Sklearn多种算法实现人脸补全的项目实践

2023-03-10 09:04:36 作者：qq_30895747

本文主要介绍了Sklearn多种算法实现人脸补全的项目实践，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧

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 np

2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)

到此这篇关于Sklearn多种算法实现人脸补全的项目实践的文章就介绍到这了,更多相关Sklearn 人脸补全内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！