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.codeboarding/Core_Processing_Engine.json Normal file
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{
"description": "Overview of the DeepFace library's abstract components, focusing on the Core Processing Engine and its interactions with fundamental modules for facial analysis.",
"components": [
{
"name": "Core Processing Engine",
"description": "The central orchestrator of the DeepFace library, responsible for coordinating and executing all high-level face-related tasks. It integrates and manages the execution flow between specialized modules for face detection, facial representation, verification, recognition, demography analysis, and real-time streaming, ensuring a cohesive and efficient facial analysis pipeline. It acts as the \"brain\" that ties together the various deep learning functionalities.",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.detection",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/detection.py",
"reference_start_line": 0,
"reference_end_line": 0
},
{
"qualified_name": "deepface.modules.representation",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/representation.py",
"reference_start_line": 0,
"reference_end_line": 0
},
{
"qualified_name": "deepface.modules.verification",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/verification.py",
"reference_start_line": 0,
"reference_end_line": 0
},
{
"qualified_name": "deepface.modules.demography",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/demography.py",
"reference_start_line": 0,
"reference_end_line": 0
},
{
"qualified_name": "deepface.modules.recognition",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/recognition.py",
"reference_start_line": 0,
"reference_end_line": 0
},
{
"qualified_name": "deepface.modules.streaming",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/streaming.py",
"reference_start_line": 0,
"reference_end_line": 0
}
]
},
{
"name": "Model Management Component",
"description": "Responsible for building, loading, and managing the various deep learning models (detectors, recognizers, demography models) required for face analysis. It ensures models are initialized and available.",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.modeling",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/modeling.py",
"reference_start_line": 0,
"reference_end_line": 0
},
{
"qualified_name": "deepface.models.Detector",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Detector.py",
"reference_start_line": 9,
"reference_end_line": 27
},
{
"qualified_name": "deepface.models.FacialRecognition",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/FacialRecognition.py",
"reference_start_line": 15,
"reference_end_line": 47
},
{
"qualified_name": "deepface.models.Demography",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Demography.py",
"reference_start_line": 15,
"reference_end_line": 67
}
]
},
{
"name": "Face Detection Component",
"description": "Identifies and extracts facial regions from input images or video frames, providing normalized and aligned face images for subsequent processing. It's a prerequisite for most other tasks.",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.detection",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/detection.py",
"reference_start_line": 0,
"reference_end_line": 0
},
{
"qualified_name": "deepface.models.face_detection",
"reference_file": "deepface/models/face_detection.py",
"reference_start_line": 0,
"reference_end_line": 0
}
]
},
{
"name": "Facial Representation Component",
"description": "Converts detected facial images into numerical embeddings (vectors) that capture unique facial features, enabling quantitative comparisons between faces.",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.representation",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/representation.py",
"reference_start_line": 0,
"reference_end_line": 0
},
{
"qualified_name": "deepface.models.facial_recognition",
"reference_file": "deepface/models/facial_recognition.py",
"reference_start_line": 0,
"reference_end_line": 0
}
]
},
{
"name": "Verification Component",
"description": "Compares two facial embeddings to determine if they belong to the same individual, calculating distances and applying thresholds for identity confirmation.",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.verification",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/verification.py",
"reference_start_line": 0,
"reference_end_line": 0
}
]
},
{
"name": "Recognition Component",
"description": "Searches for a given face within a database of known faces, identifying potential matches based on facial embeddings and verification logic.",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.recognition",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/recognition.py",
"reference_start_line": 0,
"reference_end_line": 0
}
]
},
{
"name": "Demography Analysis Component",
"description": "Analyzes detected faces to predict demographic attributes such as age, gender, emotion, and race using specialized deep learning models.",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.demography",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/demography.py",
"reference_start_line": 0,
"reference_end_line": 0
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{
"qualified_name": "deepface.models.demography",
"reference_file": "deepface/models/demography.py",
"reference_start_line": 0,
"reference_end_line": 0
}
]
},
{
"name": "Streaming Analysis Component",
"description": "Manages the real-time processing of video streams for continuous face detection, recognition, and demographic analysis, applying other DeepFace functionalities in a live context.",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.streaming",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/streaming.py",
"reference_start_line": 0,
"reference_end_line": 0
}
]
},
{
"name": "Image Utilities Component",
"description": "Provides common utility functions for image loading, resizing, alignment, and basic manipulation, serving as a foundational support for all image-processing tasks.",
"referenced_source_code": [
{
"qualified_name": "deepface.commons.image_utils",
"reference_file": "/mnt/e/StartUp/deepface/deepface/commons/image_utils.py",
"reference_start_line": 0,
"reference_end_line": 0
}
]
},
{
"name": "Logging Component",
"description": "Handles the logging of information, warnings, and errors throughout the DeepFace system, providing crucial insights for debugging and monitoring.",
"referenced_source_code": [
{
"qualified_name": "deepface.commons.logger",
"reference_file": "/mnt/e/StartUp/deepface/deepface/commons/logger.py",
"reference_start_line": 0,
"reference_end_line": 0
}
]
}
],
"components_relations": [
{
"relation": "Orchestrates",
"src_name": "Core Processing Engine",
"dst_name": "Face Detection Component"
},
{
"relation": "Orchestrates",
"src_name": "Core Processing Engine",
"dst_name": "Facial Representation Component"
},
{
"relation": "Orchestrates",
"src_name": "Core Processing Engine",
"dst_name": "Verification Component"
},
{
"relation": "Orchestrates",
"src_name": "Core Processing Engine",
"dst_name": "Recognition Component"
},
{
"relation": "Orchestrates",
"src_name": "Core Processing Engine",
"dst_name": "Demography Analysis Component"
},
{
"relation": "Orchestrates",
"src_name": "Core Processing Engine",
"dst_name": "Streaming Analysis Component"
},
{
"relation": "Utilizes",
"src_name": "Face Detection Component",
"dst_name": "Model Management Component"
},
{
"relation": "Uses",
"src_name": "Face Detection Component",
"dst_name": "Image Utilities Component"
},
{
"relation": "Utilizes",
"src_name": "Facial Representation Component",
"dst_name": "Model Management Component"
},
{
"relation": "Uses",
"src_name": "Facial Representation Component",
"dst_name": "Image Utilities Component"
},
{
"relation": "Relies on",
"src_name": "Verification Component",
"dst_name": "Facial Representation Component"
},
{
"relation": "Utilizes",
"src_name": "Verification Component",
"dst_name": "Model Management Component"
},
{
"relation": "Uses",
"src_name": "Recognition Component",
"dst_name": "Facial Representation Component"
},
{
"relation": "Uses",
"src_name": "Recognition Component",
"dst_name": "Face Detection Component"
},
{
"relation": "Leverages",
"src_name": "Recognition Component",
"dst_name": "Verification Component"
},
{
"relation": "Utilizes",
"src_name": "Demography Analysis Component",
"dst_name": "Model Management Component"
},
{
"relation": "Uses",
"src_name": "Demography Analysis Component",
"dst_name": "Image Utilities Component"
},
{
"relation": "Leverages",
"src_name": "Streaming Analysis Component",
"dst_name": "Core Processing Engine"
},
{
"relation": "Reports to",
"src_name": "Core Processing Engine",
"dst_name": "Logging Component"
},
{
"relation": "Reports to",
"src_name": "Model Management Component",
"dst_name": "Logging Component"
},
{
"relation": "Reports to",
"src_name": "Face Detection Component",
"dst_name": "Logging Component"
}
]
}

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```mermaid
graph LR
Core_Processing_Engine["Core Processing Engine"]
Model_Management_Component["Model Management Component"]
Face_Detection_Component["Face Detection Component"]
Facial_Representation_Component["Facial Representation Component"]
Verification_Component["Verification Component"]
Recognition_Component["Recognition Component"]
Demography_Analysis_Component["Demography Analysis Component"]
Streaming_Analysis_Component["Streaming Analysis Component"]
Image_Utilities_Component["Image Utilities Component"]
Logging_Component["Logging Component"]
Core_Processing_Engine -- "Orchestrates" --> Face_Detection_Component
Core_Processing_Engine -- "Orchestrates" --> Facial_Representation_Component
Core_Processing_Engine -- "Orchestrates" --> Verification_Component
Core_Processing_Engine -- "Orchestrates" --> Recognition_Component
Core_Processing_Engine -- "Orchestrates" --> Demography_Analysis_Component
Core_Processing_Engine -- "Orchestrates" --> Streaming_Analysis_Component
Face_Detection_Component -- "Utilizes" --> Model_Management_Component
Face_Detection_Component -- "Uses" --> Image_Utilities_Component
Facial_Representation_Component -- "Utilizes" --> Model_Management_Component
Facial_Representation_Component -- "Uses" --> Image_Utilities_Component
Verification_Component -- "Relies on" --> Facial_Representation_Component
Verification_Component -- "Utilizes" --> Model_Management_Component
Recognition_Component -- "Uses" --> Facial_Representation_Component
Recognition_Component -- "Uses" --> Face_Detection_Component
Recognition_Component -- "Leverages" --> Verification_Component
Demography_Analysis_Component -- "Utilizes" --> Model_Management_Component
Demography_Analysis_Component -- "Uses" --> Image_Utilities_Component
Streaming_Analysis_Component -- "Leverages" --> Core_Processing_Engine
Core_Processing_Engine -- "Reports to" --> Logging_Component
Model_Management_Component -- "Reports to" --> Logging_Component
Face_Detection_Component -- "Reports to" --> Logging_Component
click Core_Processing_Engine href "https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/Core_Processing_Engine.md" "Details"
```
[![CodeBoarding](https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square)](https://github.com/CodeBoarding/GeneratedOnBoardings)[![Demo](https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square)](https://www.codeboarding.org/demo)[![Contact](https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square)](mailto:contact@codeboarding.org)
## Details
Overview of the DeepFace library's abstract components, focusing on the Core Processing Engine and its interactions with fundamental modules for facial analysis.
