chore: modernize the samples and sample tests

samples/automl/automlVisionDataset.js

@@ -266,7 +266,7 @@ require(`yargs`) // eslint-disable-line
     computeRegion: {
       alias: `c`,
       type: `string`,
-      default: process.env.REGION_NAME,
+      default: 'us-central1',
       requiresArg: true,
       description: `region name e.g. "us-central1"`,
     },

samples/automl/automlVisionModel.js

@@ -428,7 +428,7 @@ require(`yargs`) // eslint-disable-line
     computeRegion: {
       alias: `c`,
       type: `string`,
-      default: process.env.REGION_NAME,
+      default: 'us-central1',
       requiresArg: true,
       description: `region name e.g. "us-central1"`,
     },

samples/automl/automlVisionPredict.js

@@ -83,7 +83,7 @@ require(`yargs`) // eslint-disable-line
     computeRegion: {
       alias: `c`,
       type: `string`,
-      default: process.env.REGION_NAME,
+      default: 'us-central1',
       requiresArg: true,
       description: `region name e.g. "us-central1"`,
     },

samples/faceDetection.js

@@ -33,105 +33,77 @@ const fs = require('fs');
  * Uses the Vision API to detect faces in the given file.
  */
 // [START vision_face_detection_tutorial_send_request]
-function detectFaces(inputFile, callback) {
+async function detectFaces(inputFile) {
   // Make a call to the Vision API to detect the faces
   const request = {image: {source: {filename: inputFile}}};
-  client
-    .faceDetection(request)
-    .then(results => {
-      const faces = results[0].faceAnnotations;
-      const numFaces = faces.length;
-      console.log('Found ' + numFaces + (numFaces === 1 ? ' face' : ' faces'));
-      callback(null, faces);
-    })
-    .catch(err => {
-      console.error('ERROR:', err);
-      callback(err);
-    });
+  const results = await client.faceDetection(request);
+  const faces = results[0].faceAnnotations;
+  const numFaces = faces.length;
+  console.log(`Found ${numFaces} face${numFaces === 1 ? '' : 's'}.`);
+  return faces;
 }
 // [END vision_face_detection_tutorial_send_request]
 
 /**
  * Draws a polygon around the faces, then saves to outputFile.
  */
 // [START vision_face_detection_tutorial_process_response]
-function highlightFaces(inputFile, faces, outputFile, Canvas, callback) {
-  fs.readFile(inputFile, (err, image) => {
-    if (err) {
-      return callback(err);
-    }
-
-    const Image = Canvas.Image;
-    // Open the original image into a canvas
-    const img = new Image();
-    img.src = image;
-    const canvas = new Canvas.Canvas(img.width, img.height);
-    const context = canvas.getContext('2d');
-    context.drawImage(img, 0, 0, img.width, img.height);
+async function highlightFaces(inputFile, faces, outputFile, Canvas) {
+  const {promisify} = require('util');
+  const readFile = promisify(fs.readFile);
+  const image = await readFile(inputFile);
+  const Image = Canvas.Image;
+  // Open the original image into a canvas
+  const img = new Image();
+  img.src = image;
+  const canvas = new Canvas.Canvas(img.width, img.height);
+  const context = canvas.getContext('2d');
+  context.drawImage(img, 0, 0, img.width, img.height);
 
-    // Now draw boxes around all the faces
-    context.strokeStyle = 'rgba(0,255,0,0.8)';
-    context.lineWidth = '5';
+  // Now draw boxes around all the faces
+  context.strokeStyle = 'rgba(0,255,0,0.8)';
+  context.lineWidth = '5';
 
-    faces.forEach(face => {
-      context.beginPath();
-      let origX = 0;
-      let origY = 0;
-      face.boundingPoly.vertices.forEach((bounds, i) => {
-        if (i === 0) {
-          origX = bounds.x;
-          origY = bounds.y;
-        }
-        context.lineTo(bounds.x, bounds.y);
-      });
-      context.lineTo(origX, origY);
-      context.stroke();
+  faces.forEach(face => {
+    context.beginPath();
+    let origX = 0;
+    let origY = 0;
+    face.boundingPoly.vertices.forEach((bounds, i) => {
+      if (i === 0) {
+        origX = bounds.x;
+        origY = bounds.y;
+      }
+      context.lineTo(bounds.x, bounds.y);
     });
+    context.lineTo(origX, origY);
+    context.stroke();
+  });
 
