最近一直在玩數獨，突發奇想實現圖像識別求解數獨，輸入到輸出平均需要0.5s。

整體思路大概就是識別出圖中數字生成list，然后求解。

輸入輸出demo

數獨采用的是微軟自帶的Microsoft sudoku軟件隨便截取的圖像，如下圖所示：

經過程序求解后，得到的結果如下圖所示：

def getFollow(varset, terminalset, first_dic, production_list):

follow_dic = {}

done = {}

for var in varset:

follow_dic[var] = set()

done[var] = 0

follow_dic["A1"].add("#")

# for var in terminalset:

#???? follow_dic[var]=set()

#???? done[var] = 0

for var in follow_dic:

getFollowForVar(var, varset, terminalset, first_dic, production_list, follow_dic, done)

return follow_dic

def getFollowForVar(var, varset, terminalset, first_dic, production_list, follow_dic, done):

if done[var] == 1:

return

for production in production_list:

if var in production.right:

##index這里在某些極端情況下有bug，比如多次出現var，index只會返回最左側的

if production.right.index(var) != len(production.right) - 1:

follow_dic[var] = first_dic[production.right[production.right.index(var) + 1]] | follow_dic[var]

# 沒有考慮右邊有非終結符但是為null的情況

if production.right[len(production.right) - 1] == var:

if var != production.left[0]:

# print(var, "吸納", production.left[0])

getFollowForVar(production.left[0], varset, terminalset, first_dic, production_list, follow_dic,

done)

follow_dic[var] = follow_dic[var] | follow_dic[production.left[0]]

done[var] = 1

程序具體流程

程序整體流程如下圖所示：

讀入圖像后，根據求解輪廓信息找到數字所在位置，以及不包含數字的空白位置，提取數字信息通過KNN識別，識別出數字；無數字信息的在list中置0；生成未求解數獨list，之后求解數獨，將信息在原圖中顯示出來。

def initProduction():

production_list = []

production = Production(["A1"], ["A"], 0)

production_list.append(production)

production = Production(["A"], ["E", "I", "(", ")", "{", "D", "}"], 1)

production_list.append(production)

production = Production(["E"], ["int"], 2)

production_list.append(production)

production = Production(["E"], ["float"], 3)

production_list.append(production)

production = Production(["D"], ["D", ";", "B"], 4)

production_list.append(production)

production = Production(["B"], ["F"], 5)

production_list.append(production)

production = Production(["B"], ["G"], 6)

production_list.append(production)

production = Production(["B"], ["M"], 7)

production_list.append(production)

production = Production(["F"], ["E", "I"], 8)

production_list.append(production)

production = Production(["G"], ["I", "=", "P"], 9)

production_list.append(production)

production = Production(["P"], ["K"], 10)

production_list.append(production)

production = Production(["P"], ["K", "+", "P"], 11)

production_list.append(production)

production = Production(["P"], ["K", "-", "P"], 12)

production_list.append(production)

production = Production(["I"], ["id"], 13)

production_list.append(production)

production = Production(["K"], ["I"], 14)

production_list.append(production)

production = Production(["K"], ["number"], 15)

production_list.append(production)

production = Production(["K"], ["floating"], 16)

production_list.append(production)

production = Production(["M"], ["while", "(", "T", ")", "{", "D", ";", "}"], 18)

production_list.append(production)

production = Production(["N"], ["if", "(", "T", ")", "{", "D",";", "}", "else", "{", "D", ";","}"], 19)

production_list.append(production)

production = Production(["T"], ["K", "L", "K"], 20)

production_list.append(production)

production = Production(["L"], [">"], 21)

production_list.append(production)

production = Production(["L"], ["

production_list.append(production)

production = Production(["L"], [">="], 23)

production_list.append(production)

production = Production(["L"], ["<="], 24)

production_list.append(production)

production = Production(["L"], ["=="], 25)

production_list.append(production)

production = Production(["D"], ["B"], 26)

production_list.append(production)

production = Production(["B"], ["N"], 27)

production_list.append(production)

return production_list

source = [[5, "int", " 關鍵字"], [1, "lexicalanalysis", " 標識符"], [13, "(", " 左括號"], [14, ")", " 右括號"], [20, "{", " 左大括號"],

[4, "float", " 關鍵字"], [1, "a", " 標識符"], [15, ";", " 分號"], [5, "int", " 關鍵字"], [1, "b", " 標識符"],

[15, ";", " 分號"], [1, "a", " 標識符"], [12, "=", " 賦值號"], [3, "1.1", " 浮點數"], [15, ";", " 分號"], [1, "b", " 標識符"],

[12, "=", " 賦值號"], [2, "2", " 整數"], [15, ";", " 分號"], [8, "while", "? 關鍵字"], [13, "(", " 左括號"],

[1, "b", " 標識符"], [17, "

[1, "b", " 標識符"], [12, "=", " 賦值號"], [1, "b", " 標識符"], [9, "+", " 加 號"], [2, "1", " 整數"], [15, ";", " 分號"],

[1, "a", " 標識符"], [12, "=", " 賦值號"], [1, "a", " 標識符"], [9, "+", " 加號"], [2, "3", " 整數"], [15, ";", " 分號"],

[21, "}", " 右大括號"], [15, ";", " 分號"], [6, "if", " 關鍵字"], [13, "(", " 左括號"], [1, "a", " 標識符"],

[16, ">", " 大于號"], [2, "5", " 整數"], [14, ")", " 右括號"], [20, "{", " 左大括號"], [1, "b", " 標識符"],

[12, "=", " 賦值號"], [1, "b", " 標識符"], [10, "-", " 減號"], [2, "1", " 整數"], [15, ";", " 分號"], [21, "}", " 右大括號"],

[7, "else", " 關鍵字"], [20, "{", " 左大括號"], [1, "b", " 標識符"], [12, "=", " 賦值號"], [1, "b", " 標識符"],

[9, "+", " 加號"], [2, "1", " 整數"], [15, ";", " 分號"], [21, "}", " 右大括號"], [21, "}", " 右大括號"]]

以上就是Python識別處理照片中的條形碼的詳細內容，更多關于python 識別條形碼的資料請關注我們其它相關文章！

本文標題: Python識別處理照片中的條形碼

本文地址: http://www.cppcns.com/jiaoben/python/364454.html

與50位技術專家面對面20年技術見證，附贈技術全景圖

總結

以上是生活随笔為你收集整理的python条形码识别系统_Python识别处理照片中的条形码的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。