### Core Processing Engine [[Expand]](./Core_Processing_Engine.md)
The central orchestrator of the DeepFace library, responsible for coordinating and executing all high-level face-related tasks. It integrates and manages the execution flow between specialized modules for face detection, facial representation, verification, recognition, demography analysis, and real-time streaming, ensuring a cohesive and efficient facial analysis pipeline. It acts as the "brain" that ties together the various deep learning functionalities.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/detection.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.detection` (0:0)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/representation.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.representation` (0:0)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/verification.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.verification` (0:0)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/demography.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.demography` (0:0)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/recognition.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.recognition` (0:0)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/streaming.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.streaming` (0:0)</a>
### Model Management Component
Responsible for building, loading, and managing the various deep learning models (detectors, recognizers, demography models) required for face analysis. It ensures models are initialized and available.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/modeling.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.modeling` (0:0)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Detector.py#L9-L27" target="_blank" rel="noopener noreferrer">`deepface.models.Detector` (9:27)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/FacialRecognition.py#L15-L47" target="_blank" rel="noopener noreferrer">`deepface.models.FacialRecognition` (15:47)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Demography.py#L15-L67" target="_blank" rel="noopener noreferrer">`deepface.models.Demography` (15:67)</a>
### Face Detection Component
Identifies and extracts facial regions from input images or video frames, providing normalized and aligned face images for subsequent processing. It's a prerequisite for most other tasks.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/detection.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.detection` (0:0)</a>
- `deepface.models.face_detection` (0:0)
### Facial Representation Component
Converts detected facial images into numerical embeddings (vectors) that capture unique facial features, enabling quantitative comparisons between faces.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/representation.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.representation` (0:0)</a>
- `deepface.models.facial_recognition` (0:0)
### Verification Component
Compares two facial embeddings to determine if they belong to the same individual, calculating distances and applying thresholds for identity confirmation.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/verification.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.verification` (0:0)</a>
### Recognition Component
Searches for a given face within a database of known faces, identifying potential matches based on facial embeddings and verification logic.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/recognition.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.recognition` (0:0)</a>
### Demography Analysis Component
Analyzes detected faces to predict demographic attributes such as age, gender, emotion, and race using specialized deep learning models.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/demography.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.demography` (0:0)</a>
- `deepface.models.demography` (0:0)
### Streaming Analysis Component
Manages the real-time processing of video streams for continuous face detection, recognition, and demographic analysis, applying other DeepFace functionalities in a live context.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/streaming.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.streaming` (0:0)</a>
### Image Utilities Component
Provides common utility functions for image loading, resizing, alignment, and basic manipulation, serving as a foundational support for all image-processing tasks.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/commons/image_utils.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.commons.image_utils` (0:0)</a>
### Logging Component
Handles the logging of information, warnings, and errors throughout the DeepFace system, providing crucial insights for debugging and monitoring.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/commons/logger.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.commons.logger` (0:0)</a>
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)

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.codeboarding/DeepFace_API_Entry_Points.json Normal file
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{
"description": "Abstract Components Overview: This section provides a detailed overview of the core components within the DeepFace library, focusing on their structure, purpose, and interactions. These components are fundamental to the library's functionality, enabling various facial analysis tasks from detection to recognition and demographic analysis.",
"components": [
{
"name": "DeepFace API & Entry Points",
"description": "This component serves as the primary interface for users to interact with the DeepFace library. It encompasses both the direct Python API (`deepface.DeepFace`) for programmatic access and the web API (`deepface.api.src.app`, `deepface.api.src.modules.core.routes`, `deepface.api.src.modules.core.service`) for web-based requests. It handles initial user requests and delegates them for core processing.",
"referenced_source_code": [
{
"qualified_name": "deepface.DeepFace",
"reference_file": "/mnt/e/StartUp/deepface/deepface/DeepFace.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.api.src.app",
"reference_file": "/mnt/e/StartUp/deepface/deepface/api/src/app.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.api.src.modules.core.routes",
"reference_file": "/mnt/e/StartUp/deepface/deepface/api/src/modules/core/routes.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.api.src.modules.core.service",
"reference_file": "/mnt/e/StartUp/deepface/deepface/api/src/modules/core/service.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Core API Component",
"description": "This component orchestrates the main DeepFace functionalities, acting as a central hub that coordinates calls to other specialized components like face detection, facial representation, and demography analysis. It encapsulates the core logic for various DeepFace operations (e.g., `verify`, `analyze`, `find`).",
"referenced_source_code": [
{
"qualified_name": "deepface.DeepFace",
"reference_file": "/mnt/e/StartUp/deepface/deepface/DeepFace.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.commons.functions",
"reference_file": "deepface/commons/functions.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Model Management Component",
"description": "Responsible for loading, caching, and managing the various deep learning models used by DeepFace for tasks like face detection, facial recognition, and demography analysis. It ensures models are efficiently loaded and available when needed, handling model-specific configurations and weights.",
"referenced_source_code": [
{
"qualified_name": "deepface.DeepFace",
"reference_file": "/mnt/e/StartUp/deepface/deepface/DeepFace.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.commons.functions",
"reference_file": "deepface/commons/functions.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.basemodels",
"reference_file": "deepface/basemodels/",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Face Detection Component",
"description": "This component is dedicated to detecting faces within images. It provides an abstraction over various face detection algorithms (e.g., OpenCV, SSD, RetinaFace, MTCNN, MediaPipe, YuNet, YOLO, Dlib, CenterFace) and returns detected face regions. The `getClassHierarchy` output shows various `*Client` classes inheriting from `deepface.models.Detector.Detector`.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.Detector",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Detector.py",
"reference_start_line": 9,
"reference_end_line": 27
},
{
"qualified_name": "deepface.models.face_detection",
"reference_file": "deepface/models/face_detection/",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Facial Representation Component",
"description": "This component is responsible for generating numerical representations (embeddings) of faces using various facial recognition models (e.g., ArcFace, FaceNet, DeepFace, VGG-Face, OpenFace, DeepID, Dlib, SFace, GhostFaceNet, Buffalo_L). These embeddings are crucial for tasks like verification and recognition. The `getClassHierarchy` output shows various `*Client` classes inheriting from `deepface.models.FacialRecognition.FacialRecognition`.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.FacialRecognition",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/FacialRecognition.py",
"reference_start_line": 15,
"reference_end_line": 47
},
{
"qualified_name": "deepface.models.facial_recognition",
"reference_file": "deepface/models/facial_recognition/",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Demography Analysis Component",
"description": "This component analyzes detected faces to predict demographic attributes such as age, emotion, gender, and race. It utilizes specialized models for each of these tasks. The `getClassHierarchy` output shows various `*Client` classes inheriting from `deepface.models.Demography.Demography`.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.Demography",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Demography.py",
"reference_start_line": 15,
"reference_end_line": 67
},
{
"qualified_name": "deepface.models.demography",
"reference_file": "deepface/models/demography/",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Image Utilities Component",
"description": "Provides common image processing functionalities such as pre-processing, alignment, resizing, and normalization, which are essential for preparing images for face detection and analysis models.",
"referenced_source_code": [
{
"qualified_name": "deepface.commons.functions",
"reference_file": "deepface/commons/functions.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Logging Component",
"description": "Manages the logging of events, errors, and debugging information throughout the DeepFace library, aiding in monitoring and troubleshooting.",
"referenced_source_code": [
{
"qualified_name": "deepface.commons.logger",
"reference_file": "/mnt/e/StartUp/deepface/deepface/commons/logger.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
}
],
"components_relations": [
{
"relation": "calls",
"src_name": "DeepFace API & Entry Points",
"dst_name": "Core API Component"
},
{
"relation": "calls",
"src_name": "DeepFace API & Entry Points",
"dst_name": "Model Management Component"
},
{
"relation": "calls",
"src_name": "Core API Component",
"dst_name": "Face Detection Component"
},
{
"relation": "calls",
"src_name": "Core API Component",
"dst_name": "Facial Representation Component"
},
{
"relation": "calls",
"src_name": "Core API Component",
"dst_name": "Demography Analysis Component"
},
{
"relation": "calls",
"src_name": "Core API Component",
"dst_name": "Model Management Component"
},
{
"relation": "calls",
"src_name": "Core API Component",
"dst_name": "Image Utilities Component"
},
{
"relation": "calls",
"src_name": "Face Detection Component",
"dst_name": "Image Utilities Component"
},
{
"relation": "calls",
"src_name": "Facial Representation Component",
"dst_name": "Image Utilities Component"
},
{
"relation": "calls",
"src_name": "Demography Analysis Component",
"dst_name": "Image Utilities Component"
},
{
"relation": "calls",
"src_name": "DeepFace API & Entry Points",
"dst_name": "Logging Component"
},
{
"relation": "calls",
"src_name": "Core API Component",
"dst_name": "Logging Component"
},
{
"relation": "calls",
"src_name": "Model Management Component",
"dst_name": "Logging Component"
},
{
"relation": "calls",
"src_name": "Face Detection Component",
"dst_name": "Logging Component"
},
{
"relation": "calls",
"src_name": "Facial Representation Component",
"dst_name": "Logging Component"
},
{
"relation": "calls",
"src_name": "Demography Analysis Component",
"dst_name": "Logging Component"
},
{
"relation": "calls",
"src_name": "Image Utilities Component",
"dst_name": "Logging Component"
}
]
}

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@ -0,0 +1,120 @@
```mermaid
graph LR
DeepFace_API_Entry_Points["DeepFace API & Entry Points"]
Core_API_Component["Core API Component"]
Model_Management_Component["Model Management Component"]
Face_Detection_Component["Face Detection Component"]
Facial_Representation_Component["Facial Representation Component"]
Demography_Analysis_Component["Demography Analysis Component"]
Image_Utilities_Component["Image Utilities Component"]
Logging_Component["Logging Component"]
DeepFace_API_Entry_Points -- "calls" --> Core_API_Component
DeepFace_API_Entry_Points -- "calls" --> Model_Management_Component
Core_API_Component -- "calls" --> Face_Detection_Component
Core_API_Component -- "calls" --> Facial_Representation_Component
Core_API_Component -- "calls" --> Demography_Analysis_Component
Core_API_Component -- "calls" --> Model_Management_Component
Core_API_Component -- "calls" --> Image_Utilities_Component
Face_Detection_Component -- "calls" --> Image_Utilities_Component
Facial_Representation_Component -- "calls" --> Image_Utilities_Component
Demography_Analysis_Component -- "calls" --> Image_Utilities_Component
DeepFace_API_Entry_Points -- "calls" --> Logging_Component
Core_API_Component -- "calls" --> Logging_Component
Model_Management_Component -- "calls" --> Logging_Component
Face_Detection_Component -- "calls" --> Logging_Component
Facial_Representation_Component -- "calls" --> Logging_Component
Demography_Analysis_Component -- "calls" --> Logging_Component
Image_Utilities_Component -- "calls" --> Logging_Component
click DeepFace_API_Entry_Points href "https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/DeepFace_API_Entry_Points.md" "Details"
```
[![CodeBoarding](https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square)](https://github.com/CodeBoarding/GeneratedOnBoardings)[![Demo](https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square)](https://www.codeboarding.org/demo)[![Contact](https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square)](mailto:contact@codeboarding.org)
## Details
Abstract Components Overview: This section provides a detailed overview of the core components within the DeepFace library, focusing on their structure, purpose, and interactions. These components are fundamental to the library's functionality, enabling various facial analysis tasks from detection to recognition and demographic analysis.
### DeepFace API & Entry Points [[Expand]](./DeepFace_API_Entry_Points.md)
This component serves as the primary interface for users to interact with the DeepFace library. It encompasses both the direct Python API (`deepface.DeepFace`) for programmatic access and the web API (`deepface.api.src.app`, `deepface.api.src.modules.core.routes`, `deepface.api.src.modules.core.service`) for web-based requests. It handles initial user requests and delegates them for core processing.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/DeepFace.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.DeepFace` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/api/src/app.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.api.src.app` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/api/src/modules/core/routes.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.api.src.modules.core.routes` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/api/src/modules/core/service.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.api.src.modules.core.service` (1:1)</a>
### Core API Component
This component orchestrates the main DeepFace functionalities, acting as a central hub that coordinates calls to other specialized components like face detection, facial representation, and demography analysis. It encapsulates the core logic for various DeepFace operations (e.g., `verify`, `analyze`, `find`).
