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Newtonsoft.Json
Newtonsoft.Json 是.Net平台操作Json的工具，他的介绍就不多说了，笔者最近在弄接口，需要操作Json。
以某个云计算平台的Token为例，边操作边讲解。
Json 转为 Model
将 Model 转为 Json
将 LINQ 转为 JSON
Linq 操作
命名空间、类型、方法大全
另外附上 百度AI 文字识别 Json 及其模型类
Newtonsoft.Json 将字符串转为对象，是根据类型对象名称进行的，大小写不分，但是名称要一致要，哪怕你的json只有一个
" a " : 1public class Test public int aa{get;set;}也是不能对应的。
有复杂层次的 json，可以使用 “类中类” 来映射，要注意 List<T>/Array/ArrayList的类型的使用。
Json 转为 Model
新建一个 Json 文件，名字随意，例如 json1.json
把以下内容粘贴进去
"refresh_token": "25.ea2f85ebd48df85fe5400000.18640.282335-15533349", "expires_in": 2592010, "session_key": "9mzdWr3n8ncMeDgX8zjhkhlW8khb5cdZtPevPbPwQGBg==", "access_token": "24.ac0ca9fakhlkyhl552017858.282335-15533349", "scope": "audio_voice_assistant_get audio_tts_post public vis-ocr_ocr nlp_simnet nlp_wclassify_watermark brain_ocr_scope vis-classify_car brain_gif_antiporn brain_ocr_general brain_ocr_general_basic brain_ocr_generer vis-classify_animal brain_politician brain_unit_utterance brain_imgquality_general brain_nlp_simnet brain_nlp_depparser vis-classify_plant brain_solution brain_ocr_plate_number brain_nlp_wordembedding brain_nlp_dnnlm_cn_legacy brain_nlp_simnet_legacy brain_nlp_commain_animal_classify brain_plant_classify brain_solution_iocr brain_realtime_product brain_nlp_lexer_custom brain_kgbody_analysis brain_body_attr brain_ocr_vat_invoice brain_advanced_general_classify brain_numbers brain_body_number vis-faceverify_FACE_auth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi", "session_secret": "2ca66d464545c77a4767f709873be4"定义一个模型，文件名为 AccessTokenModel.cs
public class AccessTokenModel public string refresh_token { get; set; } public string expires_in { get; set; }//： Access Token的有效期(秒为单位，一般为1个月) public string scope { get; set; } public string session_key { get; set; } public string access_token { get; set; }//： 要获取的Access Token public string session_secret { get; set; }打开 Program.cs 文件
public static void Main(string[] args) FileStream fs = new FileStream(@"请修改成你的文件路径\json1.json", FileMode.Open); StreamReader fileStream = new StreamReader(fs); string str = ""; string line; while ((line = fileStream.ReadLine()) != null) str += line; }
//上面的代码没有意义，只是将Json文件的内容加载到字符串中
JObject jObject = new JObject(); //新建 操作对象 AccessTokenModel a = JsonConvert.DeserializeObject<AccessTokenModel>(str); Console.WriteLine(a.access_token); //随意输出一个属性 Console.ReadKey();JsonConvert.DeserializeObject<要转化的模型类>("字符串对象");之后可以很方便的把Json文件的内容存放到数据库中。
把Json文件改成以下的样子
"refresh_token": "25.ea2f85ebd48df85fe5400000.18640.282335-15533349", "expires_in": 2592010, "session_key": "9mzdWr3n8ncMeDgX8zjhkhlW8khb5cdZtPevPbPwQGBg==", "access_token": "24.ac0ca9fakhlkyhl552017858.282335-15533349", "scope": "audio_voice_assistant_get audio_tts_post public vis-ocr_ocr nlp_simnet nlp_wclassify_watermark brain_ocr_scope vis-classify_car brain_gif_antiporn brain_ocr_general brain_ocr_general_basic brain_ocr_generer vis-classify_animal brain_politician brain_unit_utterance brain_imgquality_general brain_nlp_simnet brain_nlp_depparser