PowerMiner/src/ObjectDetector.java

/* Copyright 2018 The TensorFlow Authors. 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.
You may obtain a copy of the License at

    http://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.
==============================================================================*/

import java.awt.AWTException;
import java.awt.Graphics2D;
import java.awt.Point;
import java.awt.Rectangle;
import java.awt.Robot;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.File;
import java.io.IOException;
import java.io.PrintStream;
import java.nio.ByteBuffer;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import javax.imageio.ImageIO;

import org.opencv.core.Core;
import org.opencv.core.Rect2d;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Tensor;
import org.tensorflow.types.UInt8;

/**
 * Java inference for the Object Detection API at:
 * https://github.com/tensorflow/models/blob/master/research/object_detection/
 */
public class ObjectDetector {
  
	SavedModelBundle model;
	Robot robot;
	
	public ObjectDetector() throws AWTException {
		this.model = SavedModelBundle.load("/home/dpapp/raccoon_dataset/results/checkpoint_56749/saved_model/", "serve");
		this.robot = new Robot();
	}	
	
	// Goal: reduce this to < 50 ms
	public ArrayList<DetectedObject> getObjectsInImage(BufferedImage image, double scoreThreshold) throws Exception {
		List<Tensor<?>> outputs = null;
		ArrayList<DetectedObject> detectedObjectsInImage = new ArrayList<DetectedObject>();
		//int count = 0;
		makeImageTensor(image);
        try (Tensor<UInt8> input = makeImageTensor(image)) {
          outputs =
              model
                  .session()
                  .runner()
                  .feed("image_tensor", input)
                  .fetch("detection_scores")
                  .fetch("detection_classes")
                  .fetch("detection_boxes")
                  .run();
        }
        
        try (Tensor<Float> scoresT = outputs.get(0).expect(Float.class);
        	Tensor<Float> classesT = outputs.get(1).expect(Float.class);
            Tensor<Float> boxesT = outputs.get(2).expect(Float.class)) {
           
            int maxObjects = (int) scoresT.shape()[1];
            float[] scores = scoresT.copyTo(new float[1][maxObjects])[0];
            float[] classes = classesT.copyTo(new float[1][maxObjects])[0];
            float[][] boxes = boxesT.copyTo(new float[1][maxObjects][4])[0];

            for (int i = 0; i < scores.length; ++i) {
              if (scores[i] > scoreThreshold) {
            	  //count++;
            	  detectedObjectsInImage.add(new DetectedObject(scores[i], classes[i], boxes[i]));
              }
            }
          }
       // return count;
        return detectedObjectsInImage;
	}
	
	
	// This is too slow -> running object detection each time reduces the framerate to 3fps, which messses up object tracking
	public boolean isObjectPresentInBoundingBoxInImage(ArrayList<DetectedObject> detectedObjects, Rect2d boundingBox, String objectClass) throws Exception {
		for (DetectedObject detectedObject : detectedObjects) {
			if (detectedObject.getDetectionClass().equals(objectClass)) {
				if ((Math.abs(detectedObject.getBoundingRect2d().x - boundingBox.x) < 10) &&
					(Math.abs(detectedObject.getBoundingRect2d().y - boundingBox.y) < 10) &&
					(Math.abs(detectedObject.getBoundingRect2d().width - boundingBox.width) < 10) &&
					(Math.abs(detectedObject.getBoundingRect2d().height - boundingBox.height) < 10)) {
					return true;
				}
			}
		}
		return false;
	}
    
	public ArrayList<DetectedObject> getObjectsOfClassInList(ArrayList<DetectedObject> detectedObjects, String objectClass) {
		ArrayList<DetectedObject> detectedObjectsOfType = new ArrayList<DetectedObject>();
		for (DetectedObject detectedObject : detectedObjects) {
			if (detectedObject.getDetectionClass().equals(objectClass)) {
				detectedObjectsOfType.add(detectedObject);
			}
		}
		return detectedObjectsOfType;
	}

  private static Tensor<UInt8> makeImageTensor(BufferedImage image) throws IOException {
	BufferedImage formattedImage = convertBufferedImage(image, BufferedImage.TYPE_3BYTE_BGR);
	byte[] data = ((DataBufferByte) formattedImage.getData().getDataBuffer()).getData();
	bgr2rgb(data);  
    final long BATCH_SIZE = 1;
    final long CHANNELS = 3;
    long[] shape = new long[] {BATCH_SIZE, formattedImage.getHeight(), formattedImage.getWidth(), CHANNELS};
    return Tensor.create(UInt8.class, shape, ByteBuffer.wrap(data));
  }
  
  private static BufferedImage convertBufferedImage(BufferedImage sourceImage, int bufferedImageType) {
    BufferedImage image = new BufferedImage(sourceImage.getWidth(), sourceImage.getHeight(), bufferedImageType);
    Graphics2D g2d = image.createGraphics();
    g2d.drawImage(sourceImage, 0, 0, null);
    g2d.dispose();
    return image;
  }
  
  private static void bgr2rgb(byte[] data) {
	    for (int i = 0; i < data.length; i += 3) {
	      byte tmp = data[i];
	      data[i] = data[i + 2];
	      data[i + 2] = tmp;
	    }
	  }
  
  public BufferedImage captureScreenshotGameWindow() throws IOException, AWTException {
		Rectangle area = new Rectangle(Constants.GAME_WINDOW_OFFSET_X, Constants.GAME_WINDOW_OFFSET_Y, Constants.GAME_WINDOW_WIDTH, Constants.GAME_WINDOW_HEIGHT);
		return robot.createScreenCapture(area);
	  }
}