python3 用svm实现人脸识别

柔光的暖阳◎ 2024-04-17 06:01 78阅读 0赞

1，代码实现：

打开spyder,新建svm\_sklearn.py

# -*- coding: utf-8 -*-
    """
    Created on Sun Aug 25 01:15:48 2019
    
    @author: 14355
    """
    
    from __future__ import print_function
     
    from time import time
    import logging
    import matplotlib.pyplot as plt
     
    from sklearn.model_selection import train_test_split
    from sklearn.datasets import fetch_lfw_people
    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__)#输出文件开头注释的内容"""  """
    
    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)
    # introspect the images arrays to find the shapes (for plotting)
    n_samples, h, w = lfw_people.images.shape
    # for machine learning we use the 2 data directly (as relative pixel
    # positions info is ignored by this model)
    X = lfw_people.data#图片数据
    n_features = X.shape[1]#矩阵列数特征点数据1850
    
    # the label to predict is the id of the person
    y = lfw_people.target#y是label,有7个目标时，0-6之间取值
    
    target_names = lfw_people.target_names#实际有哪些名字,这个是一个字符串
    n_classes = target_names.shape[0]#shape[0]--行维数 shape[1]--列维数
    print("Total dataset size:")
    print("n_samples: %d\nn_features: %d\nn_classes: %d" % (n_samples,n_features,n_classes))
    
    # Split into a training set and a test set using a stratified k fold
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.25)
    print(X_train.shape,y_train.shape,X_test.shape,y_test.shape)
    # 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 =PCA(svd_solver='randomized',n_components=n_components,whiten=True)#降低维度，提取特征，（此处特征值位度较高）
    pca.fit(X,y)#训练如何降维
    print("done in %0.3fs" % (time() - t0))
    eigenfaces = pca.components_.reshape((n_components,h,w))#三维
    #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': [0.0001, 0.003, 0.0035,0.004,0.0045], }
    #clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
    clf=GridSearchCV(SVC(kernel='rbf',class_weight=None),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("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()

2，打印结果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70 1][]

图片对比结果如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70 2][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70 3][]

3，结果分析：

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/201908251938364.PNG?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20190825193908649.PNG?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20190825194012391.PNG?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/20190825194150456.PNG?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1,size_16,color_FFFFFF,t_70