safeai-face 0.0.3.1

SafeAI Face Detection & Face Retrieval

SafeAI face detection and face retrieval library

Installation

pip install safeai-face

Functions

Face Detection

The [ face_detection ] function is used to detect faces in an image using a YOLO-based model. It supports both simple face detection and tracking over multiple frames if needed. The function returns a list of detected faces, each containing the bounding box coordinates, tracking ID (if enabled), and confidence score.

Example Usage

import cv2
from safevision_face import face_detection

image = cv2.imread("image_path")
detection = face_detection(image, do_track=True)

#output : [{'box': (56, 16, 169, 167), 'track_id': 1, 'score': 0.8751850724220276}]

Parameters

• image_bgr(np.ndarray)

  The input image in BGR format. This is typically read using OpenCV (cv2.imread).

• conf(float, default: 0.4)

  The confidence threshold for face detection. Detections with confidence scores below this value are ignored.

• iou(float, default: 0.4)

  The confidence threshold for face detection. Detections with confidence scores below this value are ignored.

• do_track(bool, , default: False)

  A boolean flag indicating whether to enable tracking. If True, the function will use a tracker to assign unique IDs to detected faces across frames.

• tracker_config(str, default: "bytetrack.yaml")

  The configuration file for the tracker, used when do_track is set to True.

Face Extraction

The [ face_extraction ] function extracts a feature embedding vector from a given face image using the EdgeFace model. This embedding is a numerical representation of the face, which can be used for tasks like face recognition, clustering, or similarity comparison.

Example Usage

from safevision_face import face_extraction

vec = face_extraction(image)

#output : a vector of 512 dimensions

Parameters

• image_bgr(np.ndarray)

  The input image in BGR format. Typically, this is a cropped face image obtained from a face detection model.

Database Init

The [ db_set ] function initializes a connection to a Milvus database and creates a collection for storing vector data if it does not already exist. Milvus is a vector database commonly used for managing embeddings for similarity search and machine learning tasks.

Example Usage

from safevision_face import db_set

client = db_set(
    db_path="your_db_path/test.db", 
    collection_name="your_collection_name",
    dimension=512,
    metric_type="COSINE",
    indexing="IVF_FLAT",
    nlist=128
)

#output : Milvus client instance

Parameters

• db_path(str)

  The URI of the Milvus database.

• collection_name(str)

  The name of the collection in the Milvus database.

• dimension(int, default: 512)

  The dimensionality of the vectors to be stored in the collection. This should match the dimension of the embeddings being used.

• metric_type(str, default: COSINE)

  The distance metric used for similarity searches in the collection.

• indexing(str, default: IVF_FLAT)

  The index type determines how Milvus organizes and searches for vectors, directly impacting query performance and accuracy.

• nlist(int, defatult: 128)

  Defines the number of clusters into which the vector space is divided for the IVF index types.

• auto_id(bool, default: True)

  Whether to enable automatic ID generation for records in the collection.

• enable_dynamic_field(bool, default: True)

  Whether to allow dynamic fields in the collection. Dynamic fields let you store non-fixed schema attributes.

Database Insert

The [ db_insert ] function is used to add a record into a specific collection in a Milvus database. The record contains both a vector (embedding) and associated metadata, enabling vector-based similarity searches while preserving contextual information about the stored data.

from safevision_face import db_insert

db_insert(
    client,
    collection_name="your_collection_name",
    vector=embedding_vector,
    orig_path="/some/orig_path.jpg",
    crop_path="/some/crop_path.jpg",
    timestamp="20250101_120000",
    tracking_id=123,
    cam_id="cam_number"
)

#output : The function returns True after successfully inserting the record into the collection.

Parameters

• client(MilvusCLient)

  An instance of the MilvusClient connected to the database.

• collection_name(str)

  The name of the collection in which the data will be inserted.

• vector(np.ndarray)

  A vector (embedding) to be stored in the database. This represents the numerical representation of data, such as facial embeddings for similarity search.

• orig_path(str)

  The file path of the original image associated with the vector.

• crop_path(str)

  The file path of the cropped image associated with the vector.

• timestamp(str)

  A timestamp indicating when the data was generated or captured.

• tracking_id(int)

  An ID used for tracking individuals or objects across frames or locations.

• cam_id(str)

  The identifier of the camera or device from which the data was captured.

Database Search

The [ db_search ] function performs a similarity search in a Milvus collection by comparing a query image's embedding (vector) against stored vectors. The results include the top matches that meet a specified similarity threshold.

from safevision_face import db_search

results = db_search(
    client,
    collection_name="face_collection",
    metric_type="COSINE",
    query_image=image_bgr,
    top_k=1,
    threshold=0.45,
    extractor_func=face_extraction,
    nprobe=16
)

#output : [{'score': 0.7510387301445007, 'entity': {'orig_path': '/some/orig_path.jpg', 'crop_path': '/some/crop_path.jpg', 'timestamp': '20250101_120000', 'tracking_id': 123, 'cam_id': 'cam102'}}]

Parameters

• client(MilvusCLient)

  An instance of the MilvusClient connected to the Milvus database, enabling search operations on a specific collection.

• collection_name(str)

  The name of the collection in the Milvus database where the search will be performed.

• query_image(np.ndarray)

  The image (in numpy array format) for which the similarity search is conducted. This image will be converted into an embedding using the provided extractor_func.

• metric_type(str, default: COSINE)

  The distance metric used for similarity searches in the collection.

• nprobe(int, default: 16)

  It determines how many clusters are searched during a query in an IVF-based index.

  Higher nprobe searches mor clusters, improving accuracy but increasing query latency.

• top_k(int, default: 5)

  The maximum number of top matches to retrieve from the database.

• threshold(float, default: 0.45)

  The minimum similarity score for a match to be considered valid. Matches with a score below this value will be filtered out.

  0.45 means

• extractor_func

  A function to extract the vector (embedding) from the query image. The function should take an image (numpy array) as input and return its embedding.