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on_boarding.md

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    Image_Preprocessing_Feature_Extraction["Image Preprocessing & Feature Extraction"]
    Human_Centric_Vision_Core["Human-Centric Vision Core"]
    Post_processing_Analysis_Utilities["Post-processing & Analysis Utilities"]
    Model_Configuration_Assets["Model & Configuration Assets"]
    Examples_Training["Examples & Training"]
    Image_Preprocessing_Feature_Extraction -- "feeds into" --> Human_Centric_Vision_Core
    Human_Centric_Vision_Core -- "passes outputs to" --> Post_processing_Analysis_Utilities
    Model_Configuration_Assets -- "provides assets to" --> Human_Centric_Vision_Core
    Examples_Training -- "interacts with" --> Human_Centric_Vision_Core
    Examples_Training -- "leverages" --> Image_Preprocessing_Feature_Extraction
    Examples_Training -- "utilizes" --> Post_processing_Analysis_Utilities
    click Image_Preprocessing_Feature_Extraction href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/deepgaze/Image_Preprocessing_Feature_Extraction.md" "Details"
    click Human_Centric_Vision_Core href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/deepgaze/Human_Centric_Vision_Core.md" "Details"
    click Examples_Training href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/deepgaze/Examples_Training.md" "Details"
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Details

The deepgaze architecture is designed as a modular, pipeline-driven computer vision library, specializing in human-centric analysis. It initiates with an Image Preprocessing & Feature Extraction stage, which prepares visual data by generating saliency maps and performing color analysis. This preprocessed data then flows into the Human-Centric Vision Core, the central processing unit for face detection, landmark identification, and head pose estimation. The outputs from this core are subsequently refined and analyzed by Post-processing & Analysis Utilities, which handle tasks like mask manipulation and contour analysis. Supporting these operational components are the Model & Configuration Assets, providing essential pre-trained models and configuration files, and the Examples & Training module, which facilitates model development and showcases the library's functionalities. This clear separation of concerns and sequential data flow makes deepgaze highly suitable for visual diagram representation, highlighting the progression of data through distinct processing stages.

Image Preprocessing & Feature Extraction [Expand]

Responsible for initial image transformations, color-based analysis, and the extraction of fundamental visual features like saliency maps and motion cues. It acts as the first stage in many computer vision pipelines.

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Human-Centric Vision Core [Expand]

The central component for analyzing human-related visual information, encompassing functionalities for detecting faces, identifying facial landmarks, and estimating head orientation using both traditional computer vision techniques and deep learning models.

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Post-processing & Analysis Utilities

Provides a suite of tools for the post-processing and analysis of processed image data, particularly binary masks and contours generated by other modules. It focuses on extracting geometric properties and visualizing results.

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Model & Configuration Assets

Serves as the repository for all pre-trained models (TensorFlow checkpoints for CNNs) and configuration files (XML files for Haar cascades) essential for the operation of the vision modules.

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Examples & Training [Expand]

Contains various scripts and utilities primarily for demonstrating the capabilities of the deepgaze library and for training the deep learning models used within the Human-Centric Vision Core.

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FAQ