VGG16SparkJavaWebApp.java

package org.deeplearning4j.VGGwebDemo;

import org.nd4j.linalg.factory.Nd4j;
import org.deeplearning4j.nn.modelimport.keras.trainedmodels.Utils.ImageNetLabels;
import org.datavec.image.loader.NativeImageLoader;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.trainedmodels.TrainedModels;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.VGG16ImagePreProcessor;
import javax.servlet.MultipartConfigElement;
import java.io.File;
import java.io.InputStream;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.StandardCopyOption;
import static spark.Spark.options;
import static spark.Spark.get;
import static spark.Spark.post;
import static spark.Spark.staticFiles;
import java.util.Collections;
import java.util.Map;
import java.util.TreeMap;
import org.nd4j.shade.jackson.databind.ObjectMapper;

/**
 * Created by tomhanlon on 1/25/17.
 */
public class VGG16SparkJavaWebApp {
  public static void main(String[] args) throws Exception {

    File locationToSave = new File("vgg16.zip");
    ComputationGraph vgg16 = ModelSerializer.restoreComputationGraph(locationToSave);


    // make upload directory
    File uploadDir = new File("upload");
    uploadDir.mkdir(); // create the upload directory if it doesn't exist

    // form
    String form = "<form method='post' action='getPredictions' enctype='multipart/form-data'>\n" +
        "    <input type='file' name='uploaded_file'>\n" +
        "    <button>Upload picture</button>\n" +
        "</form>";

    staticFiles.location("/Users/tomhanlon/SkyMind/webcontent");// Static files
    //CorsFilter.apply();
    //options("/", (req, res) -> {
    //Appease something
    //   });
    options("/*", (req, res) -> "Hello World");
    get("/hello", (req, res) -> "Hello World");
    get("VGGpredict", (req, res) -> form);
    //post("getPredictions",(req, res) -> "GET RESULTS");

    post("/getPredictions", (req, res) -> {

      Path tempFile = Files.createTempFile(uploadDir.toPath(), "", "");

      req.attribute("org.eclipse.jetty.multipartConfig", new MultipartConfigElement("/temp"));

      try (InputStream input = req.raw().getPart("uploaded_file").getInputStream()) { // getPart needs to use same "name" as input field in form
        Files.copy(input, tempFile, StandardCopyOption.REPLACE_EXISTING);
      }

      //logInfo(req, tempFile);
      //return "<h1>You uploaded this image:<h1><img src='" + tempFile.getFileName() + "'>";

      long pipelineTime = System.currentTimeMillis();

      File file = tempFile.toFile();
      //File file = new File(path);
      NativeImageLoader loader = new NativeImageLoader(224, 224, 3);
      INDArray image = loader.asMatrix(file);
      file.delete();
      DataNormalization scaler = new VGG16ImagePreProcessor();
      scaler.transform(image);
      //System.out.print(image);

      long ffTime = System.currentTimeMillis();
      INDArray[] output = vgg16.output(false,image);
      ffTime = System.currentTimeMillis() - ffTime;

      // sort to get top 5
      INDArray[] sorted = Nd4j.sortWithIndices(output[0], 1, false);
      // sorted map for results
      //Map<Float, String> map = new TreeMap<Float, String>(Collections.reverseOrder());
      //VGGResults vggResults = new VGGResults(label,pred);
      VGGResults[] vggResultsArray  = new VGGResults[5];

      // finish benchmark
      pipelineTime = System.currentTimeMillis() - pipelineTime;

      // Get top 5
      for (int i = 0; i < 5; i++) {

        // Get prediction percent
        Float prediction = sorted[1].getFloat(i) * 100;

        // extract label for prediction
        String Label = ImageNetLabels.getLabel(sorted[0].getInt(i));

        // put both in Result array
        vggResultsArray[i] = new VGGResults(Label, prediction);

      }
      // Jackson obect mapper
      // ##### I AM HERE #####
      ObjectMapper mapper = new ObjectMapper();
      String predictions = mapper.writeValueAsString(vggResultsArray);
      //String predictions = mapper.writeValueAsString(map);
      String predictionmunge = "{" +
          "\"data\":" + predictions +
          ", \"performance\":{ \"feedforward\":" + ffTime + ",\"total\":" + pipelineTime + "}" +
          ", \"network\":{ \"parameters\":" + vgg16.numParams() + ",\"layers\":" + vgg16.getNumLayers() + "}}";
      // return "<h4> '" + predictions  + "' </h4>" +
      //        "Would you like to try another" +
      //        form;
      return predictionmunge ;
      //return "<h1>Your image is: '" + tempFile.getName(1).toString() + "' </h1>";

    });
  }
}