• scikit-learn一般实例之七:使用多输出评估器进行人脸完成


    本例将展示使用多输出评估期来实现图像完成.目标是根据给出的上半部分人脸预测人脸的下半部分.

    第一列展示的是真实的人脸,接下来的列分别展示了随机森林,K近邻,线性回归和岭回归对人脸下半部分的预测.

    # coding:utf-8
    
    from pylab import *
    
    
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.datasets import fetch_olivetti_faces
    from sklearn.utils.validation import check_random_state
    from sklearn.ensemble import ExtraTreesRegressor
    from sklearn.neighbors import KNeighborsRegressor
    from sklearn.linear_model import LinearRegression
    from sklearn.linear_model import RidgeCV
    
    # 加载人脸数据集
    data = fetch_olivetti_faces()
    targets = data.target
    
    data = data.images.reshape((len(data.images), -1))
    train = data[targets < 30]
    test = data[targets >= 30]  # 在独立的人上进行测试
    
    # 在人群子集上进行测试
    n_faces = 5
    rng = check_random_state(4)
    face_ids = rng.randint(test.shape[0], size=(n_faces, ))
    test = test[face_ids, :]
    
    n_pixels = data.shape[1]
    X_train = train[:, :np.ceil(0.5 * n_pixels)]  # 上半部分人脸
    y_train = train[:, np.floor(0.5 * n_pixels):]  # 下半部分人脸
    X_test = test[:, :np.ceil(0.5 * n_pixels)]
    y_test = test[:, np.floor(0.5 * n_pixels):]
    
    # 拟合估测器
    ESTIMATORS = {
        "Extra trees": ExtraTreesRegressor(n_estimators=10, max_features=32,
                                           random_state=0),
        "K-nn": KNeighborsRegressor(),
        "Linear regression": LinearRegression(),
        "Ridge": RidgeCV(),
    }
    ESTIMATORS_zh = {
        "Extra trees":u"随机树",
        "K-nn": u"K近邻",
        "Linear regression":u"线性回归" ,
        "Ridge": u"岭回归",
    
    }
    
    y_test_predict = dict()
    for name, estimator in ESTIMATORS.items():
        estimator.fit(X_train, y_train)
        y_test_predict[name] = estimator.predict(X_test)
    
    # 绘制完成的人脸
    
    myfont = matplotlib.font_manager.FontProperties(fname="Microsoft-Yahei-UI-Light.ttc")
    print myfont
    mpl.rcParams['axes.unicode_minus'] = False
    
    image_shape = (64, 64)
    
    n_cols = 1 + len(ESTIMATORS)
    plt.figure(figsize=(2. * n_cols, 2.26 * n_faces))
    plt.suptitle(u"采用多输出估测器进行人脸完成", size=16,fontproperties=myfont)
    
    for i in range(n_faces):
        true_face = np.hstack((X_test[i], y_test[i]))
        sub = plt.subplot(n_faces, n_cols, i * n_cols + 1)
        if not i:
           sub.set_title(u"真实人脸",fontproperties=myfont)
        sub.axis("off")
        sub.imshow(true_face.reshape(image_shape),
                   cmap=plt.cm.gray,
                   interpolation="nearest")
    
        for j, est in enumerate(sorted(ESTIMATORS)):
            completed_face = np.hstack((X_test[i], y_test_predict[est][i]))
            sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j)
            if not i:
                sub.set_title(ESTIMATORS_zh[est],fontproperties=myfont)
            sub.axis("off")
            sub.imshow(completed_face.reshape(image_shape),
                       cmap=plt.cm.gray,
                       interpolation="nearest")
    plt.show()
    
    
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  • 原文地址:https://www.cnblogs.com/taceywong/p/5931480.html
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