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https://github.com/bububa/facenet
face detction/recognization golang lib using tensorflow facenet
https://github.com/bububa/facenet
deep face-detection face-recognition facenet tensorflow
Last synced: about 5 hours ago
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face detction/recognization golang lib using tensorflow facenet
- Host: GitHub
- URL: https://github.com/bububa/facenet
- Owner: bububa
- License: apache-2.0
- Created: 2021-10-10T05:36:34.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2021-10-15T13:05:17.000Z (about 3 years ago)
- Last Synced: 2024-06-19T05:46:26.447Z (5 months ago)
- Topics: deep, face-detection, face-recognition, facenet, tensorflow
- Language: Go
- Homepage:
- Size: 19.5 MB
- Stars: 4
- Watchers: 1
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Golang lib for detect/recognize by tensorflow facenet
[](https://pkg.go.dev/github.com/bububa/facenet)
[](https://github.com/bububa/facenet/actions/workflows/go.yml)
[](https://github.com/bububa/facenet/actions/workflows/goreleaser.yml)
[](https://github.com/bububa/facenet)
[](https://goreportcard.com/report/github.com/bububa/facenet)
[](https://github.com/bububa/facenet/blob/master/LICENSE)
[](https://GitHub.com/bububa/facenet/releases/)
## Prerequest
1. libtensorfow 1.x
Follow the instruction [Install TensorFlow for C](https://www.tensorflow.org/install/lang_c#macos)
2. facenet tenorflow saved_model [Google Drive](https://drive.google.com/drive/folders/1SV59OmZRrYBC1n-5r52rb0H4BtoRNDZ3?usp=sharing)
3. build the executable
4. download font(optional) [Google Drive](https://drive.google.com/drive/folders/1h1ezExfKkZuHQqdAurZTvYSxQeef1I7m?usp=sharing)
```bash
# generated to ./bin/facenet
make facenet
```
## Demo
## Install
go get -u github.com/bububa/facenet
## Usage
### Train faces
```bash
./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
```bash
./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
```bash
./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
```bash
./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](https://www.libjpeg-turbo.org/) (use `-tags jpeg` to build without `CGo`)
- On Linux/RPi native Go [V4L](https://github.com/korandiz/v4l) implementation is used to capture images.
### Use Opencv4
```bash
make cvcamera
```
### On linux/Pi
```bash
# 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`
```bash
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
```golang
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
}
```