-    // Write the result to a file
-    console.log('Writing to file ' + outputFile);
-    const writeStream = fs.createWriteStream(outputFile);
-    const pngStream = canvas.pngStream();
+  // Write the result to a file
+  console.log(`Writing to file ${outputFile}`);
+  const writeStream = fs.createWriteStream(outputFile);
+  const pngStream = canvas.pngStream();
 
-    pngStream.on('data', chunk => {
-      writeStream.write(chunk);
-    });
-    pngStream.on('error', console.log);
-    pngStream.on('end', callback);
+  await new Promise((resolve, reject) => {
+    pngStream
+      .on('data', chunk => writeStream.write(chunk))
+      .on('error', reject)
+      .on('end', resolve);
   });
 }
 // [END vision_face_detection_tutorial_process_response]
 
 // Run the example
 // [START vision_face_detection_tutorial_run_application]
-function main(inputFile, outputFile, Canvas, callback) {
+async function main(inputFile, outputFile) {
+  const Canvas = require('canvas');
   outputFile = outputFile || 'out.png';
-  detectFaces(inputFile, (err, faces) => {
-    if (err) {
-      return callback(err);
-    }
-
-    console.log('Highlighting...');
-    highlightFaces(inputFile, faces, outputFile, Canvas, err => {
-      if (err) {
-        return callback(err);
-      }
-      console.log('Finished!');
-      callback(null, faces);
-    });
-  });
+  const faces = await detectFaces(inputFile);
+  console.log('Highlighting...');
+  await highlightFaces(inputFile, faces, outputFile, Canvas);
+  console.log('Finished!');
 }
 // [END vision_face_detection_tutorial_run_application]
 
-exports.main = main;
-
-if (module === require.main) {
-  if (process.argv.length < 3) {
-    console.log('Usage: node faceDetection <inputFile> [outputFile]');
-    // eslint-disable-next-line no-process-exit
-    process.exit(1);
-  }
-  const inputFile = process.argv[2];
-  const outputFile = process.argv[3];
-  exports.main(inputFile, outputFile, require('canvas'), console.log);
-}
+const args = process.argv.slice(2);
+main(...args).catch(console.error);

samples/package.json

@@ -1,30 +1,31 @@
 {
   "name": "nodejs-docs-samples-vision",
-  "version": "0.0.1",
   "private": true,
   "license": "Apache-2.0",
   "author": "Google Inc.",
   "engines": {
     "node": ">=8"
   },
+  "files": [
+    "*.js"
+  ],
   "scripts": {
-    "test": "mocha system-test/*.test.js --timeout 600000"
+    "test": "mocha system-test --timeout 600000"
   },
   "dependencies": {
-    "@google-cloud/automl": "^0.1.1",
+    "@google-cloud/automl": "^0.1.3",
     "@google-cloud/vision": "^0.23.0",
-    "async": "^2.6.1",
     "mathjs": "^5.0.4",
     "natural": "^0.6.1",
     "redis": "^2.8.0",
     "yargs": "^12.0.0",
     "canvas": "^2.0.0"
   },
   "devDependencies": {
-    "@google-cloud/nodejs-repo-tools": "^3.0.0",
     "@google-cloud/storage": "^2.0.0",
+    "chai": "^4.2.0",
+    "execa": "^1.0.0",
     "mocha": "^5.0.0",
     "uuid": "^3.2.1"
-  },
-  "optionalDependencies": {}
+  }
 }

samples/quickstart.js

@@ -16,20 +16,19 @@
 'use strict';
 
 // [START vision_quickstart]
-// Imports the Google Cloud client library
-const vision = require('@google-cloud/vision');
+async function quickstart() {
+  // Imports the Google Cloud client library
+  const vision = require('@google-cloud/vision');
 
-// Creates a client
-const client = new vision.ImageAnnotatorClient();
+  // Creates a client
+  const client = new vision.ImageAnnotatorClient();
 
-// Performs label detection on the image file
-async function main() {
+  // Performs label detection on the image file
   const [result] = await client.labelDetection('./resources/wakeupcat.jpg');
   const labels = result.labelAnnotations;
   console.log('Labels:');
   labels.forEach(label => console.log(label.description));
 }
-main().catch(err => {
-  console.error('ERROR:', err);
-});
 // [END vision_quickstart]
+
+quickstart().catch(console.error);

samples/system-test/.eslintrc.yml

@@ -1,6 +1,3 @@
 ---
 env:
   mocha: true
-rules:
-  node/no-unpublished-require: off
-  no-empty: off