基于python机器学习人脸自动补全

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression,Ridge,Lasso
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.datasets import fetch_olivetti_faces

faces=fetch_olivetti_faces()
data=faces['data']
target=faces['target']
#data.shape

#人脸补全
#人脸数据一分为二,上半部分作为数据,下半部分作为target
face_up,face_down=data[:,:2048],data[:,2048:]
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(face_up,face_down,test_size=0.1)

#5个算法分别识别
estimators={'knn':KNeighborsRegressor(),
           'LinearRe':LinearRegression(),
          'Ridge':Ridge(alpha=0.1),
          'Lasso':Lasso(alpha=0.5),
          'ExtraTree':ExtraTreesRegressor()}

#face_down[2048]
result_ = {}

for key,estimator in estimators.items():
     estimator.fit(x_train,y_train)
     y_ = estimator.predict(x_test)
     result_[key] = y_

plt.figure(figsize=(2*6,10*2))
for i in range(10):
    if i :
        axes=plt.subplot(10,6,i*6+1)
    else:
        axes=plt.subplot(10,6,1,title='True Face')
    axes.axis('off')

    face_up=x_test[i]
    face_down=y_test[i]
    face_full=np.hstack((face_up,face_down))
    face_image=face_full.reshape((64,64))
    axes.imshow(face_image,cmap='gray')

    for j,key in enumerate(result_):
        if i :
            axes=plt.subplot(10,6,i*6+2+j)
        else:

            axes=plt.subplot(10,6,2+j,title=key)
        face_up=x_test[i]
        y_=result_[key]
        face_down_predict=y_[i]
        face_full_predict=np.hstack((face_up,face_down_predict))
        face_image_predict=face_full_predict.reshape((64,64))
        axes.imshow(face_image_predict,cmap='gray')