# Import the libraries
import cv2
import os
import numpy as np
[docs]class Eigenfaces:
"""
Class that provides easy access to the Eigenfaces algorithm.
"""
def __init__(self, numComponents=0, threshold=-1):
"""
Set the default values.
:param numComponents: The number of components (default 0).
:param threshold: The threshold (default -1).
"""
if numComponents < 0:
numComponents = 0
# Creates the eigenface object passing a threshold variable by
# parameter
if threshold >= 0:
self.faceRec = cv2.face.createEigenFaceRecognizer(
num_components=numComponents, threshold=threshold)
else:
self.faceRec = cv2.face.createEigenFaceRecognizer(
num_components=numComponents) # threshold=DBL_MAX
self.algorithmTrained = False
[docs] def getAlgorithmName(self):
"""
Get the algorithm name.
:return: The algorithm name.
"""
return "Eigenfaces"
[docs] def train(self, images, labels):
"""
Train the face recognition algorithm
:param images: A slice with all images for training.
:param labels: A slice with all labels corresponding to the images.
"""
self.faceRec.train(images, np.array(labels))
self.algorithmTrained = True
[docs] def predict(self, image):
"""
Predict the image. Given a new image this function will make the prediction.
:param image: The image we want to predict.
:return: The subject ID (label) and the confidence.
"""
# Check if the algorithm was trained
if self.algorithmTrained is False:
print "The face recognition algorithm was not trained."
sys.exit()
# Return the subject ID (label) and the confidence
return self.faceRec.predict(image)