sklearn例程:使用多個模型預測完善人臉

例程簡介

本示例說明了使用多個模型估計器來完成圖像。目標是根據給定臉部的上半部分來預測其下半部分。

圖像的第一列顯示真實麵孔。後麵幾列展示了極端隨機的樹、k近鄰、線性回歸和嶺回歸（extremely randomized trees, k nearest neighbors, linear regression 和 ridge regression）如何完成預測這些麵孔的下半部分。

代碼實現[Python]


# -*- coding: utf-8 -*- 
print(__doc__)

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

# Load the faces datasets
data = fetch_olivetti_faces()
targets = data.target

data = data.images.reshape((len(data.images), -1))
train = data[targets = 30]  # Test on independent people

# 在人臉圖片子集上做測試
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]
# Upper half of the faces
X_train = train[:, :(n_pixels + 1) // 2]
# Lower half of the faces
y_train = train[:, n_pixels // 2:]
X_test = test[:, :(n_pixels + 1) // 2]
y_test = test[:, n_pixels // 2:]

# 擬合多個模型(Fit estimators)
ESTIMATORS = {
    "Extra trees": ExtraTreesRegressor(n_estimators=10, max_features=32,
                                       random_state=0),
    "K-nn": KNeighborsRegressor(),
    "Linear regression": LinearRegression(),
    "Ridge": RidgeCV(),
}

y_test_predict = dict()
for name, estimator in ESTIMATORS.items():
    estimator.fit(X_train, y_train)
    y_test_predict[name] = estimator.predict(X_test)

# 輸出完善的人臉圖像
image_shape = (64, 64)

n_cols = 1 + len(ESTIMATORS)
plt.figure(figsize=(2. * n_cols, 2.26 * n_faces))
plt.suptitle("Face completion with multi-output estimators", size=16)

for i in range(n_faces):
    true_face = np.hstack((X_test[i], y_test[i]))

    if i:
        sub = plt.subplot(n_faces, n_cols, i * n_cols + 1)
    else:
        sub = plt.subplot(n_faces, n_cols, i * n_cols + 1,
                          title="true faces")

    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]))

        if i:
            sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j)

        else:
            sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j,
                              title=est)

        sub.axis("off")
        sub.imshow(completed_face.reshape(image_shape),
                   cmap=plt.cm.gray,
                   interpolation="nearest")

plt.show()

代碼執行

代碼運行時間大約:0分3.749秒。
運行代碼輸出的文本內容如下:

downloading Olivetti faces from https://ndownloader.figshare.com/files/5976027 to /home/circleci/scikit_learn_data

運行代碼輸出的圖片內容如下:

Face completion with a multi-output estimators

源碼下載

Python版源碼文件: plot_multioutput_face_completion.py
Jupyter Notebook版源碼文件: plot_multioutput_face_completion.ipynb

參考資料

Face completion with a multi-output estimators