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start_face_recon_app.py
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start_face_recon_app.py
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#!/usr/bin/env python
import sys
import os
import numpy as np
from face_recognition_system.videocamera import VideoCamera
from face_recognition_system.detectors import FaceDetector
import face_recognition_system.operations as op
import cv2
from cv2 import __version__
def get_images(frame, faces_coord, shape):
""" Perfrom transformation on original and face images.
This function draws the countour around the found face given by faces_coord
and also cuts the face from the original image. Returns both images.
"""
if shape == "rectangle":
faces_img = op.cut_face_rectangle(frame, faces_coord)
frame = op.draw_face_rectangle(frame, faces_coord)
faces_img = op.normalize_intensity(faces_img)
faces_img = op.resize(faces_img)
return (frame, faces_img)
def add_person(people_folder, shape):
""" Funtion to add pictures of a person
"""
person_name = input('What is the name of the new person: ').lower()
folder = people_folder + person_name
if not os.path.exists(folder):
input("I will now take 20 pictures. Press ENTER when ready.")
os.mkdir(folder)
video = VideoCamera()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
counter = 1
timer = 0
cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE)
cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
while counter < 21:
frame = video.get_frame()
face_coord = detector.detect(frame)
if len(face_coord):
frame, face_img = get_images(frame, face_coord, shape)
# save a face every second, we start from an offset '5' because
# the first frame of the camera gets very high intensity
# readings.
if timer % 100 == 5:
cv2.imwrite(folder + '/' + str(counter) + '.jpg',
face_img[0])
print ('Images Saved:' + str(counter))
counter += 1
cv2.imshow('Saved Face', face_img[0])
cv2.imshow('Video Feed', frame)
cv2.waitKey(50)
timer += 5
else:
print ("This name already exists.")
sys.exit()
def recognize_people(people_folder, shape):
""" Start recognizing people in a live stream with your webcam
"""
try:
people = [person for person in os.listdir(people_folder)]
except:
print ("Have you added at least one person to the system?")
sys.exit()
print ("This are the people in the Recognition System:")
for person in people:
print ("-" + person)
print (30 * '-')
print (" POSSIBLE RECOGNIZERS TO USE")
print (30 * '-')
print ("1. EigenFaces")
print ("2. FisherFaces")
print ("3. LBPHFaces")
print (30 * '-')
choice = check_choice()
detector = FaceDetector('face_recognition_system/frontal_face.xml')
if choice == 1:
recognizer = cv2.face.createEigenFaceRecognizer()
threshold = 4000
elif choice == 2:
recognizer = cv2.face.createFisherFaceRecognizer()
threshold = 300
elif choice == 3:
recognizer = cv2.face.createLBPHFaceRecognizer()
threshold = 80
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
labels_people[i] = person
for image in os.listdir(people_folder + person):
images.append(cv2.imread(people_folder + person + '/' + image, 0))
labels.append(i)
try:
recognizer.train(images, np.array(labels))
except:
print ("\nOpenCV Error: Do you have at least two people in the database?\n")
sys.exit()
video = VideoCamera()
while True:
frame = video.get_frame()
faces_coord = detector.detect(frame, False)
if len(faces_coord):
frame, faces_img = get_images(frame, faces_coord, shape)
for i, face_img in enumerate(faces_img):
if __version__ == "3.1.0":
collector = cv2.face.MinDistancePredictCollector()
recognizer.predict(face_img, collector)
conf = collector.getDist()
pred = collector.getLabel()
else:
pred, conf = recognizer.predict(face_img)
print ("Prediction: " + str(pred))
print ('Confidence: ' + str(round(conf)))
print ('Threshold: ' + str(threshold))
if conf < threshold:
cv2.putText(frame, labels_people[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 2),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1,
cv2.LINE_AA)
else:
cv2.putText(frame, "Unknown",
(faces_coord[i][0], faces_coord[i][1]),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255, 255), 1, cv2.LINE_AA)
cv2.imshow('Video', frame)
if cv2.waitKey(100) & 0xFF == 27:
sys.exit()
def check_choice():
""" Check if choice is good
"""
is_valid = 0
while not is_valid:
try:
choice = int(input('Enter your choice [1-3] : '))
if choice in [1, 2, 3]:
is_valid = 1
else:
print ("'%d' is not an option.\n" % choice)
except ValueError:
print ("%s is not an option.\n" % str(error).split(": ")[1])
return choice
if __name__ == '__main__':
print (30 * '-')
print (" POSSIBLE ACTIONS")
print (30 * '-')
print ("1. Add person to the recognizer system")
print ("2. Start recognizer")
print ("3. Exit")
print (30 * '-')
CHOICE = check_choice()
PEOPLE_FOLDER = "face_recognition_system/people/"
SHAPE = "rectangle"
if CHOICE == 1:
if not os.path.exists(PEOPLE_FOLDER):
os.makedirs(PEOPLE_FOLDER)
add_person(PEOPLE_FOLDER, SHAPE)
elif CHOICE == 2:
os.system("sudo modprobe bcm2835-v4l2") # Required for Raspi-Camera Module
recognize_people(PEOPLE_FOLDER, SHAPE)
elif CHOICE == 3:
sys.exit()