目錄python svm實(shí)現(xiàn)手寫(xiě)數(shù)字識(shí)別——直接可用1、訓(xùn)練1.1、訓(xùn)練數(shù)據(jù)集下載——已轉(zhuǎn)化成csv文件1.2 、訓(xùn)練源碼2、預(yù)測(cè)單張圖片2.1、待預(yù)測(cè)圖像2.2、預(yù)測(cè)源碼2.3、預(yù)測(cè)結(jié)果python svm實(shí)現(xiàn)手寫(xiě)數(shù)字識(shí)別——直接可用

最近在做個(gè)圍棋識(shí)別的項(xiàng)目，需要識(shí)別下面的數(shù)字，如下圖：

我發(fā)現(xiàn)現(xiàn)在網(wǎng)上很多代碼是良莠不齊，…真是一言難盡，于是記錄一下，能夠運(yùn)行成功并識(shí)別成功的一個(gè)源碼。

1、訓(xùn)練1.1、訓(xùn)練數(shù)據(jù)集下載——已轉(zhuǎn)化成csv文件

下載地址

1.2 、訓(xùn)練源碼

train.py

import pandas as pdfrom sklearn.decomposition import PCAfrom sklearn import svmfrom sklearn.externals import joblibimport timeif __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))2、預(yù)測(cè)單張圖片2.1、待預(yù)測(cè)圖像

2.2、預(yù)測(cè)源碼

from sklearn.externals import joblibimport cv2if __name__ =='__main__': img = cv2.imread('img_temp.jpg', 0) #test = img.reshape(1,1444) 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])2.3、預(yù)測(cè)結(jié)果

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