Face-Detection-Recognition

This project presents a full process of creating face-detection & face-recognition web-application service using deep_utils library.

The following modules and models are used in the project:

1. pytorch
2. flask
3. docker
4. docker-compose
5. nginx
6. face-detection: ultra-light
7. face-recognition: vggface2-senet50

Dataset

The dataset should a directory of images that should be like follows:

├── dataset
│   ├──people
│   │   ├── person-1
│   │   │  ├──images
│   │   │  │  ├──image-name-1.jpg
│   │   │  │  ├──image-name-2.jpg
│   │   │  │  ├──...
│   │   ├── person-2
│   │   │  ├──images
│   │   │  │  ├──image-name-1.jpg
│   │   │  │  ├──image-name-2.jpg
│   │   │  │  ├──...
│   │   ├──...
...

dataset images are as follows:

Ali

Farshad

Pooya

Sahriar

Vahid

Notes:

1. person-name should be the name or ssn-id of the people that will be recognized.
2. image-names are arbitrary and can be anything.

Preparation:

run the following code to prepare the dataset

python data_preparation.py --dataset_dir dataset/people

By running the above code, faces of images will be cropped and saved in cropped folders under people/person-names. In addition, the encoding of each person is extracted and saved in people/encodings. Finally, a single file called people.pkl is created in people directory which contains a dictionary of names and encodings which will, eventually, used to recognize persons in a video. The output should be like the following picture.

API

To set up api first one need to download the weights.

cd api/app/weights
./get_weights.sh

File people.pkl should be copied from dataset/people/people.pkl to api/app/weights/people.pkl. The file exists for the sample dataset.

Run the project:

1. using docker & docker-compose...

cd api
docker-compose up --build

2. Using python codes:

cd api/app
python entry_point.py

Send a request:

1. using curl request:

(echo -n '{"image": "'; base64 ./dataset/test/friends.jpg; echo '"}') | curl -H "Content-Type: application/json" -d @-  http://127.0.0.1:8000/face_recognition

output:

{"face_00":["shahriar",0.016,[177.10394287109375,448.4017333984375,364.7297668457031,599.50244140625]],"face_01":["unknown",0.024,[250.45748901367188,50.30693817138672,483.1863098144531,222.37037658691406]],"face_02":["unknown",0.021,[115.46611785888672,210.6558837890625,209.75440979003906,288.85455322265625]]}

2. Using clinet.py:

python api/client.py --endpoint http://127.0.0.1:8000/face_recognition --img_address ./dataset/test/friends.jpg 

output:

Note:

The recognition model for pytorch is not accurate! You may wanna use: https://github.com/ageitgey/face_recognition until I fix this issue. However, the docker and rest API are both fine and can be used!

References:

1. https://github.com/pooya-mohammadi/deep_utils
2. https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB
3. https://github.com/cydonia999/VGGFace2-pytorch.git