test_model.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''Risk2S'''
'''
字符型图片验证码识别完整过程及Python实现
https://www.cnblogs.com/beer/p/5672678.html
'''
import os
from PIL import Image
import numpy as np
import sys


def dir_check(path):
	isExists = os.path.exists(path)
	if isExists != True:
		os.makedirs(path)
		print('文件夹' + path + ' 创建成功')
	else:
		pass


def get_bin_table(threshold=140):
	"""
	获取灰度转二值的映射table
	:param threshold:
	:return:
	"""
	table = []
	for i in range(256):
		if i < threshold:
			table.append(0)
		else:
			table.append(1)

	return table


def get_crop_imgs(img):
	"""
	按照图片的特点,进行切割,这个要根据具体的验证码来进行工作. # 见原理图
	:param img:
	:return:
	"""
	child_img_list = []
	for i in range(4):
		x = 2 + i * (8 + 5)  # 见原理图
		y = 0
		child_img = img.crop((x, y, x + 8, y + 18))
		child_img_list.append(child_img)
	return child_img_list


def get_feature(img):
	"""
	获取指定图片的特征值,
	1. 按照每排的像素点,高度为18,则有18个维度,然后为8列,总共144个维度
	:param img_path:
	:return:一个维度为18（高度）的列表
	"""
	width, height = img.size

	pixel_cnt_list = []
	height = 10
	for y in range(height):
		pix_cnt_x = 0
		for x in range(width):
			#  == 0 如果按照示例程序无法提取特征值，如果没有出现，就看看当前像素点的值进行自我处理。
			if img.getpixel((x, y)) < 240:  # 黑色点
				pix_cnt_x += 1

		pixel_cnt_list.append(pix_cnt_x)

	for x in range(width):
		pix_cnt_y = 0
		for y in range(height):
			if img.getpixel((x, y)) < 240:  # 黑色点
				pix_cnt_y += 1

		pixel_cnt_list.append(pix_cnt_y)

	return pixel_cnt_list


# 得到特征值文件
# def get_fea_file(img_path,n,flag_print):
# 遍历图片文件
# for i in os.walk(img_path + '/' + str(n)):
#     im_list = i[2]
# print(im_list)
# for im_file in im_list:
#     im_name = './' + img_path + '/' + str(n) + '/' + im_file
#     # print(im_name)
#     im = Image.open(im_name)
#     #  特征选择
#     im_feature = get_feature(im)
#     # print(im_feature)
#     z_index = 1
#     if os.path.exists(str(n) + '_feature.txt'):
#         ctreate_file = False
#     else:
#         ctreate_file = True
#     # f = open(str(n) + '_feature.txt', 'a')
#     # f = open(str(n) + '_feature.txt', 'a')
#     #单个特征值文件
#     f = open('feature.txt', 'a')
#     if ctreate_file:
#         if flag_print:
#             for z in im_feature:
#                 if z_index == 1:
#                     print(n, end=' ')
#                     f.write(str(n) + ' ')
#                 print(str(z_index) + ':' + str(z), end=' ')
#                 f.write(str(z_index) + ':' + str(z) + ' ')
#                 z_index += 1
#             print('')
#             f.write('\n')
#         else:
#             for z in im_feature:
#                 if z_index == 1:
#                     f.write(str(n) + ' ')
#                 f.write(str(z_index) + ':' + str(z) + ' ')
#                 z_index += 1
#             f.write('\n')
# f.close()

def train_svm_model():
	"""
	训练并生成model文件
	:return:
	"""
	y, x = svm_read_problem(svm_root + '/train_pix_feature_xy.txt')
	model = svm_train(y, x)
	svm_save_model(model_path, model)


def train_model_main2(model_path, file):
	# LibSVM
	# 按照 libSVM 指定的格式生成一组带特征值和标记值的向量文件
	svm_path = r"C:\Python36\risk_down\libsvm"
	sys.path.append(svm_path + r"\python")
	import svmutil
	# y, x = svmutil.svm_read_problem('./' + str(n) + '_feature.txt')
	y, x = svmutil.svm_read_problem(file)
	# ---------------------------------------------------
	model = svmutil.svm_train(y[:50], x[:50], '-c 4')
	# print(model_path,file.split('/')[2][0])
	model_path = './' + model_path + '/' + file.split('/')[2][0] + "_feature.model"
	svmutil.svm_save_model(model_path, model)


# p_label,p_acc,p_val = svmutil.svm_predict(y[0:], x[0:],model)
# print(p_label,p_acc,p_val)


# 得到测试的特征值文件
def get_fea_file(img_path, flag_print, model_path, total_name):
	# 遍历图片文件
	for i in os.walk(model_path):
		img_path = i[0]
		im_list = i[2]
	# model文件
	im_list = list(filter(lambda x: x[-3:] == 'txt', im_list))
	g = open(total_name, 'a')
	for file in im_list:
		model_name = './' + model_path + '/' + file
		f = open(model_name, 'r')
		# 单个特征值文件
		g.write(f.read())
	return total_name


def svm_model_test(svm_root, model_path):
	"""
	使用测试集测试模型
	:return:
	"""
	svm_path = r"C:\Python36\risk_down\libsvm"
	sys.path.append(svm_path + r"\python")
	import svmutil
	# 训练总模型
	y, x = svmutil.svm_read_problem('./' + 'total_feature.txt')


# model = svmutil.svm_train(y[:], x[:], '-c 4')
# svmutil.svm_save_model('total_feature.model', model)
# yt, xt = svmutil.svm_read_problem(svm_root + 'last_test_pix_xy_new.txt')

# model = svmutil.svm_load_model(model_path)
# p_label, p_acc, p_val = svmutil.svm_predict(yt, xt, model)  # p_label即为识别的结果
# cnt = 0
# for item in p_label:
# 	print('%d' % item, end=',')
# 	cnt += 1
# 	if cnt % 8 == 0:
# 		print('')

import sys


def delete(filepath):
	f = open(filepath, 'r')
	fnew = open(filepath + '_new.txt', 'w')  # 将结果存入新的文本中
	for line in f.readlines():  # 对每一行先删除空格，\n等无用的字符，再检查此行是否长度为0
		data = line.strip()
		if len(data) != 0:
			fnew.write(data)
			fnew.write('\n')
	f.close()
	fnew.close()


def main():
	img_path = "model_img"
	model_path = "model_file"
	total_name = 'total_feature.txt'
	dir_check(img_path)
	# 删除文本中的空行
	delete(total_name)
	# 是否打印特征值
	# flag_print = True
	flag_print = False
	# 到达第二阶段 模型测试
	# total_name = get_fea_file(img_path, flag_print, model_path, total_name)
	# svm_model_test('./', "num_feature.model")


if __name__ == '__main__':
	main()