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1 导入需要的类库
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression,Ridge,Lasso
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import numpy as np2拉取数据集
faces=datasets.fetch_olivetti_faces()
images=faces.images
display(images.shape)
index=np.random.randint(0,400,size=1)[0]
img=images[index]
plt.figure(figsize=(3,3))
plt.imshow(img,cmap=plt.cm.gray)3 处理图片数据（将人脸图片分为上下两部分）
index=np.random.randint(0,400,size=1)[0]
up_face=images[:,:32,:]
down_face=images[:,32:,:]
axes=plt.subplot(1,3,1)
axes.imshow(up_face[index],cmap=plt.cm.gray)
axes=plt.subplot(1,3,2)
axes.imshow(down_face[index],cmap=plt.cm.gray)
axes=plt.subplot(1,3,3)
axes.imshow(images[index],cmap=plt.cm.gray)4 创建模型
X=faces.data
x=X[:,:2048]
y=X[:,2048:]
estimators={}
estimators['linear']=LinearRegression()
estimators['ridge']=Ridge(alpha=0.1)
estimators['lasso']=Lasso(alpha=1)
estimators['knn']=KNeighborsRegressor(n_neighbors=5)
estimators['tree']=DecisionTreeRegressor()
estimators['forest']=RandomForestRegressor()5 训练数据
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)
result={}
print
for key,model in estimators.items():
print(key)
model.fit(x_train,y_train)
y_=model.predict(x_test)
result[key]=y_6展示测试结果
plt.figure(figsize=(40,40))
for i in range(0,10):
#第一列，上半张人脸
axes=plt.subplot(10,8,8*i+1)
up_face=x_test[i].reshape(32,64)
axes.imshow(up_face,cmap=plt.cm.gray)
axes.axis('off')
if i==0:
axes.set_title('up-face')
#第8列，整张人脸
axes=plt.subplot(10,8,8*i+8)
down_face=y_test[i].reshape(32,64)
full_face=np.concatenate([up_face,down_face])
axes.imshow(full_face,cmap=plt.cm.gray)
axes.axis('off')
if i==0:
axes.set_title('full-face')
#绘制预测人脸
for j,key in enumerate(result):
axes=plt.subplot(10,8,i*8+2+j)
y_=result[key]
predice_face=y_[i].reshape(32,64)
pre_face=np.concatenate([up_face,predice_face])
axes.imshow(pre_face,cmap=plt.cm.gray)
axes.axis('off')
if i==0:
axes.set_title(key)全部代码
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression,Ridge,Lasso
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import numpy as np
faces=datasets.fetch_olivetti_faces()
images=faces.images
display(images.shape)
index=np.random.randint(0,400,size=1)[0]
img=images[index]
plt.figure(figsize=(3,3))
plt.imshow(img,cmap=plt.cm.gray)
index=np.random.randint(0,400,size=1)[0]
up_face=images[:,:32,:]
down_face=images[:,32:,:]
axes=plt.subplot(1,3,1)
axes.imshow(up_face[index],cmap=plt.cm.gray)
axes=plt.subplot(1,3,2)
axes.imshow(down_face[index],cmap=plt.cm.gray)
axes=plt.subplot(1,3,3)
axes.imshow(images[index],cmap=plt.cm.gray)
X=faces.data
x=X[:,:2048]
y=X[:,2048:]
estimators={}
estimators['linear']=LinearRegression()
estimators['ridge']=Ridge(alpha=0.1)
estimators['lasso']=Lasso(alpha=1)
estimators['knn']=KNeighborsRegressor(n_neighbors=5)
estimators['tree']=DecisionTreeRegressor()
estimators['forest']=RandomForestRegressor()
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)
result={}
print
for key,model in estimators.items():
print(key)
model.fit(x_train,y_train)
y_=model.predict(x_test)
result[key]=y_
plt.figure(figsize=(40,40))
for i in range(0,10):
#第一列，上半张人脸
axes=plt.subplot(10,8,8*i+1)
up_face=x_test[i].reshape(32,64)
axes.imshow(up_face,cmap=plt.cm.gray)
axes.axis('off')
if i==0:
axes.set_title('up-face')
#第8列，整张人脸
axes=plt.subplot(10,8,8*i+8)
down_face=y_test[i].reshape(32,64)
full_face=np.concatenate([up_face,down_face])
axes.imshow(full_face,cmap=plt.cm.gray)
axes.axis('off')
if i==0:
axes.set_title('full-face')
#绘制预测人脸
for j,key in enumerate(result):
axes=plt.subplot(10,8,i*8+2+j)
y_=result[key]
predice_face=y_[i].reshape(32,64)
pre_face=np.concatenate([up_face,predice_face])
axes.imshow(pre_face,cmap=plt.cm.gray)
axes.axis('off')
if i==0:
axes.set_title(key)以上就是Sklearn实现人脸补全的方法有哪些的详细内容，更多关于Sklearn实现人脸补全的方法有哪些的资料请关注九品源码其它相关文章！