Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.dataset.api.preprocessor;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.MultiDataSet;
import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.serializer.NormalizerType;
/**
* A preprocessor specifically for images that applies min max scaling to one or more of the feature arrays
* in a MultiDataSet.
* Can take a range, so pixel values can be scaled from 0->255 to minRange->maxRange
* default minRange = 0 and maxRange = 1;
* If pixel values are not 8 bits, you can specify the number of bits as the third argument in the constructor
* For values that are already floating point, specify the number of bits as 1
*
* @author Alex Black (MultiDataSet version), Susan Eraly (original ImagePreProcessingScaler)
*/
public class ImageMultiPreProcessingScaler implements MultiDataNormalization {
private double minRange, maxRange;
private double maxPixelVal;
private int[] featureIndices;
public ImageMultiPreProcessingScaler(int... featureIndices) {
this(0, 1, 8, featureIndices);
}
public ImageMultiPreProcessingScaler(double a, double b, int[] featureIndices) {
this(a, b, 8, featureIndices);
}
/**
* Preprocessor can take a range as minRange and maxRange
* @param a, default = 0
* @param b, default = 1
* @param maxBits in the image, default = 8
* @param featureIndices Indices of feature arrays to process. If only one feature array is present,
* this should always be 0
*/
public ImageMultiPreProcessingScaler(double a, double b, int maxBits, int[] featureIndices) {
if(featureIndices == null || featureIndices.length == 0){
throw new IllegalArgumentException("Invalid feature indices: the indices of the features arrays to apply "
+ "the normalizer to must be specified. MultiDataSet/MultiDataSetIterators with only a single feature"
+ " array, this should be set to 0. Otherwise specify the indexes of all the feature arrays to apply"
+ " the normalizer to.");
}
//Image values are not always from 0 to 255 though
//some images are 16-bit, some 32-bit, integer, or float, and those BTW already come with values in [0..1]...
//If the max expected value is 1, maxBits should be specified as 1
maxPixelVal = Math.pow(2, maxBits) - 1;
this.minRange = a;
this.maxRange = b;
this.featureIndices = featureIndices;
}
@Override
public void fit(MultiDataSetIterator iterator) {
//No op
}
@Override
public void preProcess(MultiDataSet multiDataSet) {
for( int i=0; i1
if (this.maxRange - this.minRange != 1)
f.muli(this.maxRange - this.minRange); //Scaled to minRange -> maxRange
if (this.minRange != 0)
f.addi(this.minRange); //Offset by minRange
}
}
@Override
public void revertFeatures(INDArray[] features, INDArray[] featuresMask) {
revertFeatures(features);
}
@Override
public void revertFeatures(INDArray[] features) {
for( int i=0; i
