Examples with LabelFrame com.google.cloud.videointelligence.v1p3beta1.LabelFrame used on opensource projects

Example 1 with LabelFrame

use of com.google.cloud.videointelligence.v1p3beta1.LabelFrame in project java-video-intelligence by googleapis.

the class StreamingAutoMlClassification method streamingAutoMlClassification.

// Perform streaming video classification with an AutoML Model
static void streamingAutoMlClassification(String filePath, String projectId, String modelId) throws TimeoutException, StatusRuntimeException, IOException {
    try (StreamingVideoIntelligenceServiceClient client = StreamingVideoIntelligenceServiceClient.create()) {
        Path path = Paths.get(filePath);
        byte[] data = Files.readAllBytes(path);
        // Set the chunk size to 5MB (recommended less than 10MB).
        int chunkSize = 5 * 1024 * 1024;
        int numChunks = (int) Math.ceil((double) data.length / chunkSize);
        String modelPath = String.format("projects/%s/locations/us-central1/models/%s", projectId, modelId);
        System.out.println(modelPath);
        StreamingAutomlClassificationConfig streamingAutomlClassificationConfig = StreamingAutomlClassificationConfig.newBuilder().setModelName(modelPath).build();
        StreamingVideoConfig streamingVideoConfig = StreamingVideoConfig.newBuilder().setFeature(StreamingFeature.STREAMING_AUTOML_CLASSIFICATION).setAutomlClassificationConfig(streamingAutomlClassificationConfig).build();
        BidiStream<StreamingAnnotateVideoRequest, StreamingAnnotateVideoResponse> call = client.streamingAnnotateVideoCallable().call();
        // The first request must **only** contain the audio configuration:
        call.send(StreamingAnnotateVideoRequest.newBuilder().setVideoConfig(streamingVideoConfig).build());
        // Send the requests in chunks
        for (int i = 0; i < numChunks; i++) {
            call.send(StreamingAnnotateVideoRequest.newBuilder().setInputContent(ByteString.copyFrom(Arrays.copyOfRange(data, i * chunkSize, i * chunkSize + chunkSize))).build());
        }
        // Tell the service you are done sending data
        call.closeSend();
        for (StreamingAnnotateVideoResponse response : call) {
            if (response.hasError()) {
                System.out.println(response.getError().getMessage());
                break;
            }
            StreamingVideoAnnotationResults annotationResults = response.getAnnotationResults();
            for (LabelAnnotation annotation : annotationResults.getLabelAnnotationsList()) {
                String entity = annotation.getEntity().getDescription();
                // There is only one frame per annotation
                LabelFrame labelFrame = annotation.getFrames(0);
                double offset = labelFrame.getTimeOffset().getSeconds() + labelFrame.getTimeOffset().getNanos() / 1e9;
                float confidence = labelFrame.getConfidence();
                System.out.format("At %fs segment: %s (%f)\n", offset, entity, confidence);
            }
        }
        System.out.println("Video streamed successfully.");
    }
}

Example 2 with LabelFrame

use of com.google.cloud.videointelligence.v1p3beta1.LabelFrame in project java-video-intelligence by googleapis.

the class StreamingLabelDetection method streamingLabelDetection.

