Detecta texto en un video almacenado de forma local.
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Muestra de código
Go
import (
"context"
"fmt"
"io"
"io/ioutil"
video "cloud.google.com/go/videointelligence/apiv1"
"github.com/golang/protobuf/ptypes"
videopb "google.golang.org/genproto/googleapis/cloud/videointelligence/v1"
)
// textDetection analyzes a video and extracts the text from the video's audio.
func textDetection(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()
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_TEXT_DETECTION,
},
})
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]
for _, annotation := range result.TextAnnotations {
fmt.Fprintf(w, "Text: %q\n", annotation.GetText())
// Get the first text segment.
segment := annotation.GetSegments()[0]
start, _ := ptypes.Duration(segment.GetSegment().GetStartTimeOffset())
end, _ := ptypes.Duration(segment.GetSegment().GetEndTimeOffset())
fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)
fmt.Fprintf(w, "\tConfidence: %f\n", segment.GetConfidence())
// Show the result for the first frame in this segment.
frame := segment.GetFrames()[0]
seconds := float32(frame.GetTimeOffset().GetSeconds())
nanos := float32(frame.GetTimeOffset().GetNanos())
fmt.Fprintf(w, "\tTime offset of the first frame: %fs\n", seconds+nanos/1e9)
fmt.Fprintf(w, "\tRotated bounding box vertices:\n")
for _, vertex := range frame.GetRotatedBoundingBox().GetVertices() {
fmt.Fprintf(w, "\t\tVertex x=%f, y=%f\n", vertex.GetX(), vertex.GetY())
}
}
return nil
}
Java
/**
* Detect text in a video.
*
* @param filePath the path to the video file to analyze.
*/
public static VideoAnnotationResults detectText(String filePath) throws Exception {
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.TEXT_DETECTION)
.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 results = response.getAnnotationResults(0);
// Get only the first annotation for demo purposes.
TextAnnotation annotation = results.getTextAnnotations(0);
System.out.println("Text: " + annotation.getText());
// Get the first text segment.
TextSegment textSegment = annotation.getSegments(0);
System.out.println("Confidence: " + textSegment.getConfidence());
// For the text segment display it's time offset
VideoSegment videoSegment = textSegment.getSegment();
Duration startTimeOffset = videoSegment.getStartTimeOffset();
Duration endTimeOffset = videoSegment.getEndTimeOffset();
// Display the offset times in seconds, 1e9 is part of the formula to convert nanos to seconds
System.out.println(
String.format(
"Start time: %.2f", startTimeOffset.getSeconds() + startTimeOffset.getNanos() / 1e9));
System.out.println(
String.format(
"End time: %.2f", endTimeOffset.getSeconds() + endTimeOffset.getNanos() / 1e9));
// Show the first result for the first frame in the segment.
TextFrame textFrame = textSegment.getFrames(0);
Duration timeOffset = textFrame.getTimeOffset();
System.out.println(
String.format(
"Time offset for the first frame: %.2f",
timeOffset.getSeconds() + timeOffset.getNanos() / 1e9));
// Display the rotated bounding box for where the text is on the frame.
