Examples with Pose com.amplifyframework.predictions.models.Pose used on opensource projects

Example 1 with Pose

use of com.amplifyframework.predictions.models.Pose in project amplify-android by aws-amplify.

the class AWSRekognitionService method detectCelebrities.

private List<CelebrityDetails> detectCelebrities(ByteBuffer imageData) throws PredictionsException {
    RecognizeCelebritiesRequest request = new RecognizeCelebritiesRequest().withImage(new Image().withBytes(imageData));
    // Recognize celebrities in the given image via Amazon Rekognition
    final RecognizeCelebritiesResult result;
    try {
        result = rekognition.recognizeCelebrities(request);
    } catch (AmazonClientException serviceException) {
        throw new PredictionsException("Amazon Rekognition encountered an error while recognizing celebrities.", serviceException, "See attached service exception for more details.");
    }
    List<CelebrityDetails> celebrities = new ArrayList<>();
    for (com.amazonaws.services.rekognition.model.Celebrity rekognitionCelebrity : result.getCelebrityFaces()) {
        Celebrity amplifyCelebrity = Celebrity.builder().id(rekognitionCelebrity.getId()).value(rekognitionCelebrity.getName()).confidence(rekognitionCelebrity.getMatchConfidence()).build();
        // Get face-specific celebrity details from the result
        ComparedFace face = rekognitionCelebrity.getFace();
        RectF box = RekognitionResultTransformers.fromBoundingBox(face.getBoundingBox());
        Pose pose = RekognitionResultTransformers.fromRekognitionPose(face.getPose());
        List<Landmark> landmarks = RekognitionResultTransformers.fromLandmarks(face.getLandmarks());
        // Get URL links that are relevant to celebrities
        List<URL> urls = new ArrayList<>();
        for (String url : rekognitionCelebrity.getUrls()) {
            try {
                urls.add(new URL(url));
            } catch (MalformedURLException badUrl) {
            // Ignore bad URL
            }
        }
        CelebrityDetails details = CelebrityDetails.builder().celebrity(amplifyCelebrity).box(box).pose(pose).landmarks(landmarks).urls(urls).build();
        celebrities.add(details);
    }
    return celebrities;
}

Example 2 with Pose

use of com.amplifyframework.predictions.models.Pose in project amplify-android by aws-amplify.

the class AWSRekognitionService method detectEntities.

private List<EntityDetails> detectEntities(ByteBuffer imageData) throws PredictionsException {
    DetectFacesRequest request = new DetectFacesRequest().withImage(new Image().withBytes(imageData)).withAttributes(Attribute.ALL.toString());
    // Detect entities in the given image via Amazon Rekognition
    final DetectFacesResult result;
    try {
        result = rekognition.detectFaces(request);
    } catch (AmazonClientException serviceException) {
        throw new PredictionsException("Amazon Rekognition encountered an error while detecting faces.", serviceException, "See attached service exception for more details.");
    }
    List<EntityDetails> entities = new ArrayList<>();
    for (FaceDetail face : result.getFaceDetails()) {
        // Extract details from face detection
        RectF box = RekognitionResultTransformers.fromBoundingBox(face.getBoundingBox());
        AgeRange ageRange = RekognitionResultTransformers.fromRekognitionAgeRange(face.getAgeRange());
        Pose pose = RekognitionResultTransformers.fromRekognitionPose(face.getPose());
        List<Landmark> landmarks = RekognitionResultTransformers.fromLandmarks(face.getLandmarks());
        List<BinaryFeature> features = RekognitionResultTransformers.fromFaceDetail(face);
        // Gender detection
        com.amazonaws.services.rekognition.model.Gender rekognitionGender = face.getGender();
        Gender amplifyGender = Gender.builder().value(GenderBinaryTypeAdapter.fromRekognition(rekognitionGender.getValue())).confidence(rekognitionGender.getConfidence()).build();
        // Emotion detection
        List<Emotion> emotions = new ArrayList<>();
        for (com.amazonaws.services.rekognition.model.Emotion rekognitionEmotion : face.getEmotions()) {
            EmotionType emotion = EmotionTypeAdapter.fromRekognition(rekognitionEmotion.getType());
            Emotion amplifyEmotion = Emotion.builder().value(emotion).confidence(rekognitionEmotion.getConfidence()).build();
            emotions.add(amplifyEmotion);
        }
        Collections.sort(emotions, Collections.reverseOrder());
        EntityDetails entity = EntityDetails.builder().box(box).ageRange(ageRange).pose(pose).gender(amplifyGender).landmarks(landmarks).emotions(emotions).features(features).build();
        entities.add(entity);
    }
    return entities;
}

Example 3 with Pose

use of com.amplifyframework.predictions.models.Pose in project amplify-android by aws-amplify.

the class RekognitionResultTransformersTest method testPoseConversion.

/**
 * Tests that the orientation pose from Rekognition is
 * converted to an equivalent Amplify pose object.
 */
@Test
public void testPoseConversion() {
    com.amazonaws.services.rekognition.model.Pose rekognitionPose = new com.amazonaws.services.rekognition.model.Pose().withPitch(random.nextFloat()).withRoll(random.nextFloat()).withYaw(random.nextFloat());
    Pose amplifyPose = RekognitionResultTransformers.fromRekognitionPose(rekognitionPose);
    assertEquals(rekognitionPose.getPitch(), amplifyPose.getPitch(), DELTA);
    assertEquals(rekognitionPose.getRoll(), amplifyPose.getRoll(), DELTA);
    assertEquals(rekognitionPose.getYaw(), amplifyPose.getYaw(), DELTA);
}