python svm实现手写数字识别——直接可用

一时失言乱红尘 2022-10-11 12:06 184阅读 0赞

### python svm实现手写数字识别——直接可用 ###

*  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
 *  2、预测单张图片
 *   *  2.1、待预测图像
     *  2.2、预测源码
     *  2.3、预测结果

最近在做个围棋识别的项目，需要识别下面的数字，如下图：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70_pic_center]

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 ##

百度网盘：  
**链接：https://pan.baidu.com/s/17\_XBmtQK-lSejswmQJ6YtA  
提取码：jek5**

## 1.2 、训练源码 ##

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))

# 2、预测单张图片 #

## 2.1、待预测图像 ##

![在这里插入图片描述][20210630133148508.jpg_pic_center]

## 2.2、预测源码 ##

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])

## 2.3、预测结果 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70_pic_center]: /images/20221005/01346415bdd744e7a5a0f5b78159f435.png
[20210630133148508.jpg_pic_center]: /images/20221005/b61e07eea4c740c685eaad991f365fa1.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70]: /images/20221005/88d0f22f8a4c48fd86c8f4104503a096.png