Python識別處理照片中的條形碼
最近一直在玩數(shù)獨,突發(fā)奇想實現(xiàn)圖像識別求解數(shù)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(shù)字生成list,然后求解。
輸入輸出demo
數(shù)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(jīng)過程序求解后,得到的結(jié)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(xiàn)var,index只會返回最左側(cè)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(jié)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(jù)求解輪廓信息找到數(shù)字所在位置,以及不包含數(shù)字的空白位置,提取數(shù)字信息通過KNN識別,識別出數(shù)字;無數(shù)字信息的在list中置0;生成未求解數(shù)獨list,之后求解數(shù)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(guān)鍵字"], [1, "lexicalanalysis", " 標識符"], [13, "(", " 左括號"], [14, ")", " 右括號"], [20, "{", " 左大括號"], [4, "float", " 關(guān)鍵字"], [1, "a", " 標識符"], [15, ";", " 分號"], [5, "int", " 關(guān)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(shù)"], [15, ";", " 分號"], [1, "b", " 標識符"], [12, "=", " 賦值號"], [2, "2", " 整數(shù)"], [15, ";", " 分號"], [8, "while", " 關(guān)鍵字"], [13, "(", " 左括號"], [1, "b", " 標識符"], [17, "<", " 小于號"], [2, "100", " 整數(shù)"], [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(shù)"], [15, ";", " 分號"], [1, "a", " 標識符"], [12, "=", " 賦值號"], [1, "a", " 標識符"], [9, "+", " 加號"], [2, "3", " 整數(shù)"], [15, ";", " 分號"], [21, "}", " 右大括號"], [15, ";", " 分號"], [6, "if", " 關(guān)鍵字"], [13, "(", " 左括號"], [1, "a", " 標識符"], [16, ">", " 大于號"], [2, "5", " 整數(shù)"], [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(shù)"], [15, ";", " 分號"], [21, "}", " 右大括號"], [7, "else", " 關(guān)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(shù)"], [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(nèi)容,更多關(guān)于python 識別條形碼的資料請關(guān)注腳本之家其它相關(guān)文章!
相關(guān)文章
單身狗福利?Python爬取某婚戀網(wǎng)征婚數(shù)據(jù)
今天我就當回媒婆,給男性程序員來點福利.今天目標爬取征婚網(wǎng)上呈現(xiàn)出來的女生信息保存成excel表格供大家篩選心儀的女生,需要的朋友可以參考下2021-06-06Python3 使用cookiejar管理cookie的方法
今天小編就為大家分享一篇Python3 使用cookiejar管理cookie的方法,具有很好的參考價值,希望對大家有所幫助。一起跟隨小編過來看看吧2018-12-12pandas 數(shù)據(jù)實現(xiàn)行間計算的方法
今天小編就為大家分享一篇pandas 數(shù)據(jù)實現(xiàn)行間計算的方法,具有很好的參考價值,希望對大家有所幫助。一起跟隨小編過來看看吧2018-06-06python Flask實現(xiàn)restful api service
本篇文章主要介紹了python Flask實現(xiàn)restful api service,小編覺得挺不錯的,現(xiàn)在分享給大家,也給大家做個參考。一起跟隨小編過來看看吧2017-12-12Python中不同類之間調(diào)用方法的四種方式小結(jié)
類是一種面向?qū)ο蟮木幊谭妒?它允許我們將數(shù)據(jù)和功能封裝在一個實體中,本文主要介紹了Python中不同類之間調(diào)用方法的四種方式小結(jié),具有一定的參考價值,感興趣的可以了解一下2024-02-02Python3二分查找?guī)旌瘮?shù)bisect(),bisect_left()和bisect_right()的區(qū)別
這篇文章主要介紹了Python3二分查找?guī)旌瘮?shù)bisect(),bisect_left()和bisect_right()的區(qū)別,本文通過示例代碼給大家介紹的非常詳細,對大家的學(xué)習(xí)或工作具有一定的參考借鑒價值,需要的朋友可以參考下2023-03-03以Python的Pyspider為例剖析搜索引擎的網(wǎng)絡(luò)爬蟲實現(xiàn)方法
這篇文章主要介紹了以Python的Pyspider為例剖析搜索引擎的網(wǎng)絡(luò)爬蟲實現(xiàn)方法,Pyspider是一個開源項目、用Python語言編寫十分簡潔且具有爬蟲程序的代表性,需要的朋友可以參考下2015-03-03