Google Cloud provides a diverse array of services to realize the ambition of solving real business problems, like constrained resources. An image archive & analysis plus report generation use-case can be realized with just Google Workspace & GCP APIs. The principle of mixing-and-matching Google technologies is applicable to many other challenges faced by you, your organization, or your customers. These slides are from a half- to 1-hour presentation about this case study.

1. Image archive, analysis & report
generation with Google Cloud
Wesley Chun - @wescpy
Developer Advocate, Google
Adjunct CS Faculty, Foothill College
Why and Agenda
● Organizations have real-life business problems seeking solutions
● Google Cloud (the organization) produces 2 main product groups
○ Google Cloud Platform (GCP) and Google Workspace (ex G Suite)
● May know GCP for compute, storage, data & AI/ML cloud services
● While Workspace known for its apps... also for developers(!)
● Use both to build novel solutions to unique business problems
1
Google APIs intro
2
Google Cloud
APIs
3
Image processing
workflow
4
More inspiration
5
Wrap-up

2. Developer Advocate, Google Cloud
● Mission: enable current and future
developers everywhere to be
successful using Google Cloud and
other Google developer tools & APIs
● Focus: GCP serverless (App Engine,
Cloud Functions, Cloud Run); higher
education, Google Workspace, GCP
AI/ML APIs; multi-product use cases
● Content: speak to developers globally;
make videos, create code samples,
produce codelabs (free, self-paced,
hands-on tutorials), publish blog posts
About the speaker
Previous experience / background
● Software engineer & architect for 20+ years
○ Yahoo!, Sun, HP, Cisco, EMC, Xilinx
○ Original Yahoo!Mail engineer/SWE
● Technical trainer, teacher, instructor
○ Taught Math, Linux, Python since 1983
○ Private corporate trainer
○ Adjunct CS Faculty at local SV college
● Python community member
○ Popular Core Python series author
○ Python Software Foundation Fellow
● AB (Math/CS) & CMP (Music/Piano), UC
Berkeley and MSCS, UC Santa Barbara
● Adjunct Computer Science Faculty, Foothill
College (Silicon Valley)
01
Introduction to
Google APIs
Why are you here?

4. General steps
1. Go to Cloud Console
2. Login to Google/Gmail account
(Workspace domain may require admin approval)
3. Create project (per application)
4. Enable APIs to use
5. Enable billing (CC, Free Trial, etc.)
6. Download client library(ies)
7. Create & download credentials
8. Write code
9. Run code (may need to authorize)
Google APIs: how to use
Costs and pricing
● GCP: pay-per-use
● Google Workspace: subscription
● GCP Free Trial ($300/1Q, CC req'd)
● GCP "Always Free" tier
○ Most products have free tier
○ Daily or monthly quota
○ Must exceed to incur billing
● More on both programs at
cloud.google.com/free
Cloud/GCP console
console.cloud.google.com
● Hub of all developer activity
● Applications == projects
○ New project for new apps
○ Projects have a billing acct
● Manage billing accounts
○ Financial instrument required
○ Personal or corporate credit cards,
Free Trial, and education grants
● Access GCP product settings
● Manage users & security
● Manage APIs in devconsole

5. ● View application statistics
● En-/disable Google APIs
● Obtain application credentials
Using Google APIs
goo.gl/RbyTFD
API manager aka Developers Console (devconsole)
console.developers.google.com
Three different authz credential types
● Simple: API keys (to access public data)
○ Simplest form of authorization: an API key; tied to a project
○ Allows access to public data
○ Do not put in code, lose, or upload to GitHub! (can be restricted however)
○ Supported by: Google Maps, (some) YouTube, (some) GCP, etc.
● Authorized: OAuth client IDs (to access data owned by [human] user)
○ Provides additional layer of security via OAuth2 (RFC 6749)
○ Owner must grant permission for your app to access their data
○ Access granularity determined by requested permissions (user scopes)
○ Supported by: Google Workspace, (some) YouTube, (some) GCP, etc.
● Authorized: service accounts (to access data owned by an app/robot user)
○ Provides additional layer of security via OAuth2 or JWT (RFC 7519)
○ Project owning data grants permission implicitly; requires public-private key-pair
○ Access granularity determined by Cloud IAM permissions granted to service account key-pair
○ Supported by: GCP, (some) Google Workspace, etc.