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/DeepFace.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.DeepFace` (1:1)</a>
- `deepface.commons.functions` (1:1)
### Model Management Component
Responsible for loading, caching, and managing the various deep learning models used by DeepFace for tasks like face detection, facial recognition, and demography analysis. It ensures models are efficiently loaded and available when needed, handling model-specific configurations and weights.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/DeepFace.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.DeepFace` (1:1)</a>
- `deepface.commons.functions` (1:1)
- `deepface.basemodels` (1:1)
### Face Detection Component
This component is dedicated to detecting faces within images. It provides an abstraction over various face detection algorithms (e.g., OpenCV, SSD, RetinaFace, MTCNN, MediaPipe, YuNet, YOLO, Dlib, CenterFace) and returns detected face regions. The `getClassHierarchy` output shows various `*Client` classes inheriting from `deepface.models.Detector.Detector`.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Detector.py#L9-L27" target="_blank" rel="noopener noreferrer">`deepface.models.Detector` (9:27)</a>
- `deepface.models.face_detection` (1:1)
### Facial Representation Component
This component is responsible for generating numerical representations (embeddings) of faces using various facial recognition models (e.g., ArcFace, FaceNet, DeepFace, VGG-Face, OpenFace, DeepID, Dlib, SFace, GhostFaceNet, Buffalo_L). These embeddings are crucial for tasks like verification and recognition. The `getClassHierarchy` output shows various `*Client` classes inheriting from `deepface.models.FacialRecognition.FacialRecognition`.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/FacialRecognition.py#L15-L47" target="_blank" rel="noopener noreferrer">`deepface.models.FacialRecognition` (15:47)</a>
- `deepface.models.facial_recognition` (1:1)
### Demography Analysis Component
This component analyzes detected faces to predict demographic attributes such as age, emotion, gender, and race. It utilizes specialized models for each of these tasks. The `getClassHierarchy` output shows various `*Client` classes inheriting from `deepface.models.Demography.Demography`.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Demography.py#L15-L67" target="_blank" rel="noopener noreferrer">`deepface.models.Demography` (15:67)</a>
- `deepface.models.demography` (1:1)
### Image Utilities Component
Provides common image processing functionalities such as pre-processing, alignment, resizing, and normalization, which are essential for preparing images for face detection and analysis models.
**Related Classes/Methods**:
- `deepface.commons.functions` (1:1)
### Logging Component
Manages the logging of events, errors, and debugging information throughout the DeepFace library, aiding in monitoring and troubleshooting.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/commons/logger.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.commons.logger` (1:1)</a>
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)

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.codeboarding/Deep_Learning_Models.json Normal file
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{
"description": "This subsystem encapsulates the core deep learning functionalities of `deepface`, providing specialized models for various facial analysis tasks. It is structured around distinct model types, a central utility for managing model weights, and a standardized data structure for detected faces.",
"components": [
{
"name": "Face Detection Models",
"description": "This component provides a collection of algorithms and models for accurately detecting human faces within images. Each specific detection model (e.g., CenterFaceClient, DlibClient, MediaPipeClient, OpenCvClient, YoloDetectorClient, YuNetClient, etc.) is implemented as a client class, all inheriting from the `deepface.models.Detector.Detector` base class. This design ensures a unified interface for face detection operations, consistently returning structured `FacialAreaRegion` data.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.Detector.Detector",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Detector.py",
"reference_start_line": 9,
"reference_end_line": 27
},
{
"qualified_name": "deepface.models.face_detection.CenterFace.CenterFaceClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/CenterFace.py",
"reference_start_line": 20,
"reference_end_line": 85
},
{
"qualified_name": "deepface.models.face_detection.Dlib.DlibClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/Dlib.py",
"reference_start_line": 15,
"reference_end_line": 104
},
{
"qualified_name": "deepface.models.face_detection.MediaPipe.MediaPipeClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/MediaPipe.py",
"reference_start_line": 11,
"reference_end_line": 96
},
{
"qualified_name": "deepface.models.face_detection.OpenCv.OpenCvClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/OpenCv.py",
"reference_start_line": 12,
"reference_end_line": 175
},
{
"qualified_name": "deepface.models.face_detection.Yolo.YoloDetectorClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/Yolo.py",
"reference_start_line": 36,
"reference_end_line": 121
},
{
"qualified_name": "deepface.models.face_detection.YuNet.YuNetClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/YuNet.py",
"reference_start_line": 19,
"reference_end_line": 129
}
]
},
{
"name": "Facial Recognition Models",
"description": "This component offers multiple pre-trained deep learning models designed for facial recognition, capable of generating high-dimensional embeddings (numerical representations) from face images. These embeddings are essential for tasks such as face verification (determining if two faces belong to the same person) or identification. Each model (e.g., ArcFaceClient, FaceNet128dClient, VggFaceClient, DeepIdClient, GhostFaceNetClient, OpenFaceClient, SFaceClient, DlibClient, Buffalo_L, DeepFaceClient) is represented by a client class inheriting from the `deepface.models.FacialRecognition.FacialRecognition` base class.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.FacialRecognition.FacialRecognition",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/FacialRecognition.py",
"reference_start_line": 15,
"reference_end_line": 47
},
{
"qualified_name": "deepface.models.facial_recognition.ArcFace.ArcFaceClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/facial_recognition/ArcFace.py",
"reference_start_line": 47,
"reference_end_line": 56
},
{
"qualified_name": "deepface.models.facial_recognition.Facenet.FaceNet128dClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/facial_recognition/Facenet.py",
"reference_start_line": 52,
"reference_end_line": 61
},
{
"qualified_name": "deepface.models.facial_recognition.VggFace.VggFaceClient",
"reference_file": "deepface/models/facial_recognition/VggFace.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.models.facial_recognition.DeepId.DeepIdClient",
"reference_file": "deepface/models/facial_recognition/DeepId.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.models.facial_recognition.GhostFaceNet.GhostFaceNetClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/facial_recognition/GhostFaceNet.py",
"reference_start_line": 53,
"reference_end_line": 66
},
{
"qualified_name": "deepface.models.facial_recognition.OpenFace.OpenFaceClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/facial_recognition/OpenFace.py",
"reference_start_line": 31,
"reference_end_line": 40
},
{
"qualified_name": "deepface.models.facial_recognition.SFace.SFaceClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/facial_recognition/SFace.py",
"reference_start_line": 18,
"reference_end_line": 49
},
{
"qualified_name": "deepface.models.facial_recognition.Dlib.DlibClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/facial_recognition/Dlib.py",
"reference_start_line": 17,
"reference_end_line": 55
},
{
"qualified_name": "deepface.models.facial_recognition.Buffalo_L.Buffalo_L",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/facial_recognition/Buffalo_L.py",
"reference_start_line": 10,
"reference_end_line": 94
},
{
"qualified_name": "deepface.models.facial_recognition.DeepFace.DeepFaceClient",
"reference_file": "deepface/models/facial_recognition/DeepFace.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Demography Models",
"description": "This component specializes in analyzing demographic attributes of detected faces, including age, emotion, gender, and race. It comprises specialized client classes (ApparentAgeClient, EmotionClient, GenderClient, RaceClient), all inheriting from the `deepface.models.Demography.Demography` base class. These models preprocess input, perform internal predictions, and derive demographic insights. Notably, the age, gender, and race prediction models leverage the VGGFaceClient from the Facial Recognition Models component for feature extraction.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.Demography.Demography",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Demography.py",
"reference_start_line": 15,
"reference_end_line": 67
},
{
"qualified_name": "deepface.models.demography.Age.ApparentAgeClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/demography/Age.py",
"reference_start_line": 32,
"reference_end_line": 62
},
{
"qualified_name": "deepface.models.demography.Emotion.EmotionClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/demography/Emotion.py",
"reference_start_line": 39,
"reference_end_line": 79
},
{
"qualified_name": "deepface.models.demography.Gender.GenderClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/demography/Gender.py",
"reference_start_line": 34,
"reference_end_line": 59
},
{
"qualified_name": "deepface.models.demography.Race.RaceClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/demography/Race.py",
"reference_start_line": 33,
"reference_end_line": 59
}
]
},
{
"name": "Anti-Spoofing Model (FasNet)",
"description": "This component is dedicated to detecting presentation attacks (spoofing) by analyzing facial images to determine if a real person is present or if it's a fake (e.g., photo, video replay). It includes the core `Fasnet` model and its associated backbone architectures (`MiniFASNetV2`, `MiniFASNetV1SE`), which are composed of various building blocks like `Conv_block`, `Depth_Wise`, `Linear_block`, `Residual`, and `SEModule` defined within `FasNetBackbone`.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.spoofing.FasNet.Fasnet",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNet.py",
"reference_start_line": 17,
"reference_end_line": 139
},
{
"qualified_name": "deepface.models.spoofing.FasNetBackbone.MiniFASNetV2",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNetBackbone.py",
"reference_start_line": 127,
"reference_end_line": 130
},
{
"qualified_name": "deepface.models.spoofing.FasNetBackbone.MiniFASNetV1SE",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNetBackbone.py",
"reference_start_line": 133,
"reference_end_line": 138
},
{
"qualified_name": "deepface.models.spoofing.FasNetBackbone.Conv_block",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNetBackbone.py",
"reference_start_line": 146,
"reference_end_line": 165
},
{
"qualified_name": "deepface.models.spoofing.FasNetBackbone.Depth_Wise",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNetBackbone.py",
"reference_start_line": 188,
"reference_end_line": 213
},
{
"qualified_name": "deepface.models.spoofing.FasNetBackbone.Linear_block",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNetBackbone.py",
"reference_start_line": 168,
"reference_end_line": 185
},
{
"qualified_name": "deepface.models.spoofing.FasNetBackbone.Residual",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNetBackbone.py",
"reference_start_line": 278,
"reference_end_line": 301
},
{
"qualified_name": "deepface.models.spoofing.FasNetBackbone.SEModule",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNetBackbone.py",
"reference_start_line": 255,
"reference_end_line": 275
}
]
},
{
"name": "Weight Management Utility",
"description": "This is a crucial utility component responsible for managing the lifecycle of pre-trained model weights. It handles downloading necessary weights from remote sources and loading them into the respective models, ensuring models are ready for inference. This component is fundamental as all other model components depend on it for initialization and operation.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.weight_utils",
"reference_file": "deepface/models/weight_utils.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Facial Area Region Data Structure",
"description": "This component defines a standardized data structure (`FacialAreaRegion`), which inherits from `DetectedFace`. It is used across the face detection and other related modules to encapsulate the results of face detection, including bounding box coordinates and potentially facial landmarks. It provides a consistent output format for all face detectors.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.Detector.FacialAreaRegion",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Detector.py",
"reference_start_line": 31,
"reference_end_line": 57
},
{
"qualified_name": "deepface.models.Detector.DetectedFace",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Detector.py",
"reference_start_line": 61,
"reference_end_line": 73
}
]
}
],
"components_relations": [
{
"relation": "relies on",
"src_name": "Face Detection Models",
"dst_name": "Weight Management Utility"
},
{
"relation": "produces",
"src_name": "Face Detection Models",
"dst_name": "Facial Area Region Data Structure"
},
{
"relation": "relies on",
"src_name": "Facial Recognition Models",
"dst_name": "Weight Management Utility"
},
{
"relation": "relies on",
"src_name": "Demography Models",
"dst_name": "Weight Management Utility"
},
{
"relation": "relies on",
"src_name": "Demography Models",
"dst_name": "Facial Recognition Models"
},
{
"relation": "relies on",
"src_name": "Anti-Spoofing Model (FasNet)",
"dst_name": "Weight Management Utility"
}
]
}

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```mermaid
graph LR
Face_Detection_Models["Face Detection Models"]
Facial_Recognition_Models["Facial Recognition Models"]
Demography_Models["Demography Models"]
Anti_Spoofing_Model_FasNet_["Anti-Spoofing Model (FasNet)"]
Weight_Management_Utility["Weight Management Utility"]
Facial_Area_Region_Data_Structure["Facial Area Region Data Structure"]
Face_Detection_Models -- "relies on" --> Weight_Management_Utility
Face_Detection_Models -- "produces" --> Facial_Area_Region_Data_Structure
Facial_Recognition_Models -- "relies on" --> Weight_Management_Utility
Demography_Models -- "relies on" --> Weight_Management_Utility
Demography_Models -- "relies on" --> Facial_Recognition_Models
Anti_Spoofing_Model_FasNet_ -- "relies on" --> Weight_Management_Utility
```
[![CodeBoarding](https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square)](https://github.com/CodeBoarding/GeneratedOnBoardings)[![Demo](https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square)](https://www.codeboarding.org/demo)[![Contact](https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square)](mailto:contact@codeboarding.org)
## Details
This subsystem encapsulates the core deep learning functionalities of `deepface`, providing specialized models for various facial analysis tasks. It is structured around distinct model types, a central utility for managing model weights, and a standardized data structure for detected faces.
