frogermcs

private void runInferenceOnQuantizedModel(ByteBuffer byteBufferToClassify) {
    byte[][] result = new byte[1][labels.size()];
    interpreter.run(byteBufferToClassify, result);
    float[][] resultFloats = new float[1][labels.size()];
    byte[] bytes = result[0];
    for (int i = 0; i < bytes.length; i++) {
        float resultF = (bytes[i] & 0xff) / 255.f;
        resultFloats[0][i] = resultF;

    }

frogermcs

private byte[] pixelToChannelValuesQuant(int pixel) {
    byte[] rgbVals = new byte[3];
    rgbVals[0] = (byte) ((pixel >> 16) & 0xFF);
    rgbVals[1] = (byte) ((pixel >> 8) & 0xFF);
    rgbVals[2] = (byte) ((pixel) & 0xFF);
    return rgbVals;
}

frogermcs

my_training_data.zip
  |____accordion
  | |____001.jpg
  | |____002.jpg
  | |____003.jpg
  |____bass_guitar
  | |____hofner.gif
  | |____p-bass.png
  |____clavier
    |____well-tempered.jpg

frogermcs

Not true that <{0=1, 1=1, 2=1, 3=3, 4=0, 5=2, 6=1, 7=0, 8=1, 9=2, 10=2, 11=0, ... 29=1, 30=2, 31=3}> 
        contains exactly <{0=1, 1=1, 2=1, 3=3, 4=0, 5=2, 6=1, 7=0, 8=1, 9=2, 10=2, 11=0, ... 29=1, 30=2, 31=3}>. 
        It has the following entries with matching keys but different values: {16=(expected 2 but got 3), 20=(expected 2 but got 1)}
        at com.frogermcs.imageclassificationtester.MLModelTest.testClassificationBatch(MLModelTest.java:72)

frogermcs

@RunWith(AndroidJUnit4.class)
@LargeTest
public class MLModelTest {

    /* ... */

    @Test
    public void testClassificationBatch() throws IOException {
        ModelTestActivity activity = mainActivityActivityRule.getActivity();
        ModelClassificator modelClassificator = new ModelClassificator(activity, new FlowersConfig());

frogermcs

@RunWith(AndroidJUnit4.class)
@LargeTest
public class MLModelTest {

    @Rule
    public ActivityTestRule<ModelTestActivity> mainActivityActivityRule = new ActivityTestRule<>(ModelTestActivity.class);

    @Test
    public void testClassificationUI() {
        ModelTestActivity activity = mainActivityActivityRule.getActivity();

frogermcs

public class ModelTestActivity extends AppCompatActivity {

    private ImageView ivPreview;
    private TextView tvClassification;
    private ModelClassificator modelClassificator;

    @Override
    protected void onCreate(@Nullable Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(com.frogermcs.imageclassificationtester.test.R.layout.activity_model_test);

frogermcs

from PIL import Image

VAL_BATCH_DIR = "validation_batch"
!mkdir {VAL_BATCH_DIR}

# Export batch to *.jpg files with specific naming convention.
# Make sure they are exported in the full quality, otherwise the inference
# process will return different results. 

for n in range(32):

frogermcs

public class ModelClassificator {
    private static final int MAX_CLASSIFICATION_RESULTS = 3;
    private static final float CLASSIFICATION_THRESHOLD = 0.2f;

    private final Interpreter interpreter;
    private final List<String> labels;
    private final ModelConfig modelConfig;

    public ModelClassificator(Context context,
                              ModelConfig modelConfig) throws IOException {

frogermcs

public class ClassificationFrameProcessor implements FrameProcessor {

    private final ModelClassificator modelClassificator;
    private final ClassificationListener classificationListener;

    public ClassificationFrameProcessor(ModelClassificator modelClassificator,
                                        ClassificationListener classificationListener) {
        this.modelClassificator = modelClassificator;
        this.classificationListener = classificationListener;
    }