Face Detect API
This is a simple face detection api that takes as input, an image and gives as output, detected faces on the image. This API can be used as an alternative to the paid face detection APIs currently available as it gives quite good accuracy levels. the technologies used include:
- Django
- Docker
- TensorFlow with MTCNN model
- Minio
- PostgreSQL
- RabbitMQ
- Redis
- Celery
Architecture
The architecture used is a micro-service architecture with asynchronous processing of requests. The diagram below highlights the architecture used;
Local Deployment
To deploy the API locally, run the following commands
-
Clone this repo
git clone https://github.com/urandu/face_detect_api.git -
cd face_detect_api -
Run
docker-compose up -
Wait for the necessary docker images to be pulled and started
-
On a different terminal, run
docker-compose run api python manage.py makemigrations -
Then run
docker-compose run api python manage.py migrate -
(Optional) create superuser by running
docker-compose run api python manage.py createsuperuseryou will be prompted for a username, email and password. these are the admin credentials for django admin -
To test our API, we shall send a post request to the endpoint
http://localhost:8900/api/image/
curl -i -X POST -H "Content-Type: multipart/form-data"
-F "request_id=12345" -F "callback_url=<replace with requestbin.com endpoint>" -F "image=<path to image>" http://localhost:8900/api/image/
- callback response
{
"image_id": "0a5a49c6-18dc-4b3a-b984-70476280aa13",
"request_id": "123456789",
"faces": [
{
"confidence": "0.9999778270721436",
"box": "[205, 130, 34, 44]",
"keypoints": "{'left_eye': (216, 146), 'right_eye': (233, 148), 'nose': (224, 157), 'mouth_left': (216, 165), 'mouth_right': (230, 166)}"
},
{
"confidence": "0.9999626874923706",
"box": "[652, 132, 35, 43]",
"keypoints": "{'left_eye': (662, 151), 'right_eye': (678, 148), 'nose': (670, 156), 'mouth_left': (666, 167), 'mouth_right': (679, 165)}"
},
{
"confidence": "0.9999274015426636",
"box": "[564, 338, 38, 45]",
"keypoints": "{'left_eye': (576, 355), 'right_eye': (595, 355), 'nose': (586, 364), 'mouth_left': (577, 374), 'mouth_right': (593, 374)}"
},
{
"confidence": "0.9998524188995361",
"box": "[491, 154, 31, 40]",
"keypoints": "{'left_eye': (501, 170), 'right_eye': (516, 170), 'nose': (509, 177), 'mouth_left': (502, 186), 'mouth_right': (515, 186)}"
},
{
"confidence": "0.9997138381004333",
"box": "[294, 253, 33, 41]",
"keypoints": "{'left_eye': (303, 269), 'right_eye': (319, 267), 'nose': (310, 277), 'mouth_left': (304, 285), 'mouth_right': (319, 284)}"
},
{
"confidence": "0.9995110034942627",
"box": "[293, 134, 34, 45]",
"keypoints": "{'left_eye': (304, 150), 'right_eye': (321, 151), 'nose': (313, 161), 'mouth_left': (305, 168), 'mouth_right': (320, 169)}"
},
{
"confidence": "0.999414324760437",
"box": "[406, 69, 33, 40]",
"keypoints": "{'left_eye': (414, 83), 'right_eye': (431, 82), 'nose': (423, 89), 'mouth_left': (417, 100), 'mouth_right': (431, 99)}"
},
{
"confidence": "0.9993127584457397",
"box": "[418, 246, 40, 51]",
"keypoints": "{'left_eye': (429, 265), 'right_eye': (449, 266), 'nose': (438, 275), 'mouth_left': (430, 286), 'mouth_right': (446, 287)}"
},
{
"confidence": "0.9989363551139832",
"box": "[733, 160, 40, 51]",
"keypoints": "{'left_eye': (743, 180), 'right_eye': (763, 180), 'nose': (751, 189), 'mouth_left': (744, 200), 'mouth_right': (761, 200)}"
},
{
"confidence": "0.995890200138092",
"box": "[575, 248, 31, 39]",
"keypoints": "{'left_eye': (585, 262), 'right_eye': (601, 262), 'nose': (593, 269), 'mouth_left': (585, 277), 'mouth_right': (598, 278)}"
}
],
"output_image_url": "localhost:8900/api/image/?image_id=0a5a49c6-18dc-4b3a-b984-70476280aa13"
}
Below is an example image we used:
input image
output image
Please take a look at this medium article on how we developed the API