### Face Detection Models
This component provides a collection of algorithms and models for accurately detecting human faces within images. Each specific detection model (e.g., CenterFaceClient, DlibClient, MediaPipeClient, OpenCvClient, YoloDetectorClient, YuNetClient, etc.) is implemented as a client class, all inheriting from the `deepface.models.Detector.Detector` base class. This design ensures a unified interface for face detection operations, consistently returning structured `FacialAreaRegion` data.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Detector.py#L9-L27" target="_blank" rel="noopener noreferrer">`deepface.models.Detector.Detector` (9:27)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/CenterFace.py#L20-L85" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.CenterFace.CenterFaceClient` (20:85)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/Dlib.py#L15-L104" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.Dlib.DlibClient` (15:104)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/MediaPipe.py#L11-L96" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.MediaPipe.MediaPipeClient` (11:96)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/OpenCv.py#L12-L175" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.OpenCv.OpenCvClient` (12:175)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/Yolo.py#L36-L121" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.Yolo.YoloDetectorClient` (36:121)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/YuNet.py#L19-L129" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.YuNet.YuNetClient` (19:129)</a>
### Facial Recognition Models
This component offers multiple pre-trained deep learning models designed for facial recognition, capable of generating high-dimensional embeddings (numerical representations) from face images. These embeddings are essential for tasks such as face verification (determining if two faces belong to the same person) or identification. Each model (e.g., ArcFaceClient, FaceNet128dClient, VggFaceClient, DeepIdClient, GhostFaceNetClient, OpenFaceClient, SFaceClient, DlibClient, Buffalo_L, DeepFaceClient) is represented by a client class inheriting from the `deepface.models.FacialRecognition.FacialRecognition` base class.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/FacialRecognition.py#L15-L47" target="_blank" rel="noopener noreferrer">`deepface.models.FacialRecognition.FacialRecognition` (15:47)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/facial_recognition/ArcFace.py#L47-L56" target="_blank" rel="noopener noreferrer">`deepface.models.facial_recognition.ArcFace.ArcFaceClient` (47:56)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/facial_recognition/Facenet.py#L52-L61" target="_blank" rel="noopener noreferrer">`deepface.models.facial_recognition.Facenet.FaceNet128dClient` (52:61)</a>
- `deepface.models.facial_recognition.VggFace.VggFaceClient` (1:1)
- `deepface.models.facial_recognition.DeepId.DeepIdClient` (1:1)
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/facial_recognition/GhostFaceNet.py#L53-L66" target="_blank" rel="noopener noreferrer">`deepface.models.facial_recognition.GhostFaceNet.GhostFaceNetClient` (53:66)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/facial_recognition/OpenFace.py#L31-L40" target="_blank" rel="noopener noreferrer">`deepface.models.facial_recognition.OpenFace.OpenFaceClient` (31:40)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/facial_recognition/SFace.py#L18-L49" target="_blank" rel="noopener noreferrer">`deepface.models.facial_recognition.SFace.SFaceClient` (18:49)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/facial_recognition/Dlib.py#L17-L55" target="_blank" rel="noopener noreferrer">`deepface.models.facial_recognition.Dlib.DlibClient` (17:55)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/facial_recognition/Buffalo_L.py#L10-L94" target="_blank" rel="noopener noreferrer">`deepface.models.facial_recognition.Buffalo_L.Buffalo_L` (10:94)</a>
- `deepface.models.facial_recognition.DeepFace.DeepFaceClient` (1:1)
### Demography Models
This component specializes in analyzing demographic attributes of detected faces, including age, emotion, gender, and race. It comprises specialized client classes (ApparentAgeClient, EmotionClient, GenderClient, RaceClient), all inheriting from the `deepface.models.Demography.Demography` base class. These models preprocess input, perform internal predictions, and derive demographic insights. Notably, the age, gender, and race prediction models leverage the VGGFaceClient from the Facial Recognition Models component for feature extraction.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Demography.py#L15-L67" target="_blank" rel="noopener noreferrer">`deepface.models.Demography.Demography` (15:67)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/demography/Age.py#L32-L62" target="_blank" rel="noopener noreferrer">`deepface.models.demography.Age.ApparentAgeClient` (32:62)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/demography/Emotion.py#L39-L79" target="_blank" rel="noopener noreferrer">`deepface.models.demography.Emotion.EmotionClient` (39:79)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/demography/Gender.py#L34-L59" target="_blank" rel="noopener noreferrer">`deepface.models.demography.Gender.GenderClient` (34:59)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/demography/Race.py#L33-L59" target="_blank" rel="noopener noreferrer">`deepface.models.demography.Race.RaceClient` (33:59)</a>
### Anti-Spoofing Model (FasNet)
This component is dedicated to detecting presentation attacks (spoofing) by analyzing facial images to determine if a real person is present or if it's a fake (e.g., photo, video replay). It includes the core `Fasnet` model and its associated backbone architectures (`MiniFASNetV2`, `MiniFASNetV1SE`), which are composed of various building blocks like `Conv_block`, `Depth_Wise`, `Linear_block`, `Residual`, and `SEModule` defined within `FasNetBackbone`.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNet.py#L17-L139" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNet.Fasnet` (17:139)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNetBackbone.py#L127-L130" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNetBackbone.MiniFASNetV2` (127:130)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNetBackbone.py#L133-L138" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNetBackbone.MiniFASNetV1SE` (133:138)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNetBackbone.py#L146-L165" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNetBackbone.Conv_block` (146:165)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNetBackbone.py#L188-L213" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNetBackbone.Depth_Wise` (188:213)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNetBackbone.py#L168-L185" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNetBackbone.Linear_block` (168:185)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNetBackbone.py#L278-L301" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNetBackbone.Residual` (278:301)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNetBackbone.py#L255-L275" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNetBackbone.SEModule` (255:275)</a>
### Weight Management Utility
This is a crucial utility component responsible for managing the lifecycle of pre-trained model weights. It handles downloading necessary weights from remote sources and loading them into the respective models, ensuring models are ready for inference. This component is fundamental as all other model components depend on it for initialization and operation.
**Related Classes/Methods**:
- `deepface.models.weight_utils` (1:1)
### Facial Area Region Data Structure
This component defines a standardized data structure (`FacialAreaRegion`), which inherits from `DetectedFace`. It is used across the face detection and other related modules to encapsulate the results of face detection, including bounding box coordinates and potentially facial landmarks. It provides a consistent output format for all face detectors.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Detector.py#L31-L57" target="_blank" rel="noopener noreferrer">`deepface.models.Detector.FacialAreaRegion` (31:57)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Detector.py#L61-L73" target="_blank" rel="noopener noreferrer">`deepface.models.Detector.DetectedFace` (61:73)</a>
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)

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```mermaid
graph LR
API_Core["API Core"]
Streaming_Module["Streaming Module"]
DeepFace_Facade["DeepFace Facade"]
Common_Utilities["Common Utilities"]
Models["Models"]
API_Core -- "delegates requests to" --> DeepFace_Facade
Streaming_Module -- "delegates requests to" --> DeepFace_Facade
API_Core -- "uses" --> Common_Utilities
Streaming_Module -- "uses" --> Common_Utilities
DeepFace_Facade -- "interacts with to manage and utilize" --> Models
DeepFace_Facade -- "uses for logging and image processing" --> Common_Utilities
Common_Utilities -- "provides services to" --> API_Core
Common_Utilities -- "provides services to" --> DeepFace_Facade
Common_Utilities -- "provides services to" --> Streaming_Module
Models -- "are used by" --> DeepFace_Facade
```
[![CodeBoarding](https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square)](https://github.com/CodeBoarding/GeneratedOnBoardings)[![Demo](https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square)](https://www.codeboarding.org/demo)[![Contact](https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square)](mailto:contact@codeboarding.org)
## Component Details
The `External Integration Layer` is a crucial component that acts as the primary interface between the DeepFace library and external applications or systems. It is designed to expose DeepFace's powerful facial analysis capabilities through well-defined interaction patterns, specifically a RESTful API and a real-time streaming module. This layer is fundamental because it enables DeepFace to be integrated into a wide array of applications, such as web services, mobile applications, or live video surveillance systems, without requiring direct Python code interaction from the external system.
