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
1.2 、训练源码
train.py
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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))
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2、预测单张图片
2.1、待预测图像
2.2、预测源码
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from sklearn.externals import joblib
import cv2
if __name__ = = "__main__" :
img = cv2.imread( "img_temp.jpg" , 0 )
#test = img.reshape(1,1444)![在这里插入图片描述](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] # 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 ])
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2.3、预测结果
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原文链接:https://blog.csdn.net/mao_hui_fei/article/details/118358036