README.md

There are 10 code files in this repo - 5 for C++ and 5 for Python:

1. face_detection_opencv_haar.cpp and face_detection_opencv_haar.py - For Haar based face detection

2. face_detection_opencv_dnn.cpp and face_detection_opencv_dnn.py - For OpenCV DNN based face detection

3. face_detection_dlib_hog.cpp and face_detection_dlib_hog.py - for dlib hog based face detection

4. face_detection_dlib_mmod.cpp and face_detection_dlib_mmod.py - for dlib mmod based face detection

5. run-all.cpp and run-all.py - for running all the 4 together

First of all Unzip the dlib.zip file

For C++

Compile

Add path to the properly build OpenCV with DNN GPU Support and your CUDA:

cmake -D OpenCV_DIR=~/opencv -D CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda/ .
make

For Python

Note: Our code is tested using Python 3.7.5, but it should also work with any other python3.x.

Install virtualenv:

pip install virtualenv

Create new virtual environment env and activate it:

python -m venv env
source  env/bin/activate

Install numpy:

pip install numpy

Install dlib:

pip install dlib

Create symlink to the properly build OpenCV with DNN GPU Support:

cd env/lib/python3.x/site-packages/
ln -s ~/opencv/build/cv2.so cv2.so

Run

If you don't pass any filename, it will use the web cam

For individual face detectors

C++

./face_detection_XXXX <filename>

Note: for face_detection_opencv_dnn.cpp you can pass up to 3 arguments:

• video filename, if you'd like to run inference on a video instead of a camera:

./face_detection_opencv_dnn.out <filename>

• device, if you want to use CPU instead of CPU:

./face_detection_opencv_dnn.out "" cpu

• framework to specify Caffe (caffe) or TensorFlow (tf) network to use. Caffe network is set by default:

./face_detection_opencv_dnn.out "" gpu tf

Python

python face_detection_XXXX.py -video <filename>

Note: for face_detection_opencv_dnn.py you can pass up to 3 arguments:

• filename, if you'd like to run inference on a video instead of a camera:

python face_detection_opencv_dnn.out --video <filename>

• device, if you want to use CPU instead of GPU:

python face_detection_opencv_dnn.out --video <filename> --device cpu

• framework to specify Caffe (caffe) or TensorFlow (tf) network to use. Caffe network is set by default:

python face_detection_opencv_dnn.out --video <filename> --device cpu --framework tf

For running all together

C++ ./run-all

Python python run-all.py --video

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