### API Core
This component is responsible for handling all REST API interactions. It defines the various endpoints that external systems can call to access DeepFace functionalities (e.g., face verification, analysis, detection). It manages the parsing of incoming HTTP requests, validates the input data, orchestrates the calls to the underlying DeepFace functionalities, and formats the responses before sending them back to the client. It essentially translates HTTP requests into DeepFace operations and vice-versa.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/api/src/api.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.api.src.api` (1:1)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/api/src/app.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.api.src.app` (1:1)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/api/src/modules/core/routes.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.api.src.modules.core.routes` (1:1)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/api/src/modules/core/service.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.api.src.modules.core.service` (1:1)</a>
### Streaming Module
This component specializes in processing real-time video streams for continuous facial analysis. It handles the ingestion of video frames, applies DeepFace's face detection, recognition, and demography analysis algorithms to each frame, and can output the results in a continuous manner. This is essential for applications requiring live monitoring or interactive facial analysis.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.modules.streaming` (1:1)</a>
### DeepFace Facade
This component serves as the central entry point and a simplified interface to the core DeepFace functionalities. It orchestrates calls to various internal DeepFace modules (e.g., for facial representation, verification, analysis, detection, and model management). It abstracts away the complexity of the underlying deep learning models and their specific implementations, providing a clean and consistent API for higher-level components like the `API Core` and `Streaming Module`.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/DeepFace.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.DeepFace` (1:1)</a>
### Common Utilities
This component provides a collection of shared utility functions and helper modules that are utilized across various parts of the DeepFace project, including the `External Integration Layer`. Key functionalities include robust logging for debugging and monitoring, and efficient image handling (loading, preprocessing, manipulation) which is critical for any image-based deep learning application.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/logger.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.commons.logger` (1:1)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.commons.image_utils` (1:1)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/package_utils.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.commons.package_utils` (1:1)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/folder_utils.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.commons.folder_utils` (1:1)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/weight_utils.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.commons.weight_utils` (1:1)</a>
### Models
This component is responsible for defining, loading, and managing the various deep learning models used by DeepFace for its core tasks, such as facial recognition, face detection, and demography analysis (age, gender, emotion, race). It handles the instantiation and potentially caching of these models to optimize performance.
**Related Classes/Methods**:
- `deepface.models.demography` (1:1)
- `deepface.models.face_detection` (1:1)
- `deepface.models.facial_recognition` (1:1)
- `deepface.models.spoofing` (1:1)
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)

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```mermaid
graph LR
FaceDetection["FaceDetection"]
```
[![CodeBoarding](https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square)](https://github.com/CodeBoarding/GeneratedOnBoardings)[![Demo](https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square)](https://www.codeboarding.org/demo)[![Contact](https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square)](mailto:contact@codeboarding.org)
## Component Details
Initial analysis of the deepface library, focusing on the detection module. Further analysis is needed to identify more components and their relationships across other modules like demography, modeling, and recognition. The current focus is on understanding the core functionalities within detection.py and how they contribute to the overall face analysis process. Future steps will involve analyzing other modules and establishing relationships between them to build a comprehensive understanding of the library's architecture and data flow. The 'referenced_source_code' for 'detect_faces' and 'extract_faces' is a placeholder as the exact line numbers are not yet known without the file content.
### FaceDetection
Handles face detection functionalities.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/detection.py#L191-L267" target="_blank" rel="noopener noreferrer">`deepface.modules.detection.detect_faces` (191:267)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/detection.py#L20-L188" target="_blank" rel="noopener noreferrer">`deepface.modules.detection.extract_faces` (20:188)</a>
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)

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.codeboarding/Image_Preprocessing_Utilities.json Normal file
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{
"description": "The Image & Preprocessing Utilities component serves as the foundational layer for all image-related operations within the system. It encompasses a crucial set of helper functions and modules dedicated to handling image data from its raw input state through various transformations, ensuring it is optimally prepared for subsequent deep learning model consumption. This includes functionalities for loading images from diverse sources, resizing them to required dimensions, and applying normalization techniques.",
"components": [
{
"name": "Image Loading Utilities",
"description": "This sub-component is solely responsible for abstracting the process of loading image data. It supports various input formats, including local file paths, web URLs, base64 encoded strings, and in-memory IO objects, converting them into a standardized NumPy array format. It also offers utilities for listing and hashing image files, facilitating efficient data management.",
"referenced_source_code": [
{
"qualified_name": "deepface/commons/image_utils.py",
"reference_file": "/mnt/e/StartUp/deepface/deepface/commons/image_utils.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Image Preprocessing Utilities",
"description": "This sub-component focuses on preparing image data for deep learning models. Its primary functions include resizing images to a target resolution and applying various normalization techniques to pixel values. These steps are critical to ensure consistency and optimal scale for model input.",
"referenced_source_code": [
{
"qualified_name": "deepface/modules/preprocessing.py",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/preprocessing.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
}
],
"components_relations": [
{
"relation": "Provides raw image data",
"src_name": "Image Loading Utilities",
"dst_name": "Image Preprocessing Utilities"
},
{
"relation": "Supplies raw image data",
"src_name": "Image Loading Utilities",
"dst_name": "Face Detector"
},
{
"relation": "Provides preprocessed images",
"src_name": "Image Preprocessing Utilities",
"dst_name": "Face Detector"
},
{
"relation": "Delivers preprocessed and aligned face images",
"src_name": "Image Preprocessing Utilities",
"dst_name": "Facial Recognizer"
},
{
"relation": "Supplies preprocessed and aligned face images",
"src_name": "Image Preprocessing Utilities",
"dst_name": "Demography Analyzer"
},
{
"relation": "Provides preprocessed and aligned face images",
"src_name": "Image Preprocessing Utilities",
"dst_name": "Spoofing Detector"
}
]
}

40
.codeboarding/Image_Preprocessing_Utilities.md Normal file
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```mermaid
graph LR
Image_Loading_Utilities["Image Loading Utilities"]
Image_Preprocessing_Utilities["Image Preprocessing Utilities"]
Image_Loading_Utilities -- "Provides raw image data" --> Image_Preprocessing_Utilities
Image_Loading_Utilities -- "Supplies raw image data" --> Face_Detector
Image_Preprocessing_Utilities -- "Provides preprocessed images" --> Face_Detector
Image_Preprocessing_Utilities -- "Delivers preprocessed and aligned face images" --> Facial_Recognizer
Image_Preprocessing_Utilities -- "Supplies preprocessed and aligned face images" --> Demography_Analyzer
Image_Preprocessing_Utilities -- "Provides preprocessed and aligned face images" --> Spoofing_Detector
click Image_Preprocessing_Utilities href "https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/Image_Preprocessing_Utilities.md" "Details"
```
[![CodeBoarding](https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square)](https://github.com/CodeBoarding/GeneratedOnBoardings)[![Demo](https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square)](https://www.codeboarding.org/demo)[![Contact](https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square)](mailto:contact@codeboarding.org)
## Details
The Image & Preprocessing Utilities component serves as the foundational layer for all image-related operations within the system. It encompasses a crucial set of helper functions and modules dedicated to handling image data from its raw input state through various transformations, ensuring it is optimally prepared for subsequent deep learning model consumption. This includes functionalities for loading images from diverse sources, resizing them to required dimensions, and applying normalization techniques.
### Image Loading Utilities
This sub-component is solely responsible for abstracting the process of loading image data. It supports various input formats, including local file paths, web URLs, base64 encoded strings, and in-memory IO objects, converting them into a standardized NumPy array format. It also offers utilities for listing and hashing image files, facilitating efficient data management.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/commons/image_utils.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface/commons/image_utils.py` (1:1)</a>
### Image Preprocessing Utilities [[Expand]](./Image_Preprocessing_Utilities.md)
This sub-component focuses on preparing image data for deep learning models. Its primary functions include resizing images to a target resolution and applying various normalization techniques to pixel values. These steps are critical to ensure consistency and optimal scale for model input.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/preprocessing.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface/modules/preprocessing.py` (1:1)</a>
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)

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```mermaid
graph LR
deepface_modules_modeling["deepface.modules.modeling"]
deepface_commons_weight_utils["deepface.commons.weight_utils"]
deepface_models_Demography["deepface.models.Demography"]
deepface_models_Detector["deepface.models.Detector"]
deepface_models_FacialRecognition["deepface.models.FacialRecognition"]
deepface_models_spoofing_FasNet["deepface.models.spoofing.FasNet"]
deepface_models_spoofing_FasNetBackbone["deepface.models.spoofing.FasNetBackbone"]
deepface_modules_modeling -- "loads" --> deepface_models_Demography
deepface_modules_modeling -- "loads" --> deepface_models_Detector
deepface_modules_modeling -- "loads" --> deepface_models_FacialRecognition
deepface_modules_modeling -- "loads" --> deepface_models_spoofing_FasNet
deepface_models_spoofing_FasNet -- "downloads weights via" --> deepface_commons_weight_utils
deepface_models_spoofing_FasNet -- "builds upon" --> deepface_models_spoofing_FasNetBackbone
```
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## Component Details
The Model Management System in DeepFace is crucial for handling the diverse range of deep learning models used for facial recognition, detection, demography, and anti-spoofing. It provides a unified and efficient way to load, manage, and utilize these models, abstracting away the underlying complexities of different backends and ensuring pre-trained weights are readily available.
