Automatic Installation
You can install the hybrid MROI face detection wrapper using pip
:
pip install mroi-fd
Manual Installation
This project is developed and tested on python version 3.7.4
. Please check
your python version using python --version
. If your system has a different
python version, you may want to consider using
pyenv (See Using pyenv)
First, clone and cd
into the repository:
git clone https://gitlab.com/ailabuser/bacha_hybrid_mroi_face_detection
cd bacha_hybrid_mroi_face_detection
On Windows:
Create a python virtual environment:
mkvirtualenv venv
Activate the virtual environment (deactivate
to deactivate the virtual
environment):
workon venv
Install all the required dependencies while still having the virtual environment
active:
pip install -r requirements.txt
On Linux:
Create a python virtual environment:
virtualenv -p /usr/bin/python3 venv
Activate the virtual environment (deactivate
to deactivate the virtual
environment):
source venv/bin/activate
Install all the required dependencies while still having the virtual environment
active:
pip install -r requirements.txt
Using pyenv (Linux, Windows, MacOS)
If your python version is not 3.7.4, you may want to use
pyenv. After you have installed pyenv
,
install the specific python version. On Linux, this can be done by running the
following command:
env PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install 3.7.4
Then, create a virtual environment:
pyenv virtualenv 3.7.4 bacha_mroi_face_detection
You can activate the virtual environment using pyenv
like so:
pyenv activate bacha_mroi_face_detection
Description
The face detection technique used hybrid margin-based region of interest (MROI)
approach. It is hybrid in the sense that the implementation runs one main
routine to detect a face, but switch to an escape routine when the main routine
fails. Using MROI increase the face detection speed by having the selected face
detection algorithm to only consider a sub-region (where a face was previously
detected) instead of the full frame.
There are three pre-defined selection of main routines available for you to use:
- Haar cascade classifier
- Joint cascade
- Multi-task Convolutional Neural Network (MTCNN)
When the main routine failed to detect a face, the implementation switch to the
escape routine which runs template matching algorithm.
Example
Using the hybrid MROI for your face detection implementation
In order to use your face detection algorithm with the hybrid MROI face
detector, you need to create a subclass which inherit the FaceDetector
class,
and override its detect
method. The implementation requires thedetect
method to return either a face ROI or None
; otherwise, the hybrid MROI face
detector may fail.
Here's an example, in which we use a python implementation of
MTCNN:
import cv2
from mtcnn.mtcnn import MTCNN
from mroi_fd import FaceDetector
class MROI_MTCNN(FaceDetector):
def __init__(self):
fd_obj = MTCNN()
super().__init__(fd_obj, mode=FaceDetector.FMTM)
@staticmethod
def detect(fd_obj, image):
rgb_src = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
result = fd_obj.detect_faces(rgb_src)
if len(result) > 0:
return result[0]['box']
else:
return None
Internally, this was basically how the pre-defined hyrbid MROI face detectors
(i.e., MROI_HaarCascade
and MROI_MTCNN
) was defined. Simply import them with
from mroi_fd import MROI_HaarCascade, MROI_MTCNN
Running the face detector
To use the face detector, simply instantiate the hybrid MROI face detector and
run it by invoking its run
method. Below is a simple script that runs the
face detector and feed it images in a loop.
fd = MROI_MTCNN()
fd.run()
cap = cv2.VideoCapture("/path/to/video/file")
while True:
ret, frame = cap.read()
if not ret:
break
fd.input_image(frame)
ROI = fd.get_face_region()
if ROI is not None:
x, y, w, h = ROI
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow("MROI_MTCNN Face Detector", frame)
if cv2.waitKey(1) == ord('q'):
break
fd.clean()
cap.release()
cv2.destroyAllWindows()