Contributing to the hacktoberfest 2021 with python program for face detection

[Prashima01]

Creating database

It captures images and stores them in datasets

folder under the folder name of sub_data

import cv2, sys, numpy, os
haar_file = 'haarcascade_frontalface_default.xml'

All the faces data will be

present this folder

datasets = 'datasets'

These are sub data sets of folder,

for my faces I've used my name you can

change the label here

sub_data = 'vivek'

path = os.path.join(datasets, sub_data)
if not os.path.isdir(path):
os.mkdir(path)

defining the size of images

(width, height) = (130, 100)

#'0' is used for my webcam,

if you've any other camera

attached use '1' like this

face_cascade = cv2.CascadeClassifier(haar_file)
webcam = cv2.VideoCapture(0)

The program loops until it has 30 images of the face.

count = 1
while count < 30:
(_, im) = webcam.read()
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 4)
for (x, y, w, h) in faces:
cv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2)
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (width, height))
cv2.imwrite('% s/% s.png' % (path, count), face_resize)
count += 1

cv2.imshow('OpenCV', im)
key = cv2.waitKey(10)
if key == 27:
	break

It helps in identifying the faces

import cv2, sys, numpy, os
size = 4
haar_file = 'haarcascade_frontalface_default.xml'
datasets = 'datasets'

Part 1: Create fisherRecognizer

print('Recognizing Face Please Be in sufficient Lights...')

Create a list of images and a list of corresponding names

(images, labels, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(datasets):
for subdir in dirs:
names[id] = subdir
subjectpath = os.path.join(datasets, subdir)
for filename in os.listdir(subjectpath):
path = subjectpath + '/' + filename
label = id
images.append(cv2.imread(path, 0))
labels.append(int(label))
id += 1
(width, height) = (130, 100)

Create a Numpy array from the two lists above

(images, labels) = [numpy.array(lis) for lis in [images, labels]]

OpenCV trains a model from the images

NOTE FOR OpenCV2: remove '.face'

model = cv2.face.LBPHFaceRecognizer_create()
model.train(images, labels)

Part 2: Use fisherRecognizer on camera stream

face_cascade = cv2.CascadeClassifier(haar_file)
webcam = cv2.VideoCapture(0)
while True:
(_, im) = webcam.read()
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2)
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (width, height))
# Try to recognize the face
prediction = model.predict(face_resize)
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)

	if prediction[1]<500:

	cv2.putText(im, '% s - %.0f' %

(names[prediction[0]], prediction[1]), (x-10, y-10),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
else:
cv2.putText(im, 'not recognized',
(x-10, y-10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))

cv2.imshow('OpenCV', im)

key = cv2.waitKey(10)
if key == 27:
	break