GA-Release der Personenerkennung für die Video Intelligence API
Dokumentationsseiten mit diesem Codebeispiel
Die folgenden Dokumente enthalten das Codebeispiel im Kontext:
Codebeispiel
Java
import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.videointelligence.v1.AnnotateVideoProgress;
import com.google.cloud.videointelligence.v1.AnnotateVideoRequest;
import com.google.cloud.videointelligence.v1.AnnotateVideoResponse;
import com.google.cloud.videointelligence.v1.DetectedAttribute;
import com.google.cloud.videointelligence.v1.DetectedLandmark;
import com.google.cloud.videointelligence.v1.Feature;
import com.google.cloud.videointelligence.v1.PersonDetectionAnnotation;
import com.google.cloud.videointelligence.v1.PersonDetectionConfig;
import com.google.cloud.videointelligence.v1.TimestampedObject;
import com.google.cloud.videointelligence.v1.Track;
import com.google.cloud.videointelligence.v1.VideoAnnotationResults;
import com.google.cloud.videointelligence.v1.VideoContext;
import com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient;
import com.google.cloud.videointelligence.v1.VideoSegment;
import com.google.protobuf.ByteString;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class DetectPerson {
public static void detectPerson() throws Exception {
// TODO(developer): Replace these variables before running the sample.
String localFilePath = "resources/googlework_short.mp4";
detectPerson(localFilePath);
}
// Detects people in a video stored in a local file using the Cloud Video Intelligence API.
public static void detectPerson(String localFilePath) throws Exception {
try (VideoIntelligenceServiceClient videoIntelligenceServiceClient =
VideoIntelligenceServiceClient.create()) {
// Reads a local video file and converts it to base64.
Path path = Paths.get(localFilePath);
byte[] data = Files.readAllBytes(path);
ByteString inputContent = ByteString.copyFrom(data);
PersonDetectionConfig personDetectionConfig =
PersonDetectionConfig.newBuilder()
// Must set includeBoundingBoxes to true to get poses and attributes.
.setIncludeBoundingBoxes(true)
.setIncludePoseLandmarks(true)
.setIncludeAttributes(true)
.build();
VideoContext videoContext =
VideoContext.newBuilder().setPersonDetectionConfig(personDetectionConfig).build();
AnnotateVideoRequest request =
AnnotateVideoRequest.newBuilder()
.setInputContent(inputContent)
.addFeatures(Feature.PERSON_DETECTION)
.setVideoContext(videoContext)
.build();
// Detects people in a video
// We get the first result because only one video is processed.
OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> future =
videoIntelligenceServiceClient.annotateVideoAsync(request);
System.out.println("Waiting for operation to complete...");
AnnotateVideoResponse response = future.get();
// Gets annotations for video
VideoAnnotationResults annotationResult = response.getAnnotationResultsList().get(0);
// Annotations for list of people detected, tracked and recognized in video.
for (PersonDetectionAnnotation personDetectionAnnotation :
annotationResult.getPersonDetectionAnnotationsList()) {
System.out.print("Person detected:\n");
for (Track track : personDetectionAnnotation.getTracksList()) {
VideoSegment segment = track.getSegment();
System.out.printf(
"\tStart: %d.%.0fs\n",
segment.getStartTimeOffset().getSeconds(),
segment.getStartTimeOffset().getNanos() / 1e6);
System.out.printf(
"\tEnd: %d.%.0fs\n",
segment.getEndTimeOffset().getSeconds(), segment.getEndTimeOffset().getNanos() / 1e6);
// Each segment includes timestamped objects that include characteristic--e.g. clothes,
// posture of the person detected.
TimestampedObject firstTimestampedObject = track.getTimestampedObjects(0);
// Attributes include unique pieces of clothing, poses (i.e., body landmarks)
// of the person detected.
for (DetectedAttribute attribute : firstTimestampedObject.getAttributesList()) {
System.out.printf(
"\tAttribute: %s; Value: %s\n", attribute.getName(), attribute.getValue());
}
// Landmarks in person detection include body parts.
for (DetectedLandmark attribute : firstTimestampedObject.getLandmarksList()) {
System.out.printf(
"\tLandmark: %s; Vertex: %f, %f\n",
attribute.getName(), attribute.getPoint().getX(), attribute.getPoint().getY());
}
}
}
}
}
}Node.js
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
// const gcsUri = 'GCS URI of the video to analyze, e.g. gs://my-bucket/my-video.mp4';
// Imports the Google Cloud Video Intelligence library + Node's fs library
const Video = require('@google-cloud/video-intelligence').v1;
const fs = require('fs');
// Creates a client
const video = new Video.VideoIntelligenceServiceClient();
/**
* TODO(developer): Uncomment the following line before running the sample.
