import pandas as pd
from sklearn.decomposition import PCA
from sklearn import svm
from sklearn.externals import joblib
import time
if __name__ =="__main__":
 train_num = 5000
 test_num = 7000
 data = pd.read_csv('train.csv')
 train_data = data.values[0:train_num,1:]
 train_label = data.values[0:train_num,0]
 test_data = data.values[train_num:test_num,1:]
 test_label = data.values[train_num:test_num,0]
 t = time.time()
 #PCA降維
 pca = PCA(n_components=0.8, whiten=True)
 print('start pca...')
 train_x = pca.fit_transform(train_data)
 test_x = pca.transform(test_data)
 print(train_x.shape)
 # svm訓(xùn)練
 print('start svc...')
 svc = svm.SVC(kernel = 'rbf', C = 10)
 svc.fit(train_x,train_label)
 pre = svc.predict(test_x)
 #保存模型
 joblib.dump(svc, 'model.m')
 joblib.dump(pca, 'pca.m')
 # 計(jì)算準(zhǔn)確率
 score = svc.score(test_x, test_label)
 print(u'準(zhǔn)確率：%f,花費(fèi)時(shí)間：%.2fs' % (score, time.time() - t))

from sklearn.externals import joblib
import cv2

if __name__ =="__main__":
 img = cv2.imread("img_temp.jpg", 0)
 #test = img.reshape(1,1444)![在這里插入圖片描述](https://img-blog.csdnimg.cn/20210630133136668.jpg#pic_center)
 Tp_x = 10
 Tp_y = 10
 Tp_width = 20
 Tp_height = 20
 img_temp = img[Tp_y:Tp_y + Tp_height, Tp_x:Tp_x + Tp_width]  # 參數(shù)含義分別是：y、y+h、x、x+w
 cv2.namedWindow("src", 0)
 cv2.imshow("src", img_temp)
 cv2.waitKey(1000)
 [height, width] = img_temp.shape
 print(width, height)
 res_img = cv2.resize(img_temp, (28, 28))
 test = res_img.reshape(1, 784)
 #加載模型
 svc = joblib.load("model.m")
 pca = joblib.load("pca.m")
 # svm
 print('start pca...')
 test_x = pca.transform(test)
 print(test_x.shape)
 pre = svc.predict(test_x)
 print(pre[0])