Détectez, suivez et identifiez la présence de logos dans le contenu vidéo d'un fichier local.
Pages de documentation incluant cet exemple de code
Pour afficher l'exemple de code utilisé en contexte, consultez la documentation suivante :
Exemple de code
Go
import (
"context"
"fmt"
"io"
"io/ioutil"
"time"
video "cloud.google.com/go/videointelligence/apiv1"
"github.com/golang/protobuf/ptypes"
videopb "google.golang.org/genproto/googleapis/cloud/videointelligence/v1"
)
// logoDetection analyzes a video and extracts logos with their bounding boxes.
func logoDetection(w io.Writer, filename string) error {
// filename := "../testdata/googlework_short.mp4"
ctx := context.Background()
// Creates a client.
client, err := video.NewClient(ctx)
if err != nil {
return fmt.Errorf("video.NewClient: %v", err)
}
defer client.Close()
ctx, cancel := context.WithTimeout(ctx, time.Second*180)
defer cancel()
fileBytes, err := ioutil.ReadFile(filename)
if err != nil {
return fmt.Errorf("ioutil.ReadFile: %v", err)
}
op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
InputContent: fileBytes,
Features: []videopb.Feature{
videopb.Feature_LOGO_RECOGNITION,
},
})
if err != nil {
return fmt.Errorf("AnnotateVideo: %v", err)
}
resp, err := op.Wait(ctx)
if err != nil {
return fmt.Errorf("Wait: %v", err)
}
// Only one video was processed, so get the first result.
result := resp.GetAnnotationResults()[0]
// Annotations for list of logos detected, tracked and recognized in video.
for _, annotation := range result.LogoRecognitionAnnotations {
fmt.Fprintf(w, "Description: %q\n", annotation.Entity.GetDescription())
// Opaque entity ID. Some IDs may be available in Google Knowledge
// Graph Search API (https://developers.google.com/knowledge-graph/).
if len(annotation.Entity.EntityId) > 0 {
fmt.Fprintf(w, "\tEntity ID: %q\n", annotation.Entity.GetEntityId())
}
// All logo tracks where the recognized logo appears. Each track
// corresponds to one logo instance appearing in consecutive frames.
for _, track := range annotation.Tracks {
// Video segment of a track.
segment := track.GetSegment()
start, _ := ptypes.Duration(segment.GetStartTimeOffset())
end, _ := ptypes.Duration(segment.GetEndTimeOffset())
fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)
fmt.Fprintf(w, "\tConfidence: %f\n", track.GetConfidence())
// The object with timestamp and attributes per frame in the track.
for _, timestampedObject := range track.TimestampedObjects {
// Normalized Bounding box in a frame, where the object is
// located.
box := timestampedObject.GetNormalizedBoundingBox()
fmt.Fprintf(w, "\tBounding box position:\n")
fmt.Fprintf(w, "\t\tleft : %f\n", box.GetLeft())
fmt.Fprintf(w, "\t\ttop : %f\n", box.GetTop())
fmt.Fprintf(w, "\t\tright : %f\n", box.GetRight())
fmt.Fprintf(w, "\t\tbottom: %f\n", box.GetBottom())
// Optional. The attributes of the object in the bounding box.
for _, attribute := range timestampedObject.Attributes {
fmt.Fprintf(w, "\t\t\tName: %q\n", attribute.GetName())
fmt.Fprintf(w, "\t\t\tConfidence: %f\n", attribute.GetConfidence())
fmt.Fprintf(w, "\t\t\tValue: %q\n", attribute.GetValue())
}
}
// Optional. Attributes in the track level.
for _, trackAttribute := range track.Attributes {
fmt.Fprintf(w, "\t\tName: %q\n", trackAttribute.GetName())
fmt.Fprintf(w, "\t\tConfidence: %f\n", trackAttribute.GetConfidence())
fmt.Fprintf(w, "\t\tValue: %q\n", trackAttribute.GetValue())
}
}
// All video segments where the recognized logo appears. There might be
// multiple instances of the same logo class appearing in one VideoSegment.
for _, segment := range annotation.Segments {
start, _ := ptypes.Duration(segment.GetStartTimeOffset())
end, _ := ptypes.Duration(segment.GetEndTimeOffset())
fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)
}
}
return nil
}
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.Entity;
import com.google.cloud.videointelligence.v1.Feature;
import com.google.cloud.videointelligence.v1.LogoRecognitionAnnotation;
import com.google.cloud.videointelligence.v1.NormalizedBoundingBox;
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.VideoIntelligenceServiceClient;
import com.google.cloud.videointelligence.v1.VideoSegment;
import com.google.protobuf.ByteString;
import com.google.protobuf.Duration;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
public class LogoDetection {
public static void detectLogo() throws Exception {
// TODO(developer): Replace these variables before running the sample.
