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telpirionAhrar Monsur
telpirion
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Ahrar Monsur
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feat: face and person detection samples (#362)
Adds the following region tags: video_detect_person_beta video_detect_person_gcs_beta video_detect_faces_beta video_detect_faces_gcs_beta
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‎video-intelligence/analyze.v1p3beta1.js‎

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// Copyright 2020 Google LLC
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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'use strict';
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async function detectPerson(path) {
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//[START video_detect_person_beta]
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// Imports the Google Cloud Video Intelligence library + Node's fs library
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const Video = require('@google-cloud/video-intelligence').v1p3beta1;
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const fs = require('fs');
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// Creates a client
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const video = new Video.VideoIntelligenceServiceClient();
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/**
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* TODO(developer): Uncomment the following line before running the sample.
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*/
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// const path = 'Local file to analyze, e.g. ./my-file.mp4';
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// Reads a local video file and converts it to base64
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const file = fs.readFileSync(path);
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const inputContent = file.toString('base64');
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const request = {
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inputContent: inputContent,
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features: ['PERSON_DETECTION'],
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videoContext: {
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personDetectionConfig: {
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// Must set includeBoundingBoxes to true to get poses and attributes.
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includeBoundingBoxes: true,
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includePoseLandmarks: true,
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includeAttributes: true,
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},
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},
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};
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// Detects people in a video
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const [operation] = await video.annotateVideo(request);
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const results = await operation.promise();
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console.log('Waiting for operation to complete...');
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// Gets annotations for video
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const personAnnotations =
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results[0].annotationResults[0].personDetectionAnnotations;
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for (const {tracks} of personAnnotations) {
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console.log('Person detected:');
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for (const {segment, timestampedObjects} of tracks) {
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if (segment.startTimeOffset.seconds === undefined) {
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segment.startTimeOffset.seconds = 0;
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}
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if (segment.startTimeOffset.nanos === undefined) {
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segment.startTimeOffset.nanos = 0;
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}
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if (segment.endTimeOffset.seconds === undefined) {
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segment.endTimeOffset.seconds = 0;
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}
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if (segment.endTimeOffset.nanos === undefined) {
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segment.endTimeOffset.nanos = 0;
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}
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console.log(
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`\tStart: ${segment.startTimeOffset.seconds}.` +
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`${(segment.startTimeOffset.nanos / 1e6).toFixed(0)}s`
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);
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console.log(
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`\tEnd: ${segment.endTimeOffset.seconds}.` +
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`${(segment.endTimeOffset.nanos / 1e6).toFixed(0)}s`
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);
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// Each segment includes timestamped objects that
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// include characteristic--e.g. clothes, posture
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// of the person detected.
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const [firstTimestampedObject] = timestampedObjects;
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// Attributes include unique pieces of clothing,
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// poses, or hair color.
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for (const {name, value} of firstTimestampedObject.attributes) {
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console.log(`\tAttribute: ${name}; ` + `Value: ${value}`);
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}
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// Landmarks in person detection include body parts.
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for (const {name, point} of firstTimestampedObject.landmarks) {
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console.log(`\tLandmark: ${name}; Vertex: ${point.x}, ${point.y}`);
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}
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}
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}
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// [END video_detect_person_beta]
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}
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async function detectPersonGCS(gcsUri) {
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//[START video_detect_person_gcs_beta]
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// Imports the Google Cloud Video Intelligence library
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const Video = require('@google-cloud/video-intelligence').v1p3beta1;
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// Creates a client
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const video = new Video.VideoIntelligenceServiceClient();
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/**
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* TODO(developer): Uncomment the following line before running the sample.
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*/
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// const gcsUri = 'GCS URI of the video to analyze, e.g. gs://my-bucket/my-video.mp4';
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const request = {
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inputUri: gcsUri,
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features: ['PERSON_DETECTION'],
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videoContext: {
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personDetectionConfig: {
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// Must set includeBoundingBoxes to true to get poses and attributes.
