/
faces_cluster.go
90 lines (72 loc) Β· 2.81 KB
/
faces_cluster.go
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package photoprism
import (
"fmt"
"github.com/dustin/go-humanize/english"
"github.com/photoprism/photoprism/internal/entity"
"github.com/photoprism/photoprism/internal/face"
"github.com/photoprism/photoprism/internal/query"
"github.com/photoprism/photoprism/pkg/clusters"
)
// Cluster clusters indexed face embeddings.
func (w *Faces) Cluster(opt FacesOptions) (added entity.Faces, err error) {
if w.Disabled() {
return added, fmt.Errorf("face recognition is disabled")
}
// Skip clustering if index contains no new face markers, and force option isn't set.
if opt.Force {
log.Infof("faces: enforced clustering")
} else if n := query.CountNewFaceMarkers(face.ClusterSizeThreshold, face.ClusterScoreThreshold); n < opt.SampleThreshold() {
log.Debugf("faces: skipped clustering")
return added, nil
}
// Fetch unclustered face embeddings.
embeddings, err := query.Embeddings(false, true, face.ClusterSizeThreshold, face.ClusterScoreThreshold)
log.Debugf("faces: found %s", english.Plural(len(embeddings), "unclustered sample", "unclustered samples"))
// Anything that keeps us from doing this?
if err != nil {
return added, err
} else if samples := len(embeddings); samples < opt.SampleThreshold() {
log.Debugf("faces: at least %d samples needed for clustering", opt.SampleThreshold())
return added, nil
} else {
var c clusters.HardClusterer
// See https://dl.photoprism.app/research/ for research on face clustering algorithms.
if c, err = clusters.DBSCAN(face.ClusterCore, face.ClusterDist, w.conf.Workers(), clusters.EuclideanDist); err != nil {
return added, err
} else if err = c.Learn(embeddings.Float64()); err != nil {
return added, err
}
sizes := c.Sizes()
if len(sizes) > 0 {
log.Infof("faces: found %s", english.Plural(len(sizes), "new cluster", "new clusters"))
} else {
log.Debugf("faces: found no new clusters")
}
results := make([]face.Embeddings, len(sizes))
for i := range sizes {
results[i] = face.Embeddings{}
}
guesses := c.Guesses()
for i, n := range guesses {
if n < 1 {
continue
}
results[n-1] = append(results[n-1], embeddings[i])
}
for _, cluster := range results {
if f := entity.NewFace("", entity.SrcAuto, cluster); f == nil {
log.Errorf("faces: face should not be nil - you may have found a bug")
} else if f.SkipMatching() {
log.Infof("faces: skipped cluster %s, embedding not distinct enough", f.ID)
} else if err := f.Create(); err == nil {
added = append(added, *f)
log.Debugf("faces: added cluster %s based on %s, radius %f", f.ID, english.Plural(f.Samples, "sample", "samples"), f.SampleRadius)
} else if err := f.Updates(entity.Map{"UpdatedAt": entity.TimeStamp()}); err != nil {
log.Errorf("faces: %s", err)
} else {
log.Debugf("faces: updated cluster %s", f.ID)
}
}
}
return added, nil
}