content: /* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at
  
    http://www.apache.org/licenses/LICENSE-2.0
  
  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License.
  ==============================================================================*/
  
  package com.example.android.cancerdetectiondemo;
  
  import android.app.Activity;
  import java.io.IOException;
  
  /** This classifier works with the float MobileNet model. */
  public class ImageClassifierFloatMobileNet extends ImageClassifier {
  
    /**
     * An array to hold inference results, to be feed into Tensorflow Lite as outputs. This isn't part
     * of the super class, because we need a primitive array here.
     */
    private float[][] labelProbArray = null;
  
    /**
     * Initializes an {@code ImageClassifierFloatMobileNet}.
     *
     * @param activity
     */
    ImageClassifierFloatMobileNet(Activity activity) throws IOException {
      super(activity);
      labelProbArray = new float[1][getNumLabels()];
    }
  
    @Override
    protected String getModelPath() {
      // you can download this file from
      // see build.gradle for where to obtain this file. It should be auto
      // downloaded into assets.
      return "skin_cancer_best_model.tflite";
    }
  
    @Override
    protected String getLabelPath() { return "labels_skin_cancer.txt";
    }
  
    @Override
    protected int getImageSizeX() {
      return 224;
    }
  
    @Override
    protected int getImageSizeY() {
      return 224;
    }
  
    @Override
    protected int getNumBytesPerChannel() {
      return 4; // Float.SIZE / Byte.SIZE;
    }
  
  //  @Override
  //  protected void addPixelValue(int pixelValue) {
  //    imgData.putFloat(((pixelValue >> 16) & 0xFF) / 255.f);
  //    imgData.putFloat(((pixelValue >> 8) & 0xFF) / 255.f);
  //    imgData.putFloat((pixelValue & 0xFF) / 255.f);
  //  }
  
    @Override
    protected void addPixelValue(int pixelValue) {
        imgData.putFloat(((((pixelValue >> 16) & 0xFF)*2.f) / 255.f)-1.f);
        imgData.putFloat(((((pixelValue >> 8) & 0xFF)*2.f) / 255.f)-1.f);
        imgData.putFloat((((pixelValue & 0xFF)*2.f) / 255.f)-1.f);
      }
  
  
  
    @Override
    protected float getProbability(int labelIndex) {
      return labelProbArray[0][labelIndex];
    }
  
    @Override
    protected void setProbability(int labelIndex, Number value) {
      labelProbArray[0][labelIndex] = value.floatValue();
    }
  
    @Override
    protected float getNormalizedProbability(int labelIndex) {
      return labelProbArray[0][labelIndex];
    }
  
    @Override
    protected void runInference() {
      tflite.run(imgData, labelProbArray);
    }
  }
  