frogermcs

Predicted value for [0, 1] normalization. Label index: 682, confidence: 39%
Predicted value for [-1, 1] normalization. Label index: 682, confidence: 53%

frogermcs

imgUrl = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/de/Compaqarmada7800.jpg/1024px-Compaqarmada7800.jpg"
img = Image.open(requests.get(imgUrl, stream=True).raw)
img.load()
img = img.resize((224, 224), PIL.Image.ANTIALIAS)

# Normalize to [0, 1]
data = np.asarray( img, dtype="int32" ) / 255.0

# Inference on input data normalized to [0, 1]
inputImg = np.expand_dims(data,0).astype(np.float32)

frogermcs

== Input details ==
name: input
shape: [ 1 224 224 3]
type: <class 'numpy.float32’>

== Output details ==
name: MobilenetV2/Predictions/Reshape_1
shape: [ 1 1001]
type: <class 'numpy.float32’>

frogermcs

== Input details == 
name: flatten_input
shape: [ 1 28 28]
type: <class 'numpy.float32'>

== Output details ==
name: dense_2/Softmax
shape: [ 1 10]
type: <class 'numpy.float32'>

frogermcs

interpreter = tf.lite.Interpreter(model_path="mnist.tflite")
interpreter.allocate_tensors()

print("== Input details ==")
print("name:", interpreter.get_input_details()[0]['name'])
print("shape:", interpreter.get_input_details()[0]['shape'])
print("type:", interpreter.get_input_details()[0]['dtype’])

print("\n== Output details ==")
print("name:", interpreter.get_output_details()[0]['name'])

frogermcs

public abstract class ModelConfig {
    static final int FLOAT_BYTES_COUNT = 4;

    public abstract String getModelFilename();
    public abstract String getLabelsFilename();
    public abstract int getInputWidth();
    public abstract int getInputHeight();
    public abstract int getInputSize();
    public abstract int getChannelsCount();
    public abstract float getMean();

frogermcs

public class ClassificationFrameProcessor implements FrameProcessor {

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

    /* ... */

    @Override

frogermcs

public class MainActivity extends AppCompatActivity {

    private CameraView cameraView;
    private ClassificationFrameProcessor classificationFrameProcessor;

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);

frogermcs

public class SimpleObserver<T> implements Observer<T> {
    ///...

    @Override
    public void onError(Throwable e) {
      view.showGeneralErrorPopup();
      loggingManager.logException(e); // <-- !!!!!
    }
}

frogermcs

@AutoHandler
public interface PaymentErrorListener {

    @ErrorCode(CODE_UNSUPPORTED_PREPAID_CARD)
    void onUnsupportedPrepaidCardError();

    @ErrorCode(CODE_UNSUPPORTED_CONSUMER_CARD)
    void onUnsupportedConsumerCardError();

    @ErrorCode(CODE_INSUFFICIENT_FUNDS)