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test: included new benchmark tests

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esimov
2021-10-21 14:19:34 +03:00
parent a18c4ebad5
commit 7b6884c981
3 changed files with 156 additions and 45 deletions
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51
core/flploc_test.go
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@@ -3,6 +3,7 @@ package pigo_test
import (
"io/ioutil"
"log"
"runtime"
"testing"
pigo "github.com/esimov/pigo/core"
@@ -159,7 +160,37 @@ func TestFlploc_LandmarkDetectorShouldReturnCorrectDetectionPoints(t *testing.T)
}
}
func BenchmarkFlploc(b *testing.B) {
func BenchmarkFlplocReadCascadeDir(b *testing.B) {
for i := 0; i < b.N; i++ {
plc.ReadCascadeDir("../cascade/lps/")
}
}
func BenchmarkFlplocGetLendmarkPoint(b *testing.B) {
pl := pigo.PuplocCascade{}
plc, err := pl.UnpackCascade(puplocCascade)
if err != nil {
b.Fatalf("error reading the cascade file: %s", err)
}
pixs := pigo.RgbToGrayscale(srcImg)
cParams.Pixels = pixs
flploc := &pigo.Puploc{Row: 10, Col: 10, Scale: 20, Perturbs: 50}
// For benchmarking we are using common values for left and right eye.
puploc := plc.RunDetector(*flploc, *imgParams, 0.0, false)
b.ResetTimer()
runtime.GC()
for i := 0; i < b.N; i++ {
plc.GetLandmarkPoint(puploc, puploc, *imgParams, 63, false)
}
}
func BenchmarkFlplocDetection(b *testing.B) {
var faces []pigo.Detection
pg := pigo.NewPigo()
// Unpack the binary file. This will return the number of cascade trees,
// the tree depth, the threshold and the prediction from tree's leaf nodes.
@@ -174,19 +205,19 @@ func BenchmarkFlploc(b *testing.B) {
b.Fatalf("error reading the cascade file: %s", err)
}
var faces []pigo.Detection
pixs := pigo.RgbToGrayscale(srcImg)
cParams.Pixels = pixs
b.ResetTimer()
runtime.GC()
// Run the classifier over the obtained leaf nodes and return the detection results.
// The result contains quadruplets representing the row, column, scale and detection score.
faces = classifier.RunCascade(*cParams, 0.0)
// Calculate the intersection over union (IoU) of two clusters.
faces = classifier.ClusterDetections(faces, 0.1)
for i := 0; i < b.N; i++ {
pixs := pigo.RgbToGrayscale(srcImg)
cParams.Pixels = pixs
// Run the classifier over the obtained leaf nodes and return the detection results.
// The result contains quadruplets representing the row, column, scale and detection score.
faces = classifier.RunCascade(*cParams, 0.0)
// Calculate the intersection over union (IoU) of two clusters.
faces = classifier.ClusterDetections(faces, 0.1)
for _, face := range faces {
if face.Scale > 50 {
// left eye

76
core/pigo_test.go
View File

@@ -5,6 +5,7 @@ import (
"io/ioutil"
"log"
"path/filepath"
"runtime"
"testing"
pigo "github.com/esimov/pigo/core"
@@ -68,43 +69,82 @@ func TestPigo_InputImageShouldBeGrayscale(t *testing.T) {
}
func TestPigo_Detector_ShouldDetectFace(t *testing.T) {
// Unpack the binary file. This will return the number of cascade trees,
// Unpack the facefinder binary cascade file. This will return the number of cascade trees,
// the tree depth, the threshold and the prediction from tree's leaf nodes.
classifier, err := p.Unpack(pigoCascade)
p, err := p.Unpack(pigoCascade)
if err != nil {
t.Fatalf("error reading the cascade file: %s", err)
}
// Run the classifier over the obtained leaf nodes and return the detection results.
// The result contains quadruplets representing the row, column, scale and detection score.
faces := classifier.RunCascade(*cParams, 0.0)
faces := p.RunCascade(*cParams, 0.0)
// Calculate the intersection over union (IoU) of two clusters.
faces = classifier.ClusterDetections(faces, 0.1)
faces = p.ClusterDetections(faces, 0.1)
if len(faces) == 0 {
t.Fatalf("face should've been detected")
}
}
func BenchmarkPigo(b *testing.B) {
func BenchmarkPigoUnpackCascade(b *testing.B) {
pg := pigo.NewPigo()
// Unpack the binary file. This will return the number of cascade trees,
// the tree depth, the threshold and the prediction from tree's leaf nodes.
classifier, err := pg.Unpack(pigoCascade)
if err != nil {
log.Fatalf("Error reading the cascade file: %s", err)
}
var dets []pigo.Detection
b.ResetTimer()
for i := 0; i < b.N; i++ {
pixs := pigo.RgbToGrayscale(srcImg)
cParams.Pixels = pixs
// Unpack the facefinder binary cascade file.
_, err := pg.Unpack(pigoCascade)
if err != nil {
log.Fatalf("error reading the cascade file: %s", err)
}
}
}
func BenchmarkPigoFaceDetection(b *testing.B) {
var dets []pigo.Detection
pg := pigo.NewPigo()
p, err := pg.Unpack(pigoCascade)
if err != nil {
log.Fatalf("error reading the cascade file: %s", err)
}
pixs := pigo.RgbToGrayscale(srcImg)
cParams.Pixels = pixs
b.ResetTimer()
runtime.GC()
for i := 0; i < b.N; i++ {
// Run the classifier over the obtained leaf nodes and return the detection results.
// The result contains quadruplets representing the row, column, scale and detection score.
dets = classifier.RunCascade(*cParams, 0.0)
dets = p.RunCascade(*cParams, 0.0)
// Calculate the intersection over union (IoU) of two clusters.
dets = classifier.ClusterDetections(dets, 0.1)
dets = p.ClusterDetections(dets, 0.1)
}
_ = dets
}
func BenchmarkPigoClusterDetection(b *testing.B) {
var dets []pigo.Detection
pg := pigo.NewPigo()
p, err := pg.Unpack(pigoCascade)
if err != nil {
log.Fatalf("error reading the cascade file: %s", err)
}
pixs := pigo.RgbToGrayscale(srcImg)
cParams.Pixels = pixs
b.ResetTimer()
runtime.GC()
// Run the classifier over the obtained leaf nodes and return the detection results.
// The result contains quadruplets representing the row, column, scale and detection score.
dets = p.RunCascade(*cParams, 0.0)
for i := 0; i < b.N; i++ {
// Calculate the intersection over union (IoU) of two clusters.
dets = p.ClusterDetections(dets, 0.1)
}
_ = dets
}

