SVM训练代码

import cv2
import os
import numpy as np
from numpy.linalg import norm
from args import args
import time

class StatModel(object):
    def load(self, fn):
        self.model = self.model.load(fn)

    def save(self, fn):
        self.model.save(fn)


class SVM(StatModel):
    def __init__(self, C=1, gamma=0.5):
        self.model = cv2.ml.SVM_create()
        self.model.setGamma(gamma)
        self.model.setC(C)
        self.model.setKernel(cv2.ml.SVM_RBF)
        self.model.setType(cv2.ml.SVM_C_SVC)
        # 不能保证包括所有省份

    # 训练svm
    def train(self, samples, responses):
        self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)

    # 调用svm
    def predict(self, samples):
        r = self.model.predict(samples)
        return r[1].ravel()


# 定义参数
SZ = 20  # 训练图片长宽
L = 136
W = 36


# MAX_WIDTH = args.MAX_WIDTH  # 原始图片最大宽度
# Min_Area = args.Min_Area  # 车牌区域允许最大面积
# PROVINCE_START = args.PROVINCE_START
# provinces = args.provinces

# 来自opencv的sample，用于svm训练
def deskew(img):
    m = cv2.moments(img)
    if abs(m['mu02']) < 1e-2:
        return img.copy()
    skew = m['mu11'] / m['mu02']
    M
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