• Added conversion scripts to convert original MXNet models to ONNX and TensorRT
• Added demo inference scripts for ArcFace and RetinaFace using ONNX and TensorRT backends

• Added GPU support and switched to FastApi, more details in changelog.

InsightFace REST API for easy deployment of face recognition services. Code is heavily based on API code in official DeepInsight InsightFace repository.

This repository provides source code for building face recognition REST API and Dockerfiles for fast deployment.

1. MXNet
2. PyTorch
3. OpenCV
4. FastApi
5. Docker

Extract endpoint accepts list of images and return faces bounding boxes with corresponding embeddings.

API accept JSON in following format:

{
  "images":{
      "data":[
          base64_encoded_image1,  
          base64_encoded_image2
      ]
  },
  "max_size":[640,480]
}

Where max_size is maximum image dimension, images with dimensions greater than max_size will be downsized to provided value.

If max_size is set to 0, image won't be resized.

To call API from Python you can use following sample code:

import os
import json
import base64
import requests

def file2base64(path):
    with open(path, mode='rb') as fl:
        encoded = base64.b64encode(fl.read()).decode('ascii')
        return encoded


def extract_vecs(ims,max_size=[640,480]):
    target = [file2base64(im) for im in ims]
    req = {"images": {"data": target},"max_size":max_size}
    resp = requests.post('http://localhost:18081/extract', json=req)
    data = resp.json()
    return data
    
images_path = 'src/api/test_images'
images = os.path.listdir(images_path)
data = extract_vecs(images)

Response is in following format:

[
    [
        {"vec": [0.322431242,0.53545632,], "det": 0, "prob": 0.999, "bbox": [100,100,200,200]},
        {"vec": [0.235334567,-0.2342546,], "det": 1, "prob": 0.998, "bbox": [200,200,300,300]},
    ],
    [
        {"vec": [0.322431242,0.53545632,], "det": 0, "prob": 0.999, "bbox": [100,100,200,200]},
        {"vec": [0.235334567,-0.2342546,], "det": 1, "prob": 0.998, "bbox": [200,200,300,300]},
    ]
]

First level is list in order the images were sent, second level are faces detected per each image as dictionary containing face embedding, bounding box, detection probability and detection number.

1. Clone repo.
2. Execute deploy.sh from repo's root.
3. Go to http://localhost:18081 to access documentation and try API

If you have multiple GPU's with enough GPU memory you can try running multiple containers by editing n_gpu and n_con parameters in deploy.sh.

You would need load balancer like HAProxy to work with multiple containers, example HAProxy config will be added later.

• Merge TensorRT and ONNX backends in main REST API
• Add Triton Inference Server as execution backend
• Add Cython postprocessing of Retinaface predictions.
• Add CenterFace detector

Conversion scripts:

• Added conversion of MXNet models to ONNX using Python
• Added conversion of ONNX to TensorRT using Python
• Added demo inference scripts for ArcFace and Retinaface using ONNX and TensorRT backends

REST API:

• no changes

• REST API code refactored to FastAPI
• Detection/Recognition code is now based on official Insightface Python package.
• TensorFlow MTCNN replaced with PyTorch version
• Added RetinaFace detector
• Added InsightFace gender/age detector
• Added support for GPU inference
• Resize function refactored for fixed image proportions (significant speed increase and memory usage optimization)