1. Image Processing Platform
TRR ARB
Team04: Hao Wu, Junran Liu, Meiyi Yang, Vinny DeGenova, Xiangchen Zhao, Xinhui Liu, Yifan Liu

2. Outline Project Overview Project Demo Test Cases and Results
Quality Focal Point
Transition Plan

3. Image Processing Platform
Project Overview
Junran Liu

4. Operational Concept Overview
Our system is an image processing platform, aiming at recognizing images into different classes.
Image Recognition
Model Retraining

5. Operational Concept Overview
Assumptions
Users are willing to upload images
It’s more convenient for users to recognize images by our system than by themselves
Our clients will use our system
Stakeholders
Initiatives
Value Propositions
Beneficiaries
Development Team
Users
Clients
Build a new image processing platform
Divide system into two separate pipelines
Integration our pipeline into company's current system
Marketing Campaign
Users upload images
An easier way for trainers to train the model
Scope company’s current system
An easier way to classify images
Increase the efficiency to classify images
Trainers

6. Operational Concept Overview

7. Operational Concept Overview

8. Transition Objective & Strategy
Category
Description
Extent of Capability Transitioned
Full Operations
Degree of Post-transition Developer Support
None
Degree of Validation of Operational Satisfaction of Stakeholder Objectives
Client understand how to maintain our project;
Project Satisfy the requirements in the Winbook;
Pass all the acceptance testing cases;
Nature of Product Transition
New System

9. Transition Objective & Strategy
We provide two strategies for the transition phase:
AWS PC
More details is on the Transition Plan Session.

10. Image Processing Platform
Project Demo
Hao Wu, Xiangchen Liu

11. Demo
Homepage Introduction
Image Recognition
Model Retraining

12. Homepage Introduction

13. Image Recognition(1) Image Recognition Page Model selecting
Each model has a description
Choose the model by yourself
Image uploading
Image format check
Image number limitation

14. Image Recognition(2)
Testing Result

15. Model Retraining
Topic Management
Image Uploading
Model Training

16. Topic Management Page What is ‘topic’ Topic Management
Topic is the group name of same images
Topic is defined by used
Topic name is necessary
Topic Management
Add Topic
Delete Topic
Modify Topic
Topic Restrictions
Name Length
Duplication
Can be empty

17. Image Uploading Image Selecting Image Receiving Image Management
Image Format Check
Image Size Check
Image number limitation
Image Receiving
Make sure every Image have a
unique file name
Receive the image one by one
Record image information
Image Management
Add New Images
Delete Images
Modify Image Files Name

18. Model Training Training Mode Training Preparation
Debug Mode
Fast Training Mode
Balance Training Mode
Accuracy Priority Mode
Training Preparation
All images are set
Work queue is empty
Async Training Processing
Async Training
Show the progress bar

19. Image Processing Platform
Test Cases and Results
Xinhui Liu, Yifan Liu

20. Overview Test Level Test phase System Testing Delivery
Integration Testing
Development
Unit Testing
Development
Delivery
Acceptance Testing
Delivery

21. Overview White box testing Black box testing
Use white box testing to verify the Tensorflow algorithm
Use white box testing to verify the Django Framework
Black box testing
Use black box testing to verify various functionalities, such as image uploading, model retraining, and image recognition.

22. TC – 01: Test Cases for Image Uploading
Number
Test Item
Result
TC-01-01
Test upper bound of the image uploading capability for model retraining part—uploading over 1000 images at the same time
Pass
TC-01-02
Test image uploading capability for the model retraining part—uploading images
TC-01-03
Test image uploading limitation for the image recognition part—check if the system can show an alert while image number is more than 6
TC-01-04
Test image uploading limitation for the model retraining part—check if the system can show an alert while image number is less than 30.
TC-01-05
Test image renaming capability—renaming an image and showing an alert while the name is duplicated

23. TC – 01: Test Cases for Image Uploading
Number
Test Item
Result
TC-01-06
Test large image filtering capability—uploading a set of images, check if the system can filter oversized images(20M) and upload the rest images
Pass
TC-01-07
Test invalid image filtering capability—uploading a set of images, check if the system can filter images with invalid format and upload the rest images
TC-01-08
Test topic checking capability for the model retraining part—check if the system can show an alert while uploading images without a topic
TC-01-09
Test image deleting capability—after images have been uploaded, check if the system can allow users to delete images
TC-01-10
Test image partially uploading capability—while a set of images is been uploading, interrupt the system, check if the system can save images that have been uploaded already

