Extend flploc benchmark tests

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,