photoprism/internal/face/net_test.go
2021-08-11 13:21:22 +02:00

131 lines
2.7 KiB
Go

package face
import (
"os"
"path/filepath"
"strings"
"testing"
"github.com/photoprism/photoprism/pkg/fastwalk"
"github.com/stretchr/testify/assert"
)
func TestNet(t *testing.T) {
expected := map[string]int{
"1.jpg": 1,
"2.jpg": 1,
"3.jpg": 1,
"4.jpg": 1,
"5.jpg": 1,
"6.jpg": 1,
"7.jpg": 0,
"8.jpg": 0,
"9.jpg": 0,
"10.jpg": 0,
"11.jpg": 0,
"12.jpg": 1,
"13.jpg": 0,
"14.jpg": 0,
"15.jpg": 0,
"16.jpg": 1,
"17.jpg": 1,
"18.jpg": 2,
"19.jpg": 0,
}
faceindices := map[string][]int{
"18.jpg": {0, 1},
"1.jpg": {2},
"4.jpg": {3},
"5.jpg": {4},
"6.jpg": {5},
"2.jpg": {6},
"12.jpg": {7},
"16.jpg": {8},
"17.jpg": {9},
"3.jpg": {10},
}
faceindexToPersonid := [11]int{
0, 1, 1, 1, 2, 0, 1, 0, 0, 1, 0,
}
var embeddings [11][]float32
faceNet := NewNet(modelPath, "testdata/cache", false)
if err := fastwalk.Walk("testdata", func(fileName string, info os.FileMode) error {
if info.IsDir() || strings.HasPrefix(filepath.Base(fileName), ".") || strings.Contains(fileName, "cache") {
return nil
}
t.Run(fileName, func(t *testing.T) {
baseName := filepath.Base(fileName)
faces, err := faceNet.Detect(fileName)
if err != nil {
t.Fatal(err)
}
t.Logf("found %d faces in '%s'", len(faces), baseName)
if len(faces) > 0 {
t.Logf("results: %#v", faces)
for i, f := range faces {
if len(f.Embeddings) > 0 {
embeddings[faceindices[baseName][i]] = f.Embeddings[0]
} else {
embeddings[faceindices[baseName][i]] = nil
}
}
}
if i, ok := expected[baseName]; ok {
assert.Equal(t, i, len(faces))
assert.Equal(t, i, faces.Count())
if faces.Count() == 0 {
assert.Equal(t, 100, faces.Uncertainty())
} else {
assert.Truef(t, faces.Uncertainty() >= 0 && faces.Uncertainty() <= 50, "uncertainty should be between 0 and 50")
}
t.Logf("uncertainty: %d", faces.Uncertainty())
} else {
t.Logf("unknown test result for %s", baseName)
}
})
return nil
}); err != nil {
t.Fatal(err)
}
// Distance Matrix
correct := 0
for i := 0; i < len(embeddings); i++ {
for j := 0; j < len(embeddings); j++ {
if i >= j {
continue
}
dist := EuclidianDistance(embeddings[i], embeddings[j])
t.Logf("Dist for %d %d (faces are %d %d) is %f", i, j, faceindexToPersonid[i], faceindexToPersonid[j], dist)
if faceindexToPersonid[i] == faceindexToPersonid[j] {
if dist < 1.21 {
correct += 1
}
} else {
if dist >= 1.21 {
correct += 1
}
}
}
}
t.Logf("Correct for %d", correct)
// there are a few incorrect results
// 4 out of 55 with the 1.21 threshold
assert.True(t, correct == 51)
}