System.out.println("Rotated Bounding Box Vertices:");
List<NormalizedVertex> vertices = textFrame.getRotatedBoundingBox().getVerticesList();
for (NormalizedVertex normalizedVertex : vertices) {
System.out.println(
String.format(
"\tVertex.x: %.2f, Vertex.y: %.2f",
normalizedVertex.getX(), normalizedVertex.getY()));
}
return results;
}
}Node.js
// Imports the Google Cloud Video Intelligence library + Node's fs library
const Video = require('@google-cloud/video-intelligence');
const fs = require('fs');
const util = require('util');
// 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 = await util.promisify(fs.readFile)(path);
const inputContent = file.toString('base64');
const request = {
inputContent: inputContent,
features: ['TEXT_DETECTION'],
};
// Detects text in a video
const [operation] = await video.annotateVideo(request);
const results = await operation.promise();
console.log('Waiting for operation to complete...');
// Gets annotations for video
const textAnnotations = results[0].annotationResults[0].textAnnotations;
textAnnotations.forEach(textAnnotation => {
console.log(`Text ${textAnnotation.text} occurs at:`);
textAnnotation.segments.forEach(segment => {
const time = segment.segment;
if (time.startTimeOffset.seconds === undefined) {
time.startTimeOffset.seconds = 0;
}
if (time.startTimeOffset.nanos === undefined) {
time.startTimeOffset.nanos = 0;
}
if (time.endTimeOffset.seconds === undefined) {
time.endTimeOffset.seconds = 0;
}
if (time.endTimeOffset.nanos === undefined) {
time.endTimeOffset.nanos = 0;
}
console.log(
`\tStart: ${time.startTimeOffset.seconds || 0}` +
`.${(time.startTimeOffset.nanos / 1e6).toFixed(0)}s`
);
console.log(
`\tEnd: ${time.endTimeOffset.seconds || 0}.` +
`${(time.endTimeOffset.nanos / 1e6).toFixed(0)}s`
);
console.log(`\tConfidence: ${segment.confidence}`);
segment.frames.forEach(frame => {
const timeOffset = frame.timeOffset;
console.log(
`Time offset for the frame: ${timeOffset.seconds || 0}` +
`.${(timeOffset.nanos / 1e6).toFixed(0)}s`
);
console.log('Rotated Bounding Box Vertices:');
frame.rotatedBoundingBox.vertices.forEach(vertex => {
console.log(`Vertex.x:${vertex.x}, Vertex.y:${vertex.y}`);
});
});
});
});PHP
use Google\Cloud\VideoIntelligence\V1\VideoIntelligenceServiceClient;
use Google\Cloud\VideoIntelligence\V1\Feature;
/** Uncomment and populate these variables in your code */
// $path = 'File path to a video file to analyze';
// $options = [];
# Instantiate a client.
$video = new VideoIntelligenceServiceClient();
# Read the local video file
$inputContent = file_get_contents($path);
# Execute a request.
$features = [Feature::TEXT_DETECTION];
$operation = $video->annotateVideo([
'inputContent' => $inputContent,
'features' => $features,
]);
# Wait for the request to complete.
$operation->pollUntilComplete($options);
# Print the results.
if ($operation->operationSucceeded()) {
$results = $operation->getResult()->getAnnotationResults()[0];
# Process video/segment level label annotations
foreach ($results->getTextAnnotations() as $text) {
printf('Video text description: %s' . PHP_EOL, $text->getText());
foreach ($text->getSegments() as $segment) {
$start = $segment->getSegment()->getStartTimeOffset();
$end = $segment->getSegment()->getEndTimeOffset();
printf(' Segment: %ss to %ss' . PHP_EOL,
$start->getSeconds() + $start->getNanos() / 1000000000.0,
$end->getSeconds() + $end->getNanos() / 1000000000.0);
printf(' Confidence: %f' . PHP_EOL, $segment->getConfidence());
}
}
print(PHP_EOL);
} else {
print_r($operation->getError());
}Python
"""Detect text in a local video."""
from google.cloud import videointelligence
video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.Feature.TEXT_DETECTION]
video_context = videointelligence.VideoContext()
with io.open(path, "rb") as file:
input_content = file.read()
operation = video_client.annotate_video(
request={
"features": features,
"input_content": input_content,
"video_context": video_context,
}
)
print("\nProcessing video for text detection.")
result = operation.result(timeout=300)
# The first result is retrieved because a single video was processed.
annotation_result = result.annotation_results[0]
for text_annotation in annotation_result.text_annotations:
print("\nText: {}".format(text_annotation.text))
# Get the first text segment
text_segment = text_annotation.segments[0]
start_time = text_segment.segment.start_time_offset
end_time = text_segment.segment.end_time_offset
print(
"start_time: {}, end_time: {}".format(
start_time.seconds + start_time.microseconds * 1e-6,
end_time.seconds + end_time.microseconds * 1e-6,
)
)
print("Confidence: {}".format(text_segment.confidence))
# Show the result for the first frame in this segment.
frame = text_segment.frames[0]
time_offset = frame.time_offset
print(
"Time offset for the first frame: {}".format(
time_offset.seconds + time_offset.microseconds * 1e-6
)
)
print("Rotated Bounding Box Vertices:")
for vertex in frame.rotated_bounding_box.vertices:
print("\tVertex.x: {}, Vertex.y: {}".format(vertex.x, vertex.y))¿Qué sigue?
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