An overview of Mobile Image Recognition, a presentation of Moodstocks' approach and a focus on Moodstocks SDK core library for Paris Tech Talks Meetup #6. * https://moodstocks.com/ * http://www.meetup.com/Paris-Tech-Talks/events/165819522/ * https://github.com/Moodstocks/jpec

1. Supersonic image recognition
for your mobile apps
Cédric @deltheil
Paris Tech Talks #6
Moodstocks
Tech co-founder

2. Mobile Image Recognition
Your Mobile App
Reference Images + ID-s
Image Recognition Platform
query
match ID = 1234
index
user
developer
ID = 1234

3. The old way: thin client
Mobile App
Image Recognition API
client
server
reference images
send JPEG image
via HTTP
match ID or nil

4. Drawback: network latencies!

5. Our approach: thick client
Mobile App
Moodstocks API
client
server
reference images
up to millions images
Moodstocks SDK
image signatures
up to 10k images
query match ID or nil
server-side search
for large DB
sync

6. Benefit: speed
a typical search runs in a few ms

7. Moodstocks SDK
Mobile App
Wrapper
Core Library
Objective-C
Java
C

8. Moodstocks SDK
Mobile App
Wrapper
Core Library
we will now focus
on this part

9. Design Goal #1
Portability
• core library written in C99
• +99% code in common between iOS / Android
• e.g supporting Intel CPU-s on Android was… no effort!

10. Design Goal #2
Efficiency
• in-memory data structures for maximum speed
• leverage NEON capabilities (SIMD) where applicable
• a typical search runs in ~ 30 ms / 10k images (iPhone 5S)

11. Design Goal #3
Low footprint
• 100% home made, lightweight implementation
• implement & embed only what we need!
• around 800 kB for the iOS ARMv7s release build (-Os)

12. in 1/2 day
Our choices pay off

13. Core Library Architecture
Public Header File
Internal Modules
3rd Party Libraries
Image
Recognition
Pipeline

14. Public Header File
Internal Modules
3rd Party Libraries
Image
Recognition
Pipeline

15. Image Recognition Pipeline
• full-stack: the whole recognition occurs on-device!
• robust to camera noises (blur, lighting, …)
• supports 1D and 2D barcodes too
Image Recognition Pipeline

16. Public Header File
Internal Modules
3rd Party Libraries
Image
Recognition
Pipeline

17. Internal Modules (1/4)
• master-slave sync engine for image signatures
• efficient: works with diff-s & data bundles (less requests)
• fail-safe: retries + the client always stays consistent
Sync StorageLogs API client

18. Internal Modules (2/4)
Sync StorageLogs API client
• collects search results & metadata (OS, etc) along time
• a long-running thread posts batches every 30s
• tolerant to network errors -> HTTP 409 (Already Exists)

19. Internal Modules (3/4)
• persists image signatures on-disk
• persists search results events on-disk
• used as a temp on-disk buffer at sync time
Sync StorageLogs API client

20. Internal Modules (4/4)
• used for server-side recognition (send image via HTTP)
• includes a pool to re-use connections
• also used to post logs batches to the server (HTTP)
Sync StorageLogs API client

21. Public Header File
Internal Modules
3rd Party Libraries
Image
Recognition
Pipeline

22. 3rd Party Libraries (1/4)
libcurl Tokyo Cabinet jpec msgpack
• the +500M users file transfer library
• multi API -> multiple transfers in the same thread
• we use it as the backend of our sync engine + HTTP reqs.

23. 3rd Party Libraries (2/4)
libcurl Tokyo Cabinet jpec msgpack
• key / value embedded database (hash / table / B+tree)
• … and also a fantastic C library (see tcutil.h)
• we use it as the backend of our storage engine

24. 3rd Party Libraries (3/4)
libcurl Tokyo Cabinet jpec msgpack
• our home-made, tiny JPEG encoder (600 LOC-s)
• we open sourced it -> github.com/Moodstocks/jpec
• we use it for server-side requests (send image)

25. 3rd Party Libraries (4/4)
libcurl Tokyo Cabinet jpec msgpack
• efficient binary serialization format
• we use it to pack the data we synchronize

26. Thanks!
Questions? Comments? cedric AT moodstocks DOT com | @deltheil

27. Building
• we use simple Makefile-s and shell scripts
• we cross-compile w/ the iOS and Android toolchains
• we combine all the arch-s into a fat static lib (armv7, …)

28. Testing
• we write unit tests with ct (Easy Unit Testing) by @krarick
• we run them on device! e.g jailbreaked iPhone + openssh
• we use a mock to test the sync engine

29. Quality
• no warnings: we compile with -Wall -Werror
• static analysis with Clang Static Analyzer
• memory leak checks with Valgrind