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# coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: sample_face_grouping.py
DESCRIPTION:
This sample demonstrates how to group faces based on face similarity.
USAGE:
python sample_face_grouping.py
Set the environment variables with your own values before running this sample:
1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource.
2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key.
"""
import os
from dotenv import find_dotenv, load_dotenv
from shared.constants import (
CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY,
CONFIGURATION_NAME_FACE_API_ENDPOINT,
DEFAULT_FACE_API_ACCOUNT_KEY,
DEFAULT_FACE_API_ENDPOINT,
TestImages,
)
from shared import helpers
from shared.helpers import beautify_json, get_logger
class GroupFaces:
def __init__(self):
load_dotenv(find_dotenv())
self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT)
self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY)
self.logger = get_logger("sample_face_grouping")
def group(self):
from azure.core.credentials import AzureKeyCredential
from azure.ai.vision.face import FaceClient
from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel
with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client:
sample_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES)
detect_result = face_client.detect(
helpers.read_file_content(sample_file_path),
detection_model=FaceDetectionModel.DETECTION03,
recognition_model=FaceRecognitionModel.RECOGNITION04,
return_face_id=True,
)
face_ids = [str(face.face_id) for face in detect_result]
self.logger.info(f"Detect {len(face_ids)} faces from the file '{sample_file_path}': {face_ids}")
group_result = face_client.group(face_ids=face_ids)
self.logger.info(f"Group result: {beautify_json(group_result.as_dict())}")
if __name__ == "__main__":
sample = GroupFaces()
sample.group()
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