.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_face_recognition.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_applications_plot_face_recognition.py: =================================================== 使用特征脸和支持向量机进行人脸识别的示例 =================================================== 本示例中使用的数据集是“Labeled Faces in the Wild”（简称LFW）的预处理摘录： http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB) .. _LFW: http://vis-www.cs.umass.edu/lfw/ .. GENERATED FROM PYTHON SOURCE LINES 15-27 .. code-block:: Python from time import time import matplotlib.pyplot as plt from scipy.stats import loguniform from sklearn.datasets import fetch_lfw_people from sklearn.decomposition import PCA from sklearn.metrics import ConfusionMatrixDisplay, classification_report from sklearn.model_selection import RandomizedSearchCV, train_test_split from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC .. GENERATED FROM PYTHON SOURCE LINES 28-29 下载数据（如果尚未存在于磁盘上）并将其加载为 numpy 数组 .. GENERATED FROM PYTHON SOURCE LINES 29-51 .. code-block:: Python lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4) # 检查图像数组以找到形状（用于绘图） n_samples, h, w = lfw_people.images.shape # 对于机器学习，我们直接使用这两个数据（因为该模型忽略了相对像素位置信息）。 X = lfw_people.data n_features = X.shape[1] # 要预测的标签是人的ID 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) .. rst-class:: sphx-glr-script-out .. code-block:: none Total dataset size: n_samples: 1288 n_features: 1850 n_classes: 7 .. GENERATED FROM PYTHON SOURCE LINES 52-53 拆分为训练集和测试集，并保留25%的数据用于测试。 .. GENERATED FROM PYTHON SOURCE LINES 53-63 .. code-block:: Python X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25, random_state=42 ) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) .. GENERATED FROM PYTHON SOURCE LINES 64-65 计算人脸数据集上的PCA（特征脸）（视为无标签数据集）：无监督特征提取/降维 .. GENERATED FROM PYTHON SOURCE LINES 65-85 .. code-block:: Python n_components = 150 print( "Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0]) ) t0 = time() pca = PCA(n_components=n_components, svd_solver="randomized", 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)) .. rst-class:: sphx-glr-script-out .. code-block:: none Extracting the top 150 eigenfaces from 966 faces done in 0.506s Projecting the input data on the eigenfaces orthonormal basis done in 0.058s .. GENERATED FROM PYTHON SOURCE LINES 86-87 训练一个SVM分类模型 .. GENERATED FROM PYTHON SOURCE LINES 87-104 .. code-block:: Python print("Fitting the classifier to the training set") t0 = time() param_grid = { "C": loguniform(1e3, 1e5), "gamma": loguniform(1e-4, 1e-1), } clf = RandomizedSearchCV( SVC(kernel="rbf", class_weight="balanced"), param_grid, n_iter=10 ) 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_) .. rst-class:: sphx-glr-script-out .. code-block:: none Fitting the classifier to the training set done in 4.307s Best estimator found by grid search: SVC(C=np.float64(70550.73312335386), class_weight='balanced', gamma=np.float64(0.0034333176762869253)) .. GENERATED FROM PYTHON SOURCE LINES 105-106 对测试集上模型质量的定量评估 .. GENERATED FROM PYTHON SOURCE LINES 106-121 .. code-block:: Python 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)) ConfusionMatrixDisplay.from_estimator( clf, X_test_pca, y_test, display_labels=target_names, xticks_rotation="vertical" ) plt.tight_layout() plt.show() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_face_recognition_001.png :alt: plot face recognition :srcset: /auto_examples/applications/images/sphx_glr_plot_face_recognition_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Predicting people's names on the test set done in 0.029s precision recall f1-score support Ariel Sharon 0.75 0.69 0.72 13 Colin Powell 0.75 0.87 0.81 60 Donald Rumsfeld 0.83 0.74 0.78 27 George W Bush 0.89 0.95 0.92 146 Gerhard Schroeder 0.91 0.80 0.85 25 Hugo Chavez 0.90 0.60 0.72 15 Tony Blair 0.93 0.75 0.83 36 accuracy 0.86 322 macro avg 0.85 0.77 0.80 322 weighted avg 0.86 0.86 0.85 322 .. GENERATED FROM PYTHON SOURCE LINES 122-123 使用matplotlib对预测结果进行定性评估 .. GENERATED FROM PYTHON SOURCE LINES 123-138 .. code-block:: Python def plot_gallery(images, titles, h, w, n_row=3, n_col=4): """帮助函数用于绘制肖像画廊""" plt.figure(figsize=(1.8 * n_col, 2.4 * n_row)) plt.subplots_adjust(bottom=0, left=0.01, right=0.99, top=0.90, hspace=0.35) 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(()) .. GENERATED FROM PYTHON SOURCE LINES 139-140 绘制测试集部分数据的预测结果 .. GENERATED FROM PYTHON SOURCE LINES 140-153 .. code-block:: Python 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) .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_face_recognition_002.png :alt: predicted: Bush true: Bush, predicted: Bush true: Bush, predicted: Blair true: Blair, predicted: Bush true: Bush, predicted: Bush true: Bush, predicted: Bush true: Bush, predicted: Schroeder true: Schroeder, predicted: Powell true: Powell, predicted: Bush true: Bush, predicted: Bush true: Bush, predicted: Bush true: Bush, predicted: Bush true: Bush :srcset: /auto_examples/applications/images/sphx_glr_plot_face_recognition_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 154-155 绘制最具代表性的特征脸画廊 .. GENERATED FROM PYTHON SOURCE LINES 155-162 .. code-block:: Python eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])] plot_gallery(eigenfaces, eigenface_titles, h, w) plt.show() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_face_recognition_003.png :alt: eigenface 0, eigenface 1, eigenface 2, eigenface 3, eigenface 4, eigenface 5, eigenface 6, eigenface 7, eigenface 8, eigenface 9, eigenface 10, eigenface 11 :srcset: /auto_examples/applications/images/sphx_glr_plot_face_recognition_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 163-164 面部识别问题通过训练卷积神经网络可以更有效地解决，但这类模型不在scikit-learn库的范围内。感兴趣的读者应该尝试使用pytorch或tensorflow来实现这些模型。 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 5.341 seconds) .. _sphx_glr_download_auto_examples_applications_plot_face_recognition.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/applications/plot_face_recognition.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_face_recognition.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_face_recognition.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_face_recognition.zip ` .. include:: plot_face_recognition.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_