import * as Blockly from 'blockly/core';

const AI_HUE = "#55839A";

export const tuple_anchor = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField(new Blockly.FieldTextInput('anchor'), 'VAR')
            .appendField('锚点参数= (')
            .appendField(new Blockly.FieldTextInput('1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275, 6.718375, 9.01025'), 'TEXT')
            .appendField(')');
        this.setPreviousStatement(true);
        this.setNextStatement(true);
        this.setTooltip("锚点参数");
    }
};

export const tuple_calss = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField(new Blockly.FieldTextInput('calss'), 'VAR')
            .appendField('物品名称= [')
            .appendField(new Blockly.FieldTextInput("'name1', 'name2', 'name3', 'name4'"), 'TEXT')
            .appendField(']');
        this.setPreviousStatement(true);
        this.setNextStatement(true);
        this.setTooltip("将要识别的物品名称");
    }
};

export const KPU_load = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendValueInput('SUB')
            .appendField("")
            .setCheck("var");
        this.appendValueInput('path')
            .appendField("模型加载")
            .setCheck(Number);
        this.setInputsInline(true);
        this.setPreviousStatement(true);
        this.setNextStatement(true);
        this.setTooltip("从flash系统中加载模型");
    }
};

export const KPU_load1 = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendValueInput('SUB')
            .appendField("")
            .setCheck("var");
        this.appendValueInput('path')
            .appendField("模型路径")
            .setCheck(String);
        this.setInputsInline(true);
        this.setPreviousStatement(true);
        this.setNextStatement(true);
        this.setTooltip("从文件系统中加载模型");
    }
};

export const KPU_init_yolo2 = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("yolo2")
            .appendField("初始化");
        this.appendValueInput('SUB')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("网络模型")
            .setCheck("var");
        this.appendValueInput('threshold')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("概率阈值")
            .setCheck(Number);
        this.appendValueInput('nms_value')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("box_iou门限")
            .setCheck(Number);
        this.appendValueInput('anchor_num')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("锚点数")
            .setCheck(Number);
        this.appendValueInput('anchor')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("锚点参数");
        //this.setInputsInline(true);
        this.setPreviousStatement(true);
        this.setNextStatement(true);
        this.setTooltip("初始化yolo2网络");
    }
};

export const KPU_run_yolo2 = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("yolo2")
            .appendField("运行网络");
        this.appendValueInput('SUB')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("模型")
            .setCheck("var");
        this.appendValueInput('VAR')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("图像");
        this.setOutput(true);
        this.setInputsInline(true);
        this.setTooltip("运行yolo2网络");
    }
};

export const KPU_forward = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("yolo2")
            .appendField("前向运算");
        this.appendValueInput('SUB')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("模型")
            .setCheck("var");
        this.appendValueInput('VAR')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("图像");
        this.setOutput(true);
        this.setInputsInline(true);
        this.setTooltip("运行网络前向运算");
    }
};


export const KPU_analysis = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput()
            .appendField("yolo2")
            .appendField("模型解析");
        this.appendValueInput('VAR')
            .appendField("对象")
            .setCheck("var");
        this.appendDummyInput()
            .appendField("获取")
            .appendField(new Blockly.FieldDropdown([
                ["坐标-x", "x"],
                ["坐标-y", "y"],
                ["标识号", "classid"],
                ["置信度", "value"]
            ]), "key");
        this.setOutput(true);
        //this.setInputsInline(true);
        this.setTooltip("对于模型解析，获取模型识别结果的目标坐标、标识好、置信度");
    }
};

export const aionenet_nic_init = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("AI_OneNET")
            .appendField("连接WiFi");
        this.appendValueInput('account')
            .appendField("名称")
            .setCheck(String);
        this.appendValueInput('password')
            .appendField("密码")
            .setCheck(String);
        this.setInputsInline(true);
        this.setPreviousStatement(true);
        this.setNextStatement(true);
        this.setTooltip("AI-Onenet平台 连接WiFi");
    }
};

export const aionenet_token = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("AI_OneNET")
            .appendField("获鉴权码");
        this.appendValueInput('account')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("账号")
            .setCheck(String);
        this.appendValueInput('password')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("密码")
            .setCheck(String);
        this.setOutput(true);
        this.setInputsInline(true);
        this.setTooltip("AI-Onenet平台 需要注册平台才能使用账号获取用户鉴权码，鉴权码一般24小时有效");
    }
};

