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最近一直在玩数独,突发奇想实现图像识别求解数独,输入到输出平均需要0.5s。
整体思路大概就是识别出图中数字生成list,然后求解。
输入输出demo
数独采用的是微软自带的Microsoft sudoku软件随便截取的图像,如下图所示:
经过程序求解后,得到的结果如下图所示:
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这里在某些极端情况下有bug,比如多次出现var,index只会返回最左侧的 if production.right.index(var) != len(production.right) - 1: follow_dic[var] = first_dic[production.right[production.right.index(var) + 1]] | follow_dic[var] # 没有考虑右边有非终结符但是为null的情况 if production.right[len(production.right) - 1] == var: if var != production.left[0]: # print(var, "吸纳", 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
程序具体流程
程序整体流程如下图所示:
读入图像后,根据求解轮廓信息找到数字所在位置,以及不包含数字的空白位置,提取数字信息通过KNN识别,识别出数字;无数字信息的在list中置0;生成未求解数独list,之后求解数独,将信息在原图中显示出来。
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", " 关键字"], [1, "lexicalanalysis", " 标识符"], [13, "(", " 左括号"], [14, ")", " 右括号"], [20, "{", " 左大括号"], [4, "float", " 关键字"], [1, "a", " 标识符"], [15, ";", " 分号"], [5, "int", " 关键字"], [1, "b", " 标识符"], [15, ";", " 分号"], [1, "a", " 标识符"], [12, "=", " 赋值号"], [3, "1.1", " 浮点数"], [15, ";", " 分号"], [1, "b", " 标识符"], [12, "=", " 赋值号"], [2, "2", " 整数"], [15, ";", " 分号"], [8, "while", " 关键字"], [13, "(", " 左括号"], [1, "b", " 标识符"], [17, "<", " 小于号"], [2, "100", " 整数"], [14, ")", " 右括号"], [20, "{", " 左大括号"], [1, "b", " 标识符"], [12, "=", " 赋值号"], [1, "b", " 标识符"], [9, "+", " 加 号"], [2, "1", " 整数"], [15, ";", " 分号"], [1, "a", " 标识符"], [12, "=", " 赋值号"], [1, "a", " 标识符"], [9, "+", " 加号"], [2, "3", " 整数"], [15, ";", " 分号"], [21, "}", " 右大括号"], [15, ";", " 分号"], [6, "if", " 关键字"], [13, "(", " 左括号"], [1, "a", " 标识符"], [16, ">", " 大于号"], [2, "5", " 整数"], [14, ")", " 右括号"], [20, "{", " 左大括号"], [1, "b", " 标识符"], [12, "=", " 赋值号"], [1, "b", " 标识符"], [10, "-", " 减号"], [2, "1", " 整数"], [15, ";", " 分号"], [21, "}", " 右大括号"], [7, "else", " 关键字"], [20, "{", " 左大括号"], [1, "b", " 标识符"], [12, "=", " 赋值号"], [1, "b", " 标识符"], [9, "+", " 加号"], [2, "1", " 整数"], [15, ";", " 分号"], [21, "}", " 右大括号"], [21, "}", " 右大括号"]]
以上就是Python识别处理照片中的条形码的详细内容,更多关于python 识别条形码的资料请关注其它相关文章!
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