6. Google APIs client
libraries for common
languages; demos in
developers.google.com/api-
client-library
cloud.google.com/apis/docs
/cloud-client-libraries
● Why Google Believes in Open Cloud (Jun 2018 blog post & home page)
○ cloud.google.com/blog/products/gcp/why-google-believes-in-open-cloud
○ cloud.google.com/open-cloud
● Bringing the Best of Open Source to Google Cloud customers (Apr 2019)
○ Strategic partnerships w/leading {open source,data}-centric organizations
■ Confluent, DataStax, Elastic, InfluxData, MongoDB, Neo4j, Redis Labs
○ cloud.google.com/blog/products/open-source/bringing-the-best-of-open-
source-to-google-cloud-customers
○ tcrn.ch/2Z3z1qS
● Google Cloud open source blog
○ cloud.google.com/blog/products/open-source
Google Cloud open source references

7. Two different client library "styles"
● "Platform-level" client libraries (lower-level)
○ Supports multiple products as a "lowest-common denominator"
○ Manage API service endpoints (setup & use)
○ Manage authorization (API keys, OAuth client IDs, service accounts)
○ Google Workspace, Google Analytics, YouTube, Google Ads APIs, etc.
○ Install: developers.google.com/api-client-library
● "Product-level" client libraries (higher-level)
○ Custom client libraries made specifically for each product
○ Managing API service endpoints & security mostly taken care of
○ Only need to create a "client" to use API services
○ Install (Cloud/GCP & Firebase): cloud.google.com/apis/docs/cloud-client-libraries
○ Install (Maps): developers.google.com/places/web-service/client-library
● Some Google APIs families support both, e.g., Cloud
OAuth2 or
API key
HTTP-based REST APIs 1
HTTP
2
Google APIs request-response workflow
● Application makes request
● Request received by service
● Process data, return response
● Results sent to application
(typical client-server model)

8. 02
Google Cloud
Developer tools & APIs
formerly
( )

9. Google Workspace
Top-level documentation and comprehensive developers
overview video at developers.google.com/gsuite
(formerly G Suite and Google Apps)
APIs
Google Compute Engine, Google Cloud Storage
AWS EC2 & S3; Rackspace; Joyent
SaaS
Software as a Service
PaaS
Platform as a Service
IaaS
Infrastructure as a Service
Google Apps Script
Salesforce1/force.com
Google Workspace (was G Suite/Google Apps)
Yahoo!Mail, Hotmail, Salesforce, Netsuite, Office 365
Google App Engine, Cloud Functions
Heroku, Cloud Foundry, Engine Yard, AWS Lambda
Google BigQuery, Cloud SQL,
Cloud Datastore, NL, Vision, Pub/Sub
AWS Kinesis, RDS; Windows Azure SQL, Docker
Google Cloud Platform vs. Google Workspace
Workspace
APIs
GCP
APIs

10. List (first 100) files/folders in Google Drive
from __future__ import print_function
from googleapiclient import discovery
from httplib2 import Http
from oauth2client import file, client, tools
SCOPES = 'https://www.googleapis.com/auth/drive.metadata.readonly'
store = file.Storage('storage.json')
creds = store.get()
if not creds or creds.invalid:
flow = client.flow_from_clientsecrets('client_secret.json', SCOPES)
creds = tools.run_flow(flow, store)
DRIVE = discovery.build('drive', 'v3', http=creds.authorize(Http()))
files = DRIVE.files().list().execute().get('files', [])
for f in files:
print(f['name'], f['mimeType'])
Listing your files
goo.gl/ZIgf8k
Migrate SQL data to a Sheet
# read SQL data then create new spreadsheet & add rows into it
FIELDS = ('ID', 'Customer Name', 'Product Code',
'Units Ordered', 'Unit Price', 'Status')
cxn = sqlite3.connect('db.sqlite')
cur = cxn.cursor()
rows = cur.execute('SELECT * FROM orders').fetchall()
cxn.close()
rows.insert(0, FIELDS)
DATA = {'properties': {'title': 'Customer orders'}}
SHEET_ID = SHEETS.spreadsheets().create(body=DATA,
fields='spreadsheetId').execute().get('spreadsheetId')
SHEETS.spreadsheets().values().update(spreadsheetId=SHEET_ID, range='A1',
body={'values': rows}, valueInputOption='RAW').execute()
Migrate SQL data
to Sheets
goo.gl/N1RPwC

