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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"], ["<"], 22)
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, "<", " 小于號"], [2, "100", " 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"], [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"]]關于利用Python如何實現識別照片中的條形碼就分享到這里了，希望以上內容可以對大家有一定的幫助，可以學到更多知識。如果覺得文章不錯，可以把它分享出去讓更多的人看到。
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