import sys
import argparse
import cv2
import time
from libfaceid.detector import FaceDetectorModels, FaceDetector
from libfaceid.encoder import FaceEncoderModels, FaceEncoder
from libfaceid.speech_synthesizer import SpeechSynthesizerModels, SpeechSynthesizer
from libfaceid.speech_recognizer import SpeechRecognizerModels, SpeechRecognizer
# Set the window name
WINDOW_NAME = "Facial_Recognition"
# Set the input directories
INPUT_DIR_DATASET = "datasets"
INPUT_DIR_MODEL_DETECTION = "models/detection/"
INPUT_DIR_MODEL_ENCODING = "models/encoding/"
INPUT_DIR_MODEL_TRAINING = "models/training/"
INPUT_DIR_MODEL_ESTIMATION = "models/estimation/"
INPUT_DIR_AUDIOSET = "audiosets"
# Set width and height
RESOLUTION_QVGA = (320, 240)
RESOLUTION_VGA = (640, 480)
RESOLUTION_HD = (1280, 720)
RESOLUTION_FULLHD = (1920, 1080)
# Set the trigger words
TRIGGER_WORDS = ["Hey Google", "Alexa", "Activate", "Open Sesame", "Panel"]
def cam_init(cam_index, width, height):
cap = cv2.VideoCapture(cam_index)
if sys.version_info < (3, 0):
cap.set(cv2.cv.CV_CAP_PROP_FPS, 30)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, width)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, height)
else:
cap.set(cv2.CAP_PROP_FPS, 30)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
return cap
def label_face(frame, face_rect, face_id, confidence):
(x, y, w, h) = face_rect
cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 1)
if face_id is not None:
cv2.putText(frame, "{} {:.2f}%".format(face_id, confidence),
(x+5,y+h-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
trigger_word_detected = False
def speech_recognizer_callback(word):
print("Trigger word detected! '{}'".format(word))
global trigger_word_detected
trigger_word_detected = True
def process_facerecognition(model_detector, model_recognizer, model_speech_synthesizer, model_speech_recognizer, cam_index, cam_resolution):
# Initialize speech-to-text (speech recognizer) for voice-activated capability (wake-word/hot-word/trigger-word detection)
# Then wait for trigger word before starting face recognition
if True:
speech_recognizer = SpeechRecognizer(model=model_speech_recognizer, path=None)
print("\nWaiting for a trigger word: {}".format(TRIGGER_WORDS))
speech_recognizer.start(TRIGGER_WORDS, speech_recognizer_callback)
global trigger_word_detected
try:
while (trigger_word_detected == False):
time.sleep(1)
except:
pass
speech_recognizer.stop()
speech_recognizer = None
# Initialize the camera
camera = cam_init(cam_index, cam_resolution[0], cam_resolution[1])
try:
# Initialize face detection
face_detector = FaceDetector(model=model_detector, path=INPUT_DIR_MODEL_DETECTION)
# Initialize face recognizer
face_encoder = FaceEncoder(model=model_recognizer, path=INPUT_DIR_MODEL_ENCODING, path_training=INPUT_DIR_MODEL_TRAINING, training=False)
# Initialize text-to-speech (speech synthesizer) for voice-enabled capability
speech_synthesizer = SpeechSynthesizer(model=model_speech_synthesizer, path=None, path_output=None, training=False)
except:
face_encoder = None
print("Warning, check if models and trained dataset models exists!")
face_id, confidence = (None, 0)
# Start face recognition
frame_count = 0
while (True):
# Capture frame from webcam
ret, frame = camera.read()
if frame is None:
print("Error, check if camera is connected!")