String localFilePath = "path/to/your/video.mp4";
detectLogo(localFilePath);
}
public static void detectLogo(String filePath)
throws IOException, ExecutionException, InterruptedException, TimeoutException {
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
// Read file
Path path = Paths.get(filePath);
byte[] data = Files.readAllBytes(path);
// Create the request
AnnotateVideoRequest request =
AnnotateVideoRequest.newBuilder()
.setInputContent(ByteString.copyFrom(data))
.addFeatures(Feature.LOGO_RECOGNITION)
.build();
// asynchronously perform object tracking on videos
OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> future =
client.annotateVideoAsync(request);
System.out.println("Waiting for operation to complete...");
// The first result is retrieved because a single video was processed.
AnnotateVideoResponse response = future.get(300, TimeUnit.SECONDS);
VideoAnnotationResults annotationResult = response.getAnnotationResults(0);
// Annotations for list of logos detected, tracked and recognized in video.
for (LogoRecognitionAnnotation logoRecognitionAnnotation :
annotationResult.getLogoRecognitionAnnotationsList()) {
Entity entity = logoRecognitionAnnotation.getEntity();
// Opaque entity ID. Some IDs may be available in
// [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).
System.out.printf("Entity Id : %s\n", entity.getEntityId());
System.out.printf("Description : %s\n", entity.getDescription());
// All logo tracks where the recognized logo appears. Each track corresponds to one logo
// instance appearing in consecutive frames.
for (Track track : logoRecognitionAnnotation.getTracksList()) {
// Video segment of a track.
Duration startTimeOffset = track.getSegment().getStartTimeOffset();
System.out.printf(
"\n\tStart Time Offset: %s.%s\n",
startTimeOffset.getSeconds(), startTimeOffset.getNanos());
Duration endTimeOffset = track.getSegment().getEndTimeOffset();
System.out.printf(
"\tEnd Time Offset: %s.%s\n", endTimeOffset.getSeconds(), endTimeOffset.getNanos());
System.out.printf("\tConfidence: %s\n", track.getConfidence());
// The object with timestamp and attributes per frame in the track.
for (TimestampedObject timestampedObject : track.getTimestampedObjectsList()) {
// Normalized Bounding box in a frame, where the object is located.
NormalizedBoundingBox normalizedBoundingBox =
timestampedObject.getNormalizedBoundingBox();
System.out.printf("\n\t\tLeft: %s\n", normalizedBoundingBox.getLeft());
System.out.printf("\t\tTop: %s\n", normalizedBoundingBox.getTop());
System.out.printf("\t\tRight: %s\n", normalizedBoundingBox.getRight());
System.out.printf("\t\tBottom: %s\n", normalizedBoundingBox.getBottom());
// Optional. The attributes of the object in the bounding box.
for (DetectedAttribute attribute : timestampedObject.getAttributesList()) {
System.out.printf("\n\t\t\tName: %s\n", attribute.getName());
System.out.printf("\t\t\tConfidence: %s\n", attribute.getConfidence());
System.out.printf("\t\t\tValue: %s\n", attribute.getValue());
}
}
// Optional. Attributes in the track level.
for (DetectedAttribute trackAttribute : track.getAttributesList()) {
System.out.printf("\n\t\tName : %s\n", trackAttribute.getName());
System.out.printf("\t\tConfidence : %s\n", trackAttribute.getConfidence());
System.out.printf("\t\tValue : %s\n", trackAttribute.getValue());
}
}
// All video segments where the recognized logo appears. There might be multiple instances
// of the same logo class appearing in one VideoSegment.
for (VideoSegment segment : logoRecognitionAnnotation.getSegmentsList()) {
System.out.printf(
"\n\tStart Time Offset : %s.%s\n",
segment.getStartTimeOffset().getSeconds(), segment.getStartTimeOffset().getNanos());
System.out.printf(
"\tEnd Time Offset : %s.%s\n",
segment.getEndTimeOffset().getSeconds(), segment.getEndTimeOffset().getNanos());
}
}
}
}
}Node.js
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
// const localFilePath = 'path/to/your/video.mp4'
// Imports the Google Cloud client libraries
const Video = require('@google-cloud/video-intelligence');
const fs = require('fs');
// Instantiates a client
const client = new Video.VideoIntelligenceServiceClient();
// Performs asynchronous video annotation for logo recognition on a file.
async function detectLogo() {
const inputContent = fs.readFileSync(localFilePath).toString('base64');
// Build the request with the input content and logo recognition feature.
const request = {
inputContent: inputContent,
features: ['LOGO_RECOGNITION'],
};
// Make the asynchronous request
const [operation] = await client.annotateVideo(request);
// Wait for the results
const [response] = await operation.promise();
// Get the first response, since we sent only one video.
const annotationResult = response.annotationResults[0];
for (const logoRecognitionAnnotation of annotationResult.logoRecognitionAnnotations) {
const entity = logoRecognitionAnnotation.entity;
// Opaque entity ID. Some IDs may be available in
// [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).
console.log(`Entity Id: ${entity.entityId}`);
console.log(`Description: ${entity.description}`);
// All logo tracks where the recognized logo appears.