vis-classify_plant brain_solution brain_ocr_plate_number brain_nlp_wordembedding brain_nlp_dnnlm_cn_legacy brain_nlp_simnet_legacy brain_nlp_commain_animal_classify brain_plant_classify brain_solution_iocr brain_realtime_product brain_nlp_lexer_custom brain_kgbody_analysis brain_body_attr brain_ocr_vat_invoice brain_advanced_general_classify brain_numbers brain_body_number vis-faceverify_FACE_auth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi", "session_secret": "2ca66d464545c77a4767f709873be4" "refresh_token": "25.ea2f85ebd48df85fe5400000.18640.282335-15533349", "expires_in": 2592010, "session_key": "9mzdWr3n8ncMeDgX8zjhkhlW8khb5cdZtPevPbPwQGBg==", "access_token": "24.ac0ca9fakhlkyhl552017858.282335-15533349", "scope": "audio_voice_assistant_get audio_tts_post public vis-ocr_ocr nlp_simnet nlp_wclassify_watermark brain_ocr_scope vis-classify_car brain_gif_antiporn brain_ocr_general brain_ocr_general_basic brain_ocr_generer vis-classify_animal brain_politician brain_unit_utterance brain_imgquality_general brain_nlp_simnet brain_nlp_depparser vis-classify_plant brain_solution brain_ocr_plate_number brain_nlp_wordembedding brain_nlp_dnnlm_cn_legacy brain_nlp_simnet_legacy brain_nlp_commain_animal_classify brain_plant_classify brain_solution_iocr brain_realtime_product brain_nlp_lexer_custom brain_kgbody_analysis brain_body_attr brain_ocr_vat_invoice brain_advanced_general_classify brain_numbers brain_body_number vis-faceverify_FACE_auth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi", "session_secret": "2ca66d464545c77a4767f709873be4"public static void Main(string[] args) FileStream fs = new FileStream(@"请修改成你的文件路径\json1.json", FileMode.Open); StreamReader fileStream = new StreamReader(fs); string str = ""; string line; while ((line = fileStream.ReadLine()) != null) str += line; //上面的代码没有意义，只是将Json文件的内容加载到字符串中 JObject jObject = new JObject(); //新建 操作对象 List<AccessTokenModel> a = JsonConvert.DeserializeObject<List<AccessTokenModel>>(str); foreach (var i in a) Console.WriteLine(i.access_token); Console.ReadKey();将Model转为Json
能够将模型对象转为 Json。
继续使用上面的 AccessTokenModel.cs 文件，
public static void Main(string[] args) AccessTokenModel accessTokenModel = new AccessTokenModel(); accessTokenModel.access_token = "test1"; accessTokenModel.expires_in = "test2"; accessTokenModel.refresh_token = "test3"; accessTokenModel.scope = "test4"; accessTokenModel.session_key = "test5"; accessTokenModel.session_secret = " test6"; JObject jObject = new JObject(); string str = JsonConvert.SerializeObject(accessTokenModel); //转为字符串 Console.WriteLine(str); Console.ReadKey();JsonConvert.SerializeObject(a模型对象)；运行后可以看到控制台输出的是Json字符串了，你可以继续把他放到Json文件中，这里不再赘述。
将 LINQ 转为 JSON
下面这个是从官网直接copy的例子，Jarray 是其框架提供的一种类型。
在控制台运行后会发现输出的字符是已经格式化的。
public static void Main(string[] args) JArray array = new JArray(); array.Add("Manual text"); array.Add(new DateTime(2000, 5, 23)); JObject o = new JObject(); o["MyArray"] = array; string json = o.ToString(); // "MyArray": [ // "Manual text", // "2000-05-23T00:00:00" Console.WriteLine(json); Console.ReadKey();Linq 操作
