record_faces.py.orig

import numpy as np
import cv2

# instantiate a camera object to capture images
cam = cv2.VideoCapture(1)

# create a haar-cascade object for face detection
face_cas = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')

# create a placeholder for storing the data
data = []
ix = 0	# current frame number

while True:
	# retrieve the ret (boolean) and frame from camera
	ret, frame = cam.read()

	# 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"

# now we destroy the windows we have created
cv2.destroyAllWindows()

# convert the data to a numpy format
data = np.asarray(data)

# print the shape as a sanity-check
print data.shape

# save the data as a numpy matrix in an encoded format
np.save('face_01', data)

# We'll run the script for different people and store
# the data into multiple files