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docs(samples): add video-intelligence beta samples (#156)
* Adding video-intelligence beta samples * Skipping tests till fixed
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‎automl/test/automlVideoIntelligenceClassificationDataset.v1beta1.test.js

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/**
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* Copyright 2019, Google LLC
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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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*/
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'use strict';
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const {assert} = require('chai');
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const execa = require('execa');
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/** Tests for AutoML Video Intelligence Classification "Dataset API" sample. */
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const cmdDataset = 'node automlVideoIntelligenceDataset.js';
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// TODO(developer): Before running the test cases,
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// set the environment variables PROJECT_ID, REGION_NAME and
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// change the value of datasetId
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const projectId = process.env.PROJECT_ID;
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//const computeRegion = process.env.REGION_NAME;
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const bucket = projectId + '-video';
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const datasetName = 'test_video_dataset';
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const filter = 'videoClassificationDatasetMetadata:*';
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const datasetId = 'VCN1802794449273618432';
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const importDataCsv = 'gs://automl-video-demo-data/hmdb_split1.csv';
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const exec = async cmd => (await execa.shell(cmd)).stdout;
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describe.skip(`DatasetAPI`, () => {
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it(`should create, import and delete a dataset`, async () => {
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// Create dataset
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let output = await exec(`${cmdDataset} create-dataset "${datasetName}"`);
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const parsedOut = output.split('\n');
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const outputDatasetId = parsedOut[1].split(':')[1].trim();
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assert.match(output, /Dataset display name:/);
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// Import data
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output = await exec(
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`${cmdDataset} import-data "${outputDatasetId}" "${importDataCsv}"`
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);
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assert.match(output, /Processing import.../);
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// Delete dataset
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output = await exec(`${cmdDataset} delete-dataset "${outputDatasetId}"`);
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assert.match(output, /Dataset delete details:/);
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});
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it(`should list datasets`, async () => {
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// List dataset
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const output = await exec(`${cmdDataset} list-datasets "${filter}"`);
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assert.match(output, /List of datasets:/);
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});
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it(`should get preexisting dataset`, async () => {
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// Get dataset
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const output = await exec(`${cmdDataset} get-dataset "${datasetId}"`);
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assert.match(output, /Dataset display name:/);
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});
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it(`should export dataset`, async () => {
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// Export data
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const outputUri = 'gs://' + bucket + '/' + datasetId;
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const output = await exec(
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`${cmdDataset} export-data "${datasetId}" "${outputUri}"`
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);
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assert.match(output, /Processing export.../);
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});
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});

‎automl/test/automlVideoIntelligenceClassificationModel.v1beta1.test.js

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/**
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* Copyright 2019, Google LLC
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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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*/
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'use strict';
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const {assert} = require('chai');
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const execa = require('execa');
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/** Tests for AutoML Video Intelligence Classification "Model API" sample. */
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const cmdModel = 'node automlVideoIntelligenceModel.js';
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// TODO(developer): Before running the test cases,
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// set the environment variables PROJECT_ID, REGION_NAME and
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// change the values of datasetId
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//const projectId = process.env.PROJECT_ID;
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//const computeRegion = process.env.REGION_NAME;
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const filter = 'videoClassificationModelMetadata:*';
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const datasetId = 'VCN1653190499151904768';
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const testModelName = 'test_video_model';
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const exec = async cmd => (await execa.shell(cmd)).stdout;
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describe.skip(`Video Intelligence ModelAPI`, () => {
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it(`should create a model`, async () => {
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// Create model
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let output = await exec(
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`${cmdModel} create-model "${datasetId}" "${testModelName}"`
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);
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const operationName = output
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.split('\n')[0]
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.split(':')[1]
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.trim();
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assert.match(output, /Training started.../);
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output = await exec(`${cmdModel} get-operation-status "${operationName}"`);
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assert.match(output, /Operation details:/);
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});
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it(`should list models, get and delete a model. list, get and display model
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evaluations from preexisting models`, async () => {
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// List models
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let output = await exec(`${cmdModel} list-models "${filter}"`);
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const parsedOut = output.split('\n');
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const ouputModelId = parsedOut[3].split(':')[1].trim();
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assert.match(output, /List of models:/);
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// Get model
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output = await exec(`${cmdModel} get-model "${ouputModelId}"`);
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assert.match(output, /Model name:/);
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// List model evaluations
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output = await exec(`${cmdModel} list-model-evaluations "${ouputModelId}"`);
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const parsedModelEvaluation = output.split('\n');
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const modelEvaluationId = parsedModelEvaluation[3].split(':')[1].trim();
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assert.match(output, /Model evaluation Id:/);
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// Get model evaluation
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output = await exec(
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`${cmdModel} get-model-evaluation "${ouputModelId}" ` +
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`"${modelEvaluationId}"`
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);
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assert.match(output, /Model evaluation Id:/);
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// Display evaluation
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output = await exec(`${cmdModel} display-evaluation "${ouputModelId}"`);
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assert.match(output, /Model Evaluation ID:/);
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// Delete model
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output = await exec(`${cmdModel} delete-model "${ouputModelId}"`);
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assert.match(output, /Model delete details:/);
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});
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it(`should list and get operation status`, async () => {
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// List operation status
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let output = await exec(`${cmdModel} list-operations-status`);
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const operationFullId = output
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.split('\n')[3]
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.split(':')[1]
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.trim();
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assert.match(output, /Operation details:/);
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// Get operation status
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// Poll operation status, here confirming that operation is not complete yet
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output = await exec(
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`${cmdModel} get-operation-status "${operationFullId}"`
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);
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assert.match(output, /Operation details:/);
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});
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});

