https://github.com/pooya-mohammadi/face-detection-recognition
This project presents a full process of creating face-detection & face-recognition web-application service using deep_utils library
https://github.com/pooya-mohammadi/face-detection-recognition
deep-utils docker docker-compose docker-image pytorch semet50 ultralight vggface2
Last synced: 21 days ago
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This project presents a full process of creating face-detection & face-recognition web-application service using deep_utils library
- Host: GitHub
- URL: https://github.com/pooya-mohammadi/face-detection-recognition
- Owner: pooya-mohammadi
- Created: 2022-06-25T12:15:14.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-01-19T16:22:03.000Z (9 months ago)
- Last Synced: 2025-04-25T13:04:38.974Z (6 months ago)
- Topics: deep-utils, docker, docker-compose, docker-image, pytorch, semet50, ultralight, vggface2
- Language: Python
- Homepage:
- Size: 898 KB
- Stars: 9
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# 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
```commandline
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.
```commandline
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...
```commandline
cd api
docker-compose up --build
```
2. Using python codes:
```commandline
cd api/app
python entry_point.py
```
## Send a request:
1. using `curl` request:
```commandline
(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:
```commandline
{"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`:
```commandline
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