Sklearn多种算法实现人脸补全的项目实践
目录
- 1 导入需要的类库
- 2拉取数据集
- 3 处理图片数据(将人脸图片分为上下两部分)
- 4 创建模型
- 5 训练数据
- 6展示测试结果
- 全部代码
1 导入需要的类库
import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression,Ridge,javascriptLasso 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=pltjs.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('o编程客栈ff') 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 impor编程客栈t 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 开发者_JAVA入门 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('offjs') 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)
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