package face import ( "encoding/json" "fmt" "strings" "github.com/montanaflynn/stats" "github.com/photoprism/photoprism/pkg/clusters" ) // Embeddings represents a face embedding cluster. type Embeddings []Embedding // NewEmbeddings creates a new embeddings from inference results. func NewEmbeddings(inference [][]float32) Embeddings { result := make(Embeddings, len(inference)) var v []float32 var i int for i, v = range inference { e := NewEmbedding(v) if e.CanMatch() { result[i] = e } } return result } // Empty tests if embeddings are empty. func (embeddings Embeddings) Empty() bool { if len(embeddings) < 1 { return true } return len(embeddings[0]) < 1 } // Count returns the number of embeddings. func (embeddings Embeddings) Count() int { if embeddings.Empty() { return 0 } return len(embeddings) } // Kind returns the type of face e.g. regular, kids, or ignored. func (embeddings Embeddings) Kind() (result Kind) { for _, e := range embeddings { if k := e.Kind(); k > result { result = k } } return result } // One tests if there is exactly one embedding. func (embeddings Embeddings) One() bool { return embeddings.Count() == 1 } // First returns the first face embedding. func (embeddings Embeddings) First() Embedding { if embeddings.Empty() { return NullEmbedding } return embeddings[0] } // Float64 returns embeddings as a float64 slice. func (embeddings Embeddings) Float64() [][]float64 { result := make([][]float64, len(embeddings)) for i, e := range embeddings { result[i] = e } return result } // Contains tests if another embeddings is contained within a radius. func (embeddings Embeddings) Contains(other Embedding, radius float64) bool { for _, e := range embeddings { if d := e.Dist(other); d < radius { return true } } return false } // Dist returns the minimum distance to an embedding. func (embeddings Embeddings) Dist(other Embedding) (dist float64) { dist = -1 for _, e := range embeddings { if d := e.Dist(other); d < dist || dist < 0 { dist = d } } return dist } // JSON returns the embeddings as JSON bytes. func (embeddings Embeddings) JSON() []byte { var noResult = []byte("") if embeddings.Empty() { return noResult } if result, err := json.Marshal(embeddings); err != nil { return noResult } else { return result } } // EmbeddingsMidpoint returns the embeddings vector midpoint. func EmbeddingsMidpoint(embeddings Embeddings) (result Embedding, radius float64, count int) { // Return if there are no embeddings. if embeddings.Empty() { return Embedding{}, 0, 0 } // Count embeddings. count = len(embeddings) // Only one embedding? if count == 1 { // Return embedding if there is only one. return embeddings[0], 0.0, 1 } dim := len(embeddings[0]) // No embedding values? if dim == 0 { return Embedding{}, 0.0, count } result = make(Embedding, dim) // The mean of a set of vectors is calculated component-wise. for i := 0; i < dim; i++ { values := make(stats.Float64Data, count) for j := 0; j < count; j++ { values[j] = embeddings[j][i] } if m, err := stats.Mean(values); err != nil { log.Warnf("embeddings: %s", err) } else { result[i] = m } } // Radius is the max embedding distance + 0.01 from result. for _, emb := range embeddings { if d := clusters.EuclideanDist(result, emb); d > radius { radius = d + 0.01 } } return result, radius, count } // UnmarshalEmbeddings parses face embedding JSON. func UnmarshalEmbeddings(s string) (result Embeddings, err error) { if s == "" { return result, fmt.Errorf("cannot unmarshal empeddings, empty string provided") } else if !strings.HasPrefix(s, "[[") { return result, fmt.Errorf("cannot unmarshal empeddings, invalid json provided") } err = json.Unmarshal([]byte(s), &result) return result, err }