底库比对挂在规则库中，人脸三剑客只剩下一个人脸检测，给人脸赋特征值

panlei

2019-07-06 a6d98e51a7a4972501673900097d76aaf8751547

底库比对挂在规则库中，人脸三剑客只剩下一个人脸检测，给人脸赋特征值

 go.mod

@@ -5,7 +5,7 @@
require (
    basic.com/dbapi.git v0.0.0-20190701055817-73bca225181f
    basic.com/pubsub/cache.git v0.0.0-20190625115705-58cdde93bbe0
    basic.com/pubsub/protomsg.git v0.0.0-20190705101637-65381a182a3c
    basic.com/pubsub/protomsg.git v0.0.0-20190706093248-8cd3a0511c78
    basic.com/valib/deliver.git v0.0.0-20190531095353-25d8c3b20051
    basic.com/valib/gopherdiscovery.git v0.0.0-20190605034340-15d89d8b4e28
    github.com/Microsoft/go-winio v0.4.12 // indirect

 go.sum

@@ -2,8 +2,8 @@
basic.com/dbapi.git v0.0.0-20190701055817-73bca225181f/go.mod h1:eDXPnxaz6jZPDvBSk7ya7oSASWPCuUEgRTJCjsfKt/Q=
basic.com/pubsub/cache.git v0.0.0-20190625115705-58cdde93bbe0 h1:gbAslJB/3DATBlr4b95cs8sMFNSqRxL7KUwXYnyvp14=
basic.com/pubsub/cache.git v0.0.0-20190625115705-58cdde93bbe0/go.mod h1:gHLJZz2ee1cGL0X0ae69fs56bAxkDgEQwDhhXZJNUcY=
basic.com/pubsub/protomsg.git v0.0.0-20190705101637-65381a182a3c h1:gVWzsRxEiQ7oqx0xHDHKbk1LF1hj32Xjp9n65K5Q3mI=
basic.com/pubsub/protomsg.git v0.0.0-20190705101637-65381a182a3c/go.mod h1:un5NV5VWQoblVLZfx1Rt5vyLgwR0jI92d3VJhfrJhWU=
basic.com/pubsub/protomsg.git v0.0.0-20190706093248-8cd3a0511c78 h1:rjF01FTFUvFc5zj3PQ1MN17z+FVYZZQInhtzn+vhMSk=
basic.com/pubsub/protomsg.git v0.0.0-20190706093248-8cd3a0511c78/go.mod h1:un5NV5VWQoblVLZfx1Rt5vyLgwR0jI92d3VJhfrJhWU=
basic.com/valib/deliver.git v0.0.0-20190531095353-25d8c3b20051 h1:9flC2o3kasaM2Y6I+mY+mxmve/pyAY/UzGQZLT3lFHM=
basic.com/valib/deliver.git v0.0.0-20190531095353-25d8c3b20051/go.mod h1:bkYiTUGzckyNOjAgn9rB/DOjFzwoSHJlruuWQ6hu6IY=
basic.com/valib/gopherdiscovery.git v0.0.0-20190605034340-15d89d8b4e28 h1:3hejanzPEBvZSSvjIqayB83/6/6SLLrX9oNZAdiYELg=

 insertdata/insertDataToEs.go

@@ -150,7 +150,7 @@
                    race,
                    face.ThftRes.Smile,
                    face.ThftRes.Beauty,
                    "",
                    string(face.Feature),
                    []string{strings.Split(resp["fileUrl"].(string), "/")[1]},
                    "暂无集群",
                    localConfig.ServerId,

