Extend flploc benchmark tests

core/flploc_test.go

@@ -74,3 +74,59 @@ func TestFlploc_LandmarkPointsFinderShouldReturnDetectionPoints(t *testing.T) {
 		t.Fatalf("should have been detected facial landmark points: %s", err)
 	}
 }
+
+func BenchmarkFlploc(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 {
+		b.Fatalf("error reading the cascade file: %s", err)
+	}
+
+	pl := pigo.PuplocCascade{}
+	plc, err := pl.UnpackCascade(puplocCascade)
+	if err != nil {
+		b.Fatalf("error reading the cascade file: %s", err)
+	}
+
+	var faces []pigo.Detection
+
+	b.ResetTimer()
+
+	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
+				puploc := &pigo.Puploc{
+					Row:      face.Row - int(0.075*float32(face.Scale)),
+					Col:      face.Col - int(0.175*float32(face.Scale)),
+					Scale:    float32(face.Scale) * 0.25,
+					Perturbs: 50,
+				}
+				leftEye := plc.RunDetector(*puploc, *imgParams, 0.0, false)
+
+				// right eye
+				puploc = &pigo.Puploc{
+					Row:      face.Row - int(0.075*float32(face.Scale)),
+					Col:      face.Col + int(0.185*float32(face.Scale)),
+					Scale:    float32(face.Scale) * 0.25,
+					Perturbs: 50,
+				}
+				rightEye := plc.RunDetector(*puploc, *imgParams, 0.0, false)
+
+				plc.FindLandmarkPoints(leftEye, rightEye, *imgParams, 63, "left")
+
+			}
+		}
+	}
+	_ = faces
+}

core/pigo_test.go

@@ -11,6 +11,7 @@ import (
 )
 
 var (
+	p           = pigo.NewPigo()
 	pigoCascade []byte
 	srcImg      *image.NRGBA
 )
@@ -47,6 +48,43 @@ func init() {
 	}
 }
 
+func TestPigo_UnpackCascadeFileShouldNotBeNil(t *testing.T) {
+	var (
+		err  error
+		pigo = pigo.NewPigo()
+	)
+	p, err = pigo.Unpack(pigoCascade)
+	if err != nil {
+		t.Fatalf("failed unpacking the cascade file: %v", err)
+	}
+}
+
+func TestPigo_InputImageShouldBeGrayscale(t *testing.T) {
+	// Since an image converted grayscale has only one channel,we should assume
+	// that the grayscale image array length is the source image length / 4.
+	if len(imgParams.Pixels) != len(srcImg.Pix)/4 {
+		t.Fatalf("the source image should be converted to grayscale")
+	}
+}
+
+func TestPigo_Detector_ShouldDetectFace(t *testing.T) {
+	// 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 := 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)
+	// Calculate the intersection over union (IoU) of two clusters.
+	faces = classifier.ClusterDetections(faces, 0.1)
+	if len(faces) == 0 {
+		t.Fatalf("should have been detected eyes: %s", err)
+	}
+}
+
 func BenchmarkPigo(b *testing.B) {
 	pg := pigo.NewPigo()
 	// Unpack the binary file. This will return the number of cascade trees,

core/puploc_test.go

@@ -34,14 +34,6 @@ func TestPuploc_UnpackCascadeFileShouldNotBeNil(t *testing.T) {
 	}
 }
 
-func TestPuploc_InputImageShouldBeGrayscale(t *testing.T) {
-	// Since an image converted grayscale has only one channel,we should assume
-	// that the grayscale image array length is the source image length / 4.
-	if len(imgParams.Pixels) != len(srcImg.Pix)/4 {
-		t.Fatalf("the source image should be converted to grayscale")
-	}
-}
-
 func TestPuploc_Detector_ShouldDetectEyes(t *testing.T) {
 	p := pigo.NewPigo()
 	// Unpack the binary file. This will return the number of cascade trees,