org.nd4j.linalg.dataset.api.preprocessor.RGBtoGrayscaleDataSetPreProcessor Maven / Gradle / Ivy
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* Copyright (c) 2015-2019 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.
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* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.dataset.api.preprocessor;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.dataset.api.DataSetPreProcessor;
import org.nd4j.linalg.factory.Nd4j;
/**
* The RGBtoGrayscaleDataSetPreProcessor will turn a DataSet of a RGB image into a grayscale one.
* NOTE: Expects data format to be NCHW. After processing, the channel dimension is eliminated. (NCHW -> NHW)
*
* @author Alexandre Boulanger
*/
public class RGBtoGrayscaleDataSetPreProcessor implements DataSetPreProcessor {
private static final float RED_RATIO = 0.3f;
private static final float GREEN_RATIO = 0.59f;
private static final float BLUE_RATIO = 0.11f;
@Override
public void preProcess(DataSet dataSet) {
Preconditions.checkNotNull(dataSet, "Encountered null dataSet");
if(dataSet.isEmpty()) {
return;
}
INDArray originalFeatures = dataSet.getFeatures();
long[] originalShape = originalFeatures.shape();
// result shape is NHW
INDArray result = Nd4j.create(originalShape[0], originalShape[2], originalShape[3]);
for(long n = 0, numExamples = originalShape[0]; n < numExamples; ++n) {
// Extract channels
INDArray itemFeatures = originalFeatures.slice(n, 0); // shape is CHW
INDArray R = itemFeatures.slice(0, 0); // shape is HW
INDArray G = itemFeatures.slice(1, 0);
INDArray B = itemFeatures.slice(2, 0);
// Convert
R.muli(RED_RATIO);
G.muli(GREEN_RATIO);
B.muli(BLUE_RATIO);
R.addi(G).addi(B);
result.putSlice((int)n, R);
}
dataSet.setFeatures(result);
}
}