YOLO/Darknet是目前比较流行的Object Detection算法（后面统一称为Darknet），在GPU上的表现不但速度快而且准确率很高。但是使用起来不方便，只提供了命令行接口和简单的Python接口。所以我想用RESTful来实现一个云端的Darknet服务kai。
选择用Go的原因不是考虑并发，而是goroutine之间的同步能方便的处理，适合实现Pipeline的功能。问题来了，Darknet是c语言实现的，那Go必须得用cgo进行封装，才能调用c函数。目标是为了实现三个基本功能：1. 图片检测 2. 视频检测 3. 摄像头检测。为了方便使用我修改了Darknet的部分代码，然后重新定义下面几个函数：
// Set a gpu device
void set_gpu(int gpu);
// Recognize a image
void image_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename,
float thresh, float hier_thresh, char *outfile);
// Recognize a video
void video_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename,
float thresh, float hier_thresh, char *outfile);
// Recognize a camera stream
void camera_detector(char *datacfg, char *cfgfile, char *weightfile, int camindex,
float thresh, float hier_thresh, char *outpath);
有了这几个函数，就好办了，下面用cgo导入相应的库和头文件即可：
// #cgo pkg-config: opencv
// #cgo linux LDFLAGS: -ldarknet -lm -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -lcudnn
// #cgo darwin LDFLAGS: -ldarknet
// #include "yolo.h"
import "C"
// SetGPU set a gpu device you want
func SetGPU(gpu int) {
C.set_gpu(C.int(gpu))
}
// ImageDetector recognize a image
func ImageDetector(dc, cf, wf, fn string, t, ht float64, of ...string) {
...
}
// VideoDetector recognize a video
func VideoDetector(dc, cf, wf, fn string, t, ht float64, of ...string) {
...
}
// CameraDetector recognize a camera stream
func CameraDetector(dc, cf, wf string, i int, t, ht float64, of ...string) {
...
}
这样对Darknet的封装go-yolo就完成了。
下面进入主题，介绍一下kai的实现。
kai的设计目标如下：
- 后端基于Darknet（不支持训练）
- 提供RESTful接口进行图片和视频的检测
- 支持Amazon S3下载和上传
- 支持Ftp下载和上传
- 支持检测结果持久化到MongoDB
架构图是这样的
Kai
这里重点介绍一下Kai的Pipeline机制，这里的Pipeline包括下载（Download），检测（Yolo）和上（Upload）传这一系列流程。
先上个图：
Pipeline
这里的难点在于下载（Download），检测（Yolo）和上传（Upload）这三个步骤可以配置不同的Goroutine数量，而这三步之间是一个同步操作。
- 首先需要定义3个buffered channel来进行同步
// KaiServer represents the server for processing all job requests
type KaiServer struct {
net.Listener
logger *logging.Logger
config types.ServerConfig
listenAddr string
listenNetwork string
router *Router
server *http.Server
db db.Storage
// jobDownBuff is the buffered channel for job downloading
jobDownBuff chan types.Job
// jobDownBuff is the buffered channel for job todo
jobTodoBuff chan types.Job
// jobDownBuff is the buffered channel for job done
jobDoneBuff chan types.Job
}
- Pipeline的执行流程如下
// Pipeline contains downloading, processing and uploading a job
func Pipeline(logger *logging.Logger, config types.ServerConfig, dbInstance db.Storage, jobDownBuff chan types.Job,
jobTodoBuff chan types.Job, jobDoneBuff chan types.Job, job types.Job) {
logger.Infof("pipeline-job %+v", job)
// download a job
setupAndDownloadJob(logger, config.System, dbInstance, job, jobDownBuff)
// jobDownBuff -> jobTodoBuff -> jobDoneBuff
yoloJob(logger, config, dbInstance, jobDownBuff, jobTodoBuff, jobDoneBuff)
// upload a job
uploadJob(logger, dbInstance, jobDoneBuff)
}
- 下载（Download）
// setupAndDownloadJob setup and download jobs into jobDownBuff
func setupAndDownloadJob(logger *logging.Logger, config types.SystemConfig,
dbInstance db.Storage, job types.Job, jobDownBuff chan<- types.Job) {
go func() {
logger.Infof("start setup and download a job: %+v", job)
newJob, err := SetupJob(logger, job.ID, dbInstance, config)
job = *newJob
if err != nil {
logger.Error("setup-job failed", err)
return
}
downloadFunc := downloaders.GetDownloadFunc(job.Source)
if err := downloadFunc(logger, config, dbInstance, job.ID); err != nil {
logger.Error("download failed", err)
job.Status = types.JobError
job.Details = err.Error()
dbInstance.UpdateJob(job.ID, job)
return
}
jobDownBuff <- job
}()
}
- 检测（Yolo）
func yoloJob(logger *logging.Logger, config types.ServerConfig, dbInstance db.Storage,
jobDownBuff <-chan types.Job, jobTodoBuff chan types.Job, jobDoneBuff chan types.Job) {
go func() {
job, ok := <-jobDownBuff
if !ok {
logger.Info("job download buffer is closed")
return
}
logger.Infof("start a yolo job: %+v", job)
// limit the number of job in the jobTodoBuff
jobTodoBuff <- job
jobTodo, ok := <-jobTodoBuff
if !ok {
logger.Info("job todo buffer is closed")
return
}
nGpu := config.System.NGpu
t := yolo.NewTask(config.Yolo, jobTodo.Media.Cate, nGpu, jobTodo.LocalSource, jobTodo.LocalDestination)
logger.Debugf("yolo task: %+v", *t)
yolo.StartTask(t, logger, dbInstance, jobTodo.ID)
jobDoneBuff <- job
}()
}
- 上传（Upload）
func uploadJob(logger *logging.Logger, dbInstance db.Storage, jobDoneBuff <-chan types.Job) {
go func() {
jobDone, ok := <-jobDoneBuff
if !ok {
logger.Info("job done buffer is closed")
return
}
logger.Infof("start a upload job: %+v", jobDone)
uploadFunc := uploaders.GetUploadFunc(jobDone.Destination)
if err := uploadFunc(logger, dbInstance, jobDone.ID); err != nil {
logger.Error("upload failed", err)
jobDone.Status = types.JobError
jobDone.Details = err.Error()
dbInstance.UpdateJob(jobDone.ID, jobDone)
return
}
logger.Info("erasing temporary files")
if err := CleanSwap(dbInstance, jobDone.ID); err != nil {
logger.Error("erasing temporary files failed", err)
}
jobDone.Status = types.JobFinished
dbInstance.UpdateJob(jobDone.ID, jobDone)
logger.Infof("end a job: %+v", jobDone)
}()
}
到此，这个项目主要机制都已经介绍完了，如果大家有兴趣的可以去点击下面的项目主页。
项目链接：
go-yolo: https://github.com/ZanLabs/go-yolo
kai: https://github.com/ZanLabs/kai
网友评论