136 lines
2.7 KiB
Go
136 lines
2.7 KiB
Go
package face
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import (
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"os"
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"path/filepath"
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"testing"
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"github.com/photoprism/photoprism/pkg/fastwalk"
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"github.com/stretchr/testify/assert"
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)
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var modelPath, _ = filepath.Abs("../../assets/facenet")
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func TestNet(t *testing.T) {
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expected := map[string]int{
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"1.jpg": 1,
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"2.jpg": 1,
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"3.jpg": 1,
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"4.jpg": 1,
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"5.jpg": 1,
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"6.jpg": 1,
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"7.jpg": 0,
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"8.jpg": 0,
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"9.jpg": 0,
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"10.jpg": 0,
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"11.jpg": 0,
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"12.jpg": 1,
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"13.jpg": 0,
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"14.jpg": 0,
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"15.jpg": 0,
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"16.jpg": 1,
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"17.jpg": 1,
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"18.jpg": 2,
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"19.jpg": 0,
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}
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faceIndices := map[string][]int{
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"18.jpg": {1, 0},
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"1.jpg": {2},
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"4.jpg": {3},
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"5.jpg": {4},
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"6.jpg": {5},
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"2.jpg": {6},
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"12.jpg": {7},
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"16.jpg": {8},
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"17.jpg": {9},
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"3.jpg": {10},
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}
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faceIndexToPersonID := [11]int{
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0, 1, 1, 1, 2, 0, 1, 0, 0, 1, 0,
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}
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var embeddings = make(Embeddings, 11)
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faceNet := NewNet(modelPath, "testdata/cache", false)
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if err := fastwalk.Walk("testdata", func(fileName string, info os.FileMode) error {
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if info.IsDir() || filepath.Base(filepath.Dir(fileName)) != "testdata" {
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return nil
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}
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t.Run(fileName, func(t *testing.T) {
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baseName := filepath.Base(fileName)
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faces, err := faceNet.Detect(fileName, 20, false, -1)
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if err != nil {
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t.Fatal(err)
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}
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// for i, f := range faces {
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// t.Logf("FACE %d IN %s: %#v", i, fileName, f.Area)
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// }
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if len(faces) > 0 {
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for i, f := range faces {
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if len(f.Embeddings) > 0 {
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// t.Logf("FACE %d IN %s: %#v", i, fileName, f.Embeddings)
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embeddings[faceIndices[baseName][i]] = f.Embeddings[0]
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} else {
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embeddings[faceIndices[baseName][i]] = nil
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}
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}
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}
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if i, ok := expected[baseName]; ok {
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assert.Equal(t, i, faces.Count())
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if faces.Count() == 0 {
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assert.Equal(t, 100, faces.Uncertainty())
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} else {
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assert.Truef(t, faces.Uncertainty() >= 0 && faces.Uncertainty() <= 50, "uncertainty should be between 0 and 50")
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}
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t.Logf("uncertainty: %d", faces.Uncertainty())
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} else {
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t.Logf("unknown test result for %s", baseName)
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}
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})
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return nil
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}); err != nil {
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t.Fatal(err)
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}
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// Distance Matrix
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correct := 0
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for i := 0; i < len(embeddings); i++ {
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for j := 0; j < len(embeddings); j++ {
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if i >= j {
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continue
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}
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dist := embeddings[i].Distance(embeddings[j])
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t.Logf("Dist for %d %d (faces are %d %d) is %f", i, j, faceIndexToPersonID[i], faceIndexToPersonID[j], dist)
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if faceIndexToPersonID[i] == faceIndexToPersonID[j] {
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if dist < 1.21 {
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correct += 1
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}
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} else {
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if dist >= 1.21 {
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correct += 1
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}
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}
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}
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}
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t.Logf("Correct for %d", correct)
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// there are a few incorrect results
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// 3 out of 55 with the 1.21 threshold
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assert.Equal(t, 52, correct)
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}
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