### deepface.modules.modeling
This module acts as the central orchestrator for the Model Management System. Its primary function, `build_model`, is responsible for dynamically loading and caching various deep learning models (facial recognition, detection, demography, anti-spoofing) based on the requested task and model name. It ensures that models are loaded efficiently, often employing a singleton pattern to reuse already initialized models, thereby providing a unified interface for the rest of the DeepFace system to access pre-trained models.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/modeling.py#L32-L104" target="_blank" rel="noopener noreferrer">`deepface.modules.modeling:build_model` (32:104)</a>
### deepface.commons.weight_utils
This utility module is vital for managing pre-trained model weights. Its core function, `download_weights_if_necessary`, handles the downloading, caching, and verification of model weights from external URLs. It supports various compression types and ensures that the necessary model files are available locally before models are loaded, directly addressing the system's need for efficient weight management and utilization.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/weight_utils.py#L27-L76" target="_blank" rel="noopener noreferrer">`deepface.commons.weight_utils:download_weights_if_necessary` (27:76)</a>
### deepface.models.Demography
This is an abstract base class that defines the common interface and structure for all demography-related models (e.g., age, gender, emotion, race prediction) within DeepFace. It specifies methods like `predict` and internal preprocessing utilities, ensuring a consistent API for demography models.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/models/Demography.py#L20-L21" target="_blank" rel="noopener noreferrer">`deepface.models.Demography:predict` (20:21)</a>
### deepface.models.Detector
This abstract base class defines the common interface and structure for all face detection models (e.g., OpenCV, MTCNN, RetinaFace) within DeepFace. It specifies the `detect_faces` method, which is crucial for locating facial areas in images before further analysis.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/models/Detector.py#L11-L27" target="_blank" rel="noopener noreferrer">`deepface.models.Detector:detect_faces` (11:27)</a>
### deepface.models.FacialRecognition
This abstract base class defines the common interface and structure for all facial recognition models (e.g., VGG-Face, FaceNet, ArcFace) within DeepFace. It specifies the `forward` method for generating facial embeddings, which are central to identity verification and recognition tasks.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/models/FacialRecognition.py#L21-L47" target="_blank" rel="noopener noreferrer">`deepface.models.FacialRecognition:forward` (21:47)</a>
### deepface.models.spoofing.FasNet
This class represents a concrete implementation of an anti-spoofing model, specifically the Mini Face Anti Spoofing Net. It demonstrates how specific deep learning models are structured, initialized, and integrated into the DeepFace system, including the loading of its specific pre-trained weights and performing anti-spoofing analysis.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/models/spoofing/FasNet.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNet` (1:1)</a>
### deepface.models.spoofing.FasNetBackbone
This module defines the core neural network architectures, such as `MiniFASNet`, that serve as the backbone for anti-spoofing models like `FasNet`. It encapsulates the complex convolutional layers, depth-wise separable convolutions, and residual blocks that form the deep learning model's structure.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/models/spoofing/FasNetBackbone.py#L356-L465" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNetBackbone:MiniFASNet` (356:465)</a>
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)

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{
"description": "This section provides a detailed overview of the `Model & Weight Management` component within the DeepFace library, along with its closely related components. These components are fundamental to the library's operation, enabling the dynamic loading, management, and application of various deep learning models for facial analysis tasks.",
"components": [
{
"name": "Model & Weight Management",
"description": "This component is the central factory and manager for all deep learning models within DeepFace. It is responsible for dynamically loading, building, caching, and ensuring the availability of various pre-trained models and their associated weights. It abstracts the complexities of model instantiation and weight handling, providing a unified interface for accessing different model types (demography, face detection, facial recognition, anti-spoofing).",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.modeling",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/modeling.py",
"reference_start_line": 0,
"reference_end_line": 0
},
{
"qualified_name": "deepface.commons.weight_utils",
"reference_file": "/mnt/e/StartUp/deepface/deepface/commons/weight_utils.py",
"reference_start_line": 0,
"reference_end_line": 0
}
]
},
{
"name": "Demography Models",
"description": "This component encapsulates the logic and models for analyzing demographic attributes (age, emotion, gender, race) from detected faces. It provides specialized client classes for each attribute, all inheriting from a common `Demography` base class, ensuring a consistent interface for demographic analysis.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.Demography.Demography",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Demography.py",
"reference_start_line": 15,
"reference_end_line": 67
},
{
"qualified_name": "deepface.models.demography.Age.ApparentAgeClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/demography/Age.py",
"reference_start_line": 32,
"reference_end_line": 62
},
{
"qualified_name": "deepface.models.demography.Emotion.EmotionClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/demography/Emotion.py",
"reference_start_line": 39,
"reference_end_line": 79
},
{
"qualified_name": "deepface.models.demography.Gender.GenderClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/demography/Gender.py",
"reference_start_line": 34,
"reference_end_line": 59
},
{
"qualified_name": "deepface.models.demography.Race.RaceClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/demography/Race.py",
"reference_start_line": 33,
"reference_end_line": 59
}
]
},
{
"name": "Face Detection Models",
"description": "This component provides a collection of algorithms and models specifically designed for detecting human faces within images. Each detection model is implemented as a client class inheriting from a `Detector` base class, offering a standardized way to perform face detection and return structured `FacialAreaRegion` data.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.Detector.Detector",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Detector.py",
"reference_start_line": 9,
"reference_end_line": 27
},
{
"qualified_name": "deepface.models.face_detection.CenterFace.CenterFaceClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/CenterFace.py",
"reference_start_line": 20,
"reference_end_line": 85
},
{
"qualified_name": "deepface.models.face_detection.Dlib.DlibClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/Dlib.py",
"reference_start_line": 15,
"reference_end_line": 104
},
{
"qualified_name": "deepface.models.face_detection.MediaPipe.MediaPipeClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/MediaPipe.py",
"reference_start_line": 11,
"reference_end_line": 96
},
{
"qualified_name": "deepface.models.face_detection.OpenCv.OpenCvClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/OpenCv.py",
"reference_start_line": 12,
"reference_end_line": 175
},
{
"qualified_name": "deepface.models.face_detection.Yolo.YoloDetectorClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/face_detection/Yolo.py",
"reference_start_line": 36,
"reference_end_line": 121
}
]
},
{
"name": "Facial Recognition Models",
"description": "This component houses various pre-trained models for facial recognition, enabling the generation of high-dimensional embeddings from face images. These embeddings are crucial for tasks like face verification and identification. Each model is represented by a client class inheriting from the `FacialRecognition` base class.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.FacialRecognition.FacialRecognition",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/FacialRecognition.py",
"reference_start_line": 15,
"reference_end_line": 47
},
{
"qualified_name": "deepface.models.facial_recognition.ArcFace.ArcFaceClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/facial_recognition/ArcFace.py",
"reference_start_line": 47,
"reference_end_line": 56
},
{
"qualified_name": "deepface.models.facial_recognition.Facenet.FaceNet128dClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/facial_recognition/Facenet.py",
"reference_start_line": 52,
"reference_end_line": 61
},
{
"qualified_name": "deepface.models.facial_recognition.VGGFace.VggFaceClient",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/facial_recognition/VGGFace.py",
"reference_start_line": 46,
"reference_end_line": 77
}
]
},
{
"name": "Anti-Spoofing Model (FasNet)",
"description": "This component is dedicated to detecting presentation attacks (spoofing) by analyzing facial images to determine if a real person is present or if it's a fake (e.g., photo, video replay). It includes the core `FasNet` model and its associated backbone architectures.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.spoofing.FasNet.FasNet",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNet.py",
"reference_start_line": 0,
"reference_end_line": 0
},
{
"qualified_name": "deepface.models.spoofing.FasNetBackbone.MiniFASNetV2",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNetBackbone.py",
"reference_start_line": 127,
"reference_end_line": 130
},
{
"qualified_name": "deepface.models.spoofing.FasNetBackbone.MiniFASNetV1SE",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/spoofing/FasNetBackbone.py",
"reference_start_line": 133,
"reference_end_line": 138
}
]
},
{
"name": "Facial Area Region Data Structure",
"description": "This component defines a standardized data structure (`FacialAreaRegion`) used across the face detection and other related modules to encapsulate the results of face detection, including bounding box coordinates and potentially facial landmarks. It ensures consistent data exchange between components.",
"referenced_source_code": [
{
"qualified_name": "deepface.models.Detector.FacialAreaRegion",
"reference_file": "/mnt/e/StartUp/deepface/deepface/models/Detector.py",
"reference_start_line": 31,
"reference_end_line": 57
}
]
}
],
"components_relations": [
{
"relation": "provides model instances for",
"src_name": "Model & Weight Management",
"dst_name": "Demography Models"
},
{
"relation": "provides model instances for",
"src_name": "Model & Weight Management",
"dst_name": "Face Detection Models"
},
{
"relation": "provides model instances for",
"src_name": "Model & Weight Management",
"dst_name": "Facial Recognition Models"
},
{
"relation": "provides model instances for",
"src_name": "Model & Weight Management",
"dst_name": "Anti-Spoofing Model (FasNet)"
},
{
"relation": "relies on",
"src_name": "Demography Models",
"dst_name": "Model & Weight Management"
},
{
"relation": "processes",
"src_name": "Demography Models",
"dst_name": "Facial Area Region Data Structure"
},
{
"relation": "relies on",
"src_name": "Face Detection Models",
"dst_name": "Model & Weight Management"
},
{
"relation": "produces",
"src_name": "Face Detection Models",
"dst_name": "Facial Area Region Data Structure"
},
{
"relation": "relies on",
"src_name": "Facial Recognition Models",
"dst_name": "Model & Weight Management"
},
{
"relation": "processes",
"src_name": "Facial Recognition Models",
"dst_name": "Facial Area Region Data Structure"
},
{
"relation": "relies on",
"src_name": "Anti-Spoofing Model (FasNet)",
"dst_name": "Model & Weight Management"
},
{
"relation": "processes",
"src_name": "Anti-Spoofing Model (FasNet)",
"dst_name": "Facial Area Region Data Structure"
}
]
}

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```mermaid
graph LR
Model_Weight_Management["Model & Weight Management"]
Demography_Models["Demography Models"]
Face_Detection_Models["Face Detection Models"]
Facial_Recognition_Models["Facial Recognition Models"]
Anti_Spoofing_Model_FasNet_["Anti-Spoofing Model (FasNet)"]
Facial_Area_Region_Data_Structure["Facial Area Region Data Structure"]
Model_Weight_Management -- "provides model instances for" --> Demography_Models
Model_Weight_Management -- "provides model instances for" --> Face_Detection_Models
Model_Weight_Management -- "provides model instances for" --> Facial_Recognition_Models
Model_Weight_Management -- "provides model instances for" --> Anti_Spoofing_Model_FasNet_
Demography_Models -- "relies on" --> Model_Weight_Management
Demography_Models -- "processes" --> Facial_Area_Region_Data_Structure
Face_Detection_Models -- "relies on" --> Model_Weight_Management
Face_Detection_Models -- "produces" --> Facial_Area_Region_Data_Structure
Facial_Recognition_Models -- "relies on" --> Model_Weight_Management
Facial_Recognition_Models -- "processes" --> Facial_Area_Region_Data_Structure
Anti_Spoofing_Model_FasNet_ -- "relies on" --> Model_Weight_Management
Anti_Spoofing_Model_FasNet_ -- "processes" --> Facial_Area_Region_Data_Structure
click Model_Weight_Management href "https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/Model_Weight_Management.md" "Details"
```
[![CodeBoarding](https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square)](https://github.com/CodeBoarding/GeneratedOnBoardings)[![Demo](https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square)](https://www.codeboarding.org/demo)[![Contact](https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square)](mailto:contact@codeboarding.org)
## Details
This section provides a detailed overview of the `Model & Weight Management` component within the DeepFace library, along with its closely related components. These components are fundamental to the library's operation, enabling the dynamic loading, management, and application of various deep learning models for facial analysis tasks.
### Model & Weight Management [[Expand]](./Model_Weight_Management.md)
This component is the central factory and manager for all deep learning models within DeepFace. It is responsible for dynamically loading, building, caching, and ensuring the availability of various pre-trained models and their associated weights. It abstracts the complexities of model instantiation and weight handling, providing a unified interface for accessing different model types (demography, face detection, facial recognition, anti-spoofing).