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includeBoundingBoxes: true,
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includePoseLandmarks: true,
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includeAttributes: true,
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},
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},
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};
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// Detects people in a video
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const [operation] = await video.annotateVideo(request);
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const results = await operation.promise();
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console.log('Waiting for operation to complete...');
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// Gets annotations for video
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const personAnnotations =
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results[0].annotationResults[0].personDetectionAnnotations;
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for (const {tracks} of personAnnotations) {
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console.log('Person detected:');
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for (const {segment, timestampedObjects} of tracks) {
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if (segment.startTimeOffset.seconds === undefined) {
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segment.startTimeOffset.seconds = 0;
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}
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if (segment.startTimeOffset.nanos === undefined) {
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segment.startTimeOffset.nanos = 0;
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}
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if (segment.endTimeOffset.seconds === undefined) {
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segment.endTimeOffset.seconds = 0;
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}
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if (segment.endTimeOffset.nanos === undefined) {
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segment.endTimeOffset.nanos = 0;
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}
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console.log(
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`\tStart: ${segment.startTimeOffset.seconds}` +
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`.${(segment.startTimeOffset.nanos / 1e6).toFixed(0)}s`
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);
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console.log(
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`\tEnd: ${segment.endTimeOffset.seconds}.` +
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`${(segment.endTimeOffset.nanos / 1e6).toFixed(0)}s`
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);
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// Each segment includes timestamped objects that
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// include characteristic--e.g. clothes, posture
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// of the person detected.
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const [firstTimestampedObject] = timestampedObjects;
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// Attributes include unique pieces of clothing,
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// poses, or hair color.
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for (const {name, value} of firstTimestampedObject.attributes) {
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console.log(`\tAttribute: ${name}; ` + `Value: ${value}`);
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}
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// Landmarks in person detection include body parts.
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for (const {name, point} of firstTimestampedObject.landmarks) {
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console.log(`\tLandmark: ${name}; Vertex: ${point.x}, ${point.y}`);
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}
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}
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}
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// [END video_detect_person_beta]
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}
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async function detectFaces(path) {
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//[START video_detect_faces_beta]
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// Imports the Google Cloud Video Intelligence library + Node's fs library
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const Video = require('@google-cloud/video-intelligence').v1p3beta1;
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const fs = require('fs');
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// Creates a client
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const video = new Video.VideoIntelligenceServiceClient();
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/**
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* TODO(developer): Uncomment the following line before running the sample.
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*/
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// const path = 'Local file to analyze, e.g. ./my-file.mp4';
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// Reads a local video file and converts it to base64
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const file = fs.readFileSync(path);
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const inputContent = file.toString('base64');
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const request = {
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inputContent: inputContent,
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features: ['FACE_DETECTION'],
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videoContext: {
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faceDetectionConfig: {
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// Must set includeBoundingBoxes to true to get facial attributes.
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includeBoundingBoxes: true,
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includeAttributes: true,
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},
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},
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};
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// Detects faces in a video
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const [operation] = await video.annotateVideo(request);
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const results = await operation.promise();
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console.log('Waiting for operation to complete...');
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// Gets annotations for video
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const faceAnnotations =
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results[0].annotationResults[0].faceDetectionAnnotations;
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for (const {tracks} of faceAnnotations) {
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console.log('Face detected:');
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for (const {segment, timestampedObjects} of tracks) {
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if (segment.startTimeOffset.seconds === undefined) {
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segment.startTimeOffset.seconds = 0;
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}
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if (segment.startTimeOffset.nanos === undefined) {
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segment.startTimeOffset.nanos = 0;
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}
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if (segment.endTimeOffset.seconds === undefined) {
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segment.endTimeOffset.seconds = 0;
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}
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if (segment.endTimeOffset.nanos === undefined) {
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segment.endTimeOffset.nanos = 0;
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}
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console.log(
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`\tStart: ${segment.startTimeOffset.seconds}` +
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`.${(segment.startTimeOffset.nanos / 1e6).toFixed(0)}s`
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);
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console.log(
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`\tEnd: ${segment.endTimeOffset.seconds}.` +
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`${(segment.endTimeOffset.nanos / 1e6).toFixed(0)}s`
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);
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// Each segment includes timestamped objects that
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// include characteristics of the face detected.