框架提供了对 Jobject 对象的Linq操作支持
using Newtonsoft.Json.Linq;之后你可以像操作数组、集合或者Context一样方便。
命名空间、类型、方法大全
本来想翻译一下的，英语太差，算了。在常用的类型前面加粗吧
ClassesClassDescription DefaultJsonNameTableThe default JSON name table implementation.笔者在弄了一段时间的百度 Ai 平台的SDK，封装了OCR SDK，由于现在在找实习工作，所以有部分没有弄完，有兴趣可以添加笔者的微信免费获取。微信在右侧导航栏。
百度AI 识别文字，返回Json结果, 名字随意.格式建议为 json，如果使用记事本保存，注意编码格式是 utf-8，因为c# string默认为utf8，不然会乱码。
"log_id": 3413661945235258919, "direction": 0, "words_result_num": 2, "words_result": [ "vertexes_location": [ "y": 81, "x": 51 "y": 81, "x": 151 "y": 103, "x": 151 "y": 103, "x": 51 "probability": { "variance": 0.0, "average": 0.999861, "min": 0.999627 "chars": [ "char": "今", "location": { "width": 17, "top": 83, "left": 60, "height": 20 "char": "天", "location": { "width": 17, "top": 83, "left": 78, "height": 20 "char": "除", "location": { "width": 12, "top": 83, "left": 103, "height": 20 "char": "了", "location": { "width": 16, "top": 83, "left": 116, "height": 20 "char": "皮", "location": { "width": 13, "top": 83, "left": 140, "height": 20 "min_finegrained_vertexes_location": [ "y": 81, "x": 51 "y": 81, "x": 151 "y": 103, "x": 151 "y": 103, "x": 51 "finegrained_vertexes_location": [ "y": 81, "x": 51 "y": 81, "x": 71 "y": 81, "x": 90 "y": 81, "x": 110 "y": 81, "x": 129 "y": 81, "x": 149 "y": 81, "x": 151 "y": 91, "x": 151 "y": 100, "x": 151 "y": 103, "x": 151 "y": 103, "x": 132 "y": 103, "x": 112 "y": 103, "x": 93 "y": 103, "x": 73 "y": 103, "x": 54 "y": 103, "x": 51 "y": 93, "x": 51 "y": 84, "x": 51 "location": { "width": 102, "top": 81, "left": 51, "height": 24 "words": "今天除了皮" "vertexes_location": [ "y": 109, "x": 52 "y": 109, "x": 152 "y": 130, "x": 152 "y": 130, "x": 52 "probability": { "variance": 8E-05, "average": 0.9907, "min": 0.973259 "chars": [ "char": "又", "location": { "width": 16, "top": 111, "left": 61, "height": 20 "char": "啥", "location": { "width": 12, "top": 111, "left": 85, "height": 20 "char": "也", "location": { "width": 16, "top": 111, "left": 98, "height": 20 "char": "没", "location": { "width": 15, "top": 111, "left": 123, "height": 20 "char": "干", "location": { "width": 13, "top": 111, "left": 141, "height": 20 "min_finegrained_vertexes_location": [ "y": 109, "x": 52 "y": 109, "x": 152 "y": 130, "x": 152 "y": 130, "x": 52 "finegrained_vertexes_location": [ "y": 109, "x": 52 "y": 109, "x": 71 "y": 109, "x": 91 "y": 109, "x": 110 "y": 109, "x": 129 "y": 109, "x": 149 "y": 109, "x": 152 "y": 119, "x": 152 "y": 129, "x": 152 "y": 130, "x": 152 "y": 130, "x": 133 "y": 130, "x": 113 "y": 130, "x": 94 "y": 130, "x": 74 "y": 130, "x": 55 "y": 130, "x": 52 "y": 121, "x": 52 "y": 111, "x": 52 "location": { "width": 102, "top": 109, "left": 52, "height": 22 "words": "又啥也没干" "language": -1对应的模型 ，将 cs 文件，名字 GeneralModel.cs
