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dengwenjie
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/*
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* Copyright 2023 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
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* with the License. A copy of the License is located at
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*
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* http://aws.amazon.com/apache2.0/
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*
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* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
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* OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
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* and limitations under the License.
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*/
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package smartai.examples.face;
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import ai.djl.ModelException;
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import ai.djl.inference.Predictor;
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import ai.djl.modality.Classifications;
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import ai.djl.modality.Input;
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import ai.djl.modality.Output;
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import ai.djl.modality.cv.Image;
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import ai.djl.ndarray.NDList;
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import ai.djl.repository.zoo.Criteria;
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import ai.djl.repository.zoo.ZooModel;
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import ai.djl.translate.NoBatchifyTranslator;
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import ai.djl.translate.TranslateException;
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import ai.djl.translate.TranslatorContext;
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import ai.djl.util.JsonUtils;
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import ai.djl.util.Utils;
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import com.google.gson.reflect.TypeToken;
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import java.io.IOException;
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import java.io.InputStream;
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import java.lang.reflect.Type;
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import java.net.URL;
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import java.nio.file.Paths;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Map;
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public class PythonTranslator implements NoBatchifyTranslator<byte[], Classifications> {
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private ZooModel<Input, Output> model;
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private Predictor<Input, Output> predictor;
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@Override
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public void prepare(TranslatorContext ctx) throws ModelException, IOException {
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if (predictor == null) {
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Criteria<Input, Output> criteria =
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Criteria.builder()
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.setTypes(Input.class, Output.class)
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.optModelPath(Paths.get("src/test/python"))
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.optEngine("Python")
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.build();
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model = criteria.loadModel();
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predictor = model.newPredictor();
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}
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}
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// @Override
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// public NDList processInput(TranslatorContext ctx, String url)
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// throws IOException, TranslateException {
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// Input input = new Input();
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// try (InputStream is = new URL(url).openStream()) {
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// input.add("data", Utils.toByteArray(is));
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// }
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// input.addProperty("Content-Type", "image/jpeg");
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// // calling preprocess() function in model.py
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// input.addProperty("handler", "preprocess");
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// Output output = predictor.predict(input);
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// if (output.getCode() != 200) {
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// throw new TranslateException("Python preprocess() failed: " + output.getMessage());
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// }
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//
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// return output.getDataAsNDList(ctx.getNDManager());
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// }
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@Override
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public NDList processInput(TranslatorContext ctx, byte[] image)
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throws IOException, TranslateException {
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Input input = new Input();
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input.add("data", image);
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input.addProperty("Content-Type", "image/jpeg");
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// calling preprocess() function in model.py
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input.addProperty("handler", "preprocess");
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Output output = predictor.predict(input);
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if (output.getCode() != 200) {
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throw new TranslateException("Python preprocess() failed: " + output.getMessage());
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}
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return output.getDataAsNDList(ctx.getNDManager());
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}
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@Override
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public Classifications processOutput(TranslatorContext ctx, NDList list)
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throws TranslateException {
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Input input = new Input();
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input.add("data", list);
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// calling postprocess() function in processing.py
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input.addProperty("handler", "postprocess");
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Output output = predictor.predict(input);
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if (output.getCode() != 200) {
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throw new TranslateException("Python postprocess() failed: " + output.getMessage());
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}
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String json = output.getData().getAsString();
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System.out.println("json:" + json);
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return null;
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}
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public void close() {
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if (predictor != null) {
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predictor.close();
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model.close();
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predictor = null;
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model = null;
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}
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}
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}
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package smartai.examples.face;
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import ai.djl.Application;
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import ai.djl.Device;
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import ai.djl.MalformedModelException;
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import ai.djl.inference.Predictor;
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import ai.djl.modality.Classifications;
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import ai.djl.modality.audio.Audio;
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import ai.djl.modality.audio.AudioFactory;
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import ai.djl.modality.audio.translator.SpeechRecognitionTranslatorFactory;
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import ai.djl.repository.Artifact;
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import ai.djl.repository.MRL;
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import ai.djl.repository.zoo.Criteria;
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import ai.djl.repository.zoo.ModelNotFoundException;
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import ai.djl.repository.zoo.ModelZoo;
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import ai.djl.repository.zoo.ZooModel;
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import ai.djl.translate.TranslateException;
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import lombok.extern.slf4j.Slf4j;
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import java.io.IOException;
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import java.nio.file.Files;
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import java.nio.file.Path;
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import java.nio.file.Paths;
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import java.util.List;
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import java.util.Map;
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/**
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* @author dwj
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* @date 2025/7/29
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*/
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@Slf4j
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public class Test {
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public static void main(String[] args) throws ModelNotFoundException, MalformedModelException, IOException, TranslateException {
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// PythonTranslator translator = new PythonTranslator();
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// Criteria<byte[], Classifications> criteria =
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// Criteria.builder()
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// .setTypes(byte[].class, Classifications.class)
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// .optModelPath(Paths.get("/Users/wenjie/Documents/develop/model/arcfaceresnet100-11-int8.onnx"))
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// .optEngine("OnnxRuntime")
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// .optTranslator(translator)
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// .build();
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// String path = "/Users/wenjie/Downloads/facetest/jsy.jpg";
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// try (ZooModel<byte[], Classifications> model = criteria.loadModel();
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// Predictor<byte[], Classifications> predictor = model.newPredictor()) {
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// byte[] data = Files.readAllBytes(Paths.get(path));
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// Classifications ret = predictor.predict(data);
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// System.out.println(ret);
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// }
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//
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// // unload python model
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// translator.close();
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// Load model.