‎automl/test/automlVideoIntelligenceClassificationPredict.v1beta1.test.js

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/**
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* Copyright 2019, Google LLC
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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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*/
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'use strict';
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const {assert} = require('chai');
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const execa = require('execa');
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/** Tests for AutoML Video Intelligence Classification "Prediction API" sample.
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*/
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const cmdPredict = 'node automlVideoIntelligencePrediction.js';
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// TODO(developer): Before running the test cases,
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// set the environment variables PROJECT_ID, REGION_NAME and
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// change the values of modelId, inputUri and outputUriPrefix
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//const projectId = process.env.PROJECT_ID;
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//const computeRegion = process.env.REGION_NAME;
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const modelId = 'VCN5018751611309129728';
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const inputUri = 'gs://video-intelligence/input-csv/annotateVideo.csv';
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const outputUriPrefix = 'gs://video-intelligence/';
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const exec = async cmd => (await execa.shell(cmd)).stdout;
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describe.skip(`Video Intelligence PredictionAPI`, () => {
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it(`should run prediction from preexisting model`, async () => {
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// Run prediction on 'annotate_video.csv' from gcs inputUri
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const output = await exec(
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`${cmdPredict} predict "${modelId}" "${inputUri}" "${outputUriPrefix}"`
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);
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assert.match(output, /Operation name:/);
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});
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});

‎automl/video-intelligence/classification/create-dataset.v1beta1.js

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/**
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* Copyright 2019, Google LLC
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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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*/
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`use strict`;
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async function main(
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projectId = 'YOUR_PROJECT_ID',
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computeRegion = 'YOUR_REGION_NAME',
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datasetName = 'YOUR_DATASET_NAME'
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) {
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// [START automl_video_intelligence_classification_create_dataset]
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const automl = require(`@google-cloud/automl`);
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const util = require(`util`);
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const client = new automl.v1beta1.AutoMlClient();
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/**
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* Demonstrates using the AutoML client to create a dataset.
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* TODO(developer): Uncomment the following lines before running the sample.
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*/
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// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
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// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
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// const datasetName = '[DATASET_NAME]' e.g., "myDataset”;
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// A resource that represents Google Cloud Platform location.
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const projectLocation = client.locationPath(projectId, computeRegion);
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// Set dataset name and metadata.
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const myDataset = {
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displayName: datasetName,
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videoClassificationDatasetMetadata: {},
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};
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// Create a dataset with the dataset metadata in the region.
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client
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.createDataset({parent: projectLocation, dataset: myDataset})
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.then(responses => {
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const dataset = responses[0];
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// Display the dataset information.
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console.log(`Dataset name: ${dataset.name}`);
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console.log(`Dataset Id: ${dataset.name.split(`/`).pop(-1)}`);
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console.log(`Dataset display name: ${dataset.displayName}`);
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console.log(`Dataset example count: ${dataset.exampleCount}`);
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console.log(
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`Video classification dataset metadata: ${util.inspect(
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dataset.videoClassificationDatasetMetadata,
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false,
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null
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)}`
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);
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})
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.catch(err => {
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console.error(err);
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});
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// [END automl_video_intelligence_classification_create_dataset]
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}
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main(...process.argv.slice(2)).catch(console.error());

‎automl/video-intelligence/classification/create-model.v1beta1.js

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/**
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* Copyright 2019, Google LLC
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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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*/
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`use strict`;
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async function main(
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projectId = 'YOUR_PROJECT_ID',
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computeRegion = 'YOUR_REGION_NAME',
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datasetId = 'YOUR_DATASET_ID',
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modelName = 'MODEL_NAME'
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) {
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// [START automl_video_intelligence_classification_create_model]
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const automl = require(`@google-cloud/automl`);
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const client = new automl.v1beta1.AutoMlClient();
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/**
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* Demonstrates using the AutoML client to create a model.
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* TODO(developer): Uncomment the following lines before running the sample.
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*/
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// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
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// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
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// const datasetId = '[DATASET_ID]' e.g., "VCN7209576908164431872";
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// const modelName = '[MODEL_NAME]' e.g., "myModel";
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// A resource that represents Google Cloud Platform location.
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const projectLocation = client.locationPath(projectId, computeRegion);
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// Set datasetId, model name and model metadata for the dataset.
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const myModel = {
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displayName: modelName,
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datasetId: datasetId,
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videoClassificationModelMetadata: {},
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};
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// Create a model with the model metadata in the region.
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client
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.createModel({parent: projectLocation, model: myModel})
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.then(responses => {
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const initialApiResponse = responses[1];
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console.log(`Training operation name: ${initialApiResponse.name}`);
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console.log(`Training started...`);
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})
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.catch(err => {
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console.error(err);
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});
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// [END automl_video_intelligence_classification_create_model]
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}
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main(...process.argv.slice(2)).catch(console.error());

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