图像处理案例03

1 . 步骤

1. 读入数据，把数据划分为训练集和测试集
2. 用hog提取特征
3. 用SVM训练数据
4. 测试、评价模型
5. 保存模型
6. 加载模型，应用模型

2 . 代码

import os
import cv2
import sklearn
import numpy as np
from skimage.feature import hog
from skimage import data,exposure
from sklearn import svm
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import seaborn as sn
import pandas  as pd
from joblib import dump,load
from sklearn.metrics import confusion_matrix

# 1. 读入数据
# 1.1 处理数据，获取图像的地址和标签，划分训练集、测试集，对数据集打乱顺序
data_path = r'./hand_nums'
tmp_train = os.listdir(data_path+'/train')
tmp_test = os.listdir(data_path+'/test')
train_x ,test_x = [],[]
## 1.2 获取图像的地址，把训练集和测试集的特征和标签
test_y ,test_y = [],[]
for i in tmp_train:
    if i.endswith('.bmp'):
        train_x.append(data_path+'/train/'+i)
        train_y.append(int(i.split('-'