export const aionenet_API = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("AI_OneNET")
            .appendField("调取API");
        this.appendValueInput('VAR')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("图像");
        this.appendDummyInput()
            .appendField("识别")
            .appendField(new Blockly.FieldDropdown([
                ["人脸检测", "FACE_RECO"],
                ["人脸分析", "FACE_ATTRIBUTE"],
                ["人体检测", "BODY_RECO"],
                ["图像抄表", "AMMETER_READ"],
                ["内容测评", "IDENTIFY_PORN"],
                ["车牌信息", "NUMBER_PLATE_RECOGNITION"],
                ["宠物种类", "CAT_DOG_DETECTION"],
                ["火灾检测", "FIRE_DETECTION"]
            ]), "api");
        this.appendValueInput('token')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("鉴权码")
            .setCheck(String);
        this.setOutput(true);
        this.setInputsInline(true);
        this.setTooltip("AI-Onenet平台 调用平台API，返回列表识别结果参数");
    }
};

export const ailocal_training = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("AI_Local")
            .appendField("模型训练");
        this.appendValueInput('calss')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("物品");
        this.appendValueInput('sample')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("训练量")
            .setCheck(Number);
        this.appendValueInput('save')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("保存")
            .setCheck(String);
        this.setInputsInline(true);
        this.setPreviousStatement(true);
        this.setNextStatement(true);
        this.setTooltip("AI-Local本地模型训练 需要识别的物品名称、每个物品训练数量、保存的名称");
    }
};

export const ailocal_loading = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("AI_Local")
            .appendField("模型加载");
        this.appendValueInput('path')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("路径")
            .setCheck(String);
        this.setInputsInline(true);
        this.setPreviousStatement(true);
        this.setNextStatement(true);
        this.setTooltip("AI-Local 加载已经训练好的本地模型");
    }
};


export const ailocal_predict = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("AI_Local")
            .appendField("运行模型");
        this.appendValueInput('calss')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("物品");
        this.appendValueInput('VAR')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("图像");
        this.setOutput(true);
        this.setInputsInline(true);
        this.setTooltip("AI-Local 采集图像运行模型将返回识别的物品名、置信度");
    }
};

//---开始------------新增---20210302---------------------------------------------------

export const ai_face_init = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("AI_Face")
            .appendField("初始化 加载");
        this.appendValueInput('FD')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("模型FD:")
            .setCheck(String);
        this.appendValueInput('LD')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("模型LD:")
            .setCheck(String);
        this.appendValueInput('FE')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("模型FE:")
            .setCheck(String);
        //this.setInputsInline(true);
        this.setPreviousStatement(true);
        this.setNextStatement(true);
        this.setTooltip("人脸分辨，初始化");
    }
};

export const ai_face_train = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput("")
            .appendField("AI_Face")
            .appendField("运行识别");
        this.appendValueInput('names')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("人名");
        this.appendValueInput('VAR')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("图象");
        this.appendValueInput('threshold')
            .setAlign(Blockly.inputs.Align.RIGHT)
            .appendField("阈值")
            .setCheck(Number);
        this.setOutput(true);
        this.setInputsInline(true);
        this.setTooltip("人脸分辨，识别到人脸返回True，无人脸返回False");
    }
};

export const ai_face_info = {
    init: function () {
        this.setColour(AI_HUE);
        this.appendDummyInput()
            .appendField("AI_Face")
            .appendField("识别解析");
        this.appendDummyInput()
            .appendField("获取")
            .appendField(new Blockly.FieldDropdown([
                ["识别人名", "info_name"],
                ["置信度 %", "info_score"],
                ["脸部坐标", "info_face"],
                ["三官坐标", "info_organs"]
            ]), "key");
        this.setOutput(true);
        this.setInputsInline(true);
        this.setTooltip("人脸分辨,识别到人物名称，置信度，脸部坐标，三官(眼睛x2、鼻子、嘴巴*2)坐标");
    }
};

//---开始------------新增---20210302---------------------------------------------------