使用python svm实现直接可用的手写数字识别
python svm实现手写数字识别——直接可用
最近在做个围棋识别的项目，需要识别下面的数字，如下图：
我发现现在网上很多代码是良莠不齐，…真是一言难尽，于是记录一下，能够运行成功并识别成功的一个源码。
1、训练1.1、训练数据集下载——已转化成csv文件下载地址
1.2 、训练源码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降维 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训练 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’) # 计算准确率 score = svc.score(test_x, test_label) print(u’准确率：%f,花费时间：%.2fs’ % (score, time.time() - t))2、预测单张图片2.1、待预测图像
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] # 参数含义分别是：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) #加载模型 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、预测结果
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