当前位置 博文首页 > 使用python svm实现直接可用的手写数字识别
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。
下载地址
train.py
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))
from sklearn.externals import joblib
import cv2
if __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])
