# if the camera is working fine, we proceed to extract the face
if ret == True:
	# convert the current frame to grayscale
	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

	# apply the haar cascade to detect faces in the current frame
	# the other parameters 1.3 and 5 are fine tuning parameters
	# for the haar cascade object
	faces = face_cas.detectMultiScale(gray, 1.3, 5)

	# for each face object we get, we have
	# the corner coords (x, y)
	# and the width and height of the face
	for (x, y, w, h) in faces:

		# get the face component from the image frame
		face_component = frame[y:y+h, x:x+w, :]

		# resize the face image to 50X50X3
		fc = cv2.resize(face_component, (50, 50))

		# store the face data after every 10 frames
		# only if the number of entries is less than 20
		if ix%10 == 0 and len(data) < 20:
			data.append(fc)

		# for visualization, draw a rectangle around the face
		# in the image
		cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
	ix += 1	# increment the current frame number
	cv2.imshow('frame', frame)	# display the frame

	# if the user presses the escape key (ID: 27)
	# or the number of images hits 20, we stop
	# recording.
	if cv2.waitKey(1) == 27 or len(data) >= 20:
		break
else:
	# if the camera is not working, print "error"
	print "error"

### FaceRec.py
```python
import numpy as np
import cv2

# instantiate the camera object and haar cascade
cam = cv2.VideoCapture(1)
face_cas = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')

# declare the type of font to be used on output window
font = cv2.FONT_HERSHEY_SIMPLEX

# load the data from the numpy matrices and convert to linear vectors
f_01 = np.load('face_01.npy').reshape((20, 50*50*3))	# Shubham
f_02 = np.load('face_02.npy').reshape((20, 50*50*3))	# Prateek
f_03 = np.load('face_03.npy').reshape((20, 50*50*3))	# Laksh

print f_01.shape, f_02.shape, f_03.shape

# create a look-up dictionary
names = {
	0: 'Shubham',
	1: 'Prateek', 
	2: 'Laksh',
}

# create a matrix to store the labels
labels = np.zeros((60, 1))
labels[:20, :] = 0.0	# first 20 for shubham (0)
labels[20:40, :] = 1.0	# next 20 for prateek (1)
labels[40:, :] = 2.0	# last 20 for laksh (2)

# combine all info into one data array
data = np.concatenate([f_01, f_02, f_03])	# (60, 7500)
print data.shape, labels.shape	# (60, 1)

# the distance and knn functions we defined earlier
def distance(x1, x2):
    return np.sqrt(((x1-x2)**2).sum())

def knn(x, train, targets, k=5):
    m = train.shape[0]
    dist = []
    for ix in range(m):
        # compute distance from each point and store in dist
        dist.append(distance(x, train[ix]))
    dist = np.asarray(dist)
    indx = np.argsort(dist)
    sorted_labels = labels[indx][:k]
    counts = np.unique(sorted_labels, return_counts=True)
    return counts[0][np.argmax(counts[1])]

while True:
	# get each frame
	ret, frame = cam.read()

	if ret == True:
		# convert to grayscale and get faces
		gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
		faces = face_cas.detectMultiScale(gray, 1.3, 5)

		# for each face
		for (x, y, w, h) in faces:
			face_component = frame[y:y+h, x:x+w, :]
			fc = cv2.resize(face_component, (50, 50))

			# after processing the image and rescaling
			# convert to linear vector using .flatten()
			# and pass to knn function along with all the data

			lab = knn(fc.flatten(), data, labels)
			# convert this label to int and get the corresponding name
			text = names[int(lab)]

			# display the name
			cv2.putText(frame, text, (x, y), font, 1, (255, 255, 0), 2)

			# draw a rectangle over the face
			cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
		cv2.imshow('face recognition', frame)

		if cv2.waitKey(1) == 27:
			break
	else:
		print 'Error'

cv2.destroyAllWindows()