 main.go

@@ -187,7 +187,7 @@
                }
                logger.Info("------当前算法name",sdkinfo.SdkName,"--------------看看人脸检测中有几个目标：",len(faceParam.Faces))
                for _, info := range faceParam.Faces {
                    photoMap := ruleserver.PhotoMap{Rects: rectFormat(info.Pos.RcFace), Score: float64(info.Pos.Quality)*100, IsYolo:false,ThftRes:*(info.Result)}
                    photoMap := ruleserver.PhotoMap{Rects: rectFormat(info.Pos.RcFace), Score: float64(info.Pos.Quality)*100, IsYolo:false,ThftRes:*(info.Result),Feature:info.Feats}
                    arg.Photo = append(arg.Photo, photoMap)
                }
                args.Sdkdata = append(args.Sdkdata,&arg)
@@ -195,55 +195,55 @@
                continue
            }
        }
        if sdkinfo.Sdktype == "FaceExtract" { // 人脸提取
            arg := ruleserver.SdkData{}
            arg.TaskId = m.Tasklab.Taskid
            arg.SdkId = sdkinfo.Sdkid
            arg.SdkName = sdkinfo.SdkName
            arg.IsYolo = false
            bdata, err := util.UnCompress(m.Data)
            if err != nil {
                panic("解压缩图片时出现错误")
            }
            i := protomsg.Image{}
            err = proto.Unmarshal(bdata, &i)
            arg.ImageWidth = int(i.Width)
            arg.ImageHeight = int(i.Height)
            // 暂时写死，sdk还没有这俩算法
            arg.KeepRight = false
            arg.IsStatic = false
            if len(sdkinfo.Sdkdata) > 1 {
                faceParam := protomsg.ParamFaceFeature{}
                err = proto.Unmarshal(sdkinfo.Sdkdata, &faceParam)
                if err != nil {
                    logger.Info("解析FACE sdk数据时出现错误", err)
                    continue
                }
                for _,faceExtCom := range faceParam.ExtComp  {
                    binfos := protomsg.Binfos{}
                    err := proto.Unmarshal(faceExtCom.Comp, &binfos)
                    if err != nil {
                        logger.Info("解析人脸比对数据时出现错误", err)
                        continue
                    }
                    logger.Info("_________________________________________________________________解析出的人脸的相似的底库数据",binfos.Infos[0].CompareScore)
                }
                logger.Info("------当前算法name",sdkinfo.SdkName,"--------------看看人脸提取中有几个目标：",len(faceParam.ExtComp))
                for _, extComp := range faceParam.ExtComp {
                    baseinfos := protomsg.Binfos{}
                    err1 := proto.Unmarshal(extComp.Comp, &baseinfos)
                    if err1 != nil {
                        logger.Info("解析FACE sdk数据时出现错误", err)
                        continue
                    }
                    photoMap := ruleserver.PhotoMap{Rects: rectFormat(extComp.Pos.Pos.RcFace), Score: float64(extComp.Pos.Pos.Quality)*100, IsYolo:false,ThftRes:*(extComp.Pos.Result),Liker:baseinfos.Infos}
                    arg.Photo = append(arg.Photo, photoMap)
                }
                args.Sdkdata = append(args.Sdkdata,&arg)
            } else {
                continue
            }
        }
        //if sdkinfo.Sdktype == "FaceExtract" { // 人脸提取
        //    arg := ruleserver.SdkData{}
        //    arg.TaskId = m.Tasklab.Taskid
        //    arg.SdkId = sdkinfo.Sdkid
        //    arg.SdkName = sdkinfo.SdkName
        //    arg.IsYolo = false
        //    bdata, err := util.UnCompress(m.Data)
        //    if err != nil {
        //        panic("解压缩图片时出现错误")
        //    }
        //    i := protomsg.Image{}
        //    err = proto.Unmarshal(bdata, &i)
        //    arg.ImageWidth = int(i.Width)
        //    arg.ImageHeight = int(i.Height)
        //    // 暂时写死，sdk还没有这俩算法
        //    arg.KeepRight = false
        //    arg.IsStatic = false
        //    if len(sdkinfo.Sdkdata) > 1 {
        //        faceParam := protomsg.ParamFaceFeature{}
        //        err = proto.Unmarshal(sdkinfo.Sdkdata, &faceParam)
        //        if err != nil {
        //            logger.Info("解析FACE sdk数据时出现错误", err)
        //            continue
        //        }
        //        for _,faceExtCom := range faceParam.ExtComp  {
        //            binfos := protomsg.Binfos{}
        //            err := proto.Unmarshal(faceExtCom.Comp, &binfos)
        //            if err != nil {
        //                logger.Info("解析人脸比对数据时出现错误", err)
        //                continue
        //            }
        //            logger.Info("_________________________________________________________________解析出的人脸的相似的底库数据",binfos.Infos[0].CompareScore)
        //        }
        //        logger.Info("------当前算法name",sdkinfo.SdkName,"--------------看看人脸提取中有几个目标：",len(faceParam.ExtComp))
        //        for _, extComp := range faceParam.ExtComp {
        //            baseinfos := protomsg.Binfos{}
        //            err1 := proto.Unmarshal(extComp.Comp, &baseinfos)
        //            if err1 != nil {
        //                logger.Info("解析FACE sdk数据时出现错误", err)
        //                continue
        //            }
        //            photoMap := ruleserver.PhotoMap{Rects: rectFormat(extComp.Pos.Pos.RcFace), Score: float64(extComp.Pos.Pos.Quality)*100, IsYolo:false,ThftRes:*(extComp.Pos.Result),Liker:baseinfos.Infos}
        //            arg.Photo = append(arg.Photo, photoMap)
        //        }
        //        args.Sdkdata = append(args.Sdkdata,&arg)
        //    } else {
        //        continue
        //    }
        //}

