facenet

README

Golang lib for detect/recognize by tensorflow facenet

Prerequest

1. libtensorfow 1.x Follow the instruction Install TensorFlow for C
2. facenet tenorflow saved_model Google Drive
3. build the executable
4. download font(optional) Google Drive

# generated to ./bin/facenet
make facenet

Demo

Install

go get -u github.com/bububa/facenet

Usage

Train faces

./bin/facenet -model=./models/facenet -db=./models/people.db -train={image folder for training} -output={fold path for output thumbs(optional)}

the train folder include folders which name is the label with images inside

Update distinct labels

./bin/facenet -model=./models/facenet -db=./models/people.db -update={labels for update seperated by comma} -output={fold path for output thumbs(optional)}

Delete distinct labels from people model

./bin/facenet -model=./models/facenet -db=./models/people.db -delete={labels for delete seperated by comma} -output={fold path for output thumbs(optional)}

Detect faces for image

./bin/facenet -model=./models/facenet -db=./models/people.db -detect={the image file path for detecting} -font={font folder for output image(optional)} -output={fold path for output thumbs(optional)}

Camera & Server

Requirements

• libjpeg-turbo (use -tags jpeg to build without CGo)
• On Linux/RPi native Go V4L implementation is used to capture images.

Use Opencv4

make cvcamera

On linux/Pi

# use native Go V4L implementation is used to capture images
make linux_camera

Use image/jpeg instead of libjpeg-turbo

use jpeg build tag to build with native Go image/jpeg instead of libjpeg-turbo

go build -o=./bin/cvcamera -tags=cv4,jpeg ./cmd/camera

Usage as Server

Usage of camera:
  -bind string
	Bind address (default ":56000")
  -delay int
	Delay between frames, in milliseconds (default 10)
  -width float
	Frame width (default 640)
  -height float
	Frame height (default 480)
  -index int
	Camera index
  -model string
    saved_mode path
  -db string
    classifier db

User as lib

import (
    "log"

	"github.com/llgcode/draw2d"

    "github.com/bububa/facenet"
)

func main() {
    estimator, err := facenet.New(
        facenet.WithModel("./models/facenet"),
        facenet.WithDB("./models/people.db"),
        facenet.WithFontPath("./font"),
    )
    if err != nil {
       log.Fatalln(err)
    }
	err = estimator.SetFont(&draw2d.FontData{
		Name: "NotoSansCJKsc",
		//Name:   "Roboto",
		Family: draw2d.FontFamilySans,
		Style:  draw2d.FontStyleNormal,
	}, 9)
	if err != nil {
		log.Fatalln(err)
	}

    // Delete labels
    {
        labels := []string{"xxx", "yyy"}
        for _, label := range labels {
            if deleted := estimator.DeletePerson(label); deleted {
                log.Printf("[INFO] person: %s deleted\n", label)
                continue
            }
            log.Printf("[WRN] person: %s not found\n", label)
        }
        err := estimator.SaveDB("./models/people.db")
        if err != nil {
            log.Fatalln(err)
        }
    }

    // Detect faces
    {
        img, _ := loadImage(imgPath)
        minSize := 20
		markers, err := instance.DetectFaces(img, minSize)
		if err != nil {
			log.Fatalln(err)
		}
		for _, marker := range markers.Markers() {
			if marker.Error() != nil {
				log.Printf("label: %s, %v\n", marker.Label(), marker.Error())
			} else {
				log.Printf("label: %s, distance:%f\n", marker.Label(), marker.Distance())
			}
		}
		if outputPath != "" {
            txtColor := "#FFF"
            successColor := "#4CAF50"
            failedColor := "#F44336"
            strokeWidth := 2
            successMarkerOnly := false
			markerImg := estimator.DrawMarkers(markers, txtColor, successColor, failedColor, 2, successMarkerOnly)
			if err := saveImage(markerImg, outputPath); err != nil {
				log.Fatalln(err)
			}
		}
    }