// Perform streaming video label detection
static void streamingLabelDetection(String filePath) throws IOException, TimeoutException, StatusRuntimeException {
    try (StreamingVideoIntelligenceServiceClient client = StreamingVideoIntelligenceServiceClient.create()) {
        Path path = Paths.get(filePath);
        byte[] data = Files.readAllBytes(path);
        // Set the chunk size to 5MB (recommended less than 10MB).
        int chunkSize = 5 * 1024 * 1024;
        int numChunks = (int) Math.ceil((double) data.length / chunkSize);
        StreamingLabelDetectionConfig labelConfig = StreamingLabelDetectionConfig.newBuilder().setStationaryCamera(false).build();
        StreamingVideoConfig streamingVideoConfig = StreamingVideoConfig.newBuilder().setFeature(StreamingFeature.STREAMING_LABEL_DETECTION).setLabelDetectionConfig(labelConfig).build();
        BidiStream<StreamingAnnotateVideoRequest, StreamingAnnotateVideoResponse> call = client.streamingAnnotateVideoCallable().call();
        // The first request must **only** contain the audio configuration:
        call.send(StreamingAnnotateVideoRequest.newBuilder().setVideoConfig(streamingVideoConfig).build());
        // Send the requests in chunks
        for (int i = 0; i < numChunks; i++) {
            call.send(StreamingAnnotateVideoRequest.newBuilder().setInputContent(ByteString.copyFrom(Arrays.copyOfRange(data, i * chunkSize, i * chunkSize + chunkSize))).build());
        }
        // Tell the service you are done sending data
        call.closeSend();
        for (StreamingAnnotateVideoResponse response : call) {
            StreamingVideoAnnotationResults annotationResults = response.getAnnotationResults();
            for (LabelAnnotation annotation : annotationResults.getLabelAnnotationsList()) {
                String entity = annotation.getEntity().getDescription();
                // There is only one frame per annotation
                LabelFrame labelFrame = annotation.getFrames(0);
                double offset = labelFrame.getTimeOffset().getSeconds() + labelFrame.getTimeOffset().getNanos() / 1e9;
                float confidence = labelFrame.getConfidence();
                System.out.format("%fs: %s (%f)\n", offset, entity, confidence);
            }
        }
    }
}

Example 3 with LabelFrame

use of com.google.cloud.videointelligence.v1p3beta1.LabelFrame in project java-video-intelligence by googleapis.

the class StreamingAutoMlActionRecognition method streamingAutoMlActionRecognition.

// Perform streaming video action recognition
static void streamingAutoMlActionRecognition(String filePath, String projectId, String modelId) throws IOException, TimeoutException, StatusRuntimeException {
    try (StreamingVideoIntelligenceServiceClient client = StreamingVideoIntelligenceServiceClient.create()) {
        Path path = Paths.get(filePath);
        byte[] data = Files.readAllBytes(path);
        // Set the chunk size to 5MB (recommended less than 10MB).
        int chunkSize = 5 * 1024 * 1024;
        int numChunks = (int) Math.ceil((double) data.length / chunkSize);
        String modelPath = String.format("projects/%s/locations/us-central1/models/%s", projectId, modelId);
        System.out.println(modelPath);
        StreamingAutomlActionRecognitionConfig streamingAutomlActionRecognitionConfig = StreamingAutomlActionRecognitionConfig.newBuilder().setModelName(modelPath).build();
        StreamingVideoConfig streamingVideoConfig = StreamingVideoConfig.newBuilder().setFeature(StreamingFeature.STREAMING_AUTOML_ACTION_RECOGNITION).setAutomlActionRecognitionConfig(streamingAutomlActionRecognitionConfig).build();
        BidiStream<StreamingAnnotateVideoRequest, StreamingAnnotateVideoResponse> call = client.streamingAnnotateVideoCallable().call();
        // The first request must **only** contain the video configuration:
        call.send(StreamingAnnotateVideoRequest.newBuilder().setVideoConfig(streamingVideoConfig).build());
        // Send the requests in chunks
        for (int i = 0; i < numChunks; i++) {
            call.send(StreamingAnnotateVideoRequest.newBuilder().setInputContent(ByteString.copyFrom(Arrays.copyOfRange(data, i * chunkSize, i * chunkSize + chunkSize))).build());
        }
        // Tell the service you are done sending data
        call.closeSend();
        for (StreamingAnnotateVideoResponse response : call) {
            if (response.hasError()) {
                System.out.println(response.getError().getMessage());
                break;
            }
            StreamingVideoAnnotationResults annotationResults = response.getAnnotationResults();
            for (LabelAnnotation annotation : annotationResults.getLabelAnnotationsList()) {
                String entity = annotation.getEntity().getDescription();
                // There is only one frame per annotation
                LabelFrame labelFrame = annotation.getFrames(0);
                double offset = labelFrame.getTimeOffset().getSeconds() + labelFrame.getTimeOffset().getNanos() / 1e9;
                float confidence = labelFrame.getConfidence();
                System.out.format("At %fs segment: %s (%f)\n", offset, entity, confidence);
            }
        }
        System.out.println("Video streamed successfully.");
    }
}