11. Storage: listing buckets
from __future__ import print_function
from googleapiclient import discovery
GCS = discovery.build('storage', 'v1')
BUCKET = YOUR_BUCKET
# send bucket name & return fields to API, display results
print('n** Objects in bucket %r...' % BUCKET)
FIELDS = 'items(name,size)'
files = GCS.objects().list(bucket=BUCKET, fields=FIELDS
).execute().get('items') or [{'name': '(none)', 'size': 'NaN'}]
for f in files:
print(' %s (%s)' % (f['name'], f['size']))
Vision: image analysis & metadata extraction
# set Vision API request body and create endpoint to API
IMG = 'https://google.com/services/images/section-work-card-img_2x.jpg'
body = {'requests': [
{
'image': {
'source': {'imageUri': IMG},
},
'features': [
{'type': 'LABEL_DETECTION'},
{'type': 'FACE_DETECTION'},
],
},
]}
VISION = discovery.build('vision', 'v1', developerKey=API_KEY)

12. labeling = VISION.images().annotate(body=body).execute().get('responses')
for labels in labeling:
if 'labelAnnotations' in labels:
print('** Labels detected (and confidence score):')
for label in labels['labelAnnotations']:
print(' {0:.2f}%t{1}'.format(label['score']*100.,
label['description']))
if 'faceAnnotations' in labels:
print('n** Facial features detected (and likelihood):')
for label, value in labels['faceAnnotations'][0].items():
if label.endswith('Likelihood'):
print(' {}:t{}'.format(
label.split('Likelihood')[0].title(),
value.lower().replace('_', ' ')))
Vision: image analysis & metadata extraction
$ python viz_demo.py
** Labels detected (and confidence score):
96.84% Table
95.62% Furniture
95.35% Smile
93.17% Window
91.78% Tableware
88.44% Lamp
86.70% Comfort
84.65% Interior design
83.56% Sky
83.16% Desk
** Facial features detected (and likelihood):
Joy: very likely
Sorrow: very unlikely
Anger: very unlikely
Surprise: very unlikely
Underexposed: very unlikely
Blurred: very unlikely
Headwear: very unlikely
Vision: image analysis & metadata extraction
g.co/codelabs/vision-python

13. 03
Image
processing
workflow
Main use case

15. Image: Gerd Altmann from Pixabay

16. Cloud
Vision
Google Workspace GCP
Cloud image processing workflow
Cloud
Storage
Drive
Sheets
Archive
image
Categorize
image
Record
results
Cloud image processing workflow
from __future__ import print_function
import argparse, base64, io, webbrowser
from googleapiclient import discovery, http
from httplib2 import Http
from oauth2client import file, client, tools
k_ize = lambda b: '%6.2fK' % (b/1000.) # bytes to kBs
FILE = 'YOUR_IMG_ON_DRIVE'
BUCKET = 'YOUR_BUCKET_NAME'
PARENT = '' # YOUR IMG FILE PREFIX
SHEET = 'YOUR_SHEET_ID'
TOP = 5 # TOP # of VISION LABELS TO SAVE
DEBUG = False
# process credentials for OAuth2 tokens
SCOPES = (
'https://www.googleapis.com/auth/drive.readonly',
'https://www.googleapis.com/auth/devstorage',
'https://www.googleapis.com/auth/cloud-vision',
'https://www.googleapis.com/auth/spreadsheets',
)
store = file.Storage('storage.json')
creds = store.get()
if not creds or creds.invalid:
flow = client.flow_from_clientsecrets(
'client_secret.json', SCOPES)
creds = tools.run_flow(flow, store)
# create API service endpoints
HTTP = creds.authorize(Http())
DRIVE = discovery.build('drive', 'v3', http=HTTP)
GCS = discovery.build('storage', 'v1', http=HTTP)
VISION = discovery.build('vision', 'v1', http=HTTP)
SHEETS = discovery.build('sheets', 'v4', http=HTTP)