    // Training
    // check cmd/facenet
}

Documentation

Overview

Package facenet for face detection/recognization by pigo and facenet

Index

• Constants
• type Estimator
  • func New(opts ...Option) (*Estimator, error)
  • func (ins *Estimator) AddPerson(items ...*core.Person)
  • func (ins *Estimator) AddPersonSafe(items ...*core.Person)
  • func (ins *Estimator) BatchTrain(split float64, iterations int, verbosity int, batch int)
  • func (ins *Estimator) BatchTrainSafe(split float64, iterations int, verbosity int, batch int)
  • func (ins *Estimator) DeletePerson(name string) bool
  • func (ins *Estimator) DeletePersonSafe(name string) bool
  • func (ins *Estimator) DetectFaces(img image.Image, minSize int) (*core.FaceMarkers, error)
  • func (ins *Estimator) DetectFacesSafe(img image.Image, minSize int) (*core.FaceMarkers, error)
  • func (ins *Estimator) DrawMarkers(markers *core.FaceMarkers, txtColor string, successColor string, ...) image.Image
  • func (ins *Estimator) ExtractFace(person *core.Person, img image.Image, minSize int) (*core.FaceMarker, error)
  • func (ins *Estimator) ExtractFaceSafe(person *core.Person, img image.Image, minSize int) (*core.FaceMarker, error)
  • func (ins *Estimator) LoadDB(fname string) error
  • func (ins *Estimator) Match(embedding []float32) (*core.Person, float64, error)
  • func (ins *Estimator) MatchSafe(embedding []float32) (*core.Person, float64, error)
  • func (ins *Estimator) People() *core.People
  • func (ins *Estimator) PeopleSafe() *core.People
  • func (ins *Estimator) Predict(embedding []float32) ([]*core.Person, []float64, error)
  • func (ins *Estimator) PredictSafe(embedding []float32) ([]*core.Person, []float64, error)
  • func (ins *Estimator) SaveDB(fname string) error
  • func (ins *Estimator) SetDB(db *Storage)
  • func (ins *Estimator) SetFont(data *draw2d.FontData, size float64) error
  • func (ins *Estimator) SetFontCache(cache draw2d.FontCache) error
  • func (ins *Estimator) SetFontPath(cachePath string) error
  • func (ins *Estimator) SetFontSize(size float64)
  • func (ins *Estimator) SetModel(net *core.Net)
  • func (ins *Estimator) Train(split float64, iterations int, verbosity int)
  • func (ins *Estimator) TrainSafe(split float64, iterations int, verbosity int)
• type Option
  • func WithDB(dbPath string) Option
  • func WithFontPath(fontPath string) Option
  • func WithModel(modelPath string) Option
• type Storage
  • func NewStorage(people *core.People, classifier classifier.Classifier) *Storage
  • func (s *Storage) Add(items ...*core.Person)
  • func (s *Storage) BatchTrain(split float64, iterations int, verbosity int, batch int)
  • func (s *Storage) Delete(name string) bool
  • func (s *Storage) Load(fname string) error
  • func (s *Storage) Match(input []float32) (*core.Person, float64, error)
  • func (s *Storage) People() *core.People
  • func (s *Storage) Predict(input []float32) ([]*core.Person, []float64, error)
  • func (s *Storage) Save(fname string) error
  • func (s *Storage) SetClassifier(c classifier.Classifier)
  • func (s *Storage) Train(split float64, iterations int, verbosity int)

Constants

const (
	// PeopleFilename represents people data filename in zip
	PeopleFilename = "people.pb"
	// ClassifierFilename represents classifier data filename in zip
	ClassifierFilename = "classifier.model"
)

Types

type Estimator

type Estimator struct {
	// contains filtered or unexported fields
}

Estimator represents facenet estimator

func New

func New(opts ...Option) (*Estimator, error)

New init a new facenet Estimator

func (*Estimator) AddPerson

func (ins *Estimator) AddPerson(items ...*core.Person)

AddPerson add person to people

func (*Estimator) AddPersonSafe

func (ins *Estimator) AddPersonSafe(items ...*core.Person)

AddPersonSafe add person to people (multithread safe)

func (*Estimator) BatchTrain

func (ins *Estimator) BatchTrain(split float64, iterations int, verbosity int, batch int)

BatchTrain for trainging classifier

func (*Estimator) BatchTrainSafe

func (ins *Estimator) BatchTrainSafe(split float64, iterations int, verbosity int, batch int)

BatchTrainSafe for trainging classifier (multithread safe)

func (*Estimator) DeletePerson

func (ins *Estimator) DeletePerson(name string) bool

DeletePerson delete a person by name

func (*Estimator) DeletePersonSafe

func (ins *Estimator) DeletePersonSafe(name string) bool

DeletePersonSafe delete a person by name (multithread safe)

func (*Estimator) DetectFaces

func (ins *Estimator) DetectFaces(img image.Image, minSize int) (*core.FaceMarkers, error)

DetectFaces detect face markers from image

func (*Estimator) DetectFacesSafe

func (ins *Estimator) DetectFacesSafe(img image.Image, minSize int) (*core.FaceMarkers, error)

