senser

Demo Project opencv + Flink streaming

To start the project, run

first build

./mvnw compile -P generate-frontend

start

./mvnw spring-boot:run

starts edu.tuberlin.senser.images.MainApp

Visit http://localhost:8080/

Dependencies

• You should have git on the path
• Maven is self contained using the wrapper accessible from mvnw

How does it work

The app is orchestrated using Spring for dependency injection. edu.tuberlin.senser.images.MainApp is the main entry point. It uses classpath scanning to find components to initialize within the same package or underneath.

The scanning finds edu.tuberlin.senser.images.facedetection.video.FaceRecognizerInVideo which starts the face detection. It also gets via dependency injection a link to a Person Service, which is connected to an in memory database.

It reads from a video or video stream by opening the resource specified under the name senser.videosource in application.properties. This can be a remote resource with streaming video, or a locally stored video file.

Once a face is found, we try to recognize it, by asking the lbphFaceRecognizer

int[] plabel = new int[1];
double[] pconfidence = new double[1];

lbphFaceRecognizer.predict(face_resized, plabel, pconfidence);
LOG.info("Prediction confidence {}", pconfidence[0]);

If the confidence is sufficiently high, we know we have a known face.

For simplicity we just add each picture to the training samples, by adding it to the database

box_text = personService.registerImage(personID, face_resized, counter, confidence);

and updating the recognizer with the newly found face

lbphFaceRecognizer.update(images, label);

Since we store images into the database, we can also inspect them live. For this end a web controller is exposed as a component, edu.tuberlin.senser.images.web.controller.ImageController which listens on localhost:8080/images/{id}

Just visit https://localhost:8080/images to view a global view of all images.

So if there is a person with ID 1 found we can quickly retrieve all training samples using https://localhost:8080/images/1 . This will hit the controller, which retrieves the Person Object from the database, sets a model and forwards it to a view "faces"

model.addAttribute("person", personRepository.findOne(id));
return "faces";

faces is a view in the template cache, found in the resources section templates/faces.html, which uses Thymeleaf (similar to jsp) to dynamically render the resulting page on the server.

<tr th:each="image : ${person.images}">
    <img th:src="*{'data:image/jpg;base64,'+image.getAsBase64()}" alt="Person" />
</tr>

To start the flink streaming, the following is used StreamExample.startFlinkStream();

To just start the Face detection run

• edu.tuberlin.senser.images.facedetection.video.WatchTV

This runs face detection on a live video feed.

IDE Note

If you are running on a 64 bit OS, make sure you start your IDE in 64 bit mode as well, otherwise the dependencies might not resolve correctly.

To determine the correct opencv platform bindings, the os-maven-plugin is used. For integration issues with your IDE, check Issues with eclipse m2e or other ides.