photoprism/internal/face/detector.go

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package face
import (
_ "embed"
"fmt"
pigo "github.com/esimov/pigo/core"
"github.com/photoprism/photoprism/pkg/fs"
"github.com/photoprism/photoprism/pkg/txt"
_ "image/jpeg"
"io"
"os"
"path/filepath"
"runtime/debug"
)
//go:embed cascade/facefinder
var cascadeFile []byte
//go:embed cascade/puploc
var puplocFile []byte
var (
classifier *pigo.Pigo
plc *pigo.PuplocCascade
flpcs map[string][]*FlpCascade
)
func init() {
var err error
p := pigo.NewPigo()
// Unpack the binary file. This will return the number of cascade trees,
// the tree depth, the threshold and the prediction from tree's leaf nodes.
classifier, err = p.Unpack(cascadeFile)
if err != nil {
log.Errorf("face: %s", err)
}
pl := pigo.NewPuplocCascade()
plc, err = pl.UnpackCascade(puplocFile)
if err != nil {
log.Errorf("face: %s", err)
}
flpcs, err = ReadCascadeDir(pl, "cascade/lps")
if err != nil {
log.Errorf("face: %s", err)
}
}
var (
eyeCascades = []string{"lp46", "lp44", "lp42", "lp38", "lp312"}
mouthCascades = []string{"lp93", "lp84", "lp82", "lp81"}
)
// Detector struct contains Pigo face detector general settings.
type Detector struct {
minSize int
maxSize int
angle float64
shiftFactor float64
scaleFactor float64
iouThreshold float64
scoreThreshold float32
perturb int
}
// Detect runs the detection algorithm over the provided source image.
func Detect(fileName string) (faces Faces, err error) {
defer func() {
if r := recover(); r != nil {
log.Errorf("face: %s (panic)\nstack: %s", r, debug.Stack())
}
}()
fd := &Detector{
minSize: 20,
maxSize: 1000,
angle: 0.0,
shiftFactor: 0.1,
scaleFactor: 1.1,
iouThreshold: 0.2,
scoreThreshold: 10.0,
perturb: 63,
}
if !fs.FileExists(fileName) {
return faces, fmt.Errorf("face: file '%s' not found", txt.Quote(filepath.Base(fileName)))
}
log.Debugf("face: detecting faces in %s", txt.Quote(filepath.Base(fileName)))
det, params, err := fd.Detect(fileName)
if err != nil {
return faces, fmt.Errorf("face: %v (detect faces)", err)
}
if det == nil {
return faces, fmt.Errorf("face: no result")
}
faces, err = fd.Faces(det, params)
if err != nil {
return faces, fmt.Errorf("face: %s (faces)", err)
}
return faces, nil
}
// Detect runs the detection algorithm over the provided source image.
func (fd *Detector) Detect(fileName string) (faces []pigo.Detection, params pigo.CascadeParams, err error) {
var srcFile io.Reader
file, err := os.Open(fileName)
if err != nil {
return faces, params, err
}
defer file.Close()
srcFile = file
src, err := pigo.DecodeImage(srcFile)
if err != nil {
return faces, params, err
}
pixels := pigo.RgbToGrayscale(src)
cols, rows := src.Bounds().Max.X, src.Bounds().Max.Y
imageParams := &pigo.ImageParams{
Pixels: pixels,
Rows: rows,
Cols: cols,
Dim: cols,
}
params = pigo.CascadeParams{
MinSize: fd.minSize,
MaxSize: fd.maxSize,
ShiftFactor: fd.shiftFactor,
ScaleFactor: fd.scaleFactor,
ImageParams: *imageParams,
}
// Run the classifier over the obtained leaf nodes and return the Face results.
// The result contains quadruplets representing the row, column, scale and Face score.
faces = classifier.RunCascade(params, fd.angle)
// Calculate the intersection over union (IoU) of two clusters.
faces = classifier.ClusterDetections(faces, fd.iouThreshold)
return faces, params, nil
}
// Faces adds landmark coordinates to detected faces and returns the results.
func (fd *Detector) Faces(det []pigo.Detection, params pigo.CascadeParams) (Faces, error) {
var (
results Faces
eyesCoords []Point
landmarkCoords []Point
puploc *pigo.Puploc
)
for _, face := range det {
if face.Q < fd.scoreThreshold {
continue
}
faceCoord := NewPoint(
"face",
face.Row-face.Scale/2,
face.Col-face.Scale/2,
face.Scale,
)
if face.Scale > 50 {
// Find left eye.
puploc = &pigo.Puploc{
Row: face.Row - int(0.075*float32(face.Scale)),
Col: face.Col - int(0.175*float32(face.Scale)),
Scale: float32(face.Scale) * 0.25,
Perturbs: fd.perturb,
}
leftEye := plc.RunDetector(*puploc, params.ImageParams, fd.angle, false)
if leftEye.Row > 0 && leftEye.Col > 0 {
eyesCoords = append(eyesCoords, NewPoint(
"eye_l",
leftEye.Row,
leftEye.Col,
int(leftEye.Scale),
))
}
// Find right eye.
puploc = &pigo.Puploc{
Row: face.Row - int(0.075*float32(face.Scale)),
Col: face.Col + int(0.185*float32(face.Scale)),
Scale: float32(face.Scale) * 0.25,
Perturbs: fd.perturb,
}
rightEye := plc.RunDetector(*puploc, params.ImageParams, fd.angle, false)
if rightEye.Row > 0 && rightEye.Col > 0 {
eyesCoords = append(eyesCoords, NewPoint(
"eye_r",
rightEye.Row,
rightEye.Col,
int(rightEye.Scale),
))
}
if leftEye != nil && rightEye != nil {
for _, eye := range eyeCascades {
for _, flpc := range flpcs[eye] {
if flpc == nil {
continue
}
flp := flpc.GetLandmarkPoint(leftEye, rightEye, params.ImageParams, fd.perturb, false)
if flp.Row > 0 && flp.Col > 0 {
landmarkCoords = append(landmarkCoords, NewPoint(
eye,
flp.Row,
flp.Col,
int(flp.Scale),
))
}
flp = flpc.GetLandmarkPoint(leftEye, rightEye, params.ImageParams, fd.perturb, true)
if flp.Row > 0 && flp.Col > 0 {
landmarkCoords = append(landmarkCoords, NewPoint(
eye+"_v",
flp.Row,
flp.Col,
int(flp.Scale),
))
}
}
}
}
// Find mouth.
for _, mouth := range mouthCascades {
for _, flpc := range flpcs[mouth] {
if flpc == nil {
continue
}
flp := flpc.GetLandmarkPoint(leftEye, rightEye, params.ImageParams, fd.perturb, false)
if flp.Row > 0 && flp.Col > 0 {
landmarkCoords = append(landmarkCoords, NewPoint(
"mouth_"+mouth,
flp.Row,
flp.Col,
int(flp.Scale),
))
}
}
}
flpc := flpcs["lp84"][0]
if flpc != nil {
flp := flpc.GetLandmarkPoint(leftEye, rightEye, params.ImageParams, fd.perturb, true)
if flp.Row > 0 && flp.Col > 0 {
landmarkCoords = append(landmarkCoords, NewPoint(
"lp84",
flp.Row,
flp.Col,
int(flp.Scale),
))
}
}
}
results = append(results, Face{
Rows: params.ImageParams.Rows,
Cols: params.ImageParams.Cols,
Score: int(face.Q),
Face: faceCoord,
Eyes: eyesCoords,
Landmarks: landmarkCoords,
})
}
return results, nil
}