24. TC – 02: Test Cases for Model Retraining
Number
Test Item
Result
TC-02-01
Test asynchronous retraining capability—check if the system can start a retraining process asynchronously.
Pass
TC-02-02
Test process bar capability—check if the system can show a progress bar to reflect the retraining progress.
Fail
TC-02-03
Test result returning capability—check if the system can save the result of retraining into the database and show it on the front-end.
TC-02-04
Test multi tasking forbidden capability—check if the system can forbid the user to run multiple retraining tasks at the same time, only one retraining task is allowed at a time
TC-02-05
Test progress bar re-obtain capability—after closing the progress bar page, reopen the page, check if the system can show the retraining progress bar accurately

25. TC – 02: Test Cases for Model Retraining
Number
Test Item
Result
TC-02-06
Close and reopen web page while training
Pass
TC-02-07
Start another training progress while a training is progressing
TC-02-08
Test model retraining capability while there is an image that is failed during the uploading part—check if the system can show an alert
Fail

26. TC – 03: Test Cases for Image Recognition
Number
Test Item
Result
TC-03-01
User uploads 0 image and submit
Pass
TC-03-02
User upload 1-5 images at the same time submit
TC-03-03
User uploads more than 5 images at the same time—check if the system can show an alert
TC-03-04
User uploads images with different formats and different sizes
TC-03-05
User uploads images from different categories

27. Image Processing Platform
Quality Focal Point
Vinny DeGenova

28. Traceability Matrix OCD Requirement Use Case Accept Images as Input
WC_4040
UC_1
Provide efficient UI
WC_4044
Utilize provided machine learning algorithms
WC_4077
UC_2
Use detector model on user uploaded images
WC_4101
Provide functionality to extract and replace trained model
WC_4151
UC_3
Run system on unix based server
WC_4150
Train model by providing labeled images
WC_4148
UC_4

29. Solved Technical Debt Database Design: Resolved
Lack of System Documentation: Resolved
Training Status Output: Resolved
Lack of Test Cases: Resolved
Instagram API Integration: Resolved
Lack of Django experience: Resolved

30. Remaining Technical Debt
Lack of Automated Testing: Outstanding
Due to GPU requirement

31. Architecture User selects model to use Images selected for recognition
Model Selection
User selects model to use
Image Selection
Images selected for recognition
Image Recognition
Algorithm Performed
Results Shown to User

32. Architecture Add Label Select Label Upload Images Retrain Model
User adds a label to use
Select Label
User selects which label to use
Upload Images
Upload images to label
Retrain Model

33. Acceptance Test Cases Part 1: Image Recognition
Allow user to select model: PASS
Allow user to upload image: PASS
System performs recognition on uploaded images: PASS
Show recognition output to user: PASS

34. Acceptance Testing Part 2: Model Retraining
Allow user to upload a new label: PASS
Allow user to select a label to upload images to: PASS
Show labels to user: PASS
Allow user to upload images to retrain model: PASS
Retrain model and show progress: PASS

35. Image Processing Platform
Transition Plan
Meiyi Yang

36. Transition Preparation
Local Computer
AWS
Hardware
One PC with Ubuntu
One PC;
Software
Tensorflow;
Mysql5.7;
Python3.5;
Django1.9.2;
Celery 1.3;
Git
AWS EC2 instance with GPU (NVIDIA);
AWS RDS;
Site
N/A
Staff
Developer Team, Trainer; Tester; Client;

37. Operational testing, training, and evaluation
Provide technical support;
Provide documentation;
Testing
Provide Testing Documentation
Evaluation
Pass Acceptance Test Cases

38. More details Local AWS Dependencies Installation
Follow Documentation step by step
Included in AMIs
Source Code & Image Dataset
Git
Database
Start a MySQL service and create a new database;
Change Django Database setting;
Migrate Initial data by python script;
Launch DB instance with MySQL engine;
Run server
Start Mysql service;
Start Django service;

39. Stakeholder Roles & Responsibilities
Responsibility
Location
Development Team
Provide source code in GitHub;
Provide technical documentations;
Provide a trained model and CIFAR100 dataset;
Provide and deploy our current AWS EC2 instance’s AMI to our client AWS;
On-Campus
Trainer
Provide technical support;
Tester
Acceptance Testing Cases
Client / Users
Prepare for the
N/A

40. Milestone Plan 11/30-12/4 Share Github repository;
Date
Role
Responsibility
Location
11/30-12/4
Share Github repository;
Provide documentation;
Provide AWS AMIs;
12/5-12/9
Provide technical support;
Provide maintance

41. Required Resources Products Current Transition Strategy Source Code
Github Repository
Share Github Repository
Technical Documentation
Github Wiki
Server
AWS EC2
Provide AMIs / technical support / Documentation
Database and data
AWS RDS
Data Migration Script / technical support / documentation
Provided Image Dataset and model
CIFAR100 image dataset & trained model
Included in AMIs / Data Migration Script / technical support / documentation

42. Reference: License Tensorflow Apache 2.0 open source license Mysql5.7
GPL
Python3.5
Django
BSD License
Celery

43. Q & A

44. Thank you!