package face_test import ( "fmt" "log" "path/filepath" "github.com/Kagami/go-face" ) // Path to directory with models and test images. Here it's assumed it // points to the clone. const dataDir = "testdata" // This example shows the basic usage of the package: create an // recognizer, recognize faces, classify them using few known ones. func Example_basic() { // Init the recognizer. rec, err := face.NewRecognizer(filepath.Join(dataDir, "models")) if err != nil { log.Fatalf("Can't init face recognizer: %v", err) } // Free the resources when you're finished. defer rec.Close() // Test image with 10 faces. testImagePristin := filepath.Join(dataDir, "images", "pristin.jpg") // Recognize faces on that image. faces, err := rec.RecognizeFile(testImagePristin) if err != nil { log.Fatalf("Can't recognize: %v", err) } if len(faces) != 10 { log.Fatalf("Wrong number of faces") } // Fill known samples. In the real world you would use a lot of images // for each person to get better classification results but in our // example we just get them from one big image. var samples []face.Descriptor var cats []int32 for i, f := range faces { samples = append(samples, f.Descriptor) // Each face is unique on that image so goes to its own category. cats = append(cats, int32(i)) } // Name the categories, i.e. people on the image. labels := []string{ "Sungyeon", "Yehana", "Roa", "Eunwoo", "Xiyeon", "Kyulkyung", "Nayoung", "Rena", "Kyla", "Yuha", } // Pass samples to the recognizer. rec.SetSamples(samples, cats) // Now let's try to classify some not yet known image. testImageNayoung := filepath.Join(dataDir, "images", "nayoung.jpg") nayoungFace, err := rec.RecognizeSingleFile(testImageNayoung) if err != nil { log.Fatalf("Can't recognize: %v", err) } if nayoungFace == nil { log.Fatalf("Not a single face on the image") } catID := rec.Classify(nayoungFace.Descriptor) if catID < 0 { log.Fatalf("Can't classify") } // Finally print the classified label. It should be "Nayoung". fmt.Println(labels[catID]) }