"""
The detectors module provides face related classes and functions to detect
faces on a image and features of a face. Such as:
- FaceDetector
"""
import cv2
import os
# !/usr/bin/python
# Filename: face.py
class FaceDetector(object):
""" Class to detect faces in an image.
The face detection uses CascadeClassifiers to find the faces in the
image, this is a build functionality in the OpenCV library.
"""
def __init__(self, xml_path):
# Create classifier object
self.classifier = cv2.CascadeClassifier(xml_path)
def detect(self, image, biggest_only=True):
""" Detect face in an image.
Find the biggest face in an image and return its position and
dimensions (top, left, width and height).
:param image: the image in which to detect faces
:type image: numpy array
:return: top, left, width and height of the rectangle around the face
:rtype: tuple of length 4
"""
is_color = 4
if is_color:
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
image_gray = image
# The algorithm checks for faces of different sizes,
# the scale_factor is how much difference is between each size
# that you want to check.
# A bigger scale_factor means bigger jumps so it will perform
# faster but less accuratelly, it is recommended 1.2 or 1.3
scale_factor = 1.2
# Treshold to detect a face, it needs a minimum of min_neighbors
# neighbor pixels to return a detected a face on that pixel
min_neighbors = 5
# Sets the min_size of the face we want to detect. Default is 20x20
min_size = (30, 30)
# Change to True if we want to detect only one face
flags = cv2.CASCADE_FIND_BIGGEST_OBJECT | \
cv2.CASCADE_DO_ROUGH_SEARCH if biggest_only else \
cv2.CASCADE_SCALE_IMAGE
face_coord = self.classifier.detectMultiScale(
image_gray,
scaleFactor=scale_factor,
minNeighbors=min_neighbors,
minSize=min_size,
flags=flags
)
return face_coord