17. Cloud image processing workflow
def drive_get_file(fname):
rsp = DRIVE.files().list(q="name='%s'" % fname).execute().get['files'][0]
fileId, fname, mtype = rsp['id'], rsp['name'], rsp['mimeType']
blob = DRIVE.files().get_media(fileId).execute()
return mtype, rsp['modifiedTime'], blob
def gcs_blob_upload(fname, bucket, blob, mimetype):
body = {'name': fname, 'uploadType': 'multipart',
'contentType': mimetype}
return GCS.objects().insert(bucket, body, blob).execute()
def vision_label_img(img, top):
body = {'requests': [{'image': {'content': img}, 'features':
[{'type': 'LABEL_DETECTION', 'maxResults': top}]}]}
rsp = VISION.images().annotate(
body=body).execute().get['responses'][0]
return ', '.join('%s (%.2f%%)' % (label['description'],
label['score']*100.) for label in rsp['labelAnnotations'])
def sheet_append_row(sheet, row):
rsp = SHEETS.spreadsheets().values().append(
spreadsheetId=sheet, range='Sheet1',
body={'values': rows}).execute()
return rsp.get('updates').get('updatedCells')
def main(fname, bucket, sheet_id, top):
mtype, ftime, data = drive_get_img(fname)
gcs_blob_upload(fname, bucket, data, mtype)
rsp = vision_label_img(data, top)
sheet_append_row(sheet_id, [fname, mtype,
ftime, len(data), rsp])
API method calls in Bold
Driver calls in Bold Italics
Cloud image processing workflow
def drive_get_file(fname):
rsp = DRIVE.files().list(
q="name='%s'" % fname).execute().get['files'][0]
fileId, fname, mtype =
rsp['id'], rsp['name'], rsp['mimeType']
blob = DRIVE.files().get_media(fileId).execute()
return mtype, rsp['modifiedTime'], blob
API method calls in Bold
Driver calls in Bold Italics

18. Cloud image processing workflow
def gcs_blob_upload(fname, bucket, blob, mimetype):
body = {
'name': fname,
'uploadType': 'multipart',
'contentType': mimetype
}
return GCS.objects().insert(
bucket, body, blob).execute()
Cloud image processing workflow
def vision_label_img(img, top):
body = {'requests': [{'image': {
'content': img},
'features': [
{'type': 'LABEL_DETECTION',
'maxResults': top}]}]}
rsp = VISION.images().annotate(
body=body).execute().get['responses'][0]
return ', '.join('%s (%.2f%%)' % (
label['description'], label['score']*100.)
for label in rsp['labelAnnotations'])

19. Cloud image processing workflow
def sheet_append_row(sheet, row):
rsp = SHEETS.spreadsheets().values().append(
spreadsheetId=sheet, range='Sheet1',
body={'values': row}).execute()
return rsp.get('updates').get('updatedCells')
API method calls in Bold
Driver calls in Bold Italics
Cloud image processing workflow
def main(fname, bucket, sheet_id, top):
fname, mtype, ftime, data = drive_get_img(fname)
gcs_blob_upload(fname, bucket, data, mtype)
rsp = vision_label_img(data, top)
sheet_append_row(sheet_id,
[fname, mtype, ftime, len(data), rsp])
API method calls in Bold
Driver calls in Bold Italics

20. ● Project goal: Imagining an actual enterprise use case and solve it!
● Specific goals: free-up highly-utilized resource, archive data to
colder/cheaper storage, analyze images, generate report for mgmt
● Download image binary from Google Drive
● Upload object to Cloud Storage bucket
● Send payload for analysis by Cloud Vision
● Write back-up location & analysis results into Google Sheets
● Blog post: goo.gle/3nPxmlc (original post); Cloud X-post
● Codelab: free, online, self-paced, hands-on tutorial
● g.co/codelabs/drive-gcs-vision-sheets
● Application source code
● github.com/googlecodelabs/analyze_gsimg
App summary
04
More Inspiration
Build powerful solutions with
GCP and G Suite

