photoprism/internal/face/net.go

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package face
import (
"fmt"
"image"
"path"
"path/filepath"
"runtime/debug"
"sync"
tf "github.com/tensorflow/tensorflow/tensorflow/go"
"github.com/photoprism/photoprism/internal/crop"
"github.com/photoprism/photoprism/pkg/txt"
)
// Net is a wrapper for the TensorFlow Facenet model.
type Net struct {
model *tf.SavedModel
modelPath string
cachePath string
disabled bool
modelName string
modelTags []string
mutex sync.Mutex
}
// NewNet returns a new TensorFlow Facenet instance.
func NewNet(modelPath, cachePath string, disabled bool) *Net {
return &Net{modelPath: modelPath, cachePath: cachePath, disabled: disabled, modelTags: []string{"serve"}}
}
// Detect runs the detection and facenet algorithms over the provided source image.
func (t *Net) Detect(fileName string, minSize int, cacheCrop bool, expected int) (faces Faces, err error) {
faces, err = Detect(fileName, false, minSize)
if err != nil {
return faces, err
}
// Skip FaceNet?
if t.disabled {
return faces, nil
} else if c := len(faces); c == 0 || expected > 0 && c == expected {
return faces, nil
}
err = t.loadModel()
if err != nil {
return faces, err
}
for i, f := range faces {
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if f.Area.Col == 0 && f.Area.Row == 0 {
continue
}
if img, err := crop.ImageFromThumb(fileName, f.CropArea(), CropSize, cacheCrop); err != nil {
log.Errorf("faces: failed to decode image: %v", err)
} else if embeddings := t.getEmbeddings(img); !embeddings.Empty() {
faces[i].Embeddings = embeddings
}
}
return faces, nil
}
// ModelLoaded tests if the TensorFlow model is loaded.
func (t *Net) ModelLoaded() bool {
return t.model != nil
}
// loadModel loads the TensorFlow model.
func (t *Net) loadModel() error {
t.mutex.Lock()
defer t.mutex.Unlock()
if t.ModelLoaded() {
return nil
}
modelPath := path.Join(t.modelPath)
log.Infof("faces: loading %s", txt.Quote(filepath.Base(modelPath)))
// Load model
model, err := tf.LoadSavedModel(modelPath, t.modelTags, nil)
if err != nil {
return err
}
t.model = model
return nil
}
// getEmbeddings returns the face embeddings for an image.
func (t *Net) getEmbeddings(img image.Image) Embeddings {
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tensor, err := imageToTensor(img, CropSize.Width, CropSize.Height)
if err != nil {
log.Errorf("faces: failed to convert image to tensor: %v", err)
}
// TODO: pre-whiten image as in facenet
trainPhaseBoolTensor, err := tf.NewTensor(false)
output, err := t.model.Session.Run(
map[tf.Output]*tf.Tensor{
t.model.Graph.Operation("input").Output(0): tensor,
t.model.Graph.Operation("phase_train").Output(0): trainPhaseBoolTensor,
},
[]tf.Output{
t.model.Graph.Operation("embeddings").Output(0),
},
nil)
if err != nil {
log.Errorf("faces: %s", err)
}
if len(output) < 1 {
log.Errorf("faces: inference failed, no output")
} else {
return NewEmbeddings(output[0].Value().([][]float32))
}
return nil
}
func imageToTensor(img image.Image, imageHeight, imageWidth int) (tfTensor *tf.Tensor, err error) {
defer func() {
if r := recover(); r != nil {
err = fmt.Errorf("faces: %s (panic)\nstack: %s", r, debug.Stack())
}
}()
if imageHeight <= 0 || imageWidth <= 0 {
return tfTensor, fmt.Errorf("faces: image width and height must be > 0")
}
var tfImage [1][][][3]float32
for j := 0; j < imageHeight; j++ {
tfImage[0] = append(tfImage[0], make([][3]float32, imageWidth))
}
for i := 0; i < imageWidth; i++ {
for j := 0; j < imageHeight; j++ {
r, g, b, _ := img.At(i, j).RGBA()
tfImage[0][j][i][0] = convertValue(r)
tfImage[0][j][i][1] = convertValue(g)
tfImage[0][j][i][2] = convertValue(b)
}
}
return tf.NewTensor(tfImage)
}
func convertValue(value uint32) float32 {
return (float32(value>>8) - float32(127.5)) / float32(127.5)
}