    }
    return m

 ruleserver/ruleToformula.go

@@ -2,6 +2,7 @@

import (
    "encoding/json"
    "github.com/gogo/protobuf/proto"
    "ruleprocess/cache"
    "ruleprocess/logger"
    "sort"
@@ -9,6 +10,7 @@
    "strings"
    "time"

     bigCache "basic.com/pubsub/cache.git"
    "basic.com/pubsub/protomsg.git"
    "github.com/knetic/govaluate"
)
@@ -62,6 +64,7 @@
    IsYolo     bool    // 是否是yolo数据
    Location   Rect    // 记下每个目标的位置参数，最后给结果装配人脸数据的时候用的到
    SdkName    string
    Feature       []byte
    ThftRes    protomsg.ThftResult
    Liker      []*protomsg.Baseinfo
}
@@ -97,7 +100,7 @@
    IsYolo  bool    // 是否是yolo数据
    SdkName string
    ThftRes protomsg.ThftResult
    Liker   []*protomsg.Baseinfo // 如果是人脸的话尤其是比对，应存下他跟底库的人员的相似情况 yolo的话给nil就行
    Feature []byte
}

// 每个算法对于当前帧画面自己提取的数据
@@ -227,6 +230,42 @@
        }
    }
}
func Compare(args *SdkDatas, groupRule *protomsg.GroupRule) {
    compareFlag := false
    // 看看是否有只配人脸比对算法但没有配对比库的规则，如果有，则比对对象为全部底库
    for j := 0; j < len(groupRule.Rules); j++ {
        if groupRule.Rules[j].SdkId == "812b674b-2375-4589-919a-5c1c3278a972"{
            if groupRule.Rules[j].SdkArgAlias == "对比底库" {
                compareFlag = true
            }
        }
    }
    if compareFlag {
        logger.Info("配置了对比底库参数")
    }else {
        logger.Info("没有配置对比底库参数")
    }
    for j := 0; j < len(groupRule.Rules); j++ {
        if groupRule.Rules[j].SdkId == "812b674b-2375-4589-919a-5c1c3278a972" && groupRule.Rules[j].SdkArgAlias == "对比底库"{
            for _, sdkData := range args.Sdkdata {
                if sdkData.SdkId == "812b674b-2375-4589-919a-5c1c3278a97e" { // 搜索到人脸检测的sdkdata 现在关于人脸的只有他一个sdk，全找他
                    for _, areaMap := range sdkData.AreaMapList {
                        // 拿区域中每个人脸特征值去对比，填充其liker
                        if groupRule.Rules[j].PolygonId == areaMap.areaId {
                            for _,arg := range areaMap.args {
                                bytes := bigCache.Getdbpersonmsg(string(arg.Feature),true)
                                binfos := protomsg.Binfos{}
                                proto.Unmarshal(bytes,&binfos)
                                arg.Liker = binfos.Infos
                                logger.Info("-------------------成功给liker赋值：",arg.Liker)
                            }
                        }
                    }
                }
            }
        }
    }
}
func RunRule(args *SdkDatas, groupRule *protomsg.GroupRule, taskId string) bool {
    defer func() {
        if err := recover(); err != nil {
@@ -234,6 +273,7 @@
        }
    }()
    logger.Info("+++++++++++规则开始运行+++++++++++++++++当前规则--：", *groupRule)
    Compare(args,groupRule)
    resultSplice := []*LittleRuleResult{}
    // 先过完条件规则
    for j := 0; j < len(groupRule.Rules); j++ {
@@ -330,7 +370,7 @@
            // 配了人脸的算法才把人脸的数据甩出来打标签
            flag11 := false
            for j := 0; j < len(groupRule.Rules); j++ {
                if groupRule.Rules[j].SdkId == "812b674b-2375-4589-919a-5c1c3278a97e" || groupRule.Rules[j].SdkId == "812b674b-2375-4589-919a-5c1c3278a971" {
                if groupRule.Rules[j].SdkId == "812b674b-2375-4589-919a-5c1c3278a97e" || groupRule.Rules[j].SdkId == "812b674b-2375-4589-919a-5c1c3278a972" {
                    flag11 = true
                }
            }
@@ -396,7 +436,7 @@