DetectFacesSafe detect face markers from image (multithread safe)

func (*Estimator) DrawMarkers

func (ins *Estimator) DrawMarkers(markers *core.FaceMarkers, txtColor string, successColor string, failedColor string, strokeWidth float64, succeedOnly bool) image.Image

DrawMarkers draw face markers on image

func (*Estimator) ExtractFace

func (ins *Estimator) ExtractFace(person *core.Person, img image.Image, minSize int) (*core.FaceMarker, error)

ExtractFace extract face for a person from image

func (*Estimator) ExtractFaceSafe

func (ins *Estimator) ExtractFaceSafe(person *core.Person, img image.Image, minSize int) (*core.FaceMarker, error)

ExtractFaceSafe extract face for a person from image (multithread safe)

func (*Estimator) LoadDB

func (ins *Estimator) LoadDB(fname string) error

LoadDB load db file

func (*Estimator) Match

func (ins *Estimator) Match(embedding []float32) (*core.Person, float64, error)

Match match a person with embedding

func (*Estimator) MatchSafe

func (ins *Estimator) MatchSafe(embedding []float32) (*core.Person, float64, error)

MatchSafe match a person with embedding (multithread safe)

func (*Estimator) People

func (ins *Estimator) People() *core.People

People get people

func (*Estimator) PeopleSafe

func (ins *Estimator) PeopleSafe() *core.People

PeopleSafe get people (mutlthread safe)

func (*Estimator) Predict

func (ins *Estimator) Predict(embedding []float32) ([]*core.Person, []float64, error)

Predict returns embedding predicted results

func (*Estimator) PredictSafe

func (ins *Estimator) PredictSafe(embedding []float32) ([]*core.Person, []float64, error)

PredictSafe returns embedding predicted results (multithread safe)

func (*Estimator) SaveDB

func (ins *Estimator) SaveDB(fname string) error

SaveDB save db file

func (*Estimator) SetDB

func (ins *Estimator) SetDB(db *Storage)

SetDB set db

func (*Estimator) SetFont

func (ins *Estimator) SetFont(data *draw2d.FontData, size float64) error

SetFont set font

func (*Estimator) SetFontCache

func (ins *Estimator) SetFontCache(cache draw2d.FontCache) error

SetFontCache set font cache

func (*Estimator) SetFontPath

func (ins *Estimator) SetFontPath(cachePath string) error

SetFontPath set font cache with font cache path

func (*Estimator) SetFontSize

func (ins *Estimator) SetFontSize(size float64)

SetFontSize set font size

func (*Estimator) SetModel

func (ins *Estimator) SetModel(net *core.Net)

SetModel set face net model

func (*Estimator) Train

func (ins *Estimator) Train(split float64, iterations int, verbosity int)

Train for trainging classifier

func (*Estimator) TrainSafe

func (ins *Estimator) TrainSafe(split float64, iterations int, verbosity int)

TrainSafe for trainging classifier (multithread safe)

type Option

type Option interface {
	// contains filtered or unexported methods
}

Option face instance option interface

func WithDB

func WithDB(dbPath string) Option

WithDB set db with dbpath

func WithFontPath

func WithFontPath(fontPath string) Option

WithFontPath set font with font path

func WithModel

func WithModel(modelPath string) Option

WithModel set net model with model path

type Storage

type Storage struct {
	// contains filtered or unexported fields
}

Storage represents db storage

func NewStorage

func NewStorage(people *core.People, classifier classifier.Classifier) *Storage

NewStorage returns new Storage

func (*Storage) Add

func (s *Storage) Add(items ...*core.Person)

Add add person to people

func (*Storage) BatchTrain

func (s *Storage) BatchTrain(split float64, iterations int, verbosity int, batch int)

BatchTrain for trainging classifier

func (*Storage) Delete

func (s *Storage) Delete(name string) bool

Delete delete a person by name

func (*Storage) Load

func (s *Storage) Load(fname string) error

Load load storage from file

func (*Storage) Match

func (s *Storage) Match(input []float32) (*core.Person, float64, error)

Match returns best match result

func (*Storage) People

func (s *Storage) People() *core.People

People returns people

func (*Storage) Predict

func (s *Storage) Predict(input []float32) ([]*core.Person, []float64, error)

Predict returns predictation results

func (*Storage) Save

func (s *Storage) Save(fname string) error

Save save storage to file

func (*Storage) SetClassifier

func (s *Storage) SetClassifier(c classifier.Classifier)

SetClassifier set classifier

func (*Storage) Train

func (s *Storage) Train(split float64, iterations int, verbosity int)

Train for trainging classifier