/// <summary> /// 通用文字识别（含位置版）返回结果 /// </summary> public class GeneralModel /// <summary> /// 必选 /// 唯一的log id，用于问题定位 /// </summary> public long log_id { get; set; } /// <summary> /// 图像方向，当detect_direction=true时存在。 /// 非必选 ///- -1:未定义， ///- 0:正向， ///- 1: 逆时针90度， ///- 2:逆时针180度， ///- 3:逆时针270度 /// </summary> public int direction { get; set; } /// <summary> /// 必选 /// 识别结果数，表示words_result的元素个数 /// </summary> public int words_result_num { get; set; } /// <summary> /// 检测语言 默认值会返回 -1 /// </summary> public string language { get; set; } /// <summary> /// 定位和识别文字结果数组 /// </summary> public List<Words_result> words_result { get; set; } public class Words_result /// <summary> /// 图片中文字段四个顶点位置(矩形范围) /// </summary> public List<XY> vertexes_Location { get; set; } /// <summary> /// 可选 /// 行置信度信息；如果输入参数 probability = true 则输出 /// </summary> public Probability probability { get; set; } /// <summary> /// 每个字 /// </summary> public List<Chars> chars { get; set; } /// <summary> /// 最小细粒度顶点坐标 /// </summary> public List<XY> min_finegrained_vertexes_location { get; set; } /// <summary> /// 细粒度顶点坐标，多边形 /// </summary> public List<XY> finegrained_vertexes_location { get; set; } /// <summary> /// 文字在图片中的相对位置 /// </summary> public Location location { get; set; } /// <summary> /// 识别出的文字 /// </summary> public string words { get; set; } /// <summary> /// 坐标 /// </summary> public class XY public int x { get; set; } public int y { get; set; } /// <summary> /// 行置信度 /// </summary> public class Probability /// <summary> /// 行置信度平均值方差 /// </summary> public double variance { get; set; } /// <summary> /// 行置信度平均值 /// </summary> public double average { get; set; } /// <summary> /// 行置信度最小值 /// </summary> public double min { get; set; } /// <summary> /// 单个文字 /// </summary> public class Chars /// <summary> /// 识别的单个文字 /// </summary> public char chaR { get; set; } /// <summary> /// 该文字范围(矩形) /// </summary> public Location location { get; set; } public class Location public int left { get; set; } public int top { get; set; } public int width { get; set; } public int height { get; set; }可用控制台进行检验
static void Main(string[] args) StreamReader streamReader = new StreamReader(System.IO.File.OpenRead(@"json文件位置")); string str = ""; string jsonstr; while ((jsonstr = streamReader.ReadLine()) != null) str += jsonstr; GeneralModel generalModel = JsonConvert.DeserializeObject<GeneralModel>(str); Console.WriteLine("图片id：" + generalModel.log_id); Console.WriteLine("图像方向：" + generalModel.direction); Console.WriteLine("检测语言为：" + generalModel.language); Console.WriteLine("有几个结果：" + generalModel.words_result_num); foreach (var item in generalModel.words_result) Console.WriteLine("识别结果：" + Encoding.UTF8.GetString(Encoding.UTF8.GetBytes(item.words))); foreach (var itemi in item.vertexes_Location) Console.WriteLine("{x:" + itemi.x + ";y:" + itemi.y + "}"); Console.WriteLine("Probability:可信度:" + "行置信度平均值" + item.probability.average + ";行置信度平均值方差:" + item.probability.variance + ";行置信度平均值最小值：" + item.probability.min); foreach (var itemi in item.chars) Console.WriteLine(itemi.chaR); Console.WriteLine("位置: left:" + itemi.location.left + "; height: " + itemi.location.height + "top: " + itemi.location.top + "; width: " + itemi.location.width); Console.ReadKey();
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