21. Custom intelligence in Gmail
Analyze Google Workspace (formerly G Suite) data with GCP

22. Gmail message processing with GCP
Gmail
Cloud
Pub/Sub
Cloud
Functions
Cloud
Vision
Workspace
(formerly G Suite)
GCP
Star
message
Message
notification
Trigger
function
Extract
images
Categorize
images
Inbox augmented with Cloud Function

23. ● Gmail API: sets up notification forwarding to Cloud Pub/Sub
● developers.google.com/gmail/api/guides/push
● Pub/Sub: triggers logic hosted by Cloud Functions
● cloud.google.com/functions/docs/calling/pubsub
● Cloud Functions: "orchestrator" accessing GCP (and Google Workspace/G Suite) APIs
● Combine all of the above to add custom intelligence to Gmail
● Deep dive code blog post
● cloud.google.com/blog/products/application-development/
adding-custom-intelligence-to-gmail-with-serverless-on-gcp
● Application source code
● github.com/GoogleCloudPlatform/cloud-functions-gmail-nodejs
App summary
05
Wrap-up
Summary & resources

24. Session Summary
● Google provides more than just apps
○ We're more than search, YouTube, Android, Chrome, and Gmail
○ Much of our tech available to developers through our APIs
● Tour of Google (Cloud) APIs & developer tools
○ Workspace: not just a set of productivity apps… you can code them too!
○ GCP: compute, storage, networking, security, data & machine learning tools
○ Google Cloud serverless frees developers from infrastructure
■ 4 unique services so you can focus on building solutions
● Interesting possibilities using both GCP + Workspace together
● Other Google developer products to consider
Other Google APIs & platforms
● Firebase (mobile development platform + RT DB; ML Kit)
○ firebase.google.com & firebase.google.com/docs/ml-kit
● Google Data Studio (data visualization, dashboards, etc.)
○ datastudio.google.com/overview
○ goo.gle/datastudio-course
● Actions on Google/Assistant/DialogFlow (voice apps)
○ developers.google.com/actions
● YouTube (Data, Analytics, and Livestreaming APIs)
○ developers.google.com/youtube
● Google Maps (Maps, Routes, and Places APIs)
○ developers.google.com/maps
● Flutter (native apps [Android, iOS, web] w/1 code base[!])
○ flutter.dev

25. ● Documentation
○ GCP: cloud.google.com/{docs,appengine,functions,vision,automl,sql,compute.storage,
language,speech,text-to-speech,translate,video-intelligence,firestore,bigquery,filestore}
○ Workspace: developers.google.com/{gsuite,drive,calendar,gmail,docs,sheets,slides,apps-script}
● Introductory "codelabs" ([free] self-paced, hands-on tutorials)
○ Workspace REST APIs: g.co/codelabs/gsuite-apis-intro (featuring Drive API)
○ Apps Script: g.co/codelabs/apps-script-intro
○ App Engine: codelabs.developers.google.com/codelabs/cloud-app-engine-python
○ Cloud Vision: g.co/codelabs/vision-python (or C# or Ruby)
○ Cloud Functions: codelabs.developers.google.com/codelabs/cloud-starting-cloudfunctions
○ All others: gcplab.me (GCP) and g.co/codelabs (non-GCP)
● Videos: youtube.com/GoogleCloudPlatform (GCP), goo.gl/JpBQ40 (Workspace), and all others
● Code samples: github.com/GoogleCloudPlatform (GCP) and github.com/gsuitedevs (Workspace)
● GCP Free trial (new users) and Always Free (daily/monthly tier) - cloud.google.com/free
Know AWS/Azure? Compare w/GCP: cloud.google.com/docs/compare/{aws,azure}
Online resources
Thank you! Questions?
Wesley Chun
@wescpy
Codelab: g.co/codelabs/drive-gcs-vision-sheets
Progress bars: goo.gl/69EJVw
Slides: bit.ly/2SHybQg