Face Detection using Python and OpenCV with webcam

 Face Detection using Python and OpenCV with webcam

How to use :

1. Create a directory in your pc and name it (say project)
2. Create two python files named create_data.py and face_recognize.py, copy the first source code and second source code in it respectively.
3. Copy haarcascade_frontalface_default.xml to the project directory, you can get it in opencv or from
   here.

1. You are ready to now run the following codes.

create_data.py

# 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 = 'G:\PARAS\datasets'  //make datasets as new folder like i did in this path then sub

# These are sub data sets of folder,
# for my faces I've used my name you can
# change the label here akhi is a sub folder insde datasets where web image will store by webcamera

sub_data = 'akhi'

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

Datasets Storage :

face_recognize.py

# It helps in identifying the faces
import cv2, sys, numpy, os

size = 4
haar_file = 'haarcascade_frontalface_default.xml'
datasets = r'G:\PARAS\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, lables, 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
            lable = id
            images.append(cv2.imread(path, 0))
            lables.append(int(lable))
        id += 1
(width, height) = (130, 100)

# Create a Numpy array from the two lists above
(images, lables) = [numpy.array(lis) for lis in [images, lables]]

# OpenCV trains a model from the images
# NOTE FOR OpenCV2: remove '.face'
model = cv2.face.LBPHFaceRecognizer_create()
model.train(images, lables)

# Part 2: Use fisherRecognizer on camera stream
face_cascade = cv2.CascadeClassifier(haar_file)
webcam = cv2.VideoCapture(0, cv2.CAP_DSHOW) #captureDevice = camera
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

Opencv Python program for Face Detection

Put the haarcascade_eye.xml & haarcascade_frontalface_default.xml files in the same folder

# OpenCV program to detect face in real time
# import libraries of python OpenCV
# where its functionality resides
import cv2

# load the required trained XML classifiers
# https://github.com/Itseez/opencv/blob/master/
# data/haarcascades/haarcascade_frontalface_default.xml
# Trained XML classifiers describes some features of some
# object we want to detect a cascade function is trained
# from a lot of positive(faces) and negative(non-faces)
# images.
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# https://github.com/Itseez/opencv/blob/master
# /data/haarcascades/haarcascade_eye.xml
# Trained XML file for detecting eyes
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

# capture frames from a camera
cap = cv2.VideoCapture(0)

# loop runs if capturing has been initialized.
while 1:

    # reads frames from a camera
    ret, img = cap.read()

    # convert to gray scale of each frames
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Detects faces of different sizes in the input image
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    for (x, y, w, h) in faces:
        # To draw a rectangle in a face
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 0), 2)
        roi_gray = gray[y:y + h, x:x + w]
        roi_color = img[y:y + h, x:x + w]

        # Detects eyes of different sizes in the input image
        eyes = eye_cascade.detectMultiScale(roi_gray)

        # To draw a rectangle in eyes
        for (ex, ey, ew, eh) in eyes:
            cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 127, 255), 2)

        # Display an image in a window
    cv2.imshow('img', img)

    # Wait for Esc key to stop
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break

# Close the window
cap.release()

# De-allocate any associated memory usage
cv2.destroyAllWindows()