func putFaceToResult(rule *protomsg.Rule, am *AreaMap) []Arg {
    faces := []Arg{}
    if rule.SdkId == am.sdkId && rule.SdkId == "812b674b-2375-4589-919a-5c1c3278a97e" || rule.SdkId == "812b674b-2375-4589-919a-5c1c3278a971" {
    if rule.SdkId == am.sdkId && rule.SdkId == "812b674b-2375-4589-919a-5c1c3278a97e" || rule.SdkId == "812b674b-2375-4589-919a-5c1c3278a972" {
        if len(am.filterData) > 0 {
            for _, data := range am.filterData {
                faces = append(faces, data)
@@ -409,7 +449,7 @@

func putYolosToResult(rule *protomsg.Rule, am *AreaMap) []Rect {
    locations := []Rect{}
    if rule.SdkId == am.sdkId && rule.SdkId != "812b674b-2375-4589-919a-5c1c3278a97e" && rule.SdkId != "812b674b-2375-4589-919a-5c1c3278a971" {
    if rule.SdkId == am.sdkId && rule.SdkId != "812b674b-2375-4589-919a-5c1c3278a97e" && rule.SdkId != "812b674b-2375-4589-919a-5c1c3278a972" {
        if len(am.filterData) > 0 {
            for _, data := range am.filterData {
                locations = append(locations, data.Location)
@@ -442,7 +482,7 @@
        if threshold <= obj.Score && size <= float64(obj.Rects.Width*obj.Rects.Height) && intersectionper <= PgsInterPercent(areaPoints, obj.Rects, widthScale, heigthScale) {
            // 这步要备齐表达式里所需要的所有参数
            a.targetNum++
            arg1 := Arg{obj.Score, PgsInterPercent(areaPoints, obj.Rects, widthScale, heigthScale), float64(obj.Rects.Width * obj.Rects.Height), obj.IsYolo, obj.Rects, obj.SdkName, obj.ThftRes, obj.Liker}
            arg1 := Arg{obj.Score, PgsInterPercent(areaPoints, obj.Rects, widthScale, heigthScale), float64(obj.Rects.Width * obj.Rects.Height), obj.IsYolo, obj.Rects, obj.SdkName, obj.Feature,obj.ThftRes,[]*protomsg.Baseinfo{}}
            //logger.Println("放进去的arg：-------", arg1)
            a.args = append(a.args, arg1)
            a.filterData = append(a.filterData, arg1)
@@ -592,14 +632,14 @@
func filterRule(rule *protomsg.Rule, am *AreaMap) LittleRuleResult {
    if rule.SdkId == "812b674b-2375-4589-919a-5c1c3278a97e" || rule.SdkId == "812b674b-2375-4589-919a-5c1c3278a972" {
        // 处理的是人脸算法 如果这条规则配置的是人脸算法，过滤完条件之后直接得出结果，因为肯定没有数量条件，自己拼接
        logger.Info("规则的算法id和区域的算法id：",rule.SdkId,"===",am.sdkId)
        logger.Error("规则的算法id和区域的算法id：",rule.SdkId,"===",am.sdkId)
        if rule.SdkId == am.sdkId && rule.PolygonId == am.areaId { // 算法和区域都得对的上

            if rule.SdkId == "812b674b-2375-4589-919a-5c1c3278a972" && rule.SdkArgAlias != "time_rule"{
                // 只需要过滤阈值，过滤完后数组长度大于0即为报警，但要考虑如何对每一张都报警呢
                argValue, err := strconv.ParseFloat(rule.SdkArgValue, 64)
                if err != nil {
                    logger.Info("规则配置的阈值非法")
                    logger.Error("规则配置的阈值非法")
                    return LittleRuleResult{am.sdkName, rule.RuleWithPre + "" + "false", rule.Sort}
                }
                flag := "false"