Face Detection with OpenCV and Dlib (Deep Learning Library)

Courtesy: This is not by work. But I had just "integrated" lot of open source platforms and had tried to take cues from man of wisdom ! This is classic problem in the area of Machine Learning / Deep Learning. The problem is to identify human faces from a picture which is called Face Detection and Face Recognition is a further step ahead to identify the faces in the picture after comparing it to a existing database of facial features. Here I am explaining the step by step process involved in creating a program which will help turn your laptop running Ubuntu Linux into a facial recognition box. After installing the Ubuntu Linux LTS 16.06 or later follow these steps one by one in the terminal.

sudo apt install git cmake python-opencv python-pip django
sudo apt install libboost-all-dev
git clone https://github.com/opencv/opencv.git
git checkout tags/3.2.0
git clone https://github.com/opencv/opencv_contrib
git checkout tags/3.2.0
cd opencv
mkdir build
cd build
cmake -DOPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules ..
make -j5
pip install face_recognition

Once you have these program running , In these case I am using Intel Joule platform to generate a signal on a GPIO pen you can avoid that part and implement an action based on your project.

	import face_recognition
	import cv2
	import os
	import mraa
	import time
	import pyaudio
	import wave
	import audioop
	def playfile(filename):
	chunk = 1024
	wf = wave.open(filename, 'rb')
	p = pyaudio.PyAudio()
	stream = p.open(
	format = p.get_format_from_width(wf.getsampwidth()),
	channels = wf.getnchannels(),
	rate = wf.getframerate(),
	output = True)
	data = wf.readframes(chunk)
	audioop.rms(data, 4)
	while data != '':
	stream.write(data)
	data = wf.readframes(chunk)
	stream.close()
	p.terminate()
	# Get a reference to webcam #0 (the default one)
	video_capture = cv2.VideoCapture(0)
	known_face_encoding_array = []
	# Load a sample picture and learn how to recognize it.
	list_files = os.listdir("known")
	print "Learning about registered users faces ...."
	for file_name in list_files:
	if file_name.endswith(".png") or file_name.endswith(".jpg") :
	print file_name.split('.')[0]
	file_name_abs = "./known/" + file_name
	known_image = face_recognition.load_image_file(file_name_abs)
	known_face_encoding = face_recognition.face_encodings(known_image)[0]
	known_face_encoding_array.append((known_face_encoding,file_name))
	# Initialize some variables
	face_locations = []
	face_encodings = []
	face_names = []
	process_this_frame = True
	count = 0
	litup = 1
	playaudio = True
	x = mraa.Gpio(101) #For the onboard LED
	x.dir(mraa.DIR_OUT)
	y = mraa.Gpio(23)
	y.dir(mraa.DIR_OUT)
	#y.write(litup)
	#x.write(litup)
	print "Starting the camera now ...."
	while True:
	# Grab a single frame of video
	ret, frame = video_capture.read()
	# Resize frame of video to 1/4 size for faster face recognition processing
	small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
	# Only process every other frame of video to save time
	if process_this_frame:
	# Find all the faces and face encodings in the current frame of video
	face_locations = face_recognition.face_locations(small_frame)
	face_encodings = face_recognition.face_encodings(small_frame, face_locations)
	face_names = []
	for face_encoding in face_encodings:
	# See if the face is a match for the known face(s)
	name = "Unknown"
	for known_face_enc, file_name in known_face_encoding_array:
	match = face_recognition.compare_faces([known_face_enc], face_encoding)
	if match[0]:
	name = file_name
	x.write(litup)
	y.write(litup)
	if playaudio == True:
	playfile('Allow.wav')
	playaudio = False
	process_this_frame = False
	count = 0
	face_names.append(os.path.splitext(name)[0])
	if count%10 == 0:
	process_this_frame = True
	if count==10:
	x.write(not litup)
	y.write(not litup)
	playaudio = True
	count = 0
	count = count + 1
	print count
	# Display the results
	for (top, right, bottom, left), name in zip(face_locations, face_names):
	# Scale back up face locations since the frame we detected in was scaled to 1/4 size
	top *= 4
	right *= 4
	bottom *= 4
	left *= 4
	# Draw a box around the face
	cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
	# Draw a label with a name below the face
	cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), -1)
	font = cv2.FONT_HERSHEY_DUPLEX
	cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
	# Display the resulting image
	cv2.imshow('Video', frame)
	# Hit 'q' on the keyboard to quit!
	if cv2.waitKey(1) & 0xFF == ord('q'):
	break
	# Release handle to the webcam
	video_capture.release()
	cv2.destroyAllWindows()

view raw facerec.py hosted with ❤ by GitHub