并提取人臉特征向量

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目錄

輸出結果

代碼設計

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輸出結果

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代碼設計

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改為balancedclf = 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()

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ML之SVM：調用(sklearn的lfw_people函數在線下載55個外國人圖片文件夾數據集)來精確實現人臉識別并提取人臉特征向量

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