*/
// const path = 'Local file to analyze, e.g. ./my-file.mp4';
// Reads a local video file and converts it to base64
const file = fs.readFileSync(path);
const inputContent = file.toString('base64');
async function detectPerson() {
const request = {
inputContent: inputContent,
features: ['PERSON_DETECTION'],
videoContext: {
personDetectionConfig: {
// Must set includeBoundingBoxes to true to get poses and attributes.
includeBoundingBoxes: true,
includePoseLandmarks: true,
includeAttributes: true,
},
},
};
// Detects faces in a video
// We get the first result because we only process 1 video
const [operation] = await video.annotateVideo(request);
const results = await operation.promise();
console.log('Waiting for operation to complete...');
// Gets annotations for video
const personAnnotations =
results[0].annotationResults[0].personDetectionAnnotations;
for (const {tracks} of personAnnotations) {
console.log('Person detected:');
for (const {segment, timestampedObjects} of tracks) {
if (segment.startTimeOffset.seconds === undefined) {
segment.startTimeOffset.seconds = 0;
}
if (segment.startTimeOffset.nanos === undefined) {
segment.startTimeOffset.nanos = 0;
}
if (segment.endTimeOffset.seconds === undefined) {
segment.endTimeOffset.seconds = 0;
}
if (segment.endTimeOffset.nanos === undefined) {
segment.endTimeOffset.nanos = 0;
}
console.log(
`\tStart: ${segment.startTimeOffset.seconds}` +
`.${(segment.startTimeOffset.nanos / 1e6).toFixed(0)}s`
);
console.log(
`\tEnd: ${segment.endTimeOffset.seconds}.` +
`${(segment.endTimeOffset.nanos / 1e6).toFixed(0)}s`
);
// Each segment includes timestamped objects that
// include characteristic--e.g. clothes, posture
// of the person detected.
const [firstTimestampedObject] = timestampedObjects;
// Attributes include unique pieces of clothing, poses (i.e., body
// landmarks) of the person detected.
for (const {name, value} of firstTimestampedObject.attributes) {
console.log(`\tAttribute: ${name}; Value: ${value}`);
}
// Landmarks in person detection include body parts.
for (const {name, point} of firstTimestampedObject.landmarks) {
console.log(`\tLandmark: ${name}; Vertex: ${point.x}, ${point.y}`);
}
}
}
}
detectPerson();Python
import io
from google.cloud import videointelligence_v1 as videointelligence
def detect_person(local_file_path="path/to/your/video-file.mp4"):
"""Detects people in a video from a local file."""
client = videointelligence.VideoIntelligenceServiceClient()
with io.open(local_file_path, "rb") as f:
input_content = f.read()
# Configure the request
config = videointelligence.types.PersonDetectionConfig(
include_bounding_boxes=True,
include_attributes=True,
include_pose_landmarks=True,
)
context = videointelligence.types.VideoContext(person_detection_config=config)
# Start the asynchronous request
operation = client.annotate_video(
request={
"features": [videointelligence.Feature.PERSON_DETECTION],
"input_content": input_content,
"video_context": context,
}
)
print("\nProcessing video for person detection annotations.")
result = operation.result(timeout=300)
print("\nFinished processing.\n")
# Retrieve the first result, because a single video was processed.
annotation_result = result.annotation_results[0]
for annotation in annotation_result.person_detection_annotations:
print("Person detected:")
for track in annotation.tracks:
print(
"Segment: {}s to {}s".format(
track.segment.start_time_offset.seconds
+ track.segment.start_time_offset.microseconds / 1e6,
track.segment.end_time_offset.seconds
+ track.segment.end_time_offset.microseconds / 1e6,
)
)
# Each segment includes timestamped objects that include
# characteristic - -e.g.clothes, posture of the person detected.
# Grab the first timestamped object
timestamped_object = track.timestamped_objects[0]
box = timestamped_object.normalized_bounding_box
print("Bounding box:")
print("\tleft : {}".format(box.left))
print("\ttop : {}".format(box.top))
print("\tright : {}".format(box.right))
print("\tbottom: {}".format(box.bottom))
# Attributes include unique pieces of clothing,
# poses, or hair color.
print("Attributes:")
for attribute in timestamped_object.attributes:
print(
"\t{}:{} {}".format(
attribute.name, attribute.value, attribute.confidence
)
)
# Landmarks in person detection include body parts such as
# left_shoulder, right_ear, and right_ankle
print("Landmarks:")
for landmark in timestamped_object.landmarks:
print(
"\t{}: {} (x={}, y={})".format(
landmark.name,
landmark.confidence,
landmark.point.x, # Normalized vertex
landmark.point.y, # Normalized vertex
)
)
Nächste Schritte
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