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// Wav2Vec2 model is a speech model that accepts a float array corresponding to the raw
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// waveform of the speech signal.
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// String url = "/Users/wenjie/Downloads/20210601_u2++_conformer_exp/final.pt";
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// Criteria<Audio, String> criteria =
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// Criteria.builder()
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// .setTypes(Audio.class, String.class)
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//// .optModelUrls(url)
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// .optModelPath(Paths.get(url))
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// .optDevice(Device.cpu()) // torchscript model only support CPU
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// .optTranslatorFactory(new SpeechRecognitionTranslatorFactory())
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//// .optModelName("data.pkl")
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// .optEngine("PyTorch")
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// .build();
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//
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// // Read in audio file
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// String wave = "https://resources.djl.ai/audios/speech.wav";
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// Audio audio = AudioFactory.newInstance().fromUrl(wave);
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// try (ZooModel<Audio, String> model = criteria.loadModel();
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// Predictor<Audio, String> predictor = model.newPredictor()) {
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// String result = predictor.predict(audio);
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// log.info("Result: {}", result);
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// }
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boolean withArtifacts =
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args.length > 0 && ("--artifact".equals(args[0]) || "-a".equals(args[0]));
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if (!withArtifacts) {
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log.info("============================================================");
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log.info("user ./gradlew listModel --args='-a' to show artifact detail");
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log.info("============================================================");
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}
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Map<Application, List<Artifact>> models = ModelZoo.listModels();
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for (Map.Entry<Application, List<Artifact>> entry : models.entrySet()) {
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String appName = entry.getKey().toString();
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for (Artifact artifact : entry.getValue()) {
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if (withArtifacts) {
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log.info("{} djl://{}", appName, artifact);
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} else {
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log.info("{} {}", appName, artifact);
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}
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}
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}
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}
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}
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@@ -67,7 +67,7 @@ public class FaceDetDemo {
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//高精度模型，速度慢
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config.setModelEnum(FaceDetModelEnum.RETINA_FACE);//人脸检测模型
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//下载模型并替换本地路径，下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
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config.setModelPath("/Users/xxx/Documents/develop/model/retinaface.pt");
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config.setModelPath("/Users/wenjie/Documents/develop/face_model/retinaface.pt");
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config.setConfidenceThreshold(FaceDetectConstant.DEFAULT_CONFIDENCE_THRESHOLD);//只返回相似度大于该值的人脸
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config.setNmsThresh(FaceDetectConstant.NMS_THRESHOLD);//用于去除重复的人脸框，当两个框的重叠度超过该值时，只保留一个
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return FaceDetModelFactory.getInstance().getModel(config);
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@Test
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public void testFaceDetect(){
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try {
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FaceDetModel faceModel = FaceDetModelFactory.getInstance().getModel();
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FaceDetModel faceModel = getFaceDetModel();
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R<DetectionResponse> detectedResult = faceModel.detect(imgPath);
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if(detectedResult.isSuccess()){
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log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult.getData()));
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// if(detectedResult.isSuccess()){
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// log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult.getData()));
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// }else{
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// log.info("人脸检测失败：{}", detectedResult.getMessage());
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// }
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long start = System.currentTimeMillis();
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R<DetectionResponse> detectedResult2 = faceModel.detect("/Users/wenjie/Downloads/facetest/surprise.png");
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log.info("耗时：{}", System.currentTimeMillis() - start);
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if(detectedResult2.isSuccess()){
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log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult2.getData()));
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}else{
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log.info("人脸检测失败：{}", detectedResult.getMessage());
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log.info("人脸检测失败：{}", detectedResult2.getMessage());
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}
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} catch (Exception e) {
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e.printStackTrace();
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@@ -63,7 +63,7 @@ public class FaceRecDemo {
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//高精度模型，速度慢
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config.setModelEnum(FaceDetModelEnum.RETINA_FACE);//人脸检测模型
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//下载模型并替换本地路径，下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
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config.setModelPath("/Users/xxx/Documents/develop/model/retinaface.pt");
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// config.setModelPath("/Users/wenjie/Documents/develop/model/retinaface.pt");
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config.setConfidenceThreshold(FaceDetectConstant.DEFAULT_CONFIDENCE_THRESHOLD);//只返回相似度大于该值的人脸
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config.setNmsThresh(FaceDetectConstant.NMS_THRESHOLD);//用于去除重复的人脸框，当两个框的重叠度超过该值时，只保留一个
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config.setDevice(device);
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@@ -170,7 +170,7 @@ public class FaceRecDemo {
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FaceRecConfig config = new FaceRecConfig();
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//高精度模型，速度慢, 追求速度请更换高速模型，具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
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config.setModelEnum(FaceRecModelEnum.ELASTIC_FACE_MODEL);//人脸检测模型
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config.setModelPath("/Users/xxx/Documents/develop/model/elasticface.pt");
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config.setModelPath("/Users/wenjie/Documents/develop/model/elasticface.pt");
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//裁剪人脸：如果图片已经是裁剪过的，则请将此参数设置为false
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config.setCropFace(true);
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//开启人脸对齐：适用于人脸不正的场景，开启将提升人脸特征准确度，关闭可以提升性能
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