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const [firstTimestapedObject] = timestampedObjects;
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for (const {name} of firstTimestapedObject.attributes) {
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// Attributes include unique pieces of clothing, like glasses,
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// poses, or hair color.
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console.log(`\tAttribute: ${name}; `);
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}
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}
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}
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}
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async function detectFacesGCS(gcsUri) {
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//[START video_detect_faces_gcs_beta]
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// Imports the Google Cloud Video Intelligence library
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const Video = require('@google-cloud/video-intelligence').v1p3beta1;
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// Creates a client
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const video = new Video.VideoIntelligenceServiceClient();
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/**
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* TODO(developer): Uncomment the following line before running the sample.
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*/
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// const gcsUri = 'GCS URI of the video to analyze, e.g. gs://my-bucket/my-video.mp4';
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const request = {
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inputUri: gcsUri,
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features: ['FACE_DETECTION'],
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videoContext: {
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faceDetectionConfig: {
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// Must set includeBoundingBoxes to true to get facial attributes.
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includeBoundingBoxes: true,
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includeAttributes: true,
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},
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},
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};
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// Detects faces in a video
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const [operation] = await video.annotateVideo(request);
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const results = await operation.promise();
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console.log('Waiting for operation to complete...');
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// Gets annotations for video
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const faceAnnotations =
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results[0].annotationResults[0].faceDetectionAnnotations;
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for (const {tracks} of faceAnnotations) {
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console.log('Face detected:');
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for (const {segment, timestampedObjects} of tracks) {
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if (segment.startTimeOffset.seconds === undefined) {
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segment.startTimeOffset.seconds = 0;
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}
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if (segment.startTimeOffset.nanos === undefined) {
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segment.startTimeOffset.nanos = 0;
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}
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if (segment.endTimeOffset.seconds === undefined) {
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segment.endTimeOffset.seconds = 0;
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}
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if (segment.endTimeOffset.nanos === undefined) {
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segment.endTimeOffset.nanos = 0;
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}
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console.log(
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`\tStart: ${segment.startTimeOffset.seconds}.` +
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`${(segment.startTimeOffset.nanos / 1e6).toFixed(0)}s`
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);
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console.log(
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`\tEnd: ${segment.endTimeOffset.seconds}.` +
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`${(segment.endTimeOffset.nanos / 1e6).toFixed(0)}s`
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);
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// Each segment includes timestamped objects that
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// include characteristics of the face detected.
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const [firstTimestapedObject] = timestampedObjects;
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for (const {name} of firstTimestapedObject.attributes) {
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// Attributes include unique pieces of clothing, like glasses,
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// poses, or hair color.
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console.log(`\tAttribute: ${name}; `);
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}
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}
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}
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}
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async function main() {
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require(`yargs`)
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.demand(1)
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.command(
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`video-person-gcs <gcsUri>`,
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`Detects people in a video stored in Google Cloud Storage using the Cloud Video Intelligence API.`,
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{},
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opts => detectPersonGCS(opts.gcsUri)
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)
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.command(
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`video-person <path>`,
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`Detects people in a video stored in a local file using the Cloud Video Intelligence API.`,
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{},
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opts => detectPerson(opts.path)
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)
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.command(
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`video-faces-gcs <gcsUri>`,
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`Detects faces in a video stored in Google Cloud Storage using the Cloud Video Intelligence API.`,
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{},
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opts => detectFacesGCS(opts.gcsUri)
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)
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.command(
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`video-faces <path>`,
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`Detects faces in a video stored in a local file using the Cloud Video Intelligence API.`,
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{},
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opts => detectFaces(opts.path)
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)
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.example(`node $0 video-person ./resources/googlework_short.mp4`)
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.example(
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`node $0 video-person-gcs gs://cloud-samples-data/video/googlework_short.mp4`
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)
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.example(`node $0 video-faces ./resources/googlework_short.mp4`)
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.example(
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`node $0 video-faces-gcs gs://cloud-samples-data/video/googlework_short.mp4`
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)
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.wrap(120)
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.recommendCommands()
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.epilogue(
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`For more information, see https://cloud.google.com/video-intelligence/docs`
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)
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.help()
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.strict().argv;
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}
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main().catch(console.error);

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