数字识别

import cv2
import numpy as np
import matplotlib.pyplot as plt
# -------------------读取样本（特征）图像的值-----------------------
s = 'image\\'  # 图像所在的路径
num = 100  # 样本总数
row = 240  # 特征图像的行数
col = 240  # 特征图像的列数
a = np.zeros((num, row, col))  # 储存所有样本的数值
# print(a.shape)
n = 0  # 储存当前图像的编号
for i in range(0, 10):
    for j in range(1, 11):
        a[n, :, :] = cv2.imread(s + str(i) + '\\' + str(i) + '-' + str(j) + '.bmp', 0)
        n = n + 1
# ------------------提采样本图像的特征-------------------------------
feature = np.zeros((num, round(row/5), round(col/5)))  # 用来存储所有样本的特征值
# print(feature.shape)
# print(row)


for ni in range(0, num):
    for nr in range(0, row):
        for nc in range(0, col):
            if a[ni, nr, nc] == 255:
                feature[ni, int(nr/5), int(nc/5)] += 1
f = feature  # 简化变量的名称
#   -----------------------计算当前待识别图像的特征值--------------------
o = cv2.imread('image\\test\\5.bmp', 0)
# ###读取像素值
of = np.zeros((round(row/5), round(col/5)))  # 存储待识别图像的特征值
for nr in range(0, row):
    for nc in range(0, col):
        if o[nr, nc] == 255:
            of[int(nr/5), int(nc/5)] += 1
# ------------------------开始计算，识别数字，计算最邻近的若干个数字是多少，判断结果
d = np.zeros(100)
for i in range(1, 100):
    d[i] = np.sum((of - f[i, :, :]) * (of - f[i, :, :]))
# print(d)
d = d.tolist()
temp = []
Inf = max(d)
# print(Inf)
k = 7
for i in range(k):
    temp.append(d.index(min(d)))
    d[d.index(min(d))] = Inf
print(temp) # 看看都被识别为哪些特征值
temp = [i/10 for i in temp]
print(temp)
# 数组r用来储存结果，r[0]表示k近邻中0的个数，r[n]表示k临近中n的个数
r = np.zeros(10)
for i in temp:
    r[int(i)] += 1
print(r)
print('当前的数字可能为：' + str(np.argmax(r)))