// Each track corresponds to one logo instance appearing in consecutive frames.
for (const track of logoRecognitionAnnotation.tracks) {
console.log(
`\n\tStart Time Offset: ${track.segment.startTimeOffset.seconds}.${track.segment.startTimeOffset.nanos}`
);
console.log(
`\tEnd Time Offset: ${track.segment.endTimeOffset.seconds}.${track.segment.endTimeOffset.nanos}`
);
console.log(`\tConfidence: ${track.confidence}`);
// The object with timestamp and attributes per frame in the track.
for (const timestampedObject of track.timestampedObjects) {
// Normalized Bounding box in a frame, where the object is located.
const normalizedBoundingBox = timestampedObject.normalizedBoundingBox;
console.log(`\n\t\tLeft: ${normalizedBoundingBox.left}`);
console.log(`\t\tTop: ${normalizedBoundingBox.top}`);
console.log(`\t\tRight: ${normalizedBoundingBox.right}`);
console.log(`\t\tBottom: ${normalizedBoundingBox.bottom}`);
// Optional. The attributes of the object in the bounding box.
for (const attribute of timestampedObject.attributes) {
console.log(`\n\t\t\tName: ${attribute.name}`);
console.log(`\t\t\tConfidence: ${attribute.confidence}`);
console.log(`\t\t\tValue: ${attribute.value}`);
}
}
// Optional. Attributes in the track level.
for (const trackAttribute of track.attributes) {
console.log(`\n\t\tName: ${trackAttribute.name}`);
console.log(`\t\tConfidence: ${trackAttribute.confidence}`);
console.log(`\t\tValue: ${trackAttribute.value}`);
}
}
// All video segments where the recognized logo appears.
// There might be multiple instances of the same logo class appearing in one VideoSegment.
for (const segment of logoRecognitionAnnotation.segments) {
console.log(
`\n\tStart Time Offset: ${segment.startTimeOffset.seconds}.${segment.startTimeOffset.nanos}`
);
console.log(
`\tEnd Time Offset: ${segment.endTimeOffset.seconds}.${segment.endTimeOffset.nanos}`
);
}
}
}
detectLogo();Python
from google.cloud import videointelligence
def detect_logo(local_file_path="path/to/your/video.mp4"):
"""Performs asynchronous video annotation for logo recognition on a local file."""
client = videointelligence.VideoIntelligenceServiceClient()
with io.open(local_file_path, "rb") as f:
input_content = f.read()
features = [videointelligence.Feature.LOGO_RECOGNITION]
operation = client.annotate_video(
request={"features": features, "input_content": input_content}
)
print(u"Waiting for operation to complete...")
response = operation.result()
# Get the first response, since we sent only one video.
annotation_result = response.annotation_results[0]
# Annotations for list of logos detected, tracked and recognized in video.
for logo_recognition_annotation in annotation_result.logo_recognition_annotations:
entity = logo_recognition_annotation.entity
# Opaque entity ID. Some IDs may be available in [Google Knowledge Graph
# Search API](https://developers.google.com/knowledge-graph/).
print(u"Entity Id : {}".format(entity.entity_id))
print(u"Description : {}".format(entity.description))
# All logo tracks where the recognized logo appears. Each track corresponds
# to one logo instance appearing in consecutive frames.
for track in logo_recognition_annotation.tracks:
# Video segment of a track.
print(
u"\n\tStart Time Offset : {}.{}".format(
track.segment.start_time_offset.seconds,
track.segment.start_time_offset.microseconds * 1000,
)
)
print(
u"\tEnd Time Offset : {}.{}".format(
track.segment.end_time_offset.seconds,
track.segment.end_time_offset.microseconds * 1000,
)
)
print(u"\tConfidence : {}".format(track.confidence))
# The object with timestamp and attributes per frame in the track.
for timestamped_object in track.timestamped_objects:
# Normalized Bounding box in a frame, where the object is located.
normalized_bounding_box = timestamped_object.normalized_bounding_box
print(u"\n\t\tLeft : {}".format(normalized_bounding_box.left))
print(u"\t\tTop : {}".format(normalized_bounding_box.top))
print(u"\t\tRight : {}".format(normalized_bounding_box.right))
print(u"\t\tBottom : {}".format(normalized_bounding_box.bottom))
# Optional. The attributes of the object in the bounding box.
for attribute in timestamped_object.attributes:
print(u"\n\t\t\tName : {}".format(attribute.name))
print(u"\t\t\tConfidence : {}".format(attribute.confidence))
print(u"\t\t\tValue : {}".format(attribute.value))
# Optional. Attributes in the track level.
for track_attribute in track.attributes:
print(u"\n\t\tName : {}".format(track_attribute.name))
print(u"\t\tConfidence : {}".format(track_attribute.confidence))
print(u"\t\tValue : {}".format(track_attribute.value))
# All video segments where the recognized logo appears. There might be
# multiple instances of the same logo class appearing in one VideoSegment.
for segment in logo_recognition_annotation.segments:
print(
u"\n\tStart Time Offset : {}.{}".format(
segment.start_time_offset.seconds,
segment.start_time_offset.microseconds * 1000,
)
)
print(
u"\tEnd Time Offset : {}.{}".format(
segment.end_time_offset.seconds,
segment.end_time_offset.microseconds * 1000,
)
)
Étape suivante
Pour rechercher et filtrer des exemples de code pour d'autres produits Google Cloud, consultez l'explorateur d'exemples Google Cloud.