face-detection-recognition-main.zip

# 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: <table align="center"> <tr> <td align="center"> <a href="#"> <img src="https://raw.githubusercontent.com/pooya-mohammadi/face-detection-recognition/main/dataset/people/ali/cropped/ali_0.jpg" width="100px;" height="120px;" alt="Ali"/><br> _<b>Ali</b> </a> </td> <td align="center"> <a href="#"> <img src="https://raw.githubusercontent.com/pooya-mohammadi/face-detection-recognition/main/dataset/people/farshad/cropped/farshad_0.jpg" width="100px;" height="120px;" alt="Farshad"/><br> _{<b>Farshad</b>} </a> </td> <td align="center"> <a href="https://github.com/pooya-mohammadi"> <img src="https://raw.githubusercontent.com/pooya-mohammadi/face-detection-recognition/main/dataset/people/pooya/cropped/pooya_0.jpg" width="100px;" height="120px;" alt="Pooya"/><br> _<b>Pooya</b> </a> </td> <td align="center"> <a href="#"> <img src="https://raw.githubusercontent.com/pooya-mohammadi/face-detection-recognition/main/dataset/people/shahriar/cropped/shahriar_2_0.jpg" width="100px;" height="120px;" alt="Shahriar"/><br> _{<b>Sahriar</b>} </a> </td> <td align="center"> <a href="#"> <img src="https://raw.githubusercontent.com/pooya-mohammadi/face-detection-recognition/main/dataset/people/vahid/cropped/vahid_0.jpg" width="100px;" height="120px;" alt="Vahid"/><br> _<b>Vahid</b> </a> </td> </tr> </table> 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. </br>  # 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 --end_point http://127.0.0.1:8000/face_recognition --img_address ./dataset/test/friends.jpg ``` output:  # 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