74
core/puploc_test.go
View File

@@ -3,6 +3,7 @@ package pigo_test
import (
"io/ioutil"
"log"
"runtime"
"testing"
pigo "github.com/esimov/pigo/core"
@@ -36,18 +37,18 @@ func TestPuploc_UnpackCascadeFileShouldNotBeNil(t *testing.T) {
func TestPuploc_Detector_ShouldDetectEyes(t *testing.T) {
p := pigo.NewPigo()
// Unpack the binary file. This will return the number of cascade trees,
// Unpack the facefinder binary cascade file. This will return the number of cascade trees,
// the tree depth, the threshold and the prediction from tree's leaf nodes.
classifier, err := p.Unpack(pigoCascade)
p, err := p.Unpack(pigoCascade)
if err != nil {
t.Fatalf("error reading the cascade file: %s", err)
}
// Run the classifier over the obtained leaf nodes and return the detection results.
// The result contains quadruplets representing the row, column, scale and detection score.
faces := classifier.RunCascade(*cParams, 0.0)
faces := p.RunCascade(*cParams, 0.0)
// Calculate the intersection over union (IoU) of two clusters.
faces = classifier.ClusterDetections(faces, 0.1)
faces = p.ClusterDetections(faces, 0.1)
eyes := []pigo.Puploc{}
@@ -79,11 +80,50 @@ func TestPuploc_Detector_ShouldDetectEyes(t *testing.T) {
}
}
func BenchmarkPuploc(b *testing.B) {
func BenchmarkPuplocUnpackCascade(b *testing.B) {
pg := pigo.NewPigo()
// Unpack the binary file. This will return the number of cascade trees,
// the tree depth, the threshold and the prediction from tree's leaf nodes.
classifier, err := pg.Unpack(pigoCascade)
// Unpack the facefinder binary cascade file.
_, err := pg.Unpack(pigoCascade)
if err != nil {
log.Fatalf("error reading the cascade file: %s", err)
}
b.ResetTimer()
runtime.GC()
for i := 0; i < b.N; i++ {
pl := pigo.PuplocCascade{}
// Unpack the pupil localization cascade file.
_, err = pl.UnpackCascade(puplocCascade)
if err != nil {
b.Fatalf("error reading the cascade file: %s", err)
}
}
}
func BenchmarkPuplocDetectorRun(b *testing.B) {
pl := pigo.PuplocCascade{}
plc, err := pl.UnpackCascade(puplocCascade)
if err != nil {
b.Fatalf("error reading the cascade file: %s", err)
}
pixs := pigo.RgbToGrayscale(srcImg)
cParams.Pixels = pixs
puploc := &pigo.Puploc{Row: 10, Col: 10, Scale: 20, Perturbs: 50}
for i := 0; i < b.N; i++ {
plc.RunDetector(*puploc, *imgParams, 0.0, false)
}
}
func BenchmarkPuplocDetection(b *testing.B) {
var faces []pigo.Detection
pg := pigo.NewPigo()
p, err := pg.Unpack(pigoCascade)
if err != nil {
b.Fatalf("error reading the cascade file: %s", err)
}
@@ -94,19 +134,19 @@ func BenchmarkPuploc(b *testing.B) {
b.Fatalf("error reading the cascade file: %s", err)
}
var faces []pigo.Detection
pixs := pigo.RgbToGrayscale(srcImg)
cParams.Pixels = pixs
b.ResetTimer()
runtime.GC()
// Run the classifier over the obtained leaf nodes and return the detection results.
// The result contains quadruplets representing the row, column, scale and detection score.
faces = p.RunCascade(*cParams, 0.0)
// Calculate the intersection over union (IoU) of two clusters.
faces = p.ClusterDetections(faces, 0.1)
for i := 0; i < b.N; i++ {
pixs := pigo.RgbToGrayscale(srcImg)
cParams.Pixels = pixs
// Run the classifier over the obtained leaf nodes and return the detection results.
// The result contains quadruplets representing the row, column, scale and detection score.
faces = classifier.RunCascade(*cParams, 0.0)
// Calculate the intersection over union (IoU) of two clusters.
faces = classifier.ClusterDetections(faces, 0.1)
for _, face := range faces {
if face.Scale > 50 {
// left eye