人臉識別SVM演算法實現--參考麥子學院彭亮機器學習基礎5.2
阿新 • • 發佈:2019-01-11
#本例為人臉識別的SVM演算法 #首先fetch_lfw_people匯入資料 #其次對資料進行處理,首先得到X,y,分割資料集為訓練集和測試集,PCA降維,然後訓練 #最後檢視正確率,classification_report以及confusion_matrix 以及繪製出特徵圖和預測結果 from __future__ import print_function from time import time import logging#程式進展資訊 import matplotlib.pyplot as plt import PIL from sklearn.model_selection import train_test_split#分割資料集 #from sklearn.cross_validation import train_test_split from sklearn.datasets import fetch_lfw_people#下載資料集 from sklearn.model_selection import GridSearchCV #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.decomposition import PCA from sklearn.svm import SVC print(__doc__)#輸出檔案開頭註釋的內容""" """ # Display progress logs on stdout logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s') ############################################################################### # Download the data, if not already on disk and load it as numpy arrays lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4) #print(lfw_people) # introspect the images arrays to find the shapes (for plotting) n_samples, h, w = lfw_people.images.shape#影象矩陣的行h,列w #print(n_samples,h,w) # for machine learning we use the 2 data directly (as relative pixel # positions info is ignored by this model) X = lfw_people.data#圖片資料 n_features = X.shape[1]#特徵點資料 # the label to predict is the id of the person y = lfw_people.target#y是label,有7個目標時,0-6之間取值 target_names = lfw_people.target_names#實際有哪些名字,這個是一個字串 n_classes = target_names.shape[0]#shape[0]--行維數 shape[1]--列維數 #print(target_names) print("Total dataset size:") print("n_samples: %d" % n_samples) print("n_features: %d" % n_features) print("n_classes: %d" % n_classes) ############################################################################### # Split into a training set and a test set using a stratified k fold # split into a training and testing set X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25) ############################################################################### # Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled # dataset): unsupervised feature extraction / dimensionality reduction 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) pca =PCA(svd_solver='randomized',n_components=n_components,whiten=True) pca.fit(X,y)#訓練如何降維 print("done in %0.3fs" % (time() - t0)) eigenfaces = pca.components_.reshape((n_components,h,w))#三維 #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, 998, 1001, 999, 1002], 'gamma': [0.0025, 0.003, 0.0035], }#不停縮小範圍 #clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid) clf=GridSearchCV(SVC(kernel='rbf',class_weight=None),param_grid)#GridSearchCV()第一個引數是分類器 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("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))) ############################################################################### # Qualitative evaluation of the predictions using matplotlib 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(()) # plot the result of the prediction on a portion of the test set 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) # plot the gallery of the most significative eigenfaces eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])] plot_gallery(eigenfaces, eigenface_titles, h, w) plt.show()