break
# Detect and identify faces in the frame
faces = face_detector.detect(frame)
for (index, face) in enumerate(faces):
(x, y, w, h) = face
# Indentify face based on trained dataset (note: should run facial_recognition_training.py)
if face_encoder is not None:
face_id, confidence = face_encoder.identify(frame, (x, y, w, h))
# Set text and bounding box on face
label_face(frame, (x, y, w, h), face_id, confidence)
# Play audio file corresponding to the recognized name
if (frame_count % 30 == 0):
if len(faces) == 1 and (face_id is not None) and (face_id != "Unknown"):
speech_synthesizer.playaudio(INPUT_DIR_AUDIOSET, face_id, block=False)
# Process 1 face only
break
# Display updated frame
cv2.imshow(WINDOW_NAME, frame)
# Check for user actions
if cv2.waitKey(1) & 0xFF == 27: # ESC
break
frame_count += 1
# Release the camera
camera.release()
cv2.destroyAllWindows()
def run(cam_index, cam_resolution):
detector=FaceDetectorModels.HAARCASCADE
# detector=FaceDetectorModels.DLIBHOG
# detector=FaceDetectorModels.DLIBCNN
# detector=FaceDetectorModels.SSDRESNET
# detector=FaceDetectorModels.MTCNN
# detector=FaceDetectorModels.FACENET
encoder=FaceEncoderModels.LBPH
# encoder=FaceEncoderModels.OPENFACE
# encoder=FaceEncoderModels.DLIBRESNET
# encoder=FaceEncoderModels.FACENET
speech_synthesizer=SpeechSynthesizerModels.TTSX3
# speech_synthesizer=SpeechSynthesizerModels.TACOTRON
# speech_synthesizer=SpeechSynthesizerModels.GOOGLECLOUD
speech_recognizer=SpeechRecognizerModels.GOOGLECLOUD
# speech_recognizer=SpeechRecognizerModels.WITAI
# speech_recognizer=SpeechRecognizerModels.HOUNDIFY
process_facerecognition(detector, encoder, speech_synthesizer, speech_recognizer, cam_index, cam_resolution)
def main(args):
if sys.version_info < (3, 0):
print("Error: Python2 is slow. Use Python3 for max performance.")
return
cam_index = int(args.webcam)
resolutions = [ RESOLUTION_QVGA, RESOLUTION_VGA, RESOLUTION_HD, RESOLUTION_FULLHD ]
try:
cam_resolution = resolutions[int(args.resolution)]
except:
cam_resolution = RESOLUTION_QVGA
if args.detector and args.encoder and args.speech_synthesizer and args.speech_recognizer:
try:
detector = FaceDetectorModels(int(args.detector))
encoder = FaceEncoderModels(int(args.encoder))
speech_synthesizer = SpeechSynthesizerModels(int(args.speech_synthesizer))
speech_recognizer = SpeechRecognizerModels(int(args.speech_recognizer))
print( "Parameters: {} {} {} {}".format(detector, encoder, speech_synthesizer, speech_recognizer) )
process_facerecognition(detector, encoder, speech_synthesizer, speech_recognizer, cam_index, cam_resolution)
except:
print( "Invalid parameter" )
return
run(cam_index, cam_resolution)
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('--detector', required=False, default=0,
help='Detector model to use. Options: 0-HAARCASCADE, 1-DLIBHOG, 2-DLIBCNN, 3-SSDRESNET, 4-MTCNN, 5-FACENET')
parser.add_argument('--encoder', required=False, default=0,
help='Encoder model to use. Options: 0-LBPH, 1-OPENFACE, 2-DLIBRESNET, 3-FACENET')
parser.add_argument('--speech_synthesizer', required=False, default=0,
help='Speech synthesizer model to use. Options: 0-TTSX3, 1-TACOTRON, 2-GOOGLECLOUD')
parser.add_argument('--speech_recognizer', required=False, default=0,
help='Speech recognizer model to use. Options: 0-GOOGLECLOUD, 1-WITAI, 2-HOUNDIFY')
parser.add_argument('--webcam', required=False, default=0,
help='Camera index to use. Default is 0. Assume only 1 camera connected.)')
parser.add_argument('--resolution', required=False, default=0,
help='Camera resolution to use. Default is 0. Options: 0-QVGA, 1-VGA, 2-HD, 3-FULLHD')
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
