1 . 步驟

1. 讀入數(shù)據(jù)，把數(shù)據(jù)劃分為訓(xùn)練集和測(cè)試集
2. 用hog提取特征
3. 用SVM訓(xùn)練數(shù)據(jù)
4. 測(cè)試、評(píng)價(jià)模型
5. 保存模型
6. 加載模型，應(yīng)用模型

2 . 代碼

import os
import cv2
import sklearn
import numpy as np
from skimage.feature import hog
from skimage import data,exposure
from sklearn import svm
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import seaborn as sn
import pandas  as pd
from joblib import dump,load
from sklearn.metrics import confusion_matrix# 1. 讀入數(shù)據(jù)
# 1.1 處理數(shù)據(jù)，獲取圖像的地址和標(biāo)簽，劃分訓(xùn)練集、測(cè)試集，對(duì)數(shù)據(jù)集打亂順序
data_path = r'./hand_nums'
tmp_train = os.listdir(data_path+'/train')
tmp_test = os.listdir(data_path+'/test')
train_x ,test_x = [],[]
## 1.2 獲取圖像的地址，把訓(xùn)練集和測(cè)試集的特征和標(biāo)簽
test_y ,test_y = [],[]
for i in tmp_train:if i.endswith('.bmp'):train_x.append(data_path+'/train/'+i)train_y.append(int(i.split('-')[0]))
for i in tmp_test:if i.endswith('.bmp'):test_x.append(data_path+'/test/'+i)test_y.append(int(i.split('.')[0]))
## 1.3 打亂數(shù)據(jù)的順序
## 訓(xùn)練集、測(cè)試集的下標(biāo)
train_idx = np.arange(len(train_x))
test_idx = np.arange(len(test_x))
## 打亂順序
np.random.shuffle(train_idx)
np.random.shuffle(test_idx)
## 訓(xùn)練集、測(cè)試集打亂順序
train_x = list(np.array(train_x)[train_idx])
test_x = list(np.array(test_x)[test_idx])
train_y = list(np.array(train_y)[train_idx])
test_y = list(np.array(test_y)[test_idx])# 2. 用hog提取特征
## 2.1 hog實(shí)例化
train_feature,test_feature = [],[]
## 獲取訓(xùn)練集特征
for i in train_x:img_gray = cv2.imread(i,0)fd , img_hog = hog(img_gray,orientations=9,pixels_per_cell=(8,8),cells_per_block=(2,2),visualize=True)train_feature.append(img_hog.flatten())
## 獲取測(cè)試集特征
for i in test_x:img_gray = cv2.imread(i,0)fd,img_hog = hog(img_gray,orientations=9,pixels_per_cell=(8,8),cells_per_block=(2,2),visualize=True)test_feature.append(img_hog.flatten())# 3 SVM訓(xùn)練
clf = svm.SVC(decision_function_shape='ovo')
clf.fit(train_feature ,train_y)
# 4. 測(cè)試、評(píng)價(jià)模型
dec = clf.decision_function(test_feature)
pred_y = clf.predict(test_feature)
accuracy_score(pred_y,test_y)
# 5. 保存模型
from joblib import dump,load
## 保存模型
dump(clf,'./hand_nums/models/poly.joblib')
## 加載模型
new_cls = load('./hand_nums/models/poly.joblib')
pred_y = new_cls.predict(test_feature)
accuracy_score(pred_y,test_y)# 6. 加載模型，應(yīng)用模型
clf_poly = svm.SVC(decision_function_shape='ovo',kernel='poly')
clf_poly.fit(train_feature ,train_y)
dec = clf_poly.decision_function(test_feature)
pred_y = clf_poly.predict(test_feature)
accuracy_score(pred_y,test_y)clf_linear = svm.SVC(decision_function_shape='ovo',kernel='linear')
clf_linear.fit(train_feature ,train_y)
dec = clf_linear.decision_function(test_feature)
pred_y = clf_linear.predict(test_feature)
accuracy_score(pred_y,test_y)
cm = confusion_matrix(test_y,pred_y)df_cm = pd.Dataframe(cm,index=[i for i in ['0','1','2','3','4','5','6','7','8','9']],columns = [i for i in ['0','1','2','3','4','5','6','7','8','9']])
plt.figure(figsize=(10,7))
sn.heatmap(df_cm,annot=True,cmap='Green',fmt='d')

準(zhǔn)確率 1.0
嘻嘻😁
數(shù)據(jù)集：鏈接:https://pan.baidu.com/s/1yFCJvcswdSgGcAN6n9u-nA 密碼:ryqo