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/modeling.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.modules.modeling` (0:0)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/commons/weight_utils.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.commons.weight_utils` (0:0)</a>
### Demography Models
This component encapsulates the logic and models for analyzing demographic attributes (age, emotion, gender, race) from detected faces. It provides specialized client classes for each attribute, all inheriting from a common `Demography` base class, ensuring a consistent interface for demographic analysis.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Demography.py#L15-L67" target="_blank" rel="noopener noreferrer">`deepface.models.Demography.Demography` (15:67)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/demography/Age.py#L32-L62" target="_blank" rel="noopener noreferrer">`deepface.models.demography.Age.ApparentAgeClient` (32:62)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/demography/Emotion.py#L39-L79" target="_blank" rel="noopener noreferrer">`deepface.models.demography.Emotion.EmotionClient` (39:79)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/demography/Gender.py#L34-L59" target="_blank" rel="noopener noreferrer">`deepface.models.demography.Gender.GenderClient` (34:59)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/demography/Race.py#L33-L59" target="_blank" rel="noopener noreferrer">`deepface.models.demography.Race.RaceClient` (33:59)</a>
### Face Detection Models
This component provides a collection of algorithms and models specifically designed for detecting human faces within images. Each detection model is implemented as a client class inheriting from a `Detector` base class, offering a standardized way to perform face detection and return structured `FacialAreaRegion` data.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Detector.py#L9-L27" target="_blank" rel="noopener noreferrer">`deepface.models.Detector.Detector` (9:27)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/CenterFace.py#L20-L85" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.CenterFace.CenterFaceClient` (20:85)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/Dlib.py#L15-L104" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.Dlib.DlibClient` (15:104)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/MediaPipe.py#L11-L96" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.MediaPipe.MediaPipeClient` (11:96)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/OpenCv.py#L12-L175" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.OpenCv.OpenCvClient` (12:175)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/face_detection/Yolo.py#L36-L121" target="_blank" rel="noopener noreferrer">`deepface.models.face_detection.Yolo.YoloDetectorClient` (36:121)</a>
### Facial Recognition Models
This component houses various pre-trained models for facial recognition, enabling the generation of high-dimensional embeddings from face images. These embeddings are crucial for tasks like face verification and identification. Each model is represented by a client class inheriting from the `FacialRecognition` base class.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/FacialRecognition.py#L15-L47" target="_blank" rel="noopener noreferrer">`deepface.models.FacialRecognition.FacialRecognition` (15:47)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/facial_recognition/ArcFace.py#L47-L56" target="_blank" rel="noopener noreferrer">`deepface.models.facial_recognition.ArcFace.ArcFaceClient` (47:56)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/facial_recognition/Facenet.py#L52-L61" target="_blank" rel="noopener noreferrer">`deepface.models.facial_recognition.Facenet.FaceNet128dClient` (52:61)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/facial_recognition/VGGFace.py#L46-L77" target="_blank" rel="noopener noreferrer">`deepface.models.facial_recognition.VGGFace.VggFaceClient` (46:77)</a>
### Anti-Spoofing Model (FasNet)
This component is dedicated to detecting presentation attacks (spoofing) by analyzing facial images to determine if a real person is present or if it's a fake (e.g., photo, video replay). It includes the core `FasNet` model and its associated backbone architectures.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNet.py#L0-L0" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNet.FasNet` (0:0)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNetBackbone.py#L127-L130" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNetBackbone.MiniFASNetV2` (127:130)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/spoofing/FasNetBackbone.py#L133-L138" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNetBackbone.MiniFASNetV1SE` (133:138)</a>
### Facial Area Region Data Structure
This component defines a standardized data structure (`FacialAreaRegion`) used across the face detection and other related modules to encapsulate the results of face detection, including bounding box coordinates and potentially facial landmarks. It ensures consistent data exchange between components.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/models/Detector.py#L31-L57" target="_blank" rel="noopener noreferrer">`deepface.models.Detector.FacialAreaRegion` (31:57)</a>
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)

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.codeboarding/Utility_Infrastructure_Layer.md
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@ -1,63 +0,0 @@
```mermaid
graph LR
Utility_Infrastructure_Layer["Utility & Infrastructure Layer"]
API_Core -- "uses" --> Utility_Infrastructure_Layer
DeepFace_Facade -- "uses" --> Utility_Infrastructure_Layer
Streaming_Module -- "uses" --> Utility_Infrastructure_Layer
Models -- "uses" --> Utility_Infrastructure_Layer
click Utility_Infrastructure_Layer href "https://github.com/serengil/deepface/blob/main/.codeboarding//Utility_Infrastructure_Layer.md" "Details"
```
[![CodeBoarding](https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square)](https://github.com/CodeBoarding/GeneratedOnBoardings)[![Demo](https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square)](https://www.codeboarding.org/demo)[![Contact](https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square)](mailto:contact@codeboarding.org)
## Component Details
Analysis of the Utility & Infrastructure Layer within the DeepFace library, detailing its components, purpose, and relationships with other key modules. This layer provides foundational services like image handling, logging, file system operations, and model weight management, which are crucial for the entire DeepFace library's functionality and efficiency. It serves as a bedrock for components such as API Core, DeepFace Facade, Streaming Module, and Models, ensuring reusability and consistency across the application by centralizing common, non-domain-specific functionalities.
### Utility & Infrastructure Layer
This foundational component provides essential helper functions and cross-cutting concerns used throughout the entire DeepFace library. It includes utilities for image loading and manipulation, centralized logging, file system operations, package-related utilities, constants, and crucially, utilities for managing and loading model weights. It ensures consistency and efficiency for common, non-domain-specific tasks.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/constant.py#L0-L0" target="_blank" rel="noopener noreferrer">`constant` (0:0)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/folder_utils.py#L0-L0" target="_blank" rel="noopener noreferrer">`folder_utils` (0:0)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py#L0-L0" target="_blank" rel="noopener noreferrer">`image_utils` (0:0)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/logger.py#L0-L0" target="_blank" rel="noopener noreferrer">`logger` (0:0)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/package_utils.py#L0-L0" target="_blank" rel="noopener noreferrer">`package_utils` (0:0)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/weight_utils.py#L0-L0" target="_blank" rel="noopener noreferrer">`weight_utils` (0:0)</a>
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)

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.codeboarding/analysis.json Normal file
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{
"description": "High-level data flow overview of the `deepface` project, identifying its central modules, their responsibilities, and their interactions.",
"components": [
{
"name": "DeepFace API & Entry Points",
"description": "This component serves as the primary interface for users to interact with the DeepFace library. It encompasses both the direct Python API (`deepface.DeepFace`) for programmatic access and the web API (`deepface.api.src.app`, `deepface.api.src.modules.core.routes`, `deepface.api.src.modules.core.service`) for web-based requests. It handles initial user requests and delegates them for core processing.",
"referenced_source_code": [
{
"qualified_name": "deepface.DeepFace",
"reference_file": "/mnt/e/StartUp/deepface/deepface/DeepFace.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.api.src.app",
"reference_file": "/mnt/e/StartUp/deepface/deepface/api/src/app.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.api.src.modules.core.routes",
"reference_file": "/mnt/e/StartUp/deepface/deepface/api/src/modules/core/routes.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.api.src.modules.core.service",
"reference_file": "/mnt/e/StartUp/deepface/deepface/api/src/modules/core/service.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Core Processing Engine",
"description": "The heart of the DeepFace library, this component implements the core logic for all face-related tasks such as detection, representation, verification, demography analysis, recognition, and streaming. It orchestrates the execution flow by calling specific deep learning models and utility functions.",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.detection",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/detection.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.modules.representation",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/representation.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.modules.verification",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/verification.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.modules.demography",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/demography.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.modules.recognition",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/recognition.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.modules.streaming",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/streaming.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Model & Weight Management",
"description": "This component is responsible for the dynamic loading, building, caching, and management of various pre-trained deep learning models and their associated weights. It ensures that the necessary models and their parameters are available to the Core Processing Engine when required.",
"referenced_source_code": [
{
"qualified_name": "deepface.modules.modeling",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/modeling.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.commons.weight_utils",
"reference_file": "/mnt/e/StartUp/deepface/deepface/commons/weight_utils.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Deep Learning Models",
"description": "This component comprises the actual implementations of specialized deep learning models used for different facial analysis tasks. This includes models for face detection, facial recognition (embedding generation), demography prediction (age, gender, emotion, race), and presentation attack detection (spoofing).",
"referenced_source_code": [
{
"qualified_name": "deepface.models.demography",
"reference_file": "deepface/models/demography.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.models.face_detection",
"reference_file": "deepface/models/face_detection.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.models.facial_recognition",
"reference_file": "deepface/models/facial_recognition.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.models.spoofing",
"reference_file": "deepface/models/spoofing.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
},
{
"name": "Image & Preprocessing Utilities",
"description": "A collection of essential helper functions for handling image data. This includes functionalities for loading, resizing, aligning, normalizing, and general manipulation of images, which are crucial steps before feeding images into deep learning models.",
"referenced_source_code": [
{
"qualified_name": "deepface.commons.image_utils",
"reference_file": "/mnt/e/StartUp/deepface/deepface/commons/image_utils.py",
"reference_start_line": 1,
"reference_end_line": 1
},
{
"qualified_name": "deepface.modules.preprocessing",
"reference_file": "/mnt/e/StartUp/deepface/deepface/modules/preprocessing.py",
"reference_start_line": 1,
"reference_end_line": 1
}
]
}
],
"components_relations": [
{
"relation": "interacts with",
"src_name": "DeepFace API & Entry Points",
"dst_name": "Core Processing Engine"
},
{
"relation": "uses",
"src_name": "DeepFace API & Entry Points",
"dst_name": "Image & Preprocessing Utilities"
},
{
"relation": "orchestrates",
"src_name": "Core Processing Engine",
"dst_name": "Deep Learning Models"
},
{
"relation": "relies on",
"src_name": "Core Processing Engine",
"dst_name": "Model & Weight Management"
},
{
"relation": "processes with",
"src_name": "Core Processing Engine",
"dst_name": "Image & Preprocessing Utilities"
},
{
"relation": "manages",
"src_name": "Model & Weight Management",
"dst_name": "Deep Learning Models"
}
]
}

4
.codeboarding/codeboarding_version.json Normal file
View File

@ -0,0 +1,4 @@
{
"commit_hash": "3fd1e1f1d634eb89e3a9d30b058e3e69e604fa7d",
"code_boarding_version": "0.1.0"
}

226
.codeboarding/on_boarding.md
View File

@ -1,219 +1,87 @@
```mermaid
graph LR
DeepFace_Public_API["DeepFace Public API"]
Model_Management_System["Model Management System"]
Facial_Analysis_Engine["Facial Analysis Engine"]
Utility_Infrastructure_Layer["Utility & Infrastructure Layer"]
External_Integration_Layer["External Integration Layer"]
DeepFace_Public_API -- "Delegates to" --> Facial_Analysis_Engine
DeepFace_Public_API -- "Delegates to" --> Model_Management_System
Model_Management_System -- "Provides models to" --> Facial_Analysis_Engine
Model_Management_System -- "Uses" --> Utility_Infrastructure_Layer
Facial_Analysis_Engine -- "Requests models from" --> Model_Management_System
Facial_Analysis_Engine -- "Uses" --> Utility_Infrastructure_Layer
Utility_Infrastructure_Layer -- "Supports" --> DeepFace_Public_API
Utility_Infrastructure_Layer -- "Supports" --> Model_Management_System
Utility_Infrastructure_Layer -- "Supports" --> Facial_Analysis_Engine
Utility_Infrastructure_Layer -- "Supports" --> External_Integration_Layer
External_Integration_Layer -- "Invokes" --> DeepFace_Public_API
External_Integration_Layer -- "Uses" --> Utility_Infrastructure_Layer
click Model_Management_System href "https://github.com/serengil/deepface/blob/main/.codeboarding//Model_Management_System.md" "Details"
click Facial_Analysis_Engine href "https://github.com/serengil/deepface/blob/main/.codeboarding//Facial_Analysis_Engine.md" "Details"
click Utility_Infrastructure_Layer href "https://github.com/serengil/deepface/blob/main/.codeboarding//Utility_Infrastructure_Layer.md" "Details"
click External_Integration_Layer href "https://github.com/serengil/deepface/blob/main/.codeboarding//External_Integration_Layer.md" "Details"
DeepFace_API_Entry_Points["DeepFace API & Entry Points"]
Core_Processing_Engine["Core Processing Engine"]
Model_Weight_Management["Model & Weight Management"]
Deep_Learning_Models["Deep Learning Models"]
Image_Preprocessing_Utilities["Image & Preprocessing Utilities"]
DeepFace_API_Entry_Points -- "interacts with" --> Core_Processing_Engine
DeepFace_API_Entry_Points -- "uses" --> Image_Preprocessing_Utilities
Core_Processing_Engine -- "orchestrates" --> Deep_Learning_Models
Core_Processing_Engine -- "relies on" --> Model_Weight_Management
Core_Processing_Engine -- "processes with" --> Image_Preprocessing_Utilities
Model_Weight_Management -- "manages" --> Deep_Learning_Models
click DeepFace_API_Entry_Points href "https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/DeepFace_API_Entry_Points.md" "Details"
click Core_Processing_Engine href "https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/Core_Processing_Engine.md" "Details"
click Model_Weight_Management href "https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/Model_Weight_Management.md" "Details"
click Deep_Learning_Models href "https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/Deep_Learning_Models.md" "Details"
click Image_Preprocessing_Utilities href "https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/Image_Preprocessing_Utilities.md" "Details"
```
[![CodeBoarding](https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square)](https://github.com/CodeBoarding/GeneratedOnBoardings)[![Demo](https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square)](https://www.codeboarding.org/demo)[![Contact](https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square)](mailto:contact@codeboarding.org)
## Details
High-level data flow overview of the `deepface` project, identifying its central modules, their responsibilities, and their interactions.
