Foxy v 1.0

Modules Overview

Foxy is a versatile cross-platform software designed for real-time web scraping and video file extraction, offering powerful tools for capturing and processing data with precision.

1. Collector Module

The Collector Module manages the screenshot and snapshot capture process. It allows you to configure specific images that signal various stages of a capture session:

Start Image: Triggers the beginning of the capture session.
Optional Capture Images: Defines additional images to be captured after the session starts.
End Image: Marks the conclusion of the session.

You can also designate detection areas to monitor for changes within specific regions. If changes are detected, the system captures new images. Customizable parameters let you define the similarity threshold for "activation points" and the difference threshold for change detection. Once the end-of-session image is identified, the system automatically reanalyzes the frames to locate the start image.

The module offers an intuitive console interface to guide users through each step of the process. It supports creating multiple "Screen Sessions" within the same project, making it easy to organize captures into separate folders or groups. Each capture is managed in a messaging queue with essential metadata such as timestamps, session codes, and additional details to streamline processing in the next module.
2. Processor Module

The Processor Module lets you select projects and screen sessions created in the Collector Module and define the areas of interest within the captured images. This step is crucial for the OCR and data extraction processes.
Key Features:

Area Labeling: Define and assign names and groups to specific text areas for targeted data extraction.
Pre-Extraction Testing: Run tests to preview extraction results and adjust settings such as thresholds and data types (e.g., string, decimal, integer).
Parallel Processing: OCR and data extraction occur simultaneously with ongoing captures from the Collector Module, enabling efficient multi-tasking.

After processing, data is added to a RabbitMQ messaging queue, similar to the collection stage. You can then export the processed data to either a new or existing project database or a spreadsheet for further analysis.
Data Export and Organization

Foxy dynamically structures both the database and spreadsheet outputs based on the labeled areas:

Database: Each group of labeled areas is automatically mapped to a corresponding table.
Spreadsheet: Data is organized hierarchically with multi-level indices, offering advanced visualization and analysis. Column names derive from the area labels, and data types are assigned based on the labeling process.

Foxy Video Scraping: Real-Time Web Scraping, OCR, Data Labeling & Modeling, with Export to Databases and Spreadsheets.

• The Windows installer now includes the ability to create shortcuts.

• In the Collector module, a new option has been added to the Areas menu to copy configurations from other areas.

• A bug with links in Windows has been fixed.

FOXY is a cross-platform software available in English, Spanish, and Portuguese. It is designed to perform real-time web scraping and video file scraping using OCR.

The first module, "Collector", handles capturing screenshots or snapshots. You can configure an image to act as a signal for the start of the capture, another for optional captures after the session starts, and another to indicate the end of the session. Additionally, you can define an area to detect changes and perform captures if alterations are detected in that area. Parameters can be adjusted to define the similarity required for "activation points" and the threshold difference for the comparison area. Once the capture session starts and the image indicating the end of the session is detected, the system re-analyzes the frames in search of the image that signals the start of the session.

The system features a user-friendly console interface that guides users through all processes. It also allows creating multiple "Screen Sessions" within the same project, making it easy to organize captures into different groups or folders. All captures are placed in a messaging queue with metadata, including timestamp, session code, and other data, to facilitate processing in the second module.

The second module, "Processor", allows selecting projects created in the "Collector" module and choosing screen sessions. In this module, it is necessary to specify the areas or sectors of the image in the captures and label these areas, defining parameters that facilitate the OCR and data modeling processes. Text areas to be extracted can be labeled with a name and assigned to a group. It also offers an interface that allows performing pre-extraction tests to anticipate the final result or adjust parameters such as thresholds and the final data type of each area (string, decimal, or integer). The extraction/OCR process can run in parallel with the "Collector" module, enabling data processing while captures are being made. All processed data is loaded into a RabbitMQ messaging queue, just like in the collection process. Finally, the software allows exporting data to a new or existing project database or to a spreadsheet.

The database and spreadsheet structure is dynamic and defined based on the labeling of the "areas." For each defined group, a table is created in the database, while in the spreadsheet, the information is organized in a hierarchical format using multiple levels of indices. This allows detailed data organization, with each index level representing an additional dimension of the data, facilitating the visualization and analysis of the information. The names of the areas represent columns, and the data type is defined according to what was established during the labeling process.

Foxy Video Scraping: Web Scraping (Real-Time), OCR, Data Labeling, Data Modeling, Export to Database and Spreadsheets

FOXY is a cross-platform software available in English, Spanish, and Portuguese. It is designed to perform real-time web scraping and video file scraping using OCR.

The first module, "Collector", handles capturing screenshots or snapshots. You can configure an image to act as a signal for the start of the capture, another for optional captures after the session starts, and another to indicate the end of the session. Additionally, you can define an area to detect changes and perform captures if alterations are detected in that area. Parameters can be adjusted to define the similarity required for "activation points" and the threshold difference for the comparison area. Once the capture session starts and the image indicating the end of the session is detected, the system re-analyzes the frames in search of the image that signals the start of the session.

The system features a user-friendly console interface that guides users through all processes. It also allows creating multiple "Screen Sessions" within the same project, making it easy to organize captures into different groups or folders. All captures are placed in a messaging queue with metadata, including timestamp, session code, and other data, to facilitate processing in the second module.

The second module, "Processor", allows selecting projects created in the "Collector" module and choosing screen sessions. In this module, it is necessary to specify the areas or sectors of the image in the captures and label these areas, defining parameters that facilitate the OCR and data modeling processes. Text areas to be extracted can be labeled with a name and assigned to a group. It also offers an interface that allows performing pre-extraction tests to anticipate the final result or adjust parameters such as thresholds and the final data type of each area (string, decimal, or integer). The extraction/OCR process can run in parallel with the "Collector" module, enabling data processing while captures are being made. All processed data is loaded into a RabbitMQ messaging queue, just like in the collection process. Finally, the software allows exporting data to a new or existing project database or to a spreadsheet.

The database and spreadsheet structure is dynamic and defined based on the labeling of the "areas." For each defined group, a table is created in the database, while in the spreadsheet, the information is organized in a hierarchical format using multiple levels of indices. This allows detailed data organization, with each index level representing an additional dimension of the data, facilitating the visualization and analysis of the information. The names of the areas represent columns, and the data type is defined according to what was established during the labeling process.