TensorFlow is an open source software library for machine learning, developed by Google and currently used in many of their projects.

In this article, we will create an Android app that can recognize five types of fruits.

Machine learning model inside your mobile app

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Using the machine learnings, you can reduce the significant amount of traffic, and you can get much faster responses from your web server.Because you can extract the meaning from the raw data at client side. For example, if you are using machine learning for image recognition, you can have the machine learning model running inside your mobile application so that your mobile application can recognize what kind of object is in each image.So that you can just send the label, such as a flower or human face, to the server.That can reduce the traffic.

Build an application that is powered by machine learning

TensorFlow is very valuable for people like me because I don’t have any sophisticated mathematical background.

Implement TensorFlow in Android

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Android added a JSON integration, which makes step easier.Just add one line to the build.gradle, and the Gradle take care or the rest of steps.Under the library archive, holding TensorFlow shared object is downloaded from JCenter, linked against the application automatically.

dependencies {
 ....
 compile 'org.tensorflow:tensorflow-android:1.2.0-preview'
}

Android release inference library to integrate TensorFlow for Java Application.

Add your model to the project

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We need the pre-trained model and label file.In the previous tutorial, we train model which does the image classification on a given image.You can download the model from here.Unzip this zip file, you will get retrained_labels.txt(label for objects) and rounded_graph.pb (pre-trained model). Put retrained_labels.txt and rounded_graph.pb into android/assets directory.

public List<Recognition> recognizeImage(final Bitmap bitmap) {
     // Log this method so that it can be analyzed with systrace.
     Trace.beginSection("recognizeImage");
     Trace.beginSection("preprocessBitmap");
     // Preprocess the image data from 0-255 int to normalized float based
     // on the provided parameters.
     bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
     for (int i = 0; i < intValues.length; ++i) {
         final int val = intValues[i];
         floatValues[i * 3 + 0] = (((val >> 16) & 0xFF) - imageMean) / imageStd;
         floatValues[i * 3 + 1] = (((val >> 8) & 0xFF) - imageMean) / imageStd;
         floatValues[i * 3 + 2] = ((val & 0xFF) - imageMean) / imageStd;
     }
     Trace.endSection();
     // Copy the input data into TensorFlow.
     Trace.beginSection("feed");
     inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3);
     Trace.endSection();
     // Run the inference call.
     Trace.beginSection("run");
     inferenceInterface.run(outputNames, logStats);
     Trace.endSection();
     // Copy the output Tensor back into the output array.
     Trace.beginSection("fetch");
     inferenceInterface.fetch(outputName, outputs);
     Trace.endSection();
     // Find the best classifications.
      PriorityQueue<Recognition> pq =new PriorityQueue<Recognition>(
                        3,
                        new Comparator<Recognition>() {
                            @Override
                            public int compare(Recognition lhs, Recognition rhs) {
                                // Intentionally reversed to put high confidence at the head of the queue.
                                return Float.compare(rhs.getConfidence(), lhs.getConfidence());
                            }
                        });
        for (int i = 0; i < outputs.length; ++i) {
            if (outputs[i] > THRESHOLD) {
                pq.add(
                        new Recognition(
                                "" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i], null));
            }
        }
        final ArrayList<Recognition> recognitions = new ArrayList<Recognition>();
        int recognitionsSize = Math.min(pq.size(), MAX_RESULTS);
        for (int i = 0; i < recognitionsSize; ++i) {
            recognitions.add(pq.poll());
        }
        Trace.endSection(); // "recognizeImage"
        return recognitions;
    }

At first, create TensorFlow inference interface, opening the model file from the asset in the APK.Then, Set up the input feed using Feed API. On mobile, the input feed tends to be retrieved from various sensors like a camera, accelerometer, then run the interface. Finally, you can fetch the results using fetch method over there.You would notice that those calls are all blocking calls.So you’d want to run them in a worker thread, rather than the main thread because API would take a long time.This one is Java API.you can use regular C++ API as well.

Download this project from GitHub

Related Past

Image Classify Using TensorFlow Lite

Google Cloud Vision API in Android APP TenserFlow Lite

Train Image classifier with TensorFlow

Train your Object Detection model locally with TensorFlow