use of com.google.cloud.videointelligence.v1p1beta1.FaceDetectionAnnotation in project java-docs-samples by GoogleCloudPlatform.
the class Detect method analyzeFacesBoundingBoxes.
// [START video_face_bounding_boxes]
/**
* Detects faces' bounding boxes on the video at the provided Cloud Storage path.
*
* @param gcsUri the path to the video file to analyze.
*/
public static void analyzeFacesBoundingBoxes(String gcsUri) throws Exception {
// Instantiate a com.google.cloud.videointelligence.v1p1beta1.VideoIntelligenceServiceClient
try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
// Set the configuration to include bounding boxes
FaceConfig config = FaceConfig.newBuilder().setIncludeBoundingBoxes(true).build();
// Set the video context with the above configuration
VideoContext context = VideoContext.newBuilder().setFaceDetectionConfig(config).build();
// Create the request
AnnotateVideoRequest request = AnnotateVideoRequest.newBuilder().setInputUri(gcsUri).addFeatures(Feature.FACE_DETECTION).setVideoContext(context).build();
// asynchronously perform facial analysis on videos
OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> response = client.annotateVideoAsync(request);
System.out.println("Waiting for operation to complete...");
boolean faceFound = false;
// Display the results
for (VideoAnnotationResults results : response.get(900, TimeUnit.SECONDS).getAnnotationResultsList()) {
int faceCount = 0;
// Display the results for each face
for (FaceDetectionAnnotation faceAnnotation : results.getFaceDetectionAnnotationsList()) {
faceFound = true;
System.out.println("\nFace: " + ++faceCount);
// Each FaceDetectionAnnotation has only one segment.
for (FaceSegment segment : faceAnnotation.getSegmentsList()) {
double startTime = segment.getSegment().getStartTimeOffset().getSeconds() + segment.getSegment().getStartTimeOffset().getNanos() / 1e9;
double endTime = segment.getSegment().getEndTimeOffset().getSeconds() + segment.getSegment().getEndTimeOffset().getNanos() / 1e9;
System.out.printf("Segment location: %.3fs to %.3f\n", startTime, endTime);
}
// There are typically many frames for each face,
try {
// Here we process only the first frame.
if (faceAnnotation.getFramesCount() > 0) {
// get the first frame
FaceDetectionFrame frame = faceAnnotation.getFrames(0);
double timeOffset = frame.getTimeOffset().getSeconds() + frame.getTimeOffset().getNanos() / 1e9;
System.out.printf("First frame time offset: %.3fs\n", timeOffset);
// print info on the first normalized bounding box
NormalizedBoundingBox box = frame.getAttributes(0).getNormalizedBoundingBox();
System.out.printf("\tLeft: %.3f\n", box.getLeft());
System.out.printf("\tTop: %.3f\n", box.getTop());
System.out.printf("\tBottom: %.3f\n", box.getBottom());
System.out.printf("\tRight: %.3f\n", box.getRight());
} else {
System.out.println("No frames found in annotation");
}
} catch (IndexOutOfBoundsException ioe) {
System.out.println("Could not retrieve frame: " + ioe.getMessage());
}
}
}
if (!faceFound) {
System.out.println("No faces detected in " + gcsUri);
}
}
}
use of com.google.cloud.videointelligence.v1p1beta1.FaceDetectionAnnotation in project java-docs-samples by GoogleCloudPlatform.
the class Detect method analyzeFaceEmotions.
// [END video_face_bounding_boxes]
// [START video_face_emotions]
/**
* Analyze faces' emotions over frames on the video at the provided Cloud Storage path.
*
* @param gcsUri the path to the video file to analyze.
*/
public static void analyzeFaceEmotions(String gcsUri) throws Exception {
// Instantiate a com.google.cloud.videointelligence.v1p1beta1.VideoIntelligenceServiceClient
try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
// Set the configuration to include bounding boxes
FaceConfig config = FaceConfig.newBuilder().setIncludeEmotions(true).build();
// Set the video context with the above configuration
VideoContext context = VideoContext.newBuilder().setFaceDetectionConfig(config).build();
// Create the request
AnnotateVideoRequest request = AnnotateVideoRequest.newBuilder().setInputUri(gcsUri).addFeatures(Feature.FACE_DETECTION).setVideoContext(context).build();
// asynchronously perform facial analysis on videos
OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> response = client.annotateVideoAsync(request);
System.out.println("Waiting for operation to complete...");
boolean faceFound = false;
// Display the results
for (VideoAnnotationResults results : response.get(600, TimeUnit.SECONDS).getAnnotationResultsList()) {
int faceCount = 0;
// Display the results for each face
for (FaceDetectionAnnotation faceAnnotation : results.getFaceDetectionAnnotationsList()) {
faceFound = true;
System.out.println("\nFace: " + ++faceCount);
// Each FaceDetectionAnnotation has only one segment.
for (FaceSegment segment : faceAnnotation.getSegmentsList()) {
double startTime = segment.getSegment().getStartTimeOffset().getSeconds() + segment.getSegment().getStartTimeOffset().getNanos() / 1e9;
double endTime = segment.getSegment().getEndTimeOffset().getSeconds() + segment.getSegment().getEndTimeOffset().getNanos() / 1e9;
System.out.printf("Segment location: %.3fs to %.3f\n", startTime, endTime);
}
try {
// Print each frame's highest emotion
for (FaceDetectionFrame frame : faceAnnotation.getFramesList()) {
double timeOffset = frame.getTimeOffset().getSeconds() + frame.getTimeOffset().getNanos() / 1e9;
float highestScore = 0.0f;
String emotion = "";
// Get the highest scoring emotion for the current frame
for (EmotionAttribute emotionAttribute : frame.getAttributes(0).getEmotionsList()) {
if (emotionAttribute.getScore() > highestScore) {
highestScore = emotionAttribute.getScore();
emotion = emotionAttribute.getEmotion().name();
}
}
System.out.printf("\t%4.2fs: %14s %4.3f\n", timeOffset, emotion, highestScore);
}
} catch (IndexOutOfBoundsException ioe) {
System.out.println("Could not retrieve frame: " + ioe.getMessage());
}
}
}
if (!faceFound) {
System.out.println("No faces detected in " + gcsUri);
}
}
}
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