Implementing computer vision algorithms using Java wrapper classes

14 Sep 2023

Computer vision is a field of computer science that focuses on enabling computers to understand and interpret visual information from images or videos. It involves tasks such as object detection, image recognition, and image processing. In this blog post, we will explore how to implement computer vision algorithms using Java wrapper classes, which provide a convenient way to access and utilize popular computer vision libraries.

Java Wrapper Classes for Computer Vision Libraries

There are several popular computer vision libraries available that provide extensive functionality and pre-trained models for performing various computer vision tasks. Some of these libraries include OpenCV, TensorFlow, and DLib. To use these libraries in our Java projects, we can take advantage of the Java wrapper classes provided by these libraries.

OpenCV Java Wrapper

OpenCV is a widely used open-source computer vision library with support for various programming languages, including Java. To use OpenCV in Java, we need to import the OpenCV Java wrapper class and load the necessary native libraries. Here’s an example code snippet that demonstrates how to use the OpenCV Java wrapper for detecting and drawing bounding boxes around faces in an image:

import org.opencv.core.*;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.objdetect.CascadeClassifier;

public class FaceDetection {

    public static void main(String[] args) {

        // Load the OpenCV native library
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        // Load the cascade classifier for face detection
        CascadeClassifier faceCascade = new CascadeClassifier();
        faceCascade.load("haarcascade_frontalface_default.xml");

        // Load the input image
        Mat image = Imgcodecs.imread("input.jpg");

        // Convert the image to grayscale
        Mat grayImage = new Mat();
        Imgproc.cvtColor(image, grayImage, Imgproc.COLOR_BGR2GRAY);

        // Detect faces in the image
        MatOfRect faces = new MatOfRect();
        faceCascade.detectMultiScale(grayImage, faces);

        // Draw bounding boxes around the detected faces
        for (Rect rect : faces.toArray()) {
            Imgproc.rectangle(image, new Point(rect.x, rect.y),
                    new Point(rect.x + rect.width, rect.y + rect.height),
                    new Scalar(0, 255, 0), 3);
        }

        // Save the output image
        Imgcodecs.imwrite("output.jpg", image);

        System.out.println("Face detection completed successfully!");
    }
}

TensorFlow Java Wrapper

TensorFlow is a popular machine learning framework that also supports computer vision tasks. To use TensorFlow in Java, we can utilize the TensorFlow Java API, which provides a Java wrapper for the TensorFlow library. Here’s an example code snippet that demonstrates how to use the TensorFlow Java wrapper for image classification:

import org.tensorflow.*;
import org.tensorflow.Tensor;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.List;
import java.util.stream.Collectors;

public class ImageClassification {

    public static void main(String[] args) throws Exception {

        // Load the pre-trained TensorFlow model
        byte[] graphDef = Files.readAllBytes(Paths.get("model.pb"));
        try (Graph graph = new Graph()) {
            graph.importGraphDef(graphDef);

            // Load and preprocess the input image
            byte[] imageBytes = Files.readAllBytes(Paths.get("input.jpg"));
            Tensor tensor = Tensor.create(imageBytes);

            // Create a session and run the inference
            try (Session session = new Session(graph)) {
                List<Tensor<?>> outputs =
                        session.runner()
                                .feed("input", tensor)
                                .fetch("output")
                                .run();

                // Process the output tensors
                Tensor<?> output = outputs.get(0);
                float[] probabilities = output.copyTo(new float[1][numClasses])[0];

                // Print the top predicted classes
                List<String> labels =
                        Files.readAllLines(Paths.get("labels.txt"));
                List<String> topClasses =
                        IntStream.range(0, numClasses)
                                .mapToObj(i -> new AbstractMap.SimpleEntry<>(labels.get(i), probabilities[i]))
                                .sorted((e1, e2) -> Float.compare(e2.getValue(), e1.getValue()))
                                .limit(k)
                                .map(AbstractMap.SimpleEntry::getKey)
                                .collect(Collectors.toList());

                System.out.println("Top predicted classes: " + topClasses);
            }
        }

        System.out.println("Image classification completed successfully!");
    }
}

Conclusion

In this blog post, we explored how to implement computer vision algorithms using Java wrapper classes for popular computer vision libraries. We demonstrated the usage of OpenCV Java wrapper for face detection and TensorFlow Java wrapper for image classification. These wrapper classes provide a convenient way to leverage the functionality and pre-trained models of these computer vision libraries in Java projects. By utilizing these wrappers, developers can easily integrate computer vision capabilities into their Java applications and build powerful visual intelligence systems.

#computerVision #JavaWrapper #OpenCV #TensorFlow #ImageProcessing