This is a component of CompreFace. CompreFace is a service for face recognition: upload images with faces of known people, then upload a new image, and the service will recognize faces in it.

Not needed if only running containers:

$ python -m pip install -r requirements.txt -e srcext/insightface/python-package
$ imageio_download_bin freeimage

Only needed if using tools:

$ make tools/tmp
$ chmod +x tools/test_memory_constraints.sh

$ export FLASK_ENV=development
$ python -m src.app

There are some pre-build images on https://hub.docker.com/r/exadel/compreface-core. To use it run:

$ docker run -p 3000:3000 exadel/compreface-core:latest

Builds container (also runs main tests during the build):

$ docker build -t embedding-calculator .

To skip tests during build, use:

$ docker build -t embedding-calculator --build-arg SKIP_TESTS=true .

$ docker run -p 3000:3000 embedding-calculator

Unit tests

$ pytest -m "not integration and not performance" src tools

Integration tests

$ pytest -m integration src tools

Performance tests

$ pytest -m performance src tools

Lint checks

$ python -m pylama --options pylama.ini src tools

If DockerHub images is not enough, build an image with only the necessary set of plugins.
For changing default plugins pass needed plugin names in build arguments and build your own image.

Set plugins by build arguments FACE_DETECTION_PLUGIN and CALCULATION_PLUGIN

Pass to EXTRA_PLUGINS comma-separated names of plugins.

Notes:

• facenet.LandmarksDetector and insightface.LandmarksDetector extract landmarks from results of FaceDetector plugin without additional processing. Returns 5 points of eyes, nose and mouth.
• insightface.Landmarks2d106Detector detects 106 points of facial landmark. Points mark-up

FACE_DETECTION_PLUGIN=facenet.FaceDetector
CALCULATION_PLUGIN=facenet.Calculator
EXTRA_PLUGINS=facenet.LandmarksDetector,agegender.GenderDetector,agegender.AgeDetector,facenet.facemask.MaskDetector,facenet.PoseEstimator

Some plugins have several pre-trained models.
To use an additional model pass a name of the model after a plugin name with a separator @. For example:

FACE_DETECTION_PLUGIN=insightface.FaceDetector@retinaface_mnet025_v1

List of pre-trained models:

• facenet.Calculator

  • 20180402-114759 (default)
  • 20180408-102900

• insightface.FaceDetector

  • retinaface_r50_v1 (default)
  • retinaface_mnet025_v1
  • retinaface_mnet025_v2

• insightface.Calculator

  • arcface_r100_v1 (default)
  • arcface_resnet34
  • arcface_resnet50
  • arcface_mobilefacenet
  • arcface-r50-msfdrop75
  • arcface-r100-msfdrop75

• facenet.facemask.MaskDetector

  • inception_v3_on_mafa_kaggle123 (default)
  • mobilenet_v2_on_mafa_kaggle123

• insightface.facemask.MaskDetector

  • mobilenet_v2_on_mafa_kaggle123 (default)
  • resnet18_on_mafa_kaggle123

There are two build arguments for optimization:

• GPU_IDX - id of NVIDIA GPU device, starts from 0 (empty or -1 for disable)
• INTEL_OPTIMIZATION - enable Intel MKL optimization (true/false)

1. Install or update Docker Desktop.
2. Make sure that you have Windows version 21H2 or higher.
3. Update your NVIDIA drivers.
4. Install or update WSL2 Linux kernel.
5. Make sure the WSL2 backend is enabled in Docker Desktop.

Install the nvidia-docker2 package and dependencies on the host machine:

sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker

docker build . -t embedding-calculator-cuda -f gpu.Dockerfile
docker build . -t embedding-calculator-gpu --build-arg GPU_IDX=0 --build-arg BASE_IMAGE=embedding-calculator-cuda
docker run -p 3000:3000 --gpus all embedding-calculator-gpu

Finds faces in a given image, puts bounding boxes and saves the resulting image.

$ export IMG_NAMES=015_6.jpg
$ python -m tools.scan

Tests the accuracy of face detection.

$ make tools/benchmark_detection/tmp
$ python -m tools.benchmark_detection

Tests whether service crashes with various parameters under given RAM constraints.

$ docker build -t embedding-calculator .
$ tools/test_memory_constraints.sh $(pwd)/sample_images

Optimizes face detection library parameters with a given annotated image dataset.

$ mkdir tmp
$ python -m tools.optimize_detection_params

Perform the following steps:

1. Build and run embedding-calculator with the needed scanner backend and CPU/GPU supports
2. Run a benchmark:
   1. inside the container docker exec embedding-calculator ./benchmark
   2. or locally cd .embedding-calculator && ./benchmark.sh (require exposing API at localhost:3000)

CRLF file endings may cause this. To fix, run $ dos2unix *.

Package uWSGI is not supported on Windows. Workaround is to temporarily delete the line with the package name from requirements.txt and install without it.

Check that the component is in valid state: run tests, build container, start it

$ make
$ make up

Get project line counts per file type

$ which tokei >/dev/null || conda install -y -c conda-forge tokei && tokei --exclude srcext/