## Component Details
Final Architecture Analysis for `deepface`
Based on the Control Flow Graph (CFG) and Source Code analysis, the `deepface` project's architecture can be effectively summarized into five fundamental components. These components are chosen for their distinct responsibilities, high cohesion, and critical roles in the overall system's operation.
Why these components are fundamental:
These five components represent a clear and logical separation of concerns within the `deepface` project, making the architecture modular, maintainable, and scalable.
1. **DeepFace Public API**: This is fundamental because it defines the user experience and the primary interaction model with the library. It abstracts internal complexities, providing a clean and stable interface for developers.
2. **Model Management System**: DeepFace is built upon deep learning models. This component is absolutely critical for handling the acquisition, loading, and efficient management of these models, which are the core intellectual property and functional backbone of the library. Without it, every analysis task would need to manage its own model lifecycle.
3. **Facial Analysis Engine**: This is the "brain" of DeepFace, where all the specialized algorithms for face detection, recognition, and attribute analysis reside. It's fundamental because it encapsulates the core domain logic and the complex computational tasks that define the library's purpose.
4. **Utility & Infrastructure Layer**: This component provides essential cross-cutting services that are required by almost every other part of the system (e.g., logging, image handling). It's fundamental for ensuring consistency, reducing code duplication, and providing a robust foundation for the entire application.
5. **External Integration Layer**: This component is crucial for the broader applicability and deployability of DeepFace. By providing a REST API and streaming capabilities, it allows the library to be consumed by various applications and systems beyond direct Python scripting, making it a versatile and production-ready solution.
Together, these components form a cohesive and well-structured architecture that effectively manages the complexities of deep learning-based facial analysis, from model management to user interaction and external integration.
### DeepFace Public API
This component serves as the primary public interface and orchestration layer for the entire DeepFace library. It provides high-level, user-friendly functions (`verify`, `analyze`, `represent`, `find`, `stream`, `extract_faces`, `build_model`) that abstract the underlying complexities of facial analysis. It acts as a facade, delegating tasks to specialized internal modules and ensuring a clean, consistent API for consumers.
### DeepFace API & Entry Points [[Expand]](./DeepFace_API_Entry_Points.md)
This component serves as the primary interface for users to interact with the DeepFace library. It encompasses both the direct Python API (`deepface.DeepFace`) for programmatic access and the web API (`deepface.api.src.app`, `deepface.api.src.modules.core.routes`, `deepface.api.src.modules.core.service`) for web-based requests. It handles initial user requests and delegates them for core processing.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/DeepFace.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.DeepFace` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/api/src/app.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.api.src.app` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/api/src/modules/core/routes.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.api.src.modules.core.routes` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/api/src/modules/core/service.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.api.src.modules.core.service` (1:1)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/DeepFace.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.DeepFace` (1:1000)</a>
### Model Management System
This component is responsible for the lifecycle management of all deep learning models used by DeepFace. This includes dynamic loading, building, caching, and ensuring that pre-trained model weights are correctly downloaded and utilized. It abstracts the complexities of various model backends (e.g., TensorFlow, Keras) and provides a unified interface for accessing different types of models (facial recognition, detection, demography, anti-spoofing).
### Core Processing Engine [[Expand]](./Core_Processing_Engine.md)
The heart of the DeepFace library, this component implements the core logic for all face-related tasks such as detection, representation, verification, demography analysis, recognition, and streaming. It orchestrates the execution flow by calling specific deep learning models and utility functions.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/detection.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.modules.detection` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/representation.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.modules.representation` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/verification.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.modules.verification` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/demography.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.modules.demography` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/recognition.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.modules.recognition` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/streaming.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.modules.streaming` (1:1)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/modeling.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.modules.modeling` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/weight_utils.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.commons.weight_utils` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/models/Demography.py#L15-L67" target="_blank" rel="noopener noreferrer">`deepface.models.Demography` (15:67)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/models/Detector.py#L9-L27" target="_blank" rel="noopener noreferrer">`deepface.models.Detector` (9:27)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/models/FacialRecognition.py#L15-L47" target="_blank" rel="noopener noreferrer">`deepface.models.FacialRecognition` (15:47)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/models/spoofing/FasNet.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.models.spoofing.FasNet` (1:1000)</a>
### Facial Analysis Engine
This is the algorithmic core of DeepFace, containing the specialized modules that perform the actual facial analysis tasks. Each module within this component handles a specific aspect: face detection, facial representation (embedding generation), identity verification, identity recognition, demographic attribute prediction (age, gender, emotion, race), and image preprocessing (alignment, normalization). It encapsulates the complex machine learning logic.
### Model & Weight Management [[Expand]](./Model_Weight_Management.md)
This component is responsible for the dynamic loading, building, caching, and management of various pre-trained deep learning models and their associated weights. It ensures that the necessary models and their parameters are available to the Core Processing Engine when required.
**Related Classes/Methods**:
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/modeling.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.modules.modeling` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/commons/weight_utils.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.commons.weight_utils` (1:1)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/detection.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.modules.detection` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/representation.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.modules.representation` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/verification.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.modules.verification` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/recognition.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.modules.recognition` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/demography.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.modules.demography` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/preprocessing.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.modules.preprocessing` (1:1000)</a>
### Utility & Infrastructure Layer
This foundational component provides essential helper functions and cross-cutting concerns used throughout the entire DeepFace library. It includes utilities for image loading and manipulation, centralized logging, file system operations, package-related utilities, and constants. It ensures consistency and efficiency for common, non-domain-specific tasks.
### Deep Learning Models [[Expand]](./Deep_Learning_Models.md)
This component comprises the actual implementations of specialized deep learning models used for different facial analysis tasks. This includes models for face detection, facial recognition (embedding generation), demography prediction (age, gender, emotion, race), and presentation attack detection (spoofing).
**Related Classes/Methods**:
- `deepface.models.demography` (1:1)
- `deepface.models.face_detection` (1:1)
- `deepface.models.facial_recognition` (1:1)
- `deepface.models.spoofing` (1:1)
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.commons.image_utils` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/logger.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.commons.logger` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/folder_utils.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.commons.folder_utils` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/package_utils.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.commons.package_utils` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/commons/constant.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.commons.constant` (1:1000)</a>
### External Integration Layer
This component is responsible for exposing DeepFace functionalities to external systems and handling specific interaction patterns beyond direct Python function calls. It primarily includes the REST API, which defines endpoints, parses incoming HTTP requests, and formats responses, and the streaming module, which manages real-time facial analysis from video feeds. It acts as the bridge for integrating DeepFace into web applications or live video processing pipelines.
### Image & Preprocessing Utilities [[Expand]](./Image_Preprocessing_Utilities.md)
A collection of essential helper functions for handling image data. This includes functionalities for loading, resizing, aligning, normalizing, and general manipulation of images, which are crucial steps before feeding images into deep learning models.
**Related Classes/Methods**:
- <a href="https://github.com/serengil/deepface/blob/master/deepface/api/src/api.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.api.src.api` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/api/src/app.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.api.src.app` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/api/src/modules/core/routes.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.api.src.modules.core.routes` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/api/src/modules/core/service.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.api.src.modules.core.service` (1:1000)</a>
- <a href="https://github.com/serengil/deepface/blob/master/deepface/modules/streaming.py#L1-L1000" target="_blank" rel="noopener noreferrer">`deepface.modules.streaming` (1:1000)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/commons/image_utils.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.commons.image_utils` (1:1)</a>
- <a href="https://github.com/CodeBoarding/deepface/blob/master/.codeboarding/deepface/modules/preprocessing.py#L1-L1" target="_blank" rel="noopener noreferrer">`deepface.modules.preprocessing` (1:1)</a>
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)
### [FAQ](https://github.com/CodeBoarding/GeneratedOnBoardings/tree/main?tab=readme-ov-file#faq)

24
.github/workflows/update-docs.yml → .github/workflows/codeboarding-workflow.yml vendored
View File

@ -1,4 +1,4 @@
name: Update Documentation
name: CodeBoarding Documentation update workflow
on:
workflow_dispatch:
@ -6,15 +6,15 @@ on:
repository_url:
description: 'Repository URL to test with'
required: false
default: 'https://github.com/${{ github.repository }}'
default: 'https://github.com/serengil/deepface'
type: string
source_branch:
description: 'Source branch for comparison'
description: 'Source branch for generation'
required: false
default: 'master'
type: string
target_branch:
description: 'Target branch for comparison'
description: 'Target branch for pull request'
required: false
default: 'master'
type: string
@ -26,13 +26,16 @@ on:
options:
- '.md'
- '.rst'
schedule:
# Runs every Monday at 09:00 UTC
- cron: '0 9 * * 1'
output_directory:
description: 'Output directory for documentation files'
required: false
default: '.codeboarding'
type: string
jobs:
update-docs-action-usage:
runs-on: ubuntu-latest
timeout-minutes: 45
permissions:
contents: write
pull-requests: write
@ -59,13 +62,14 @@ jobs:
fi
- name: Fetch CodeBoarding Documentation
timeout-minutes: 30
id: codeboarding
uses: ./
uses: CodeBoarding/CodeBoarding-GHAction@v0.1.0
with:
repository_url: ${{ github.event.inputs.repository_url }}
source_branch: ${{ steps.set-branches.outputs.source_branch }}
target_branch: ${{ steps.set-branches.outputs.target_branch }}
output_directory: '.codeboarding'
output_directory: ${{ github.event.inputs.output_directory || '.codeboarding' }}
output_format: ${{ github.event.inputs.output_format || '.md' }}
- name: Display Action Results
@ -90,7 +94,7 @@ jobs:
if: steps.git-changes.outputs.has_git_changes == 'true' && steps.codeboarding.outputs.has_changes == 'true'
uses: peter-evans/create-pull-request@v5
with:
token: ${{ secrets.GITHUB_TOKEN }}
token: ${{ secrets.PAT_TOKEN }}
commit-message: "docs: update codeboarding documentation"
title: "📚 CodeBoarding Documentation Update"
body: |