Face Recognition in Real Time

 

 

https://gist.github.com/ageitgey/629d75c1baac34dfa5ca2a1928a7aeaf

installing_dlib_on_macos_for_python.md

 

 

 

https://github.com/shantnu/Webcam-Face-Detect

shantnu/Webcam-Face-Detect

 

import cv2

import sys, os

import logging as log

import datetime as dt

import face_recognition

import numpy as np

 

from time import sleep

from datetime import datetime

 

cascPath = "haarcascade_frontalface_default.xml"

faceCascade = cv2.CascadeClassifier(cascPath)

log.basicConfig(filename='webcam.log',level=log.INFO)

 

video_capture = cv2.VideoCapture(0)

anterior = 0

 

known_face_encodings = []

while True:

if not video_capture.isOpened():

print('Unable to load camera.')

sleep(5)

pass

 

# Capture frame-by-frame

ret, frame = video_capture.read()

 

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

 

faces = faceCascade.detectMultiScale(

gray,

scaleFactor=1.1,

minNeighbors=5,

minSize=(100, 100),

flags=cv2.CASCADE_SCALE_IMAGE

)

 

# Draw a rectangle around the faces

for (x, y, w, h) in faces:

face_encodings = face_recognition.face_encodings(frame, {(y, x+w, y+h, x)})

if face_encodings is not None:

distances = face_recognition.face_distance(known_face_encodings, face_encodings[0])

 

# if distances is not None and len(distances) > 0:

# print(min(distances), max(distances))

 

# min(distances) == 0.6

if distances is None or len(distances) == 0 or (len(distances) > 0 and min(distances) > 0.6):

pathname = 'images/face_%s.jpg' % datetime.now()

cv2.imwrite(pathname, frame[y:y+h, x:x+w])

 

# img = face_recognition.load_image_file(pathname)

# face_encoding = face_recognition.face_encodings(img)

 

known_face_encodings.append(face_encodings[0])

# cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

 

# if anterior != len(faces):

# anterior = len(faces)

# log.info("faces: "+str(len(faces))+" at "+str(dt.datetime.now()))

 

# Display the resulting frame

cv2.imshow('Video', frame)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

 

# Display the resulting frame

cv2.imshow('Video', frame)

 

# When everything is done, release the capture

video_capture.release()

cv2.destroyAllWindows()