Matthias Bartolo 0436103L

Through the following tasks, a deeper understanding of computer vision was achieved, with a particular focus on tools and frameworks that enabled its implementation:

In the first part of the task, a variety of Data Augmentation techniques in computer vision were explored, encompassing both theoretical and practical aspects. The objectives involved investigating the concept of data augmentation, conducting a comprehensive review of relevant literature, implementing selected techniques using TensorFlow, PyTorch, and OpenCV, and evaluating the performance of augmented images in comparison to other approaches and the original images. Valuable insights into the potential and effectiveness of data augmentation techniques in computer vision were obtained.

In the second part of the task, various tutorials were followed to delve into specific tasks that enhance computer vision capabilities. This endeavor involved experimenting with a wide range of techniques, including both Point Processing and Area Processing. Convolution techniques, including sobel kernel, box filter, and gaussian kernel, were studied, as they play a fundamental role in tasks such as edge detection and smoothing. Additionally, Morphology operations like dilation, erosion, opening, closing, and text segmentation were explored, which are crucial for image processing tasks like noise removal, shape detection, and text extraction. Feature Detection techniques, such as Corner Harris, Sci Thomasi, SIFT, SURF, and ORB, were also examined, as they enable the identification and extraction of distinctive features for tasks like object recognition and tracking.

By completing these tasks, the project achieved a comprehensive understanding of computer vision principles and techniques. The exploration of data augmentation techniques, along with the utilization of tools and frameworks, provided valuable insights into the potential and effectiveness of computer vision applications.

The repository includes the Computer Vision Tutorials and Group Project.

This tool is a simple CA intended to be executed via cli and http.

Mainly intended for development/testing environments.

go build

data_directory: ./tmp/
http_server:
    listen_address: 127.0.0.1
    listen_port: 5000
all_ca_configs:
    ca_1:
        subject:
            common_name: My CA 1
            country:
                - IT
            organization:
                - ACME Corp
            organizational_unit:
                - PKI
            locality: []
            province: []
            street_address: []
            postal_code: []
        validity:
            years: 1
            months: 1
            days: 10
        key_config:
            type: rsa
            config:
                size: 4096
        crl_ttl: 12h
        permitted_dns_domains_critical: true
        permitted_dns_domains: []
        excluded_dns_domains: []
        permitted_ip_ranges:
            - 192.168.0.0/16
            - 10.0.0.0/8
        excluded_ip_ranges: []
        permitted_email_addresses: []
        excluded_email_addresses: []
        permitted_uri_domains: []
        excluded_uri_domains: []
        http_server_options:
            users:
                my_user: my_pass

./simple-ca

openssl req \
    -nodes \
    -subj "/CN=www.example.com" \
     -addext "subjectAltName = DNS:www.example.com , DNS:www2.example.com" \
    -addext "extendedKeyUsage = serverAuth, clientAuth" \
    -addext "keyUsage=keyEncipherment" \
    -newkey rsa:2048 \
    -keyout ${KEYS_DIR}/www.example.com.key.pem \
    -out ${CSRPOOL}/www.example.com.csr.pem


openssl req \
    -in ${CSRPOOL}/www.example.com.csr.pem \
    -noout \
    -text

./simple-ca

./simple-ca http

openssl req \
    -nodes \
    -subj "/CN=www.example.com" \
     -addext "subjectAltName = DNS:www.example.com , DNS:www2.example.com" \
    -addext "extendedKeyUsage = serverAuth, clientAuth" \
    -addext "keyUsage=keyEncipherment" \
    -newkey rsa:2048 \
    -keyout ${KEYS_DIR}/www.example.com.key.pem \
    -out ${CSR_DIR}/www.example.com.csr.pem


openssl req \
    -in ${CSR_DIR}/www.example.com.csr.pem \
    -noout \
    -text

CA_ID=ca_1

curl \
    -sSLf \
    -T ${CSR_DIR}/www.example.com.csr.pem \
    --user my_user:my_pass \
    -X POST \
    http://localhost:5000/ca/$CA_ID/csr/sign

curl \
    -sSLf \
    --user my_user:my_pass \
    -X POST \
    http://localhost:5000/ca/$CA_ID/crt/revoke/12345

• About
• Benefit
• Info
• Setup
• Functions

The Google Tag Manager API provides access to Google Tag Manager configuration data for an authorized user. With this API you can manage: accounts, containers, worksapces, tags, triggers and variables

The Google Tag Manager API handles millions of operations. To protect the system from receiving more operations than it can handle, and to ensure an equitable distribution of system resources, it is necessary to employ a quota system.

https://developers.google.com/tag-manager/api/v2/reference
https://developers.google.com/tag-manager/api/v2/devguide

• 50,000 requests per project per day, which can be increased.
• 10 queries per second (QPS) per IP address
• By default, it is set to 100 requests per 100 seconds per user
• This can be adjusted to a maximum value of 1,000.

python -m venv venv
source ./venv/bin/activate (Mac) or venv\Scripts\activate (Windows)

pip install -r requirements.txt

https://console.cloud.google.com/

mkdir conf
mv xxxxx-xxxxx.json client_secrets.json

touch .env

• CLIENT_SECRETS=’conf/client_secrets.json’
• ACCOUNT_ID=’xxxx’
• CONTAINER_NAME=’xxxx’
• CONTAINER_ID=’xxxx’
• WORKSPACE_NAME=’xxxx’
• WORKSPACE_ID=’xx’

CLIENT_SECRETS = config('CLIENT_SECRETS')
ACCOUNT_ID = config('ACCOUNT_ID')
CONTAINER_NAME= config('CONTAINER_NAME')
CONTAINER_ID = config('CONTAINER_ID')
WORKSPACE_NAME = config('WORKSPACE_NAME')
WORKSPACE_ID = config('WORKSPACE_ID')

you can scan the account, container, workspace, tag, trigger, variable information by using gtm_scanner.py

python project/run_scanner.py

you can create new workspaces, tags, triggers, variables and publish these by using gtm_creator.py

python project/run_creator.py