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/*
* Copyright 2019 Amazon.com, Inc. or its affiliates. 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. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0/
*
* or in the "license" file accompanying this file. This file 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 ai.djl.modality.cv.translator;
import ai.djl.Model;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.transform.CenterCrop;
import ai.djl.modality.cv.transform.Normalize;
import ai.djl.modality.cv.transform.Resize;
import ai.djl.modality.cv.transform.ToTensor;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.translate.ArgumentsUtil;
import ai.djl.translate.Batchifier;
import ai.djl.translate.Pipeline;
import ai.djl.translate.Transform;
import ai.djl.translate.Translator;
import ai.djl.translate.TranslatorContext;
import ai.djl.util.Utils;
import java.io.IOException;
import java.io.InputStream;
import java.net.MalformedURLException;
import java.net.URL;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
/**
* Built-in {@code Translator} that provides default image pre-processing.
*
* @param the output object type
*/
public abstract class BaseImageTranslator implements Translator {
private static final float[] MEAN = {0.485f, 0.456f, 0.406f};
private static final float[] STD = {0.229f, 0.224f, 0.225f};
protected Pipeline pipeline;
private Image.Flag flag;
private Batchifier batchifier;
/**
* Constructs an ImageTranslator with the provided builder.
*
* @param builder the data to build with
*/
public BaseImageTranslator(BaseBuilder builder) {
flag = builder.flag;
pipeline = builder.pipeline;
batchifier = builder.batchifier;
}
/** {@inheritDoc} */
@Override
public Batchifier getBatchifier() {
return batchifier;
}
/** {@inheritDoc} */
@Override
public NDList processInput(TranslatorContext ctx, Image input) {
NDArray array = input.toNDArray(ctx.getNDManager(), flag);
return pipeline.transform(new NDList(array));
}
/**
* A builder to extend for all classes extending the {@link BaseImageTranslator}.
*
* @param the concrete builder type
*/
@SuppressWarnings("rawtypes")
public abstract static class BaseBuilder {
protected int width = 224;
protected int height = 224;
protected Image.Flag flag = Image.Flag.COLOR;
protected Pipeline pipeline;
protected Batchifier batchifier = Batchifier.STACK;
/**
* Sets the optional {@link ai.djl.modality.cv.Image.Flag} (default is {@link
* Image.Flag#COLOR}).
*
* @param flag the color mode for the images
* @return this builder
*/
public T optFlag(Image.Flag flag) {
this.flag = flag;
return self();
}
/**
* Sets the {@link Pipeline} to use for pre-processing the image.
*
* @param pipeline the pre-processing pipeline
* @return this builder
*/
public T setPipeline(Pipeline pipeline) {
this.pipeline = pipeline;
return self();
}
/**
* Adds the {@link Transform} to the {@link Pipeline} use for pre-processing the image.
*
* @param transform the {@link Transform} to be added
* @return this builder
*/
public T addTransform(Transform transform) {
if (pipeline == null) {
pipeline = new Pipeline();
}
pipeline.add(transform);
return self();
}
/**
* Sets the {@link Batchifier} for the {@link Translator}.
*
* @param batchifier the {@link Batchifier} to be set
* @return this builder
*/
public T optBatchifier(Batchifier batchifier) {
this.batchifier = batchifier;
return self();
}
protected abstract T self();
protected void validate() {
if (pipeline == null) {
throw new IllegalArgumentException("pipeline is required.");
}
}
protected void configPreProcess(Map arguments) {
if (pipeline == null) {
pipeline = new Pipeline();
}
width = ArgumentsUtil.intValue(arguments, "width", 224);
height = ArgumentsUtil.intValue(arguments, "height", 224);
if (arguments.containsKey("flag")) {
flag = Image.Flag.valueOf(arguments.get("flag").toString());
}
String resize = ArgumentsUtil.stringValue(arguments, "resize", "false");
if ("true".equals(resize)) {
addTransform(new Resize(width, height));
} else if (!"false".equals(resize)) {
String[] tokens = resize.split("\\s*,\\s*");
if (tokens.length > 1) {
addTransform(
new Resize(
(int) Double.parseDouble(tokens[0]),
(int) Double.parseDouble(tokens[1])));
} else {
addTransform(new Resize((int) Double.parseDouble(tokens[0])));
}
}
if (ArgumentsUtil.booleanValue(arguments, "centerCrop", false)) {
addTransform(new CenterCrop(width, height));
}
if (ArgumentsUtil.booleanValue(arguments, "toTensor", true)) {
addTransform(new ToTensor());
}
String normalize = ArgumentsUtil.stringValue(arguments, "normalize", "false");
if ("true".equals(normalize)) {
addTransform(new Normalize(MEAN, STD));
} else if (!"false".equals(normalize)) {
String[] tokens = normalize.split("\\s*,\\s*");
if (tokens.length != 6) {
throw new IllegalArgumentException("Invalid normalize value: " + normalize);
}
float[] mean = {
Float.parseFloat(tokens[0]),
Float.parseFloat(tokens[1]),
Float.parseFloat(tokens[2])
};
float[] std = {
Float.parseFloat(tokens[3]),
Float.parseFloat(tokens[4]),
Float.parseFloat(tokens[5])
};
addTransform(new Normalize(mean, std));
}
String range = (String) arguments.get("range");
if ("0,1".equals(range)) {
addTransform(a -> a.div(255f));
} else if ("-1,1".equals(range)) {
addTransform(a -> a.div(128f).sub(1));
}
if (arguments.containsKey("batchifier")) {
batchifier = Batchifier.fromString((String) arguments.get("batchifier"));
}
}
protected void configPostProcess(Map arguments) {}
}
/** A Builder to construct a {@code ImageClassificationTranslator}. */
@SuppressWarnings("rawtypes")
public abstract static class ClassificationBuilder
extends BaseBuilder {
protected SynsetLoader synsetLoader;
/**
* Sets the name of the synset file listing the potential classes for an image.
*
* @param synsetArtifactName a file listing the potential classes for an image
* @return the builder
*/
public T optSynsetArtifactName(String synsetArtifactName) {
synsetLoader = new SynsetLoader(synsetArtifactName);
return self();
}
/**
* Sets the URL of the synset file.
*
* @param synsetUrl the URL of the synset file
* @return the builder
*/
public T optSynsetUrl(String synsetUrl) {
try {
this.synsetLoader = new SynsetLoader(new URL(synsetUrl));
} catch (MalformedURLException e) {
throw new IllegalArgumentException("Invalid synsetUrl: " + synsetUrl, e);
}
return self();
}
/**
* Sets the potential classes for an image.
*
* @param synset the potential classes for an image
* @return the builder
*/
public T optSynset(List synset) {
synsetLoader = new SynsetLoader(synset);
return self();
}
/** {@inheritDoc} */
@Override
protected void validate() {
super.validate();
if (synsetLoader == null) {
synsetLoader = new SynsetLoader("synset.txt");
}
}
/** {@inheritDoc} */
@Override
protected void configPostProcess(Map arguments) {
String synset = (String) arguments.get("synset");
if (synset != null) {
optSynset(Arrays.asList(synset.split(",")));
}
String synsetUrl = (String) arguments.get("synsetUrl");
if (synsetUrl != null) {
optSynsetUrl(synsetUrl);
}
String synsetFileName = (String) arguments.get("synsetFileName");
if (synsetFileName != null) {
optSynsetArtifactName(synsetFileName);
}
}
}
protected static final class SynsetLoader {
private String synsetFileName;
private URL synsetUrl;
private List synset;
public SynsetLoader(List synset) {
this.synset = synset;
}
public SynsetLoader(URL synsetUrl) {
this.synsetUrl = synsetUrl;
}
public SynsetLoader(String synsetFileName) {
this.synsetFileName = synsetFileName;
}
public List load(Model model) throws IOException {
if (synset != null) {
return synset;
} else if (synsetUrl != null) {
try (InputStream is = synsetUrl.openStream()) {
return Utils.readLines(is);
}
}
return model.getArtifact